not for publication or presentation · not for publication or presentation c. ib10-05 spellman s,...

Not for publication or presentation

A G E N D A CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY Salt Lake City, Utah Saturday, February 16, 2013, 12:15 pm– 4:45 pm Co-Chair: Carlheinz Müller, MD, PhD, German National Bone Marrow Donor Registry Telephone: +49-731-1507-10; Fax: +49-731-1507-51; E-mail: [email protected] Co-Chair: David Miklos, MD, PhD, Stanford University Telephone: 650-725-4626; Fax: 650-724-6182; E-mail: [email protected] Co-Chair: Marcelo Fernandez-Viña, PhD, Stanford University Telephone: 650-723-7968; Fax: 650-725-4470; E-mail: [email protected] Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 612-884-8609; Fax: 612-884-8661; E-mail: [email protected] John Klein, PhD, CIBMTR Statistical Center Telephone: 414-955-8379; Fax: 414-955-6513; E-mail: [email protected] Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-955-4339; Fax: 414-955-6513; E-mail: [email protected] Co-Scientific Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Directors: Telephone: 206-667-5160; Fax: 206-667-1034; E-mail: [email protected] Stephen Spellman, MBS, CIBMTR Immunobiology Research Telephone: 612-617-8334; Fax: 612-884-8677; E-mail: [email protected] 1. Welcome and introduction (M Fernandez-Viña) 12:15 pm

a. Minutes of Immunobiology Working Committee at Tandem 2012 12:20 pm (Attachment 1)

b. Newly appointed chair: Michael Verneris, MD; University of Minnesota Medical Center - Fairview; E-mail: [email protected], Telephone: 612-626-2961

2. Completed project summary (published or submitted work) 12:25 pm a. IB07-02 Marino SR, Lin S, Maiers M, Haagenson M, Spellman S, Klein JP, Binkowski TA, Lee

SJ, van Besien K. Identification by Random Forest method of HLA class I amino acid substitutions associated with lower survival at day 100 in unrelated donor hematopoietic cell transplantation. Published. Bone Marrow Transplantation, 47:217-226, February 2012.

b. IB07-06 Fleischhauer K, Shaw B, Gooley T, Malkki M, Bardy P, Bignon JD, Dubois V,

Horowitz M, Madrigal JA, Morishima Y, Oudshoorn M, Ringden O, Spellman S, Velardi A, Zino E, Petersdorf E. Effect of T-cell-epitope matching at HLA-DPB1 in recipients of unrelated-donor haemopoietic-cell transplantation: a retrospective study. Published. Lancet Oncology, 13(4):366-374, April 2012.

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c. IB10-05 Spellman S, Klein JP, Haagenson M, Askar M, Baxter-Lowe LA, He J, Hsu S, Blasczyk

R, Hurley CK. Scoring HLA Class I Mismatches by HistoCheck Does Not Predict Clinical Outcome in Unrelated Hematopoietic Stem Cell Transplantation. Published. Biol Blood Marrow Transplant, 18(5):739-746. May 2012.

d. IB07-09 Pearce KF, Lee SJ, Haagenson M, Petersdorf EW, Norden J, Collin MP, Klein JP,

Spellman SR, Lowerson SA, Davies S, Dickinson AM. Analysis of non-HLA genomic risk factors in HLA-matched unrelated donor hematopoietic cell transplantation for chronic myeloid leukemia. Published. Haematologica, 97(7):1014-1019. July 2012.

e. IB08-02 Horan J, Wang T, Haagenson M, Spellman SR, Dehn J, Eapen M, Frangoul H, Gupta V,

Hale GA, Hurley CK, Marino S, Oudshoorn M, Reddy V, Shaw P, Lee SJ, Woolfrey A. Evaluation of HLA matching in unrelated hematopoietic stem cell transplantation for non-malignant disorders. Published. Blood, 120(14):2918-2924. October 4, 2012.

f. IB06-11s Rocha V, Spellman S, Zhang MJ, Ruggeri A, Purtill D, Brady C, Baxter-Lowe LA,

Baudoux E, Bergamaschi P, Chow R, Freed B, Koegler G, Kurtzberg J, Larghero J, Lecchi L, Nagler A, Navarette C, Prasad V, Prasath A, Price T, Pouthier F, Ratanatharathorn V, van Rood JJ, Horowitz MM, Gluckman E, Eapen M. Effect of HLA-matching recipients to donor non-inherited maternal antigens on outcomes after mismatched umbilical cord blood transplantation for hematologic malignancies. Published. Biol Blood Marrow Transplant, 18(12):1890-1896. December 2012.

g. R04-76s Petersdorf EW, Malkki M, Gooley TA, Spellman S, Haagenson M, Horowitz MM,

Wang T. MHC-resident variation affects risks after unrelated donor hematopoietic cell transplantation. In press. Science Translational Medicine, 4(144):144ra101, Published online: DOI:10.1126/scitranslmed.3003974, July 25, 2012.

h. R04-74s Venstrom JM, Pittari G, Gooley TA, Chewning J, Spellman S, Haagenson M, Gallagher

MM, Malkki M, Petersdorf E, Dupont B, Hsu KC. HLA-C dependent prevention of leukemia relapse by donor activating KIR2DS1. In press. N Engl J Med, 367(9):805-816, Published online: DOI: 10.1056/NEJMoa1200503, August 30, 2012.

i. IB07-05 Morishima Y, Kawase T, Malkki M, Morishima S, Spellman S, Kashiwase K, Kato S,

Cesbron A, Tiercy JM, Senitzer D, Verlardi A, Petersdorf EW. Significance of ethnicity in the risk of acute graft-versus-host disease and leukemia relapse after unrelated donor haematopoietic cell transplantation. Submitted.

j. IB09-08 Dobbelstein C, Ahn KW, Haagenson M, Hale GA, van Rood JJ, Miklos D, Waller

EK, Spellman SR, Fernandez-Viña M, Ganser A, Aljurf M, Bornhaeuser M, Gupta V, Marino SR, Pollack MS, Reddy V, Eder M, Lee SJ. Birth order and transplant outcome in HLA-identical sibling stem cell transplantation – an analysis on behalf of the Center for International Blood and Marrow Transplantation (CIBMTR). Submitted.

4. Research Repository update and accrual tables (S Spellman) (Attachment 2) 12:25 pm

5. Proposed studies and discussion for Immunobiology Working Committee 12:35 pm

a. Voting guidelines (C Müller)

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b. PROP 0212-01/0712-01 Interaction between SNPs and clinical data using predictive modeling on a Bayesian network framework./ Short and long term survival assessment of post HSCT transplantation using predictive modeling on a Bayesian network framework. (R Abdi/G Alterovitz/D McDermott) (Attachment 3)

c. PROP 0312-01 Role of the complement system in graft-versus-host disease (V Afshar-Kharghan /J Belmont/C Amos) (Attachment 4)

d. PROP 0712-02/IB13-01 The Impact Of MHC Class I Chain-Related Gene A (MICA) Donor-Recipient Mismatches and MICA-129 Polymorphism On Unrelated Donor Hematopoietic Stem Cell Transplants (HSCT) For Hematological Malignancies (M Askar/R Sobecks) (Attachment 5)

e. PROP 0712-03 The development of Machine Learning based classifiers to define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation (Y Louzoun) (Attachment 6)

f. PROP 1112-16 Effect of HLA-C allele matching in the context of recipient HLA-C-encoded KIR ligand grouping (C1 or C2) on the outcome of unrelated hematopoietic stem cell transplantation (HSCT) (J Fischer/M Uhrberg) (Attachment 7)

g. PROP 1112-27 Impact of donor signal-regulatory protein alpha (SIRPα) polymorphism on outcome of allogeneic hematopoietic stem cell transplantation (allo-HSCT) (A Gassas/J Danska/S Rajakumar) (Attachment 8)

h. PROP 1112-68 The effect of allele-level HLA-matching on survival after umbilical cord blood transplantation for non-malignant diseases in children. (P Veys/M Eapen) (Attachment 9)

i. PROP 1212-04 Effects of HLA Class I Amino Acid Mismatches on Stem Cell Transplant Outcomes (SR Marino/SM Lee/T Karrison/TA Binkowski/A Artz) (Attachment 10)

j. Proposal voting

6. BREAK – 30 minutes 2:15 pm 7. Studies in progress (Attachment 11) 2:45 pm

HLA GENES – CLASSICAL MATCHING (Chair: D Miklos) 2:45 pm

a. IB06-13/R04-80s HLA matching in unrelated cord blood transplants (S Rodriguez-Marino/LA Baxter-Lowe/V Rocha/M Eapen) – update

Manuscript Preparation

b. IB11-03 Evaluation of the impact of allele homozygosity at HLA loci on outcome (C Hurley/A Woolfrey/M Maiers) (Attachment 12) - update


c. IB11-04 Impact of amino acid substitutions at peptide binding pockets of HLA class I molecules on hematopoietic cell transplantation (HCT) outcomes (J Pidala/C Anasetti) (Attachment 13) - update


d. IB11-06 Evaluation of the impact of potentially non-immunogenic HLA-C allele level mismatch (M Fernandez-Viña/M Setterholm) - update


e. IB12-03 Effect of genetic ancestry matching on HSCT outcomes (A Madbouly/M Maiers/N Majhail) (Attachment 14) – update

Typing

f. IB06-02 Mismatching for low expression HLA loci in matched unrelated donor transplants (M Fernandez-Viña) - no update


g. IB09-02 Non-permissive HLA-DPB1 disparities based on T cell alloreactivity (K Fleischhauer) – no update


h. IB12-01 Impact of unrelated donor HLA-mismatch in reduced- Data File Preparation

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intensity conditioning allogeneic hematopoietic stem cell transplantation outcomes (J Koreth) – no update

i. IB12-02 Impact of unrelated donor HLA-mismatch in myeloablative conditioning allogeneic hematopoietic stem cell transplantation outcomes (J Pidala/C Anasetti) – no update

Protocol Development

CYTOKINE/CHEMOKINE (Chair: M Fernandez-Viña) 3:15 pm

a. IB08-04s Immune response gene polymorphisms in unrelated donor stem cell transplantation in children (K Müller) – no update


NK/KIR (Chair: M Fernandez-Viña) 3:15 pm a. R02-40s/R03-63s KIR Program Project/NK receptor acquisition

(J Miller/E Trachtenberg) - update Ongoing

b. IB07-03 Analysis of Killer Immunoglobulin-like Receptor(KIR) ligands in reduced intensity conditioning (RIC)allogeneic hematopoietic stem cell transplantation (HSCT) (R Sobecks/K Hsu/M Askar) (Attachment 15) – update

Analysis

c. IB12-04 Determining the Effects of HLA-C KIR Ligand Expression on Outcomes of Unrelated Hematopoietic Stem Cell Transplantation (J Venstrom) – update

Data File Preparation

d. R04-74s KIR functional significance (IHWG) (K Hsu/J Venstrom) – no update

Ongoing

e. IB08-06 Analysis of Killer Immunoglobulin-Like Receptor (KIR) ligands in umbilical cord blood transplantation (R Sobecks/V Rocha/M Eapen) – no update

Analysis

f. IB11-05s KIR genotyping and immune function in MDS patients prior to unrelated donor transplantation (E Warlick/J Miller) - no update

Typing

g. IB12-06s Natural killer cell genomics and outcomes after allogeneic transplantation for lymphoma (V Bachanova/J Miller/D Weisdorf/L Burns) – no update


OTHER GENES (Chair: C Müller) 3:35 pm a. R04-76s Identification of functional SNPs (IHWG) (E

Petersdorf) - update Ongoing

b. IB10-01 Donor and Recipient Telomere Length as Predictors of Outcomes after Hematopoietic Stem Cell Transplant in Patients with Acquired Severe Aplastic Anemia (S Gadalla) –update

Analysis

c. IB12-05/RT10-01 Plasma YKL-40 and CHI3L1 genotype to predict mortality after allogeneic hematopoietic cell transplantation (HCT) (B Kornblit) – update


d. IB08-08 Genome-Wide Association in Unrelated Donor Transplant Recipients and Donors: A Pilot Study (R Goyal) – no update


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e. IB09-04s Association of donor and recipient gene polymorphisms of drug metabolisms [GSTP, GSTT, GSTM and UGT (2B17, 2B7, 2B28)] and innate immune response [CD14, TIRAP, and NALPs (1 and 3)] with outcomes after allele matched unrelated hematopoietic stem cell transplantation (V Rocha) – no update

Analysis

f. IB09-06s/RT09-04s Genetic polymorphisms and HCT related mortality Re: Pre-HCT conditioning in matched unrelated donor HCT (T Hahn) – no update


g. IB10-03 TLR and HMGB1 gene polymorphisms in unrelated haematopoietic stem cell transplantation (K Müller/B Kornblit) – no update

Analysis

h. IB10-04s A validation study of the role of base excision repair pathway as a predictor of outcome after hematopoietic stem cell transplant (B Thyagrajan /M Arora) – no update

Analysis

i. IB11-02s Impact of CTLA4 single nucleotide polymorphisms on outcome after unrelated donor transplant (M Jagasia/W Clark/B Savani/S Sengsayadeth) – no update

Typing

SENSITIZATION/TOLERANCE (Chair: D Miklos) 3:55 pm a. IB11-01 Analysis of the NIMA effect on the outcome of unrelated

PBSC/BM transplantation (G Ehninger/JJ van Rood/ A Schmidt) – update

Typing

b. IB11-07 Effect of Pretransplant Rituximab upon ABO Mismatch Hematopoietic Cell Transplantation (D Miklos/A Logan) – update


c. R03-65s HY antigen (D Miklos) – no update Manuscript Preparation d. IB06-09s Detection of HLA antibody to the mismatched

antigen in single antigen HLA-mismatched unrelated donor transplants: Is it a predictor of graft-versus-host disease outcome? (S Arai/D Miklos) – no update


MINOR HISTOCOMPATIBILITY ANTIGENS No updates. 4:10 pm

8. Deferred studies pending accrual/funding 4:10 pm

a. IB06-10 Evaluation of the impact of the exposure to NIMA during fetal life and breast feeding and to the IPA during pregnancy on the clinical outcome of HSCT from haploidentical family members (J van Rood) – no update

Data Collection

b. IB12-07 Telomeres and incidence of leukemia recurrence and survival after hematopoietic stem cell transplantation (M Eapen) – no update

Data Collection

9. Closing remarks (C Müller) 4:15 pm

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Not for publication or presentation Attachment 1

MINUTES CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY San Diego, California Wednesday, February 1, 2012, 12:15 pm– 4:45 pm Co-Chair: Carlheinz Müller, MD, PhD, German National Bone Marrow Donor Registry Telephone: +49-731-1507-10; Fax: +49-731-1507-51; E-mail: [email protected] Co-Chair: David Miklos, MD, PhD, Stanford University Telephone: 650-725-4626; Fax: 650-724-6182; E-mail: [email protected] Co-Chair: Marcelo Fernandez-Viña, PhD, Stanford University Telephone: 650-723-7968; Fax: 650-725-4470; E-mail: [email protected] Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 612-884-8609; Fax: 612-884-8661; E-mail: [email protected] Tanya Pedersen, MPH, CIBMTR Statistical Center Telephone: 612-884-8607; Fax: 612-884-8661; E-mail: [email protected] John Klein, PhD, CIBMTR Statistical Center Telephone: 414-456-8280; Fax: 414-456-6513; E-mail: [email protected] Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-456-4339; Fax: 414-456-6513; E-mail: [email protected] Co-Scientific Dir: Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Telephone: 206-667-5160; Fax: 206-667-1034; E-mail: [email protected] Co-Scientific Dir: Stephen Spellman, MBS, CIBMTR Immunobiology Research Telephone: 612-617-8334; Fax: 612-884-8677; E-mail: [email protected] 1. Welcome and introduction

Dr. Marcelo Fernandez-Viña opened the meeting at 12:15 pm by introducing the chairs, scientific directors and statisticians of the Immunobiology working committee.

2. Minutes of Immunobiology Working Committee at Tandem 2011

Dr. Fernandez-Viña then asked for approval of the minutes from last year. They were approved as written.

3. Completed project summary (published or submitted work) a. IB06-03 Valcárcel D, Sierra J, Wang T, Kan F, Gupta V, Hale GA, Marks D, McCarthy PL, Oudshoorn

M, Petersdorf EW, Ringdén O, Setterholm M, Spellman SR, Waller EK, Gajewski JL, Marino SR, Senitzer D, Lee SJ. One antigen mismatched related vs. HLA-matched unrelated donor hematopoietic transplantation in adults with acute leukemia: CIBMTR results in the era of molecular typing. Published. Biol Blood Marrow Transplant, Vol. 17, Issue 5, Pages 640-648. May 2011.

b. IB07-01 Woolfrey A, Klein JP, Haagenson M, Spellman SR, Petersdorf E, Oudshoorn M, Gajewski J,

Hale GA, Horan J, Battiwalla M, Marino SR, Setterholm M, Ringden O, Hurley CK, Flomenberg N, Anasetti C, Fernandez-Vina M and Lee SJ. HLA-C Antigen mismatches are associated with worse outcomes in unrelated donor peripheral blood stem cell transplantation. Published. Biol Blood Marrow Transplant, Vol. 17, Issue 6, Pages 885-892, June 2011.

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c. IB06-04 Dong L, Wu T, Gao ZY, Zhang MJ, Kan F, Spellman SR, Tan XY, Zhao YL, Wang JB, Lu DP, Miklos D, Petersdorf E, Fernandez-Vina M and Lee SJ. The outcomes of family haploidentical hematopoietic stem cell transplantation in haematological malignancies are not associated with patient age. Published. Biol Blood Marrow Transplant, Vol. 17, Issue 8, Pages 1205-1213. August 2011.

d. IB07-02 Marino SR, Lin S, Maiers M, Haagenson M, Spellman S, Klein JP, Binkowski TA, Lee SJ,

and van Besien K. Identification by random forest method of HLA class I amino acid substitutions associated with lower survival at day 100 in unrelated donor hematopoietic cell transplantation. In press. Bone Marrow Transplantation. Published online: DOI:10.1038/bmt.2011.56. 28 March 2011.

e. IB10-05 Spellman S, Klein JP, Haagenson M, Askar M, Baxter-Lowe LA, He J, Hsu S, Blasczyk R,

Hurley CK. Scoring HLA Class I Mismatches by HistoCheck Does Not Predict Clinical Outcome in Unrelated Hematopoietic Stem Cell Transplantation. In press. Biol Blood Marrow Transplant, Uncorrected proof: 29 September 2011.

f. IB05-03s Shamim Z, Faucher S, Spellman S, Decker W, Haagenson M, Wang T, Lee SJ, Ryder LP,

and Muller K. Polymorphism in the genes encoding human interleukin-7 receptor-alpha and outcomes after allogeneic hematopoietic cell transplantation with matched unrelated donor. Submitted.

g. IB06-11s Rocha V, Spellman S, Zhang MJ, Ruggeri A, Purtill D, Brady C, Altamuro D, Baxter-Lowe

LA, Baudoux E, Beddard RL, Bergamaschi P, Chow R, Freed B, Koegler G, Kurtzberg J, Larghero J, Lecchi L, Mrowiec Z, Nagler A, Navarette C, Prasad V, Prasath A, Price T, Pouthier F, Ratanatharathorn V, Sander J, Sender L, van Rood JJ, Horowitz MM, Gluckman E, Eapen M. Effect of HLA-matching recipients to donor non-inherited maternal antigens on outcomes after mismatched umbilical cord blood transplantation for hematologic malignancies. Submitted.

h. IB07-09 Pearce KF, Lee SJ, Haagenson M, Petersdorf EW, Norden J, Collin MP, Klein JP, Spellman

SR, Lowerson SA, Davies S, Dickinson AM. Analysis of non-HLA genomic risk factors in HLA-matched unrelated donor hematopoietic cell transplantation for chronic myeloid leukemia. Submitted.

i. IB07-06 Fleischhauer K, Shaw B, Gooley T, Malkki M, Bardy P, Bignon JD, Horowitz M, Madrigal A,

Morishima Y, Spellman S, Velardi A, Zino E, Petersdorf E. Non-permissive HLA-DPB1 T cell epitope mismatches increase mortality after unrelated donor hematopoietic cell transplantation. Submitted.

j. R04-74s Venstrom JM, Pittari G, Gooley TA, Chewning J, Spellman S, Haagenson M, Gallagher MM,

Malkki M, Petersdorf E, Dupont B, Hsu KC. Donor activating KIR2DS1 protects against acute myeloid leukemia relapse in an HLA-dependent manner. Submitted.

Dr. Fernandez-Viña gave a quick overview of the studies that have been published and submitted over the past year.

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4. Research Repository update and accrual tables

Steve Spellman announced the new CIBMTR effort, the Forms Revision Process. All data collection forms are undergoing revision over the next two years, starting with the following: CRID (2804), Pre-TED (2400), Baseline (2000), Infectious Disease Markers (2004), HLA (2005), Infusion (2006), AML (2010/2110), ALL (2011/2111), MDS (2014/2114), JMML (2015/2115), Plasma Cell Disorders (2016/2116), Amyloidosis (2017/2117), Lymphoma (2018/2118) and Waldenstrom's Macroglobulinemia (2019/2119). The revised forms will coincide with the development of the new FormsNet application. Members are encouraged to become a member of the Forms Revision Review Committee in order to capture all the relevant information needed to produce high-quality studies. Suggestions for forms should be forwarded to the Immunobiology Working Committee Leadership or Emilie Meissner at [email protected]. An open comment period will end March 2, 2012. Mr. Spellman then presented an update on the NMDP Research Repository. The NMDP has samples on unrelated donors, cord blood units, and related donors. The unrelated donor sample collection started in 1988. The related donor sample collection started in December, 2007 as part of the SCTOD contract. Over 12,000 samples were distributed last year. Most samples are frozen aliquots, although some are stored as whole blood on filter paper. The unrelated donor samples before 2002 consisted of B-LCL, PBMC, granulocytes and serum. There are approximately 22,700 unrelated donor transplant pairs overall with over 15,000 first transplants (most with high resolution HLA typing). There are 775 single umbilical cord blood unit transplant pairs (UCBT) and 128 double UCBT triplets in the Repository. There are 1135 related paired samples, while there are 1359 related recipients and 1247 related donors. Mr. Spellman also mentioned that investigators are required to submit the interpreted results of all testing back to the NMDP/CIBMTR. The data must be made available to the HCT research community. This eliminates or reduces duplicative testing to preserve resources and sample inventory. Data is captured in the NMDP Immunobiology database and is linked to sample inventory. The NMDP Histocompatibility Advisory Group oversees the use of samples and provides final approval for release of samples.

5. Proposed studies and discussion for Immunobiology Working Committee

a. Voting guidelines Dr. Carlheinz Müller stressed the basic criteria for voting, so that the IBWC group can decide how to use our resources effectively. Voting should be on a scale from one to nine, based on impact.

b. PROP 1111-07 Is a well-matched HLA-identical unrelated male donor (10/10) an alternative to a female HLA-identical sibling donor for a male recipient in need of hematopoietic stem cell transplantation? (O Ringdén/T Erkers/J Törlén) Dr. Olle Ringdén presented this proposal. The scientific background for this proposal is the belief that female donors transplanted into male recipients leads to increased risk of acute and chronic GvHD, worse survival and leukemia-free survival, and contribute to a graft-versus-leukemia effect, so that perhaps a male matched unrelated donor might be preferable to a female related donor . The outcomes to be analyzed for this proposal would be acute GvHD (both Grades II-IV and III-IV), TRM, chronic GvHD, relapse, overall survival and relapse-free survival. The study population would include diseases of ALL, AML and CML transplanted between 1995 and 2011. Myeloablative or reduced intensity conditioning regimens would be considered. This proposal would evaluate only male recipients and would compare HLA-identical female sibling donors against 10/10 matched unrelated male donors to see which is the better donor. In the initial evaluation, there are 2553 HLA-identical female sibling donor transplants compared to 1486 10/10 matched unrelated male donor transplants available.

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Concerns from the committee were the study population should be adult only due to the difference in ages between a sibling donor and an unrelated donor; CML cases should be removed since they do not reflect current practice; donor parity, ATG use, and cytogenetics should be considered in the analysis; and that the sex mismatch impact on survival in related donor transplantation is not well established.

c. PROP 1111-08 Are major HLA-antigens targets for the graft-versus-leukemia effect? (T Erkers/O Ringdén/J Törlén) Dr. Tom Erkers presented this proposal, which would compare mismatched related donors versus unrelated single cord blood units. The study population would include ALL and AML cases transplanted between 1995 and 2011, with recipients of all ages and conditioning regimens. Outcomes to be analyzed include platelet and neutrophil engraftment, acute GVHD grades II-IV and grades III-IV, treatment-related mortality, chronic GVHD, relapse probability, overall survival, and relapse-free survival. The importance of study would be that for patients who do not have an HLA-matched related or unrelated donor, it is important to select the best source available. The preliminary tables show approximately 579 mismatched related donors available and 657 single unit cord blood transplants available. One concern raised by the committee was the difference in recipient age between the two groups, so one way around that is to cap the age at 18 years of age. Limiting the study to children would also make is more relevant since most adults are getting double cords now. Other suggestions were to only use CIBMTR data, exclude CML patients and 6/6 matched cord blood transplants, exclude transplants before 2000, require a minimum TNC in the cord transplants and evaluate engraftment. HLA is defined for related cases, but for cord blood transplants, the HLA is defined as antigen-level for HLA-A and –B and allele-level for HLA-DRB1. This is a difficult study to adjust for the differences that exist. Some centers don’t use UCB transplants after relapse. Another concern was the number of available cases because of the parsing that would take place.

d. PROP 1111-22 Impact of unrelated donor HLA-mismatch in reduced-intensity conditioning allogeneic hematopoietic stem cell transplantation outcomes (J Koreth) Dr. John Koreth presented this proposal, which would assess 8/8 vs. 7/8 vs. 6/8 HLA-match unrelated donor transplants (URD) in reduced intensity conditioning regimens (RIC) in the diseases of AML, MDS, ALL and CML, for impact of: A) Number of HLA-mismatch loci (1 or 2-loci mm vs. matched); and B) Type of HLA-mismatch (antigen or allele mm vs. matched; HLA-A, -B, -C or -DRB1 mm vs. matched.) Outcomes include overall survival as the primary outcome and Non-Relapse Mortality (NRM), relapse, aGVHD (gr II-IV) and Disease-Free Survival (DFS) as secondary outcomes. Other possible outcomes include primary graft failure (GF) and chronic GVHD. In the preliminary analysis of 3380 cases available, there are 2564 8/8 matched cases, 710 7/8 matched cases (where 235 are HLA-A mismatched (MM), 102 are HLA-B MM, 283 are HLA-C MM and 90 are HLA-DRB1 MM), and 106 6/8 matched cases. Dr. Koreth doesn’t think related donors should be included. Also, there are 3203 cases with HLA-DQB1 typing, with 2908 matched, 287 with single MM and 8 with a double MM. There are only 700 cases with HLA-DPB1 typing, of which 107 are matched, 397 are single MM and 196 are double MM. The committee raised concerns about the low number of marrow cases in the study population that may warrant restricting to PBSC. In addition, it would be important to understand the reason for receiving a RIC transplant in recipients less than 50 years old, since it may reflect additional co-morbidities. Another possibility is to limit the analysis to those over age 50. Dr. Koreth felt that the distributions should be similar across the board since these are restricted to RIC transplants.

e. PROP 1111-24 Impact of HLA mismatch and patient/donor non-HLA variables on transplantation

outcome: An updated analysis utilizing NMDP high-resolution typing project data (J Pidala/C Anasetti) Dr. Joseph Pidala presented this proposal. The study objectives include determining the impact of allele and antigen-level HLA mismatch among donor-recipient pairs among the current NMDP high-

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resolution typing project on HCT outcomes, and studying the impact of donor and recipient non-HLA variables on transplantation outcome. The study population would include adult and pediatric patients, myeloablative first unrelated bone marrow or peripheral blood stem cell HCT, hematological malignancy (AML, ALL, CML and MDS), year of transplant between 1988 and 2011 (provided minimum one year follow up at time of analysis), and the patient and donor must have complete high resolution typing for HLA-A, B, C, DRB1, DQA1, DQB1, DPA1, and DPB1. Outcomes to be analyzed include overall survival, treatment-related mortality, relapse, acute GVHD (Grades II-IV, and III-IV) and chronic GVHD (limited or extensive). The primary effect to be tested would be the impact of single allele or antigen-level mismatch on HCT outcomes, where the analysis would evaluate the outcomes according to 8/8 vs. 7/8 vs. 6/8. Secondary effects include impact of mismatch at each HLA locus on outcomes, with the comparison of allele vs. antigen-level mismatch as well as the impact of multiple mismatches on outcome. The study design would include examining the independent impact of HLA disparity on the outcomes. The multivariate analyses would be performed using the proportional hazards model, and there would be a comparison between pairs mismatched at specific HLA loci and HLA-matched pairs. Concerns were raised by the committee to make sure that directional mismatching is considered when doing the analyses. This study would be a starting point for selecting the optimal unrelated donor for transplant. Suggestions included restricting the analysis to the 8/8 vs. 7/8 vs. 6/8 in the HLA locus MM analyses. Another suggestion was to limit to more recent years of transplant to reflect a more contemporary dataset.

f. PROP 1111-50 Determining the effects of HLA-C KIR ligand expression on outcomes of unrelated

hematopoietic stem cell transplantation (J Venstrom) Dr. Jeffrey Venstrom presented this proposal, which will look at the impact of the level of expression of HLA-C alleles (low vs. high) on KIR alloreactivity. The first hypothesis is that recipients with low expression HLA-C alleles will have improved survival because they will benefit from less donor NK cell inhibition. A second hypothesis is that, in an HLA-C mm HCT, recipients with low expressing HLA-C alleles will have lower rates of aGvHD and NRM because they will benefit from less NK inhibition and less non-specific T-cell alloreactivity. HLA-C alleles will be divided into 2 functional groups based on linkage disequilibrium (LD) between the allele and the presence of the 3’UTR binding site for miR148. The aim will be to determine the influence of this grouping on HCT outcomes. The preliminary analysis showed 9588 cases available for analysis of which 5510 are 8/8 matched. The HLA-C alleles will be divided into levels of surface expression based off of 3’UTR 263 deletion or insertion. The outcomes to be analyzed include overall survival, acute GVHD, relapse, disease-free survival and non-relapse mortality. Initially, the proposal will look to see if there is a signal, and then if there is, the analysis will continue to see what the signal means. Concerns from the committee included making the study population more homogeneous, looking at the potential type of mismatches as homozygous vs. heterozygous and determining what subset of the cases listed have KIR genotyping data available. 80% of HLA-C are expected to be low expression, and there is some overlap with between the groupings according to expression level and Group 1 or 2 classifications. It is not known how miR148 regulates HLA-C expression.

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g. PROP 1111-53 Plasma YKL-40 and CHI3L1 genotype to predict mortality after allogeneic

hematopoietic cell transplantation (HCT) (B Kornblit/P Garred/L Vindelov) Dr. Brian Kornblit presented this proposal. The main objective of this study is to find an inflammatory biomarker that captures the risk that is both inherent in the patient’s comorbidities malignancy. The study will analyze YKL-40, which is an acute phase reactant with a different release profile than CRP. YKL-40 has been associated with disease severity and mortality in a variety of infectious and non-infectious inflammatory diseases. YKL-40 secretion is induced by INF-γ and IL-6. YKL-40 is released from macrophages, granulocytes, cancer cells and connective tissue cells. The exact function of YKL-40 is unknown, but some known functions include inflammation, cell proliferation and differentiation, and inflammatory cell apoptosis. CHI3L1 is the gene for YKL-40 and contains promotor SNP rs4950928, which defines a haplotype associated with plasma/serum levels. The primary aim of this proposal is to assess the prognostic value of patient and donor pre-transplant YKL-40 plasma levels and CHI3L1 genotype for the outcomes of overall survival, progression free survival, non-relapse mortality, relapse related mortality, acute GVHD and chronic GVHD. The preliminary analysis shows 510 patients available for the study. The study population runs from 2008-2010. Other stipulations of this proposal include plasma and DNA available from patients and donors, diseases are AML (88%) and MDS (12%), conditioning regimen of high dose or RIC, donor type of MUD 98% (well matched 76%), graft source of BM or PBSC, median age of 49 (18-74) years, and T-cell depletion is 1 out of 510. One concern about this study is that the minor allele frequency is about 4%, which could represent a problem with the analysis. Also, the study population’ race was a concern since it wasn’t stated in the table, although it is roughly 85% Caucasian. A suggestion was to add other markers to the analysis to ensure efficient use of the samples. The plan is to partner with an existing study in the Regimen-Related Toxicity working committee that is investigating the role of recipient CRP and IL-6 levels on outcomes. The same patient group could be used for both analyses.

h. PROP 1111-69 Natural killer cell genomics and outcomes after allogeneic transplantation for lymphoma (V Bachanova/J Miller/D Weisdorf/L Burns) Dr. Veronika Bachanova presented this proposal. This proposal will evaluate natural killer (NK) cell genomics and outcomes in lymphoma. KIR genes and HLA genes segregate independently on different chromosomes. Donor-derived NK cells permit potent alloreactivity toward tumor cells. Donor KIR repertoire is genetically determined and inherited as a haplotype. Therefore even fully HLA-matched allogeneic donors likely have a higher frequency of NK cells mismatched between KIR and cognate HLA class 1 ligands. It was shown that donors with more KIR B-gene content decrease relapse and improve survival in AML, which is a benefit in both HLA matched or HLA mismatched transplants. The hypothesis of this proposal is that the number and content of genes regulating donor –derived NK cell function have an impact on graft –versus-lymphoma effects and subsequent relapse of patients with lymphoma undergoing donor HCT. The study design will include analyzing donor samples for KIR genotype; determining haplotype AA vs. Bx, B content score, cen AA, AB, BB, tel AA, AB, BB; determining transplant outcomes and all models will include the A/B gene genotype; evaluating “missing” KIR ligand model in lymphoma; and evaluating NK immunogenetics in multivariate analysis. The preliminary analysis shows 819 patients with donor samples available. There are 185 with Follicular Lymphoma, 118 with DLBCL and 122 with MCL. One concern was raised about the power to detect a difference in this cohort. It was believed that about 400 cases are needed to detect a 7% difference in relapse. Relapse was used as the outcome because it was of most interest and did not have as much noise as overall survival. Other concerns included: (1) being able to determine whether patients received Rituximab and the impact this could have on NK cell licensing. This is a yes/no question on the form, without an associated date. Rituximab may negate any effect of NK cells; (2) heterogeneity in the disease status of the patient going into transplant; (3) the use of ATG ; and (4) determining the amount of RIC transplants in the cohort, which is about 50%.

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i. PROP 1211-03 Effect of genetic ancestry matching on HSCT outcomes. (A Madbouly/M Maiers/N

Majhail) Martin Maiers presented this proposal. Many HSCT studies are looking at race/ethnicity of patient, of donor and of patient/donor pair (matched/mismatched). These studies use self-identification or interviewer-identified ethnicity. This proposal will use Ancestry Informative Markers (AIMS) to define population structure (beyond self-identification), to compare with current race/ethnicity questionnaire in efforts to form a new ancestry focused questionnaire, to fill in blanks (unknown, other, declines), and to provide objective evaluation of this “multi” classification . Hypotheses for this proposal are that differences in genetic ancestry for HLA matched unrelated donors and recipients can identify potential transplantation determinants and that recipient and/or donor non-HLA genetic ancestry can impact transplant outcomes. The clinical study plan includes a Phase 1 pilot study where there would be SNP typing on a small sample. This would help in estimating a sample size for adequate statistical power and evaluating several patient/donor proximity metrics. The proposal would then proceed to Phase 2 which is cohort typing and analysis. This would include HLA-matched donors and recipients (10 of 10) typed for SNP AIMs as well as studying the effect of ancestry transplant outcomes with respect to genetically assigned ancestry and with respect to genetic differences between donors and recipients. Primary outcomes measures would be overall survival and TRM. One concern was about the current way of defining Hispanic as an ethnicity and that it has changed over time. Another concern mentioned clustering race into groups after typing the samples. Another concern about this study is the number of SNPs to type to form the groups.

6. 16th International Histocompatibility and Immunogenetics Workshop Collaboration updates

Steve Spellman presented these updates on behalf of Dr. Effie Petersdorf. The 16th IHIW Meeting will be held this summer in Liverpool. There are currently 23,755 cases in the IHWG data base. One study by Drs. Katharina Fleischhauer and Bronwen Shaw was accepted for publication in Lancet Oncology. The conclusions of that manuscript are that TCE matching defines permissive and non-permissive HLA‐DPB1 mismatches, and avoidance of an unrelated donor with a non-permissive TCE mismatch may provide a practical strategy for lowering the risks of mortality after unrelated donor HCT.

j. Proposal voting The proposal voting was done at this time.

7. BREAK

A break was taken at 2:05 pm and the meeting reconvened at 2:40 pm. 8. Studies in progress

IB11-04 Impact of amino acid substitutions at peptide binding pockets of HLA class I molecules on hematopoietic cell transplantation (HCT) outcomes (J Pidala/C Anasetti) – Dr. Joseph Pidala began the second half of the IBWC session with an update to his amino acid substitution (AAS) study IB11-04. The hypothesis of this study is that AAS at peptide binding pockets of the HLA class I molecule adversely affects transplantation outcome. The study population was HLA high-resolution typed and included 8/8 for HLA-A, -B, -C and –DRB1 or 7/8 with a single mismatch (MM) at HLA-A, -B or –C. The population was broken down into three groups in the initial analysis: 8/8 matched, 7/8 with an AAS residue at positions 9, 77, 99, 116 or 156 (in any combination), and 7/8 with AAS only at other positions. The analysis examined the independent impact of AAS at key (9, 77, 99, 116, and 156) residues on Grade III-IV acute GVHD, chronic GVHD, TRM, relapse and overall survival (OS). Multivariate analysis comparing the two 7/8 matched groups showed that AAS at position 116 at any HLA locus had increased Grade III-IV acute GvHD. No other significant associations were detected.

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Further multivariate analysis looked at AAS by HLA locus in the 7/8 groups and determined that for 7/8 with HLA-C mismatch, AAS at position 116 is associated with increased risk for severe acute GVHD, worse OS and AAS at position 99 is associated with increased TRM. For 7/8 with HLA-B mismatch, AAS at position 9 is associated with increased chronic GVHD. Ongoing efforts include expanding coverage of AAS at positions of interest by describing actual amino acids that are substituted at these positions and by describing donor-recipient allele pairs that inform these AAS. Another ongoing effort is the structural analysis to explain the impact of these AAS on antigen presentation, possibly by including the Risler score, charge or hydrophobicity. A concern from the audience included the p-values of the multivariate analysis as to whether they were corrected or not for multiple comparisons. They were not corrected. MINOR HISTOCOMPATIBILITY ANTIGENS – No updates were given for this section. SENSITIZATION/TOLERANCE – a. R03-65s HY antigen (D Miklos)

Dr. David Miklos presented this update. In a Blood paper from 2005, it was shown that H-Y positive antibody development correlates with increased chronic GVHD and less disease relapse. The aim of the original study was to determine the prevalence and specificity of antibody to H-Y antigens in 290 healthy female donors in relation to donor age and parity. Dr. Miklos referred to a study by Dr. Alison Loren in BBMT 2006 where there is an increased chronic GVHD risk in HLA-identical sibling donor transplants after female donor into male recipient HCT in both nulliparous and parous female donors. The technology aim of this study now is to develop H-Y microarray technology to improve detection sensitivity and fine specificity analysis of serum antibodies to H-Y proteins and their overlapping peptides. Currently, H-Y microarray identifies more seropositive female donors than ELISA. An analysis of the data will show two things: 1) Does H-Y microarray seropositivity correlate with parity info; and 2) Does H-Y seropositivity predict clinical outcomes of chronic GVHD, relapse and overall survival?

b. IB11-01 Analysis of the NIMA effect on the outcome of unrelated PBSC/BM

transplantation (G Ehninger/JJ van Rood/ A Schmidt) – Dr. Alexander Schmidt presented this study update. Dr. Schmidt stated that the hypothesis of this study is that in utero exposure of the child to non-inherited maternal antigens (NIMA) is supposed to lead to tolerance towards these antigens, and that this tolerance may improve outcomes in stem cell transplantation. This study’s primary end point is transplant-related mortality (TRM), and the secondary end points include neutrophil engraftment, platelet engraftment, incidence of grade II-IV and III-IV acute GVHD, incidence of chronic GVHD, relapse, overall mortality and treatment failure. The inclusion criteria for this study are patients with AML, ALL, CML or MDS; unrelated BM/PBSC transplantation; no T-cell depletion; patient follow-up information available for a minimum of 1 year after transplantation; patient and donor HLA typing available at high resolution at least for the HLA loci A, B, C, DRB1; donor/patient pairs must have at least one allele mismatch; and maternal HLA typing at high resolution at the mismatched locus. Patients who received second HSCT will be excluded. The current status of this study is 518 donors have been contacted of which 368 are CIBMTR cases. There are 227 typings of maternal HLA (44%), and 14 have NIMA Matches (6%) (11 on allele level, 3 on split-antigen level only). The main problem is: Are there enough patient outcome data to reach statistical significance? DKMS is in contact with EBMT Acute Leukemia Working Party (M. Mohty / E. Polge) as well as

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potentially getting further data from CIBMTR. Doing a matched case/control study does not increase the power much for this type of situation. The number of necessary cases would depend on the size of the effect on outcome. Power calculations will be run to determine the effect evaluable with this study population.

c. IB11-07 Effect of Pretransplant Rituximab upon ABO Mismatch Hematopoietic Cell

Transplantation (D Miklos/A Logan) –Dr. Aaron Logan presented this study update. This study looks at recipients of ABO minor mismatched (MM) grafts (i.e. - donor has anti-recipient ABO B cells and antibodies) to see if they experienced decreased OS and increased NRM. Survival impairment with ABO minor MM was observed in patients with leukemias but not with lymphomas. The hypothesis is that pre-HCT Rituximab in lymphoma patients may ameliorate the ABO MM effect by in vivo depletion of adoptively transferred anti-recipient ABO B cells. This study took an already existing data set from a study published in 2009, looking at Rituximab use in acute GVHD (both Grades II-IV and Grades III-IV), progression-free survival and overall survival. The multivariate analysis showed ABO mismatch had no effect on incidence of acute/chronic GVHD, or relapse/progression. Conclusions were that this dataset corroborates our finding of increased TRM and impaired OS in ABO minor mismatched allograft recipients, and that these data do not support the hypothesis that pre-HCT Rituximab ameliorates risks of ABO minor mismatched grafts (though, samples sizes are small). A manuscript describing the Stanford and CIBMTR datasets is in preparation. Other studies will consider ABO mismatch as a covariate in the multivariate analysis because of this analysis.

d. IB06-09s Detection of HLA antibody to the mismatched antigen in single antigen HLA-mismatched unrelated donor transplants: Is it a predictor of graft-versus-host disease outcome? (S Arai/D Miklos) No update was given.

e. IB09-08 A retrospective study on impact of donor and recipient birth order on outcome of HLA-identical sibling stem cell transplantation (SCT) in hematological malignancies reported to the CIBMTR (C Dobbelstein) – No update was given.

f. IB10-02 Development of GVHD prevention diagnostic test (R Somogyi/L Greller) – No update was given.

IB11-03 Evaluation of the impact of allele homozygosity at HLA loci on outcome (C Hurley/A Woolfrey/M Maiers) – Dr. Carolyn Hurley presented the study update. The study evaluates outcomes based on bidirectional and unidirectional mismatching, using HLA-A, -B, -C and –DRB1 as the loci of reference. The comparisons were 8/8 matched and homozygous, 7/8 GVH mismatch (MM) (where the donor is homozygous), 7/8 HVG MM (where the recipient is homozygous), 7/8 bidirectional MM (where both recipient and donor are heterozygous), and 6/8. Previous studies have shown HVG MM leads to more graft failure, one on bone marrow from the Seattle group in 2001 and one on cord blood from the New York Blood Center in 2011. This study looks at BM /PBSC; ALL, AML, CML, MDS; HLA-A, -B, -C, -DRB1 high resolution typing where the groups are 8/8 with at least 1 locus homozygous (n=1126), 7/8 bidirectional (1497), 7/8 HVG MM (121), 7/8 GVH MM (125), 6/8 (500). The primary endpoints

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are overall and disease free survival, and the secondary endpoints are acute GVHD, chronic GVHD, TRM, relapse, neutrophil engraftment and graft failure. The multivariate analyses showed that acute GVHD is lower for the 7/8 HVG MM group when compared to 7/8 bidirectional MM and 7/8 GVH MM groups. Otherwise, nothing came out as statistically significant. The outcome summary is to select 7/8 HVG MM over 7/8 bidirectional MM and 7/8 GVH MM donors. The writing committee for this study is working on the manuscript. The results of non-significance could be a power issue since there was a low number of cases in the 7/8 GVH MM and 7/8 HVG MM groups. The graft failure/rejection rate was minimal in this study, although it is likely underpowered. The multivariate analysis should adjust for ATG use, which it did.

OTHER GENES a. IB08-08 Genome-Wide Association in Unrelated Donor Transplant Recipients and

Donors: A Pilot Study (R Goyal) – Dr. Rakesh Goyal presented this study update. Dr. Goyal received samples on recipients and donors where the recipients either had no acute GVHD or had Grades III-IV acute GVHD. The recipient GWAS samples are now at 579 cases after typing, and the donor GWAS samples are now at 656 after typing. The p-values for potential candidates initially ranged from 10-10 to 10-6. Verification genotyping p-values now range from 10-5 to 10-2. There was also lost power due to GWAS typing, so some of the SNPs did not come out as significant after further testing. However, the question is raised about the biological rationale of these SNPs. Further testing is being done on matching for HLA-DPB1 and the effect on acute GVHD. The odds of Grades III‐IV acute GVHD are significantly lower if more HLA-DPB1 alleles match. If the patient has HLA data of HLA-DPB1*0401 mismatch, then the patient is likely to have less acute GVHD; however, if the donor has an HLA-DPB1*0401 mismatch, then the patient is more likely to have acute GVHD. Further analysis is required to verify these preliminary observations.

b. IB09-04s Association of donor and recipient gene polymorphisms of drug metabolisms [GSTP, GSTT, GSTM and UGT (2B17, 2B7, 2B28)] and innate immune response [CD14, TIRAP, and NALPs (1 and 3)] with outcomes after allele matched unrelated hematopoietic stem cell transplantation (V Rocha) – No update was given.

c. IB09-06s/RT09-04s Genetic polymorphisms and HCT related mortality Re: Pre-HCT conditioning in matched unrelated donor HCT (T Hahn) – No update was given.

d. IB10-01 Donor and Recipient Telomere Length as Predictors of Outcomes after Hematopoietic Stem Cell Transplant in Patients with Acquired Severe Aplastic Anemia (S Gadalla) – No update was given.

e. IB10-03 TLR and HMGB1 gene polymorphisms in unrelated haematopoietic stem cell transplantation (K Müller/B Kornblit) – No update was given.

f. IB10-04s A validation study of the role of base excision repair pathway as a predictor of outcome after hematopoietic stem cell transplant (B Thyagrajan /M Arora) –

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No update was given.

NK/KIR a. R02-40s/R03-63s KIR Program Project/NK receptor acquisition (J Miller/S Cooley/E

Trachtenberg) – Steve Spellman provided a verbal summary of this study. The plans are to collect a validation cohort of AML patients to test the Cen B/B effect. Approximately 1200 cases have been assembled. 650 samples went to Dr. Beth Trachtenberg’s lab last week for typing, and there are 600 additional cases that already have KIR typing in the NMDP database. This is a more contemporary cohort and will likely include more RIC and better HLA matching. The program project grant is also sponsoring a trial evaluating donor selection based on Cen B/B.

b. R04-74s KIR functional significance (IHWG) (B Dupont/K Hsu/J Venstrom) – Dr. Katharine Hsu presented this study update. At Tandem 2011, the abstract “Donor KIR2DS1 and KIR3DS1 are associated with improved outcomes following unrelated allogeneic stem cell transplantation for acute myeloid leukemia” was presented by Dr. Venstrom. The study population included 1233 cases that were 9/10 and 10/10 matched (with 9/10 having the mismatch at HLA–B or –C locus), and studied activating KIR and HLA-C ligand status. The outcomes analyzed were relapse, overall survival, TRM and GVHD. Results concluded that donor 2DS1 is associated with lower relapse. Also, donor 2DS1 effects are such that if the donor is KIR2DS1 positive and homozygous for HLA-C2 ligand, then there is a higher relapse rate. Recipient KIR ligands define susceptibility for AML relapse where recipients with homozygous HLA-C2 ligand have higher relapse than those with HLA-C1 ligand. HLA-C2/C2 recipients receiving HLA-matched HCT do not benefit from donor KIR2DS1. The study generated a subsequent abstract “KIR3DL1/S1 alleles and HLA-B alleles combine to influence unrelated HSCT outcomes” that was presented at Tandem 2012. The study population was 426 patients with myeloid diseases (AML=299, CML=27, MDS=100) where 82% HLA-matched 10/10, 96% HLA-B matched, 4% HLA-B matched on one allele, 37% of donors are 3DS1+, 96% of donors are 3DL1+, and 60% of donors and patients are HLA-Bw4+ (Bw4-I80, Bw4-T80). KIR genotyping was completed through the NMDP High Resolution KIR typing project. KIR3DL1 allele-level genotyping was performed with a combination of sequence-based typing (SBT) and sequence-specific typing (SSP). KIR3DL1 alleles were grouped into high (H), low (L) and null where H (*001, *002, *003, *008, *020, *01502, *022, *033, *035, *051 and *052); L (*005, *006, *007, *053, *028 and *029); and Null (*004). Outcomes analyzed included survival, relapse, NRM and GvHD. The multivariate analyses adjusted for patient age, disease severity, HLA matching, type of disease, T cell depletion and presence of 3DS1. Conclusions of the analyses are high affinity 3DL1-Bw4 interactions are associated with higher relapse; low-affinity 3DL1-Bw4 interactions and HLA-Bw6/Bw6 (missing ligand, unlicensed NK) are associated with lower relapse rates. Donor selection based on these observations are applicable to all patients (HLA-Bw4 vs. HLA-Bw6) and nearly all donors (96% 3DL1+); and an advantageous KIR donor can be identified for all Bw4+ patients with more than 1 URD. A question was raised as to why HLA-A ligands were ignored? The thought is that they probably won’t behave the same because they are not strong licensors. They could also dilute the result. Another question was raised about how does the patient population

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compare to a normal population? There has been testing on a normal population, but the issue is to choose the right donor because you cannot change anything about the patient.

c. IB07-03 Analysis of Killer Immunoglobulin-like Receptor (KIR) ligands in reduced intensity conditioning (RIC) allogeneic hematopoietic stem cell transplantation (HSCT) (R Sobecks/K Hsu/M Askar) – Dr. Ronald Sobecks presented this study update. The objectives of this study are to evaluate the clinical effects of donor KIR genotype and KIR ligand interactions in recipients of matched and mismatched unrelated donor RIC allogeneic HSCT for myeloid malignancies including GVHD, relapse, DFS, TRM and OS (1 and 5 yrs), and to assess the effects of donor-recipient KIR ligand interactions in unrelated donor RIC allogeneic HSCT for myeloid malignancies including graft rejection and T-cell donor chimerism (CDC). The cohort description includes cases from 1990 to 2007; 710 AML, 197 CML, 346 MDS; HLA matching of 746 (10/10), 319 (9/10), 128 (8/10), 60 (<7/10); HLA & KIR genotyping performed by NMDP; and the KIR classification done at MSKCC (by Meighan Gallagher and Katharine Hsu). KIR-HLA interactions have been assessed. For the 1051 AML/MDS cases, the missing KIR ligand model has been determined. Presence of donor inhibitory KIR where corresponding recipient HLA ligand is absent, single missing ligand where there would be the comparison of all ligands present (n=386) vs. missing C1 (n=128) vs. missing C2 (n=224) vs. missing Bw4 (n=106) vs. missing C1 or C2 & Bw4 (n=207), and multiple missing ligands. Univariate analyses show that relapse is lowest for group missing C1 or C2 & Bw4 at 1 yr (p=0.02) and at 5 yrs (p=0.03) and that there are no differences for acute GVHD (Grades II-IV at 100 days), TRM, DFS or OS. The univariate analysis for multiple missing ligands compares all ligands present vs. all others missing any KIR ligand(s), and it shows no differences in any outcomes. Also evaluated were the effects of individual donor activating KIRs by presence or absence of each individual donor activating KIR with its cognate ligand where relevant, donor 2DS1+ and donor C2 homozygote implies the highest 1 year TRM. No other significant differences were found in the univariate analysis. Three approaches for the upcoming multivariate analysis are the Missing Self model, the donor cumulative B content (with centromeric and telomeric combined) and the donor assessed for Centromeric B effect alone. The Missing Self model considers the presence of donor inhibitory KIR where donor HLA background is positive for the cognate HLA ligand while recipient lacks the ligand. Two approaches will include the single Missing Self model which assesses for each individual donor inhibitory KIR, and the multiple Missing Self model which assesses as a group (self-conserved for all KIR vs. missing self for any KIR). For the donor cumulative B content, there are 359 with zero B’s vs. 681 with 1-3 B’s vs. 9 with 4 B’s in the AML/MDS cohort. The univariate analysis showed no differences in 1 or 5 year OS, DFS, relapse, TRM or in 100 day Grades II-IV acute GVHD. The univariate analysis for the donors assessed for Centromeric B effect alone showed that Cen BB OS and DFS at 1 year is borderline statistically better than the other groups in the AML/MDS cohort. For the CML/AML/MDS cohort, Cen BB is better for one year OS, DFS and TRM in the univariate analysis. The multivariate analysis has not been started yet and the preliminary univariate results do not include any adjustments

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for other relevant co-variates.

d. IB08-06 Analysis of Killer Immunoglobulin-Like Receptor (KIR) ligands in umbilical cord blood transplantation (R Sobecks) – No update was given.

e. IB11-05s KIR genotyping and immune function in MDS patients prior to unrelated donor transplantation (E Warlick/J Miller) – No update was given.

CYTOKINE/CHEMOKINE a. IB08-04s Immune response gene polymorphisms in unrelated donor stem cell

transplantation in children (K Müller) – No update was given.

HLA GENES – CLASSICAL MATCHING a. IB11-03 Evaluation of the impact of allele homozygosity at HLA loci on outcome (C

Hurley/A Woolfrey/M Maiers) – This study update was already given.

b. IB11-04 Impact of amino acid substitutions at peptide binding pockets of HLA class I molecules on hematopoietic cell transplantation (HCT) outcomes (J Pidala/C Anasetti) – This study update was already given.

c. IB11-06 Evaluation of the impact of potentially non-immunogenic HLA-C allele level mismatch (M Fernandez-Vina/M Setterholm) – Dr. Marcelo Fernandez-Vina presented this study update. Dr. Stephanie Lee’s (et al) study “High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation” showed that poor outcomes (for OS, TRM and acute GVHD) are associated with HLA-C antigen mismatches (MM), but not HLA-C allele mismatches (MM). These results led to this study, looking at the HLA-C MM of C*03:03/C*03:04, which has the single amino acid replacement (R/G) at residue 91 (Connecting Loop). There are currently 134 cases in the data set that have a C*03:03/C*03:04 MM. The multivariate analysis compared this group against the 8/8 matched group, as well as comparing 7/8 HLA-C other allele MM, 7/8 HLA-C antigen MM, 7/8 HLA other locus MM against the baseline of 8/8 matched. Overall survival, DFS and Grades III-IV acute GVHD results do not show a significant difference between the 8/8 matched group and the 7/8 C*03:03/C*03:04 MM group. In addition, the C*03:03/C*03:04 MM group is statistically different than the other 7/8 groups for Grades III-IV acute GVHD, but this is not true for overall survival. Neutrophil engraftment at day 28 post transplant is worse for both the C*03:03/C*03:04 MM group and the 7/8 HLA-C allele MM group when compared to the 8/8 matched group. No associations with relapse or chronic GvHD in any of the 7/8 groups when compared to the 8/8 matched group. However, power calculations suggest that this study is quite underpowered and may require over 13000 cases to detect a difference between the groups. The mismatch HLA-C*03:03/C*03:04 is the most common isolated allele level mismatch (D&R European descent). There are common associations with B*15:01 (12.9 % patients with European ancestry). The outcomes of transplants with this single MM do not differ significantly from the transplants matched in 8/8 (near identical HR). Other MM (C-antigens,

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other loci) associate with worst outcomes (Survival, DFS, TRM, aGvHD). There is a significant difference in incidence of III/IV aGvHD between the MM C*03:03/C*03:04 and other mismatches. Priority should be given to 7/8 mismatches including C*03:03/C*03:04 over other mismatches (allele or antigen MM in HLA-C, A, B, DRB1). Other C-allele mismatches present similar HR to those observed for other MM. High resolution typing of HLA-C is still required in order to be able to identify these mismatches when present.

d. IB08-02 Evaluation of HLA matching requirements in unrelated hematopoietic stem cell transplantation for nonmalignant disorders (J Horan/A Woolfrey) – No update was given.

e. IB09-02 Non-permissive HLA-DPB1 disparities based on T cell alloreactivity (K Fleischhauer) – No update was given.

f. IB10-07 Use of HLA Structure and Function Parameters to Understand the Relationship between HLA Disparity and Transplant Outcomes (LA Baxter-Lowe) – No update was given.

g. IB06-02 Mismatching for low expression HLA loci in matched unrelated donor transplants (M Fernandez-Vina) – No update was given.

9. Deferred Studies Pending Accrual/Funding

a. R04-80s HLA matching in unrelated cord blood transplants(S Rodriguez-Marino) - no update

Data collection

b. IB06-10 Evaluation of the impact of the exposure to NIMA during fetal life and breast feeding and to the IPA during pregnancy on the clinical outcome of HSCT from haploidentical family members (J van Rood) – no update

Data collection

c. IB06-13 HLA disparity in unrelated cord blood transplantation: Delineation of factors contributing to transplant outcomes (L Baxter-Lowe) - no update

Data collection

d. IB08-05s Evaluation of lymphotoxin alpha (LTA) alleles in relation to relapse in AML and CML (P Posch) - no update

Data collection

e. IB10-06 Identification of Common, Clinically Significant, Minor Histocompatibility Antigens through Stem Cell Transplant Donor/Patient Polymorphism Disparities (P Armistead) – no update

Awaiting funding

f. IB11-02s Impact of CTLA4 single nucleotide polymorphisms on outcome after unrelated donor transplant (M Jagasia/W Clark/B Savani/S Sengsayadeth) – no update

Awaiting funding

10. Closing remarks

Dr. Carlheinz Müller thanked the group for attending and adjourned the meeting at 4:20 pm.

19


Accrual Summary for Immunobiology Working Committee

Characteristics of recipients of first transplants reported to the CIBMTR and NMDP

CIBMTR HLA-identical

Sibling

CIBMTR Alternative

Related

CIBMTR Unrelated (non-US)

CIBMTR Unrelated

(US)

Variable N (%) N (%) N (%) N (%)Number of patients 41827 6416 7766 27129Number of centers 463 393 198 174Age, median (range), years 30 (<1-82) 21 (<1-82) 26 (<1-75) 34 (<1-80)Age at transplant < 10 y 5804 (14) 1854 (29) 1777 (23) 5062 (19) 10-20 y 7159 (17) 1182 (18) 1283 (17) 3596 (13) 20-29 y 7615 (18) 969 (15) 1207 (16) 3395 (13) 30-39 y 8207 (20) 891 (14) 1349 (17) 3761 (14) 40-49 y 7344 (18) 805 (13) 1151 (15) 4545 (17) ≥ 50 y 5691 (14) 712 (11) 999 (13) 6764 (25)Male sex 24380 (58) 3850 (60) 4617 (59) 15796 (58)Karnofsky prior to transplant > 90% 29248 (73) 3981 (67) 5287 (72) 17844 (71)HLA-A,B,DRB1 groups – low resolution 6/6 41827 (100) 1469 (23) 896 (10) 18815 (69) 5/6 0 970 (15) 211 ( 4) 4592 (17) 4/6 0 1042 (16) 53 ( 1) 1585 ( 6) 3/6 0 N/A 0 14 (<1) Other/ < “3/6” /Unknown/TBD 0 2935 (46) 6606 (85) 2123 ( 8)High resolution available ≤ 5/8 N/A N/A 104 (15) 2644 (13) 6/8 N/A N/A 109 (16) 2143 (11) 7/8 N/A N/A 196 (28) 4476 (22) 8/8 N/A N/A 290 (41) 10793 (54)HLA high-res. typed/audited (out of 8) N/A N/A 385 ( 5) 13419 (49)Graft type Bone marrow 30355 (73) 4712 (73) 4755 (61) 13529 (50) Peripheral blood 11097 (27) 1603 (25) 1747 (23) 8910 (33) Cord blood 184 (<1) 32 (<1) 1247 (16) 4483 (17)

Other 189 (<1) 69 ( 1) 10 (<1) 172 ( 1)Conditioning regimen

Myeloablative 34420 (82) 5251 (82) 5804 (75) 19668 (72)Reduced intensity 3651 ( 9) 617 (10) 1137 (15) 4435 (16)Nonmyeloablative 1331 ( 3) 234 ( 4) 389 ( 5) 2045 ( 8)Other/To Be Determined 2425 ( 6) 314 ( 5) 436 ( 6) 981 ( 4)

Donor age, median (range), years 30 (<1-93) 33 (<1-80) 32 (<1-68) 34 (<1-61)Donor age < 10 (including UCB Tx) 4983 (12) 448 ( 7) 862 (12) 2038 (10) 10-19 7091 (17) 780 (12) 63 ( 1) 226 ( 1) 20-29 7854 (19) 1295 (21) 1483 (21) 5090 (25) 30-39 8085 (20) 1574 (25) 2064 (29) 6718 (33) 40-49 7008 (17) 1161 (19) 1478 (21) 4658 (23) ≥ 50 5802 (14) 1016 (16) 1130 (16) 1875 ( 9)

20



Characteristics of recipients of first transplants reported to the CIBMTR and NMDP

CIBMTR HLA-

identical Sibling

CIBMTR Alternative

Related


CIBMTR Unrelated

(US)

Variable N (%) N (%) N (%) N (%)Disease at transplant AML 11109 (27) 1545 (24) 1842 (24) 8375 (31) ALL 6780 (16) 1231 (19) 1664 (21) 4893 (18) Other leukemia 783 ( 2) 100 ( 2) 136 ( 2) 920 ( 3) CML 7976 (19) 973 (15) 1704 (22) 4003 (15) MDS/MPS 2770 ( 7) 348 ( 5) 783 (10) 3386 (13) Other acute leukemia 268 ( 1) 53 ( 1) 71 ( 1) 280 ( 1) Non-Hodgkin’s lymphoma 2637 ( 6) 380 ( 6) 215 ( 3) 1634 ( 6) HD-Hodgkin’s lymphoma 268 ( 1) 48 ( 1) 10 (<1) 94 (<1) MYE-plasma cell disorder, MM 975 ( 2) 148 ( 2) 39 ( 1) 133 (<1) Other malignancies 327 ( 1) 57 ( 1) 31 (<1) 69 (<1) Breast cancer 64 (<1) 22 (<1) 0 3 (<1) Severe aplastic anemia 4230 (10) 472 ( 7) 432 ( 6) 1012 ( 4) Inherited ab erythro. diff-funct. 2556 ( 6) 263 ( 4) 222 ( 3) 469 ( 2) SCID & other immune deficienc. 560 ( 1) 562 ( 9) 278 ( 4) 713 ( 3) Inherited abnormal. of platelets 20 (<1) 7 (<1) 12 (<1) 45 (<1) Inherited disorder of metabolism 263 ( 1) 152 ( 2) 199 ( 3) 664 ( 2) Histiocytic disorders 103 (<1) 40 ( 1) 96 ( 1) 336 ( 1)Autoimmune diseases 17 (<1) 4 (<1) 2 (<1) 10 (<1)

Other 25 (<1) 5 (<1) 6 (<1) 41 (<1)Disease status at transplant Early 22147 (53) 2759 (43) 4153 (53) 14131 (52) Intermediate 1125 ( 3) 234 ( 4) 377 ( 5) 1311 ( 5) Advanced 5168 (12) 1104 (17) 1258 (16) 4377 (16) Non-malignant disease/Other 13387 (32) 2319 (36) 1978 (25) 7310 (27)GVHD prophylaxis

No GVHD prophylaxis (TBD) 368 ( 1) 827 (13) 42 ( 1) 64 (<1)Ex vivo T-cell depletion alone 1215 ( 3) 461 ( 7) 195 ( 3) 1054 ( 4)Ex vivo T-cell depletion + post-tx immune suppression

1155 ( 3)

490 ( 8)

220 ( 3)

1848 ( 7)

CD34 selection alone 151 (<1) 114 ( 2) 30 (<1) 133 (<1)CD34 selection + post-tx immune suppression 279 ( 1) 113 ( 2) 36 (<1) 316 ( 1)Cyclophosphamide alone 26 (<1) 2 (<1) 0 55 (<1)Cyclophosphamide + others 401 ( 1) 101 ( 2) 34 (<1) 75 (<1)FK506 + MMF +- others 594 ( 1) 116 ( 2) 56 ( 1) 2598 (10)FK506 + MTX +- others (except MMF) 2557 ( 6) 182 ( 3) 399 ( 5) 7061 (26)FK506 + others (except MTX, MMF) 336 ( 1) 20 (<1) 32 (<1) 899 ( 3)FK506 alone 211 ( 1) 37 ( 1) 17 (<1) 496 ( 2)

21


Continued.

CIBMTR HLA-

identical Sibling

CIBMTR Alternative

Related


CIBMTR Unrelated

(US)CSA + MMF +- others (except FK506) 991 ( 2) 57 ( 1) 559 ( 7) 2769 (10)CSA + MTX +- others (except FK506,MMF) 17884 (43) 1628 (25) 4274 (55) 7086 (26)CSA + others (except FK506, MTX, MMF) 2452 ( 6) 333 ( 5) 911 (12) 1768 ( 7)CSA alone 3985 (10) 367 ( 6) 396 ( 5) 311 ( 1)Other GVHD prophylaxis 9222 (22) 1568 (24) 565 ( 7) 596 ( 2)

22



Characteristics of recipients of first transplants reported to the CIBMTR and NMDP CIBMTR

HLA-identical Sibling

CIBMTR Alternative

Related


CIBMTR Unrelated

(US)Variable N (%) N (%) N (%) N (%)Donor/recipient sex match Male/Male 12402 (32) 2048 (35) 2302 (38) 568 (32) Male/Female 8375 (22) 971 (17) 1315 (22) 352 (20) Female/Male 9893 (26) 1432 (25) 1248 (21) 494 (28) Female/Female 7500 (20) 1342 (23) 1119 (19) 362 (20)Donor/recipient CMV match Negative/Negative 9504 (23) 1472 (23) 2003 (26) 7585 (28) Negative/Positive 6168 (15) 875 (14) 1840 (24) 7688 (28) Positive/Negative 3843 ( 9) 794 (12) 1059 (14) 3435 (13) Positive/Positive 15597 (37) 2022 (32) 1995 (26) 5319 (20) Unknown 6715 (16) 1253 (20) 869 (11) 3102 (11)Year of transplant

1964-1985 4809 (11) 885 (14) 35 (<1) 13 (<1)1986 1375 ( 3) 257 ( 4) 14 (<1) 18 (<1)1987 1461 ( 3) 250 ( 4) 31 (<1) 34 (<1)1988 1619 ( 4) 249 ( 4) 53 ( 1) 96 (<1)1989 1851 ( 4) 256 ( 4) 100 ( 1) 179 ( 1)1990 1937 ( 5) 317 ( 5) 136 ( 2) 290 ( 1)1991 1883 ( 5) 250 ( 4) 171 ( 2) 410 ( 2)1992 1980 ( 5) 269 ( 4) 227 ( 3) 473 ( 2)1993 1990 ( 5) 273 ( 4) 237 ( 3) 577 ( 2)1994 1834 ( 4) 254 ( 4) 247 ( 3) 712 ( 3)1995 1910 ( 5) 322 ( 5) 323 ( 4) 855 ( 3)1996 1947 ( 5) 313 ( 5) 405 ( 5) 992 ( 4)1997 1651 ( 4) 297 ( 5) 375 ( 5) 1055 ( 4)1998 1489 ( 4) 217 ( 3) 421 ( 5) 1068 ( 4)1999 1341 ( 3) 200 ( 3) 417 ( 5) 1119 ( 4)2000 1434 ( 3) 206 ( 3) 441 ( 6) 1156 ( 4)2001 1412 ( 3) 221 ( 3) 457 ( 6) 1207 ( 4)2002 1338 ( 3) 187 ( 3) 457 ( 6) 1311 ( 5)2003 1145 ( 3) 162 ( 3) 470 ( 6) 1463 ( 5)2004 1353 ( 3) 144 ( 2) 594 ( 8) 1637 ( 6)2005 1415 ( 3) 173 ( 3) 554 ( 7) 1776 ( 7)2006 1167 ( 3) 143 ( 2) 485 ( 6) 2019 ( 7)2007 687 ( 2) 90 ( 1) 298 ( 4) 2206 ( 8)2008 1059 ( 3) 226 ( 4) 303 ( 4) 2001 ( 7)2009 872 ( 2) 144 ( 2) 260 ( 3) 1909 ( 7)2010 489 ( 1) 48 ( 1) 133 ( 2) 1260 ( 5)2011 289 ( 1) 53 ( 1) 84 ( 1) 949 ( 3)2012 90 (<1) 10 (<1) 38 (<1) 344 ( 1)Median follow-up of recipients, mos 98 (<1-465) 95 (1-443) 69 (<1-270) 70 (<1-295)

23


Accrual Summary for First Transplants with Samples Available for Recipient and/or Donor through the NMDP for Adult Unrelated Donor Transplants

Samples Available for

Recipient and Donor Samples Available for

Recipient Only Samples Available for

Donor Only Variable

N Eval

N (%) N Eval

N (%) N Eval

N (%)

Number of cases 17466 3859 3081 Number of centers 199 176 216 Age, median (range), years 17466 39 (<1-83) 3859 37 (<1-79) 3080 33 (<1-75) Age at transplant 17466 3859 3080 < 10 y 2021 (12) 442 (11) 523 (17) 10-20 y 2074 (12) 500 (13) 417 (14) 20-29 y 2280 (13) 512 (13) 423 (14) 30-39 y 2627 (15) 594 (15) 453 (15) 40-49 y 3181 (18) 730 (19) 525 (17) ≥ 50 y 5283 (30) 1081 (28) 739 (24) Male sex 17459 10116 (58) 3856 2281 (59) 3079 1846 (60) Karnofsky prior to transplant > 90% 14401 10172 (71) 3308 2295 (69) 2437 1732 (71) HLA-A,B,C,DRB1 groups – high-res 16283 1654 1219 0/8 - 3/8 64 (<1) 30 ( 2) 2 (<1) 4/8 180 ( 1) 62 ( 4) 4 (<1) 5/8 516 ( 3) 106 ( 6) 18 ( 1) 6/8 1397 ( 9) 104 ( 6) 81 ( 7) 7/8 3924 (24) 343 (21) 288 (24) 8/8 10202 (63) 1009 (61) 826 (68) HLA high-res. typed and audited 17466 13110 (75) 3859 50 ( 1) 3081 168 ( 5) Disease status at transplant 17466 3859 3081 Early 8293 (47) 1969 (51) 1342 (44) Intermediate 771 ( 4) 156 ( 4) 137 ( 4) Advanced 2679 (15) 588 (15) 499 (16) Non-malignant disease/Other 5723 (33) 1146 (30) 1103 (36) Graft type 17466 3859 3081

Bone marrow 9578 (55) 2290 (59) 1916 (62) Peripheral blood 7800 (45) 1221 (32) 1154 (37) Cord blood 0 0 0 Other 88 ( 1) 13 (<1) 11 (<1)

Conditioning regimen 17466 3859 3081 Myeloablative 11593(66) 2680 (69) 2030 (66) Reduced intensity 2746 (16) 557 (14) 438 (14) Nonmyeloablative 1244 ( 7) 277 ( 7) 192 ( 6) Other/To be determined 1883 (11) 345 ( 9) 421 (14)

Donor age, median (range), years 17466 35 (18-61) 3859 34 (18-61) 3081 36 (18-59) Donor age 17466 3859 3081 < 20 120 ( 1) 30 ( 1) 25 ( 1) 20-29 3391 (19) 851 (22) 642 (21) 30-39 4615 (26) 1046 (27) 866 (28) 40-49 3291 (19) 658 (17) 623 (20) ≥ 50 980 ( 6) 185 ( 5) 231 ( 7)

Unknown/TBD 5069 (29) 1089 (28) 694 (23)

24




Recipient and Donor Samples Available for Recipient Only

Samples Available for Donor Only

Variable

N Eval

N (%) N Eval

N (%) N Eval

N (%)

Disease at transplant 17448 3856 3065 AML 5475 (31) 1267 (33) 929 (30) ALL 2938 (17) 656 (17) 585 (19) Other leukemia 888 ( 5) 179 ( 5) 143 ( 5) CML 2705 (16) 681 (18) 473 (15) MDS/MPS 2362 (14) 490 (13) 369 (12) Non-Hodgkin’s lymphoma 1230 ( 7) 231 ( 6) 170 ( 6) HD-Hodgkin’s lymphoma 71 (<1) 15 (<1) 10 (<1) MYE-plasma cell disorder, MM 105 ( 1) 25 ( 1) 13 (<1) Other malignancies 34 (<1) 7 (<1) 12 (<1) Breast cancer 5 (<1) 0 0 Severe aplastic anemia 658 ( 4) 119 ( 3) 137 ( 4) Inherited ab erythro. diff-funct. 276 ( 2) 51 ( 1) 41 ( 1) SCID & other immune deficienc. 307 ( 2) 49 ( 1) 66 ( 2) Inherited abnormal. of platelets 21 (<1) 4 (<1) 4 (<1) Inherited disorder of metabolism 197 ( 1) 48 ( 1) 60 ( 2) Histiocytic disorders 157 ( 1) 31 ( 1) 43 ( 1) Autoimmune disease 1 (<1) 1 (<1) 1 (<1) Other 18 (<1) 2 (<1) 9 (<1) GVHD prophylaxis 15462 3498 2690

No GVHD prophylaxis (forms under review)

36 (<1) 6 (<1)

10 (<1)

Ex vivo T-cell depletion alone 676 ( 4) 186 ( 5) 142 ( 5) Ex vivo TCD + post-tx immune

suppression

1181 ( 8) 286 ( 8)

199 ( 7) CD34 selection alone 62 (<1) 11 (<1) 17 ( 1) CD34 selection + post-tx immune suppression

232 ( 2) 45 ( 1)

25 ( 1)

Cyclophosphamide alone 40 (<1) 6 (<1) 2 (<1) Cyclophosphamide + others 47 (<1) 13 (<1) 12 (<1) FK506 + MMF ± others 1388 ( 9) 295 ( 8) 193 ( 7) FK506 + MTX ± others (no

MMF)

4664 (30) 839 (24)

667 (25) FK506 + others (no MTX, MMF)

462 ( 3) 102 ( 3)

37 ( 1)

FK506 alone 269 ( 2) 70 ( 2) 44 ( 2) CSA + MMF ± others (no FK506) 821 ( 5) 291 ( 8) 165 ( 6) CSA + MTX ± others (no FK506,

MMF)

4822 (31) 1189 (34)

947 (35) CSA + others (no FK506, MTX, MMF)

408 ( 3) 91 ( 3)

111 ( 4)

CSA alone 137 ( 1) 20 ( 1) 53 ( 2) Other GVHD prophylaxis 217 ( 1) 48 ( 1) 66 ( 2)

25




Recipient and Donor Samples Available for Recipient Only


Variable N Eval

N (%)

N Eval

N (%)

N Eval

N (%)

Donor/recipient sex match 17264 3824 2972 Male/Male 6738 (39) 1523 (40) 1144 (38) Male/Female 4234 (25) 903 (24) 659 (22) Female/Male 3266 (19) 743 (19) 629 (21) Female/Female 3026 (18) 655 (17) 540 (18) Donor/recipient CMV match 17466 3859 3081 Negative/Negative 5310 (30) 1117 (29) 819 (27) Negative/Positive 5433 (31) 1125 (29) 879 (29) Positive/Negative 2275 (13) 533 (14) 382 (12) Positive/Positive 3685 (21) 880 (23) 608 (20) Unknown 763 ( 4) 204 ( 5) 393 (13) Year of transplant 17466 3859 3081

1987 1 (<1) 0 1 (<1) 1988 54 (<1) 10 (<1) 9 (<1) 1989 147 ( 1) 4 (<1) 6 (<1) 1990 206 ( 1) 20 ( 1) 28 ( 1) 1991 296 ( 2) 45 ( 1) 53 ( 2) 1992 324 ( 2) 40 ( 1) 87 ( 3) 1993 356 ( 2) 72 ( 2) 113 ( 4) 1994 451 ( 3) 114 ( 3) 101 ( 3) 1995 492 ( 3) 185 ( 5) 113 ( 4) 1996 533 ( 3) 231 ( 6) 122 ( 4) 1997 622 ( 4) 229 ( 6) 136 ( 4) 1998 576 ( 3) 270 ( 7) 126 ( 4) 1999 680 ( 4) 218 ( 6) 126 ( 4) 2000 794 ( 5) 157 ( 4) 128 ( 4) 2001 768 ( 4) 130 ( 3) 136 ( 4) 2002 652 ( 4) 120 ( 3) 357 (12) 2003 790 ( 5) 153 ( 4) 317 (10) 2004 1018 ( 6) 248 ( 6) 211 ( 7) 2005 1168 ( 7) 208 ( 5) 177 ( 6) 2006 1323 ( 8) 224 ( 6) 195 ( 6) 2007 1459 ( 8) 168 ( 4) 189 ( 6) 2008 1156 ( 7) 152 ( 4) 79 ( 3) 2009 1068 ( 6) 201 ( 5) 67 ( 2) 2010 829 ( 5) 176 ( 5) 44 ( 1) 2011 733 ( 4) 256 ( 7) 37 ( 1) 2012 970 ( 6) 228 ( 6) 123 ( 4)

Median follow-up of recipients, mo 6257 68 (<1-270) 1327 72(2-286) 1073 86(<1-287)

26


Accrual Summary for First Transplants with Samples Available for Recipient and/or Cord Blood Unit(s) through the NMDP for Cord Blood Transplants

Samples

Available - Recipient and UCB Unit(s)

Samples Available -

Recipient Only

Samples Available -

UCB Unit(s) Only

Variable N (%) N (%) N (%)Number of cases 1316 841 232Number of centers 116 110 79Number of cord blood units in transplant

One 1096 (83) 580 (69) 197 (85) Two 220 (17) 261 (31) 35 (15) Three or more (N=1 for 4) 0 0 0

Age, median (range), years 12 (<1-80) 14 (<1-73) 9 (<1-73) Age at transplant, by decade < 10 y 605 (46) 350 (42) 117 (50) 10-20 y 219 (17) 125 (15) 30 (13) 20-29 y 90 ( 7) 83 (10) 16 ( 7) 30-39 y 94 ( 7) 65 ( 8) 11 ( 5) 40-49 y 101 ( 8) 57 ( 7) 19 ( 8) ≥ 50 y 207 (16) 161 (19) 39 (17) Male sex 727 (55) 466 (55) 124 (53) Karnofsky prior to transplant > 90% 877 (77) 480 (74) 130 (73) HLA-A, B, C, DRB1 groups – high-res 0/8 – 3/8 82 ( 7) 61 ( 9) 10 ( 6) 4/8 183 (16) 116 (17) 26 (16) 5/8 355 (31) 197 (30) 48 (30) 6/8 255 (22) 161 (24) 31 (19) 7/8 147 (13) 79 (12) 32 (20) 8/8 120 (11) 49 ( 7) 15 ( 9) HLA high resolution typed and audited 474 (42) 85 (13) 2 ( 1) Disease status at transplant Early 574 (44) 346 (41) 78 (34) Intermediate 54 ( 4) 23 ( 3) 14 ( 6) Advanced 123 ( 9) 78 ( 9) 22 ( 9) Non-malignant disease/Other 565 (43) 394 (47) 118 (51) Conditioning regimen

Myeloablative 848 (64) 498 (59) 123 (53) Reduced intensity 205 (16) 112 (13) 40 (17) Nonmyeloablative 149 (11) 126 (15) 34 (15) Other/To be determined 114 ( 9) 105 (12) 35 (15)

Donor/recipient CMV match Negative/Negative 199 (15) 95 (11) 27 (12) Negative/Positive 245 (19) 122 (15) 32 (14) Positive/Negative 111 ( 8) 83 (10) 16 ( 7) Positive/Positive 235 (18) 196 (23) 35 (15) Unknown 526 (40) 345 (41) 122 (53)

27


Accrual Summary for First Transplants with Samples Available for Recipient and/or Cord Blood Unit(s) through the NMDP for Cord Blood Transplants

Samples


Samples Available -

Recipient Only

Samples Available -

UCB Unit(s) Only

Variable N (%) N (%) N (%) Disease at transplant AML 418 (32) 315 (37) 56 (24) ALL 287 (22) 201 (24) 57 (25) Other leukemia 39 ( 3) 20 ( 2) 4 ( 2) CML 32 ( 2) 11 ( 1) 5 ( 2) MDS/MPS 102 ( 8) 73 ( 9) 21 ( 9) Other acute leukemia 28 ( 2) 12 ( 1) 6 ( 3) Non-Hodgkin’s lymphoma 56 ( 4) 33 ( 4) 14 ( 6) HD-Hodgkin’s lymphoma 5 (<1) 3 (<1) 0

Plasma cell disorders, Multiple Myeloma 0 1 (<1) 0 Other malignancies 4 (<1) 1 (<1) 0 Severe aplastic anemia 39 ( 3) 21 ( 2) 2 ( 1) Inherited ab erythro. diff-funct. 39 ( 3) 30 ( 4) 11 ( 5) SCID & other immune deficienc. 99 ( 8) 38 ( 5) 15 ( 6) Inherited abnormal. of platelets 3 (<1) 5 ( 1) 1 (<1) Inherited disorder of metabolism 100 ( 8) 53 ( 6) 25 (11) Histiocytic disorders 54 ( 4) 21 ( 2) 13 ( 6) Autoimmune diseases 2 (<1) 1 (<1) 0 Other 7 ( 1) 2 (<1) 2 ( 1) GVHD prophylaxis

No GVHD prophylaxis (forms under review) 3 (<1) 2 (<1) 6 ( 3) Ex vivo T-cell depletion alone 0 2 (<1) 0 Ex vivo T-cell depletion + post-tx immune suppression

2 (<1)

0

0

CD34 selection alone 2 (<1) 0 1 (<1) CD34 selection + post-tx immune suppression 0 1 (<1) 0 FK506 + MMF +- others 218 (17) 156 (22) 30 (14) FK506 + MTX +- others (except MMF) 92 ( 7) 42 ( 6) 10 ( 5) FK506 + others (except MTX, MMF) 64 ( 5) 39 ( 5) 17 ( 8) FK506 alone 36 ( 3) 11 ( 2) 7 ( 3) CSA + MMF +- others (except FK506) 584 (46) 309 (43) 81 (38) CSA + MTX +- others (except FK506, MMF) 49 ( 4) 32 ( 4) 14 ( 7) CSA + others (except FK506, MTX, MMF) 173 (14) 94 (13) 38 (18) CSA alone 22 ( 2) 15 ( 2) 6 ( 3) Other GVHD prophylaxis 16 ( 1) 13 ( 2) 3 ( 1)

28


Accrual Summary for First Transplants with Samples Available for Recipient

and/or Cord Blood Unit(s) through the NMDP for Cord Blood Transplants

Samples


Samples Available -

Recipient Only

Samples Available -

UCB Unit(s) Only

Variable N (%) N (%) N (%)Year of transplant

2000 1 (<1) 1 (<1) 4 ( 2)2001 11 ( 1) 10 ( 1) 6 ( 3)2002 4 (<1) 8 ( 1) 2 ( 1)2003 17 ( 1) 25 ( 3) 1 (<1)2004 19 ( 1) 23 ( 3) 7 ( 3)2005 60 ( 5) 35 ( 4) 14 ( 6)2006 57 ( 4) 134 (16) 11 ( 5)2007 229 (17) 34 ( 4) 51 (22)2008 254 (19) 25 ( 3) 42 (18)2009 282 (21) 81 (10) 34 (15)2010 218 (17) 123 (15) 34 (15)2011 137 (10) 182 (22) 16 ( 7)2012 27 ( 2) 160 (19) 10 ( 4)

Median follow-up of recipients, mo 36 (3-107) 24 (1-120) 37 (3-122)

29


Accrual Summary for First Transplants with Samples Available for Recipient and/or Donor through the NMDP for Adult Related Donor Transplants

Samples Available

for Recipient and Donor

Samples Available for Recipient Only


Variable

N Eval

N (%)

N Eval

N (%)

N Eval

N (%)

Number of cases 1167 132 61Number of centers 35 23 14Age, median (range), years 1167 49 (<1-75) 132 48 (<1-74) 61 47 (<1-74)Age at transplant 1167 132 61 < 10 y 100 ( 9) 5 ( 4) 6 (10) 10-20 y 96 ( 8) 10 ( 8) 4 ( 7) 20-29 y 94 ( 8) 16 (12) 3 ( 5) 30-39 y 100 ( 9) 16 (12) 5 ( 8) 40-49 y 201 (17) 22 (17) 17 (28) ≥ 50 y 576 (49) 63 (48) 26 (43)Male sex 1167 694 (59) 132 77 (58) 61 37 (61)Karnofsky prior to transplant > 90% 138 95 (69) 12 6 (50) 5 5 (100)Disease status at transplant 1167 132 61 Early 67 ( 6) 6 ( 5) 2 ( 3) Intermediate 3 (<1) 0 0 Advanced 19 ( 2) 1 ( 1) 2 ( 3) Non-malignant disease/Other 1078 (92) 125 (95) 57 (93)Graft type 1167 132 61

Bone marrow 256 (22) 22 (17) 15 (25)Peripheral blood 906 (78) 106 (80) 46 (75)Cord blood 2 (<1) 2 ( 2) 0Other 3 (<1) 2 ( 2) 0

Conditioning regimen 1167 132 61 Myeloablative 218 (19) 36 (27) 11 (18)Reduced intensity 62 ( 5) 2 ( 2) 2 ( 3)Nonmyeloablative 42 ( 4) 6 ( 5) 4 ( 7)Other/To be determined 845 (72) 88 (67) 44 (72)

Donor age, median (range), years 1167 50 (1-76) 132 48 (27-66) 61 53 (11-73)Donor age 1167 132 61 < 20 17 ( 1) 0 1 ( 2) 20-29 14 ( 1) 1 ( 1) 0 30-39 16 ( 1) 2 ( 2) 0 40-49 31 ( 3) 5 ( 4) 2 ( 3) ≥ 50 81 ( 7) 5 ( 4) 3 ( 5)

Unknown/TBD 1008 (86) 119 (90) 55 (90)

30


Accrual Summary for First Transplants with Samples Available for Recipient and/or Donor through the NMDP for Adult Related Donor Transplants

Samples Available




Variable N Eval N (%) N Eval N (%) N Eval N (%)Disease at transplant 1167 132 61 AML 339 (29) 41 (31) 14 (23) ALL 194 (17) 25 (19) 9 (15) Other leukemia 53 ( 5) 6 ( 5) 1 ( 2) CML 59 ( 5) 3 ( 2) 3 ( 5) MDS/MPS 181 (16) 21 (16) 11 (18)

Other acute leukemia 10 ( 1) 1 ( 1) 1 ( 2) Non-Hodgkin’s lymphoma 161 (14) 18 (14) 10 (16) HD-Hodgkin’s lymphoma 18 ( 2) 6 ( 5) 3 ( 5) MYE-plasma cell disorder, MM 31 ( 3) 3 ( 2) 2 ( 3) Other malignancies 2 (<1) 0 0 Breast cancer 1 (<1) 0 0 Severe aplastic anemia 41 ( 4) 4 ( 3) 3 ( 5) Inherited ab erythro. diff-funct. 47 ( 4) 2 ( 2) 1 ( 2) SCID & other immune deficienc. 14 ( 1) 1 ( 1) 3 ( 5) Inherited abnormal. of platelets 3 (<1) 0 0 Inherited disorder of metabolism 4 (<1) 1 ( 1) 0 Histiocytic disorders 6 ( 1) 0 0 Other 3 (<1) 0 0GVHD prophylaxis 1167 132 61

Ex vivo T-cell depletion alone 1 ( 1) 0 0Ex vivo T-cell depletion + post-tx immune suppression 2 ( 1) 0 0CD34 selection alone 1 ( 1) 0 0CD34 selection + post-tx immune suppression 3 ( 2) 0 0Cyclophosphamide alone Cyclophosphamide + others 4 ( 2) 0 0FK506 + MMF +- others 11 ( 7) 0 0FK506 + MTX +- others (except MMF) 106 (64) 7 (54) 3 (50)FK506 + others (except MTX, MMF) 16 (10) 5 (38) 2 (33)FK506 alone 3 ( 2) 0 0CSA + MMF +- others (except FK506) 2 ( 1) 0 0CSA + MTX +- others (except FK506, MMF) 11 ( 7) 0 0CSA + others (except FK506, MTX, MMF) 0 1 ( 8) 0CSA alone 2 ( 1) 0 0Other GVHD prophylaxis 3 ( 2) 0 1 (17)

31


Accrual Summary for First Transplants with Samples Available for Recipient

and/or Donor through the NMDP for Adult Related Donor Transplants Samples Available




Variable N Eval

N (%) N Eval

N (%) N Eval

N (%)

Donor/recipient sex match 1108 121 59 Male/Male 370 (33) 37 (31) 24 (41) Male/Female 219 (20) 24 (20) 11 (19) Female/Male 290 (26) 35 (29) 12 (20) Female/Female 229 (21) 25 (21) 12 (20)Donor/recipient CMV match 1167 132 61 Negative/Negative 212 (18) 21 (16) 10 (16) Negative/Positive 247 (21) 23 (17) 7 (11) Positive/Negative 116 (10) 12 ( 9) 6 (10) Positive/Positive 494 (42) 62 (47) 34 (56) Unknown 98 ( 8) 14 (11) 4 ( 7)Year of transplant 1167 132 61

2007 14 ( 1) 2 ( 2) 02008 116 (10) 6 ( 5) 4 ( 7)2009 165 (14) 17 (13) 9 (15)2010 213 (18) 29 (22) 8 (13)2011 313 (27) 44 (33) 20 (33)2012 346 (30) 34 (26) 20 (33)

Median follow-up of recipients, mo 67013 (2-62)

77 13 (2-54)

38 12 (3-36)

32


Study Proposal 0212-01/0712-01 Study Title: Short and long term survival assessment of post HSCT transplantation using predictive modeling on a Bayesian network framework Reza Abdi, MD, Brigham and Women’s Hospital, Boston, MA, USA Gil Alterovitz, PhD, Brigham and Women’s Hospital, Boston, MA, USA David McDermott MD, National Institutes of Health, Bethesda, MD, USA Scientific Justification: Hematopoietic stem cell transplantation (HSCT) has emerged as the treatment of choice for various hematologic malignancies and congenital disorders. A holistic analytic approach is required to identify meaningful (e.g., potentially predictive) relationships between genetic variants and outcomes especially for complex setting such as HSCT. Bayesian networks are graphical models that represent statistical dependencies between variables. Nodes in the graph are continuous, discrete or categorical random variables of virtually any data type, including biomedical and demographic. Relationships between nodes are encoded in Bayesian network through directed arcs between nodes, and each arc is associated with a conditional probability table that describes how knowledge of the condition of one node impacts probabilistic beliefs about the likelihood of observing another node in a specific state. This approach is much more flexible than the standard Cox model in allowing variables to have different associations with the outcomes depending on the values of the surrounding nodes. The main goal of this study is to determinants the major determinant of short and long term survival post HSCT and to examine their interaction using novel Bayesian Network Analysis. Study Population: The study is designed to include largest possible cohort of HSCT patients including both HLA matched and mismatched related and unrelated donors. These patients needed to undergo HSCT for treatment of a hematological malignancy using either bone marrow or peripheral blood stem cells and myeloablative or reduced intensity/non-myeloablative conditioning. Data Requirements: Patient-related (at time of transplant):

– Age: in decades (≤9, 10-19, 20-29, 30-39, 40-49, 50-59, ≥60) – Gender: female vs. male – Donor-recipient sex matching: M/M, M/F, F/M, F/F – Karnofsky score at transplant: < 90 vs. 90-100 – Race/Ethnicity

Disease-Related: – Diseases at transplant – Disease stage at transplant: early vs. intermediate vs. advanced

Transplant-Related: – Source of stem cells: marrow (BM) vs. peripheral blood stem cells (PB) – Cell dose – Donor type: HLA-identical sibling vs. URD – Unrelated donor HLA match: HLA- A, B, C and DRB1

– 8/8 matched – 7/8 matched

– Donor age: in decades (18-29, 30-39, 40-49, >=50) – or use CIBMTR classification – Year of transplant: 2000-2011 – Gender match: M-M vs. M-F vs. F-M vs. F-F

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– Donor/recipient CMV status: -/- vs. -/+ vs. +/- vs. +/+ vs. Unknown – Conditioning regimen: myeloablative vs. reduced intensity vs. non-myeloablative – GvHD prophylaxis – T- cell depletion – Time from diagnosis to transplant for CML: ≤1 year vs. > 1 year

The analysis will include the following outcomes to limit the impact of competing risks: – 100 day mortality – entire cohort – 1 year mortality – entire cohort – 3 year mortality – entire cohort

Study Design:

– Research Strategy – Approach: We propose using models based on a Bayesian network framework approach to develop a holistic multivariate predictive model with high positive and negative predictive values for the development of short and long term patient overall survival. By holistic, we imply a model that considers a variety of features (demographic, clinical and eventually genetic) rather than one focused solely on a single aspect of the disease process. The model building process will be performed using a Bayesian network structure utilizing the K2 algorithm, which decreases the computing requirements substantially by considering possible nodes for addition sequentially. This modeling approach is also more flexible than the TAN (tree augmented naïve) approach, which hypothesizes a tree structure (1-2), and has been used in other complex biological datasets with success (3-7). The TAN approach is more appropriate for signaling cascades and other linear biological models. The modeling method used here will consist of the following steps:

– Feature selection: We will identify covariates and risk factors from the CIBMTR data collection forms to be considered by our algorithms including donor-recipient HLA matching, gender mismatch (female donor to male recipient), advanced donor age, the source and dose of stem cells, the intensity of the conditioning regimen, GVHD prophylaxis, and pre-transplant manipulation of grafts.

– Bayesian Networks: Bayesian networks are graphical models that represent statistical dependencies between variables. The ideal here is to have each possible type of variable probabilistic relationship be encoded in the data so it can be extracted. As such, heterogeneous data that allows for calculation of the conditional probability tables (CPTs) allows for the most robust models to be built. They can be updated to incorporate newly learned information while using fewer parameters than other comparable methods.13,14 A Bayesian Network Framework consists of nodes that are continuous, discrete or categorical random variables of virtually any data type. Relationships between nodes will be encoded in a Bayesian network through directed arcs between nodes, and each arc is associated with a conditional probability table that describes how knowledge of the condition of one node impacts probabilistic beliefs about the likelihood of observing another node in a specific state. Since the total number of structures is very large compared to the number of nodes, a heuristic is used to intelligently search for the most likely structure. Once an optimal structure is chosen, the parameters are initially set to represent any prior beliefs that exist about each relationship, and are then updated to reflect new information learned about each relationship directly from the data.

– Model building: We will include the largest cohort of HLA matched and unmatched of NMDP to examine the short and long term survival of these patients and assess the risk factors associated with poorer survival. Bayesian network structure and parameter estimates will be directly learned from the sample data. Model selection begins by assuming equal likelihood for all models, and then sequentially searching the possible models, assigning each network a score. The search algorithms will be bottom-up heuristics based on the K2 algorithm or similar. The K2 algorithm

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imposes an ordering on the factors in the model, and for each node, composes a list of parent nodes that includes nodes that are higher on the list. K2 will assign a node to be a parent of another node if the model, including the added parent, has a higher local marginal likelihood than the model not including the parent. Through Monte-Carlo simulation, much of the key landscape of the search space can be explored. Heuristic algorithms are required because an exhaustive network search is rendered impractical by the overwhelming number of possible models. Models will be scored and subsequently ranked as a function of the posterior probability, calculated conditionally on available data. The final model is selected to be the one with the highest posterior probability. An example of such a network is shown in Figure 1 for 59 clinical and SNP variables and 114 edges representing probabilistic dependencies to predict post-transplantation survival. A zoomed-in version in Figure 1 shows some of the key relationships. At each node, we also store probabilistic tables so that predictions can be done on prognosis based on the state of the various variables.

Typically, Bayesian model building is optimized by (1) enough variability in the input data and (2) a study population of sufficient size to create a stable model. Further increases in the sample size beyond this point bring diminishing returns for model accuracy. There is no downside to extreme heterogeneity in the input data as far as model building, but validation may be more difficult if certain patient subpopulations are not sufficient to generate stable risk estimates. Given the very large population available through CIBMTR, we will evaluate the available data to determine the appropriate samples size and type for the training and internal testing set. For example, one approach is to randomly select 80% for the training set with 20% held for internal validation.

– Model validation: Both an internal and external network validation will be performed to ensure accuracy, sensitivity and specificity. As an internal validation, n-fold cross-validation, is a common assessment technique. Cross-validation uses datapoints efficiently, as it uses every datapoint in a dataset in both training and testing, and importantly, in such a way that no datapoint is being used for both training and testing at the same time. This study leverages an unbiased cross-validation that does not select network nodes using the entire dataset, but instead only using the training sets. This is accomplished by first dividing the dataset into n non-overlapping partitions, called folds. Next, features are selected using only data from the n-1 folds, and an optimal model is built on those features. The remaining fold is used to test the network. This is repeated n times in such a way that, each time, a different fold is held out for testing. The test results are then averaged over all folds, specifically, accuracy (number of observations correctly predicted divided by the total number of observations) and

Figure 1. Bayesian network for post transplantation survival showing with a magnified via of immediate dependencies including rs10912564.

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area under the receiver operator characteristic curve (AUROC). An objective model evaluation metric will allow the predictors to be quantified as good, fair, or poor. The 20% of the reserved population will be tested in the model as an internal validation check. In this step, subpopulations that do not perform adequately may be deleted from subsequent considerations.

External validation will be performed on an entirely independent dataset, using the clinical criteria used in the final model building (for example, if subpopulations are deleted because model generation is suboptimal due to small numbers, these types of patients will not be included in the validation cohort). One approach to creating the external validation set is to select patients that were randomly excluded from the training and internal validation sets. Another approach is to use a clinically relevant non-overlapping validation set, for example, patients transplanted more recently, since the goal is to create a model that is relevant for patients transplanted in the future. However, this approach risks poor validation statistics due to differences in the training and validation set. The external validation cohort will be transferred to the investigators following completion of the initial analysis. The Bayesian network model will be used to calculate estimates for outcomes in the validation cohort that can be compared with actual outcomes to allow assessment for accuracy, sensitivity, specificity and AUROC. A model performance of greater than 70% accuracy on the test set will be considered to be a successful validation. Given a validated model, its uses are numerous in a clinical setting. First, it can be used to try to identify patient at very high risk of mortality, so that the transplant plans can be reconsidered. It can also be used to predict prognosis for the patients based on factors known before transplantation. This can allow for development of a personalized post-treatment plan.

References:

1. Zhang W: Depth-first branch-and-bound versus local search: A case study. In: Proceedings of the 17th National Conference on Artificial Intelligence 2000:930-936.

2. Pearl J: Reverend Bayes on inference engines: A distributed hierarchical approach. In: Proceedings of the National Conference on Artificial Intelligence 1982:133-136.

3. Alterovitz G, Liu J, Afkhami E, Ramoni MF: Bayesian methods for proteomics. Proteomics 2007, 7(16):2843-2855.

4. Sebastiani P, Ramoni MF, Nolan V, Baldwin CT, Steinberg MH: Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet 2005, 37(4):435-440.

5. Zollanvari A, Thomas J, Alterovitz G: A Prediction-based Bayesian framework for quantifying the interaction of demographics and genetics: application to alcohol dependence. American Medical Informatics Association Translational Bioinformatics (AMIA TBI) 2012.

6. Alterovitz G, Xiang M, Hill DP, Lomax J, Liu J, Cherkassky M, Mungall C, Harris MA, Dolan ME, Blake JA et al: Ontology Engineering. Nature Biotechnology 2009.

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Characteristics of recipients receiving first allogeneic transplants that are HLA-identical sibling donor transplants or unrelated donor transplants with high-resolution HLA typing for

HLA-A, -B, -C and –DRB1 that are 7/8 or 8/8 matcheda

Related UnrelatedCharacteristics of patients N (%) N (%)Number of patients 12039 11343 Number of centers 284 214 Recipient age, median (range), years 40 (<1-82) 43 (<1-78) Age at transplant ≤ 9 y 1381 (11) 1097 (10) 10-19 y 1524 (13) 1180 (10) 20-29 y 1432 (12) 1318 (12) 30-39 y 1577 (13) 1428 (13) 40-49 y 2313 (19) 1996 (18) 50 y and older 3812 (32) 4324 (38) Recipient race/ethnicity

Caucasian, Non-hispanic 8574 (74) 9585 (86)African American, Non-hispanic 589 ( 5) 478 ( 4)Asian, Non-hispanic 1261 (11) 215 ( 2)Pacific Islander, Non-hispanic 23 (<1) 13 (<1)Native American, Non-hispanic 35 (<1) 37 (<1)Hispanic, Caucasian race 685 ( 6) 624 ( 6)Hispanic, African American race 41 (<1) 21 (<1)Hispanic, Asian race 7 (<1) 3 (<1)Hispanic, Pacific Islander race 3 (<1) 1 (<1)Hispanic, Native American race 9 (<1) 4 (<1)Hispanic, Unknown race 111 ( 1) 87 ( 1)Other 203 ( 2) 14 (<1)

Male sex 6969 (58) 6582 (58) Karnofsky prior to transplant > 90 8212 (72) 7136 (70) HLA matching for HLA-A, -B, -C and –DRB1

7/8 HLA-A mismatch N/A 975 ( 9)7/8 HLA-B mismatch N/A 486 ( 4)7/8 HLA-C mismatch N/A 1183 (10)7/8 HLA-DRB1 mismatch N/A 325 ( 3)8/8 HLA matched N/A 8374 (74)HLA-identical siblings 12039 (100) N/A

a – Data has not been CAP-modeled.

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Continued. Related UnrelatedCharacteristics of patients N (%) N (%)Number of patients 12039 11343 Disease at transplant

AML 3620 (30) 4179 (37)ALL 1495 (12) 1753 (15)Other leukemia 446 ( 4) 561 ( 5)CML 1335 (11) 960 ( 8)MDS 1298 (11) 1770 (16)Other acute leukemia 44 (<1) 111 ( 1)NHL 1225 (10) 910 ( 8)Hodgkins Lymphoma 79 ( 1) 52 (<1)Plasma Cell Disorders, MM 295 ( 2) 42 (<1)SAA 1143 ( 9) 426 ( 4)Inherited abnormalities erythrocyte diff fxn 679 ( 6) 177 ( 2)SCIDs 240 ( 2) 198 ( 2)Inherited abnormalities of platelets 10 (<1) 15 (<1)Inherited disorders of metabolism 78 ( 1) 88 ( 1)Histiocytic disorders 52 (<1) 101 ( 1)

Disease stage at transplant

Early 6190 (51) 6409 (57)Intermediate 478 ( 4) 487 ( 4)Advanced/Late 1950 (16) 2298 (20)Other 3421 (28) 2149 (19)

Stem cell source

Bone marrow 3764 (31) 4249 (37)PBSC 8275 (69) 7094 (63)

GVHD prophylaxis

Ex vivo T-cell depletion alone 150 ( 1) 245 ( 2)Ex vivo T-cell depletion + post-tx immune suppression

102 ( 1)

318 ( 3)

CD34 selection alone 91 ( 1) 69 ( 1)CD34 selection + post-tx immune suppression 173 ( 1) 256 ( 2)Cyclophosphamide alone 26 (<1) 46 (<1)Cyclophosphamide + others 91 ( 1) 8 (<1)FK506 + MMF +- others 551 ( 5) 1546 (14)FK506 + MTX +- others (except MMF) 2291 (19) 4523 (40)FK506 + others (except MTX, MMF) 300 ( 2) 398 ( 4)FK506 alone 182 ( 2) 299 ( 3)CSA + MMF +- others (except FK506) 869 ( 7) 865 ( 8)CSA + MTX +- others (except FK506, MMF) 5393 (45) 2251 (20)CSA + others (except FK506, MTX, MMF) 451 ( 4) 231 ( 2)CSA alone 871 ( 7) 104 ( 1)Other GVHD prophylaxis 498 ( 4) 184 ( 1)

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Continued. Related UnrelatedCharacteristics of patients N (%) N (%)Number of patients 12039 11343

Conditioning regimen

Myeloablative 6881 (57) 6915 (61)Reduced intensity 2243 (19) 2693 (24)Nonmyeloablative 1303 (11) 1314 (12)Other 1612 (13) 421 ( 4)

Donor/recipient sex match

Male/Male 3851 (32) 3169 (40) Male/Female 2623 (22) 1984 (25) Female/Male 3021 (25) 1464 (18) Female/Female 2393 (20) 1335 (17)

Donor sex TBD 151 3391 Donor race/ethnicity

Caucasian, Non-hispanic 1801 (77) 6313 (87)African American, Non-hispanic 169 ( 7) 289 ( 4)Asian, Non-hispanic 114 ( 5) 122 ( 2)Pacific Islander, Non-hispanic 6 (<1) 5 (<1)Native American, Non-hispanic 9 (<1) 97 ( 1)Hispanic, Caucasian race 229 (10) 99 ( 1)Hispanic, African American race 9 (<1) 2 (<1)Hispanic, Asian race 3 (<1) 0Hispanic, Native American race 3 (<1) 6 (<1)Hispanic, Unknown race 0 295 ( 4)Other 0 12 (<1)

In vivo T-cell depleted 2421 (20) 4169 (37)

Donor/recipient CMV status

Negative/Negative 2734 (23) 3408 (30) Negative/Positive 2059 (17) 3695 (33) Positive/Negative 1237 (10) 1173 (10) Positive/Positive 5547 (46) 2164 (19) Unknown 462 ( 4) 903 ( 8)

Donor age in years, median (range) 39 (<1-85) 34 (18-61) Donor age

≤ 9 y 1148 (10) 0 10-19 y 1613 (13) 119 ( 1) 20-29 y 1468 (12) 2454 (22) 30-39 y 1702 (14) 2781 (25) 40-49 y 2503 (21) 1876 (17) 50 y and older 2972 (25) 534 ( 5)

Donor age TBD 633 3579

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Continued. Related UnrelatedCharacteristics of patients N (%) N (%)Number of patients 12039 11343 Year of transplant

2000 1334 (11) 570 ( 5)2001 1269 (11) 583 ( 5)2002 1241 (10) 553 ( 5)2003 1069 ( 9) 805 ( 7)2004 1298 (11) 1100 (10)2005 1340 (11) 1319 (12)2006 1056 ( 9) 1575 (14)2007 489 ( 4) 1578 (14)2008 1179 (10) 1217 (11)2009 932 ( 8) 1087 (10)2010 516 ( 4) 589 ( 5)2011 316 ( 3) 367 ( 3)

Median follow-up of survivors, mo (range) 54 (<1-152) 60 (2.9-150)

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Study Proposal 0312-01 Study Title: Role of the complement system in graft-versus-host disease. Vahid Afshar-Kharghan, MD, M.D. Anderson Cancer Center, Houston, TX, USA John Belmont MD, PhD, Baylor College of Medicine, Houston, TX, USA Christopher Amos PhD, M.D. Anderson Cancer Center, Houston, TX, USA Study Objectives:

1. To study the association between polymorphisms in complement system genes and severity of GVHD.

2. To identify the high risk complement haplotypes for severe GVHD. Scientific Justification: Our hypothesis is that polymorphisms in genes encoding complement proteins might affect the severity of GVHD, and is based on: 1) previous studies showing that the complement system regulates the interaction between antigen presenting cells (APCs) and alloreactive T cells, 2) our preliminary data showing complement deficiency in mice reduces GVHD-related morbidity and mortality, and 3) the presence of an association between complement polymorphisms and susceptibility to inflammatory and infectious diseases. Identifying complement genetic risk factors for GVHD might create the possibility of using anti-complement therapy in patients with high risk for severe GVHD (instead of or in addition to currently available immunosuppressive therapy). The complement system regulates the interaction between antigen presenting cells (APCs) and alloreactive T cells mice. Complement proteins regulate B and T cell activation and proliferation.1,2 The role of complement proteins in the cognate interaction between dendritic cells (DCs) and T cells has been extensively studied in allograft rejection. Local production of complement proteins by APCs residing in the allograft is important in activation of recipient CD4+ and CD8+ lymphocytes. 3,4 Complement proteins are involved essential for maturation and differentiation of DCs,5,6 and for effective antigen presentation to T cells3 and subsequent proliferation and differentiation of T cells. 3,5,7-9 Complement deficiency in mice reduces GVHD-related morbidity and mortality. Using a mouse model of GVHD (based on the disparity in MHC class I and II antigens),10 we found that plasma concentration of C3a and C5a (complement activation products) increased over time parallel to the clinical progression of GVHD. Next, we compared the severity of GVHD in complement sufficient (wild type) and deficient (C3-/-) mice. We found that C3-/- recipient mice had a significantly lower 56-day mortality rate (25%, 5 out of 20 died) compared to wild type mice (75%, 15 out of 20 died) (p=0.0008, n=20 in each group). In a separate experiment, 3 mice from each group were sacrificed, and their livers, lungs, skins, and intestines were evaluated for GVHD-related histological changes. Wild type mice had significantly more severe GVHD-induced tissue injuries compared to C3-/- mice. Variations in complement activity in normal population and disease susceptibility. Polymorphisms in complement and complement regulatory proteins cause variation in complement activity. The discovery of a link between factor H polymorphism (Y402H) and age-related macular degeneration11 was followed by reports on the association between complement polymorphisms and risk of infectious and inflammatory conditions such as, systemic lupus erythematosis,12 meningococcal infection,11 and sepsis. 13 Study Population: We can include all allogeneic donor and recipient pairs.

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Data Requirements: The variables from existing CIBMTR data collection forms that need to be analyzed in our study includes age, gender, primary diagnosis of recipient, HLA typing of donor and recipient, type of allogeneic hematopoietic stem cell transplantation, conditioning regimen, immunosuppressive therapy and GVHD prophylaxis, grade of acute GVHD and organs involved, presence and severity of chronic GVHD. We will use the following data collection forms: Recipient base line data, Confirmation of HLA typing, 100 days post-HSCT data, Six months to two years post-HSCT data, Yearly follow-up for greater than two years post-HSCT data, Pre-transplant essential data, post-transplant essential data. We will not collect any new data. Sample Requirements: We will need whole blood samples from donor and recipient pairs. DNA will be extracted in the Laboratory for Translational Genomics at the Children’s Nutrition Research Center (CNRC) at Baylor College of Medicine directed by Dr. John Belmont.

Study Design: This is a case-control study, and we will compare the frequency of different polymorphisms in the genes involved in complement activation and regulation between patients with severe (grade III-IV) GVHD (Cases) and mild (grade 0-I) GVHD (Controls). Our goal is to identify any association between single nucleotide polymorphisms (SNPs) or haplotypes of complement system genes and severity of GVHD. Because complement proteins are secreted from and complement receptors are present on both donor T cells and recipient DCs, we will genotype both donors and recipients for complement polymorphisms, and correlate the genotypes to the highest grades of acute GVHD detected in the clinical course of patients after allo-HSCT. Genotyping will be conducted at the Children’s Nutrition Research Center (CNRC) at Baylor College of Medicine directed by Dr. John Belmont. Data analysis will be conducted in MDACC under supervision of Dr. Christopher Amos. We will conduct genotyping studies at two stages. In the first stage (discovery), we will study 2522 tagged SNPs in 59 complement system genes (Figure 9) using 2000 DNA samples from CIBMTR (500 patients with severe GVHD, 500 with mild GVHD, and 1000 respective donors). In addition to tagged SNPs, we will also include functional SNPs and disease-associated mutations involving complement system genes in our studies. In the second stage (replication), we will genotype additional 2000 DNA samples from CIBMTR (500 patients with severe GVHD, 500 with mild GVHD, and 1000 respective donors) for the most promising 20 SNPs (based on the most significant results of stage 1).

– Power Calculation: Power calculations are conducted by examining power under different strengths of linkage disequilibrium (as measured by R2). We assumed about 25% of the patients will have severe GVHD. Since the underlying model predisposing to disease risk may actually follow dominant, recessive, or other models, we have performed power calculations assuming that we have fitted an additive model but the underlying model is either dominant or recessive. We are conservatively adjusting for 2600 independent tests to set the significance level. Including the entire cohort of initially genotyped subjects with those in the replication and validation phases leads to detectable odds ratios of 1.27, 1.29, 1.35, 1.41, and 1.55 for risk genotype frequencies of 50%, 40%, 30%, 20%, and 10% respectively, using a two stage analytical design.14 While there is some gain in power including validation samples, these additional samples will allow us to evaluate more fully phenotypes contributing to GVHD and explore interactions among markers and between the host and the donor.

– DNA Genotyping: We propose to use an Illumina custom Infinium SNP beadarray. This platform will give a low cost and high coverage set of SNPs for the planned candidate genes. DNA quantification, dilution, genotyping, and archiving will be performed in the Laboratory for

42


Translational Genomics at the Children’s hospital at Baylor College of Medicine directed by Dr. John Belmont.

– Quality Control (QC): Raw genotype data will be reviewed for quality prior to association analysis. Quality control for the assay is ensured through multiple levels of redundancy, as well as sample-independent and sample-dependent internal controls.

– Controlling for Population Substructure: If cases and controls are not well matched for general ancestry, numerous markers can exhibit allele frequency differences even though they are not causally associated with the trait. A practical approach is provided by multidimensional scaling analysis (MDS) as implemented in PLINK 15 using three HapMap reference populations (CEU, CHB_JPT, and YRI). We will include a panel of 200 SNPs that show significant variation among racial groups.16,17

– Association Analysis: In this phase of the analysis, we will perform candidate variant and genome-wide case-control association tests contrasting the severe GVHD cases to mild GVHD individuals who will be coded as controls. Associations tests will first be carried out for each SNP individually using logistic regression with clinical covariates as implemented in PLINK. Important known covariates are gender, age, HLA matching, conditioning chemotherapy, and immunosuppressive regimens. In order to test for either dominant or recessive inheritance, we will also evaluate all makers in model-based association tests with covariates. In addition to analyzing the SNPs on the custom panel, we will also generate imputed genotype data for HAPMAP and 1000 Genomes SNPs that have not been genotyped but are in linkage disequilibrium with SNPs using MACH software. We will use algorithms implemented in GenAbel to perform association tests with the imputed SNPs. We will compute the genomic inflation factor (GIF) and construct QQ plots to investigate the distribution of the test statistics. This will allow us to assess whether any of the association tests depart from the random expectation and whether there is a significant residual effect of population structure. We will then use permutation-based methods to evaluate apparently associated SNPs. SNPs that attain genome-wide significance (p value<5*10-8), possibly associated SNPs (p value<10-4), and functional SNPs in coding sequences will be selected for replication analysis in an independent cohort of 500 cases and 500 controls also using Taqman chemistry. The additional clinical data derived from studies at MDACC will allow us to evaluate if there are specific clinical subphenotypes that are most associated with particular variants.

– Potential Problem and Altnerative Approach: – Analysis of gene interactions is potentially of major importance in defining genetic

predisposition to GVHD, as biological interactions between donor- and recipient derived cells are expected in GVHD; and in addition to recipient’s genotype, the donor’s genotypes may also play a key role in the development of GVHD. A secondary hypothesis is that donor genotypes (alone or in combination with certain recipient genotypes) may play a role in development of GVHD, and because we are genotyping the donors and recipients, we will be able to test this hypothesis. In addition to the methods outlined in previous section, we propose additional exploratory state-of-art methods for studying the potential interaction of SNPs within and between genes and between the donor and the recipient. These include the adaptive spline and tree-based methods such as Multivariate Adaptive Regression Splines (MARS)18 and Classification and Regression Trees (CART).19 These methods can be used to generate interpretable interaction rules among SNPs and according to host and donor genotypes. Since these methods also have limitations (e.g. MARS is most applicable to situations in which there are only a few variable sites and CART only generates rules in disjunctive normal form), we plan to apply recently developed logic regression methods.20 In order to study and assess gene by gene interaction and gene by clinical variable interactions on the risk of developing GVHD, we will also apply the recently developed tree based method, FlexTree.21 Since these methods are applicable to a limited number of possible interactions, we will restrict the multilocus analysis to only a small subset of SNPs that

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includes only SNPs for which we have confirmed a marginal significance in the single SNP analyses (i.e. after completion of the validation phase of the study). We will examine ~100 SNPs (based on highest marginal significance) including no more than 5 SNPs in a single haplotype block as defined by the HapMap project.

– Targeted genotyping of SNPs does not capture all of the variability in the genic regions under study. We have chosen to use this approach because it is currently much less costly than targeted sequencing and should capture the vast majority of the variability for frequent alleles that have a substantial impact on disease development. If targeted sequencing becomes accessible as an alternative, we will switch to that technology. Most of the methods of analysis that we have proposed would remain relevant, although in that case we may also need to apply rare variant tests.22

– Acute GVHD will be the main clinical endpoint in our association studies; however, considering the clinical similarities between chronic GVHD and autoimmune disorders, and the strong association between polymorphisms of the complement system genes and autoimmune disorders, such as systemic lupus erythematosis12 and systemic sclerosis;23,24 we will also investigate the correlation between complement polymorphisms and severity of chronic GVHD.

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112, 1759-1766. 8. Sacks, S. H. (2010) Eur. J. Immunol. 40, 668-670. 9. Peng, Q., Li, K., Patel, H., Sacks, S. H., and Zhou, W. (2006) J. Immunol. 176, 3330-3341.

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11. Davila, S. et al. (2010) Nat. Genet. 42, 772-776. 12. Zhao, J. et al. (2011) PLoS. Genet. 7, e1002079. 13. Agbeko, R. S., Fidler, K. J., Allen, M. L., Wilson, P., Klein, N. J., and Peters, M. J. (2010)

Pediatr. Crit Care Med. 11, 561-567. 14. Skol, A. D., Scott, L. J., Abecasis, G. R., and Boehnke, M. (2006) Nat. Genet. 38, 209-213. 15. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., Maller, J.,

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Garchon, H. J., Pulver, A. E., Ransom, M., Gregersen, P. K., and Seldin, M. F. (2009) Mol. Med. 15, 371-383.

18. York, T. P., Eaves, L. J., and van den Oord, E. J. (2006) Stat. Med. 25, 1355-1367. 19. Garcia-Magarinos, M., Lopez-de-Ullibarri, I., Cao, R., and Salas, A. (2009) Ann. Hum. Genet.

73, 360-369. 20. Kooperberg, C. and Ruczinski, I. (2005) Genet. Epidemiol. 28, 157-170. 21. Huang, J., Lin, A., Narasimhan, B., Quertermous, T., Hsiung, C. A., Ho, L. T., Grove, J. S.,

Olivier, M., Ranade, K., Risch, N. J., and Olshen, R. A. (2004) Proc. Natl. Acad. Sci. U. S. A 101, 10529-10534.

22. Liu, D. J. and Leal, S. M. (2010) Am. J. Hum. Genet. 87, 790-801. 23. Briggs, D., Stephens, C., Vaughan, R., Welsh, K., and Black, C. (1993) Arthritis Rheum. 36,

943-954. 24. Takeuchi, F., Nabeta, H., Hong, G. H., Matsuta, K., Tokunaga, K., Tanimoto, K., and Nakano,

K. (1998) Clin. Exp. Rheumatol. 16, 55-60.

45


Characteristics of recipients receiving first allogeneic transplants for AML, ALL, CML or MDS that are high-resolution HLA-matched for HLA-A, -B, -C, –DRB1 and –DQB1 and have

donor and recipient samples availablea

Characteristics of patients N Eval N (%)Number of patients 5319 Number of centers 174 Recipient age at transplant, median (range), years 5319 42 (<1-78) ≤ 9 y 328 ( 6) 10-19 y 489 ( 9) 20-29 y 744 (14) 30-39 y 866 (16) 40-49 y 1126 (21) 50 y and older 1766 (33) Recipient race/ethnicity 5237

Caucasian, Non-hispanic 4838 (92)African American, Non-hispanic 102 ( 2)Asian, Non-hispanic 60 ( 1)Pacific Islander, Non-hispanic 5 (<1)Native American, Non-hispanic 11 (<1)Hispanic, Caucasian race 163 ( 3)Hispanic, African American race 2 (<1)Hispanic, Asian race 2 (<1)Hispanic, Unknown race 46 ( 1)Other 8 (<1)

Male sex 5319 2976 (56) Karnofsky prior to transplant > 90 4884 3435 (70) Disease at transplant 5319

AML 2403 (45)ALL 967 (18)CML 1088 (20)MDS 861 (16)

Disease stage at transplant 5319

Early 3791 (71)Intermediate 273 ( 5)Advanced/Late 1173 (22)Other 82 ( 2)

HLA-DPB1 Matching 2955

Allele matched 457 (15)Single allele mismatch 1598 (54)Double allele mismatch 900 (30)


46


Continued. Characteristics of patients N Eval N (%)Stem cell source 5319

Bone marrow 2656 (50)PBSC 2663 (50)

Conditioning regimen 5319

Myeloablative 3975 (75)Reduced intensity 915 (17)Nonmyeloablative 327 ( 6)Other 102 ( 2)

GVHD prophylaxis 5319

Ex vivo T-cell depletion alone 146 ( 3)Ex vivo T-cell depletion + post-tx immune suppression

252 ( 5)

CD34 selection alone 12 (<1)CD34 selection + post-tx immune suppression 69 ( 1)Cyclophosphamide alone 29 ( 1)Cyclophosphamide + others 8 (<1)FK506 + MMF +- others 552 (10)FK506 + MTX +- others (except MMF) 1940 (36)FK506 + others (except MTX, MMF) 174 ( 3)FK506 alone 95 ( 2)CSA + MMF +- others (except FK506) 284 ( 5)CSA + MTX +- others (except FK506, MMF) 1499 (28)CSA + others (except FK506, MTX, MMF) 96 ( 2)CSA alone 39 ( 1)Other GvHD Prophylaxis 124 ( 2)

In vivo T-cell depleted 5319 1357 (26)

Donor/recipient sex match 5319

Male/Male 1674 (40) Male/Female 1062 (25) Female/Male 688 (16) Female/Female 754 (18)

Donor sex TBD 1141

Donor/recipient CMV status 5319

Negative/Negative 1714 (32) Negative/Positive 1735 (33) Positive/Negative 567 (11) Positive/Positive 830 (16) Unknown 473 ( 9)

47


Continued. Characteristics of patients N Eval N (%)Donor age in years, median (range) 5319 35 (18-61) 18-19 y 56 ( 1) 20-29 y 1262 (24) 30-39 y 1525 (29) 40-49 y 1017 (19) 50 y and older 261 ( 5)

Donor age TBD 1198 (23) Donor race/ethnicity 3891

Caucasian, Non-hispanic 3593 (92)African American, Non-hispanic 76 ( 2)Asian, Non-hispanic 28 ( 1)Pacific Islander, Non-hispanic 3 (<1)Native American, Non-hispanic 48 ( 1)Hispanic, Caucasian race 27 ( 1)Hispanic, Native American race 1 (<1)Hispanic, Unknown race 105 ( 3)Other 10 (<1)

Year of transplant 5319

1988 7 (<1)1989 35 ( 1)1990 50 ( 1)1991 64 ( 1)1992 84 ( 2)1993 84 ( 2)1994 124 ( 2)1995 123 ( 2)1996 130 ( 2)1997 155 ( 3)1998 154 ( 3)1999 179 ( 3)2000 198 ( 4)2001 186 ( 3)2002 185 ( 3)2003 269 ( 5)2004 433 ( 8)2005 533 (10)2006 599 (11)2007 633 (12)2008 408 ( 8)2009 367 ( 7)2010 216 ( 4)2011 85 ( 2)2012 18 (<1)

Median follow-up of survivors, mo (range) 2098 60 (3-250)

48


Study Proposal 0712-02 Study Title: The Impact of MHC Class I Chain-Related Gene A (MICA) Donor-Recipient Mismatches and MICA-129 Polymorphism on Unrelated Donor Hematopoietic Stem Cell Transplants (HSCT) For Hematological Malignancies Medhat Askar, MD, PhD, Cleveland Clinic, Cleveland, OH, USA Ronald Sobecks, MD, Cleveland Clinic, OH, USA Study Objectives:

1. To investigate the impact of MICA donor recipient mismatches on the clinical outcomes of unrelated donor HSCT for hematological malignancies including acute and chronic graft versus host disease (GvHD), donor engraftment, relapse, transplant related mortality, disease free survival, overall survival and CMV activation (if data is available)

2. To investigate the impact of MICA position 129 polymorphism of HSCT recipients and donors on the above clinical outcomes

Scientific Justification: The impact of donor recipient matching at HLA loci A, B, C, DRB1, DQB1, & DPB1 on clinical outcomes of unrelated HSCT has been established by numerous studies 1-3. However the effect of MHC Class I Chain-Related Gene A (MICA) donor-recipient mismatches remains controversial. Parmar and colleagues have investigated the impact of MICA in a single center study on 236 patients (172, 73% of them were 10/10 HLA Match)4. They reported a higher rate of grade II-IV aGVHD in MICA-mismatched patients (80% vs 40%, p = 0.003) irrespective of degree of HLA matching (HLA 10/10 match: 75% vs 39%, p = 0.02) and HLA any mismatch (83% vs 46%, p = 0.003). They also found that the rate of grade II-IV gastrointestinal aGVHD was also higher in MICA-mismatched patients (35% vs 17%, p = 0.05). We also recently reported in a single center analysis on 269 patients (204, 79% of them were 10/10 matches) a significant association between 1-2 MICA mismatches and severe acute GvHD (Grades III & IV) in a multivariable model with a hazard ratio of 2.12 vs. 0 MICA mismatch (95% CI 1.17-3.83, p = 0.01)5. In addition, we investigated the potential of interaction between MICA and HLA-DPB1 mismatches in influencing outcomes using exploratory recursive partitioning analysis (RPA) with log-rank splitting. In our cohort RPA has shown that the effect of MICA mismatching was additive to the effect of HLA-DPB1 mismatching. Figure 1 shows the incidence of severe acute GvHD at 100 days as 38%, 17%, and 8% in patients with 1-2 MICA and 1-2 HLA-DPB1 mismatches (n=27), 1-2 HLA-DPB1 but no MICA mismatches (n=158), and no HLA=DP mismatches (n=43), respectively, p=0.002). Anderson and colleagues on the other hand did not observe similar effects when they typed 38 donor/recipient pairs who were 12/12 HLA matched (matched at loci HLA-A, B, C, DRB1, DRQ1, and DPB1) 6. However, in that study only 1/38 pairs (2.6%) was mismatched for MICA. The remarkably low incidence of MICA mismatches in this study is at least explained in part by the strong linkage disequilibrium between MICA and HLA-B but contrasts the 8.5% and 12.7% MICA mismatch rates observed in the Parmar and Askar studies, respectively. An alternate hypothesis to explain the influence of MICA mismatches on HSCT outcomes could be the differential strength of binding of various MICA alleles to their cognate NKG2D activating receptor. NKG2D is expressed on the surface of NK, NKT, CD8+ and TCRγδ+ T cells. Allelic variants of MICA due to a single amino acid substitution at position 129 in the alpha2 domain have been reported to result in large differences in NKG2D binding. MICA alleles with a methionine (M) or valine (V) have been classified as having strong or weak binding affinity for NKG2D, respectively. These variable affinities have been suggested to affect thresholds of NK cell triggering and T cell modulation.

49


Figure (1): The incidence of severe acute GvHD at 100 days Recipient homozygosity of V alleles (VV) has been reported to be associated with chronic GVHD in a study that primarily included myeloablative transplants 7. In this study recipient MICA-129 VV genotype was found to increase the risk of chronic GvHD in a multivariable model (HR, 1.61; 95% CI, 1.08-2.40; p =0.019). The authors hypothesized that the weak engagement of NKG2D receptors by the weak binder MICA-129 V allele may impair NK/cytotoxic T lymphocyte cell activation/costimulation, possibly skewing the TH1 pathway toward TH2 with consequent B-cell activation and Ab production which are two hallmarks of chronic GvHD pathogenesis. In contrast, we recently reported that VV recipients who underwent reduced intensity regimens had significantly less grade 3-4 acute GvHD (0 vs. 26%, respectively, p=0.022) compared to the VM/MM recipients and a trend to less chronic GvHD (8 vs. 37%, respectively, p=0.07) 8. We hypothesized that recipients with weak binding affinity (VV) for the NKG2D receptors may cause less activation of immune effector cells and consequently decrease the risk of developing GvHD. In contrast with the results reported by both Parmar et al and our group, when this alternate hypothesis was tested in the Anderson study in the 38 patients tested for MICA, no association between recipient MICA allele binding affinity and acute GVHD was observed. The investigators of the study also examined whether certain recipient HLA-B alleles (based on MICA linkage) are associated with GI acute GVHD using a second cohort of 1676 recipients and found no differences in transplantation outcomes between recipient B alleles associated with MICA alleles of different NKG2D binding affinity. In the Boukouaci study, the observed distribution in 211 recipients was 7%, 47% and 46% for MICA genotypes MM, MV, and VV respectively. In our study, the observed distribution in was 7%, 47% and 46% for MICA genotypes MM, MV, and VV respectively. To further evaluate the clinical utility of MICA-129 genotypes as a biomarker in terms of distribution of the three genotypes, we tested the MICA 129 genotype distribution among two cohorts 9. Cohort I consisted of 388 solid organ and HSCT donors (351 Caucasians and 37 African Americans) as a random sample of the population. Cohort II consisted of 530 sequential HSCT recipients (507 Caucasians & 23 Afrivan Americans). The genotype distribution of both cohorts is shown in tables 1 & 2, respectively: Table 1: MICA-129 genotype distribution among donors

MM MV VV Total Caucasians 63 (18%) 116 (33%) 172 (49%) 351 African Americans 13 (35%) 19 (51%) 5 (14%) 37 Total 76 (20%) 135 (35%) 177 (46%) 388

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Table 2: MICA-129 genotype distribution among 530 sequential HSCT recipients

MM MV VV Total Caucasians 38 (8%) 214 (42%) 255 (50%) 507 African Americans 5 (22%) 7 (30%) 11 (48%) 23 Total 43 (8%) 221 (42%) 266 (50%) 530 Our distribution study indicated that at least 8% of cohorts analyzed represented the least frequent genotype (MM). Interestingly, we observed a racial difference in genotype distribution among Caucasians and African Americans in cohort I (p= <0.0001) and in cohort II (p = 0.06). We also observed a significantly different prevalence of MM genotype among bone marrow recipients (8%) and donors of cohort I (20%) (p= <0.0001). Currently, there is a lack of consensus regarding the role of MICA mismatches and MICA 129-genotypes in HSCT clinical outcomes. This study will resolve this uncertainty and would leverage the strength of the adequate numbers available in the NMDP/CIBMTR to address the limitations of prior smaller studies or studies based on B-MICA associations rather than actual MICA genotyping which have been shown to be imperfect at least in about 10% of the cases from the studies presented. The proposed study will be powered enough to either confirm the reported role of MICA as a significant target for donor recipient matching when the choice of multiple donors is an option to improve HSCT clinical outcomes or to more definitively rule it out from matching algorithms. In addition if a role of MICA 129 genotype is confirmed we will gain additional insights into the immunobiology of GvHD. Study Population: 10/10 HLA matched adult HSCT recipients with hematological malignancies with known HLA-DPB1 typing and available DNA or equivalent samples to allow for MICA genotyping of recipients and their corresponding donors. Ideally the study population would be homogeneous favorably in terms of diseases and preparative regimens (based on available numbers that meet the above criteria). Data Requirements: Patient and donor demographics (age, gender, race), diagnosis, disease stage, acute GvHD (any, severe, GI), chronic GvHD (any, extensive), relapse, transplant related mortality, disease free survival, overall survival and if available failure of engraftment, time to complete donor chimerism, platelet and neutrophil engraftment, and CMV reactivation. Sample Requirements: 1 ml blood for DNA extraction and MICA genotyping of recipients and donors Study Design: The proposed study will be a retrospective cohort study. Donors and recipients will be genotyped for MICA using commercially available Luminex based rSSOP MICA genotyping kits (available from One Lambda or Gene Probe). Identified donor and recipient MICA genotypes will be used to determine the MICA mismatches and also to classify MICA alleles based on the amino acid present in sequence position 129 into MM, MV, or VV. Possible limitations could include inadequate extracted DNA quantity or quality which could be addressed using whole genome amplification technique. Guidance from CIBMTR biostatisticians will be sought to calculate the adequate sample size and the appropriate statistical methods that would be appropriate to investigate the study aims.

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Experience of the Investigator in the proposed methods: The first investigator is an MD with a PhD in Microbiology and Immunology and is certified by the American Board of Histocompatibility & Immunogenetics (ABHI), the American Board of Medical Laboratory Immunology (ABMLI), and the American Board of Bioanalysis as High Complexity Laboratory Director (ABB, HCLD). He is the director of a high volume full service histocompatibility and immunogentics laboratory that supports all transplant programs of a large academic institute (Cleveland Clinic) including kidney, pancreas, liver, small bowel, heart, lung, multi-organ, face, and HSCT including cord blood transplants. Our laboratory performs over 500 MICA gentyping tests a year and we have validated MICA genotyping by both rSSOP Luminex based and also Sequence based typing. We have been participants in the UCLA MICA exchange since its beginning and have not had a single discrepant result as of the present time. We have identified 2 new MICA alleles and published a number of abstracts and presented numerous presentations that involve MICA genotyping. For further information please see enclosed biosketch. References:

1. Ciurea SO, Saliba RM, Rondon G, et al. Outcomes of patients with myeloid malignancies treated with allogeneic hematopoietic stem cell transplantation from matched unrelated donors compared with one human leukocyte antigen mismatched related donors using HLA typing at 10 loci. Biol Blood Marrow Transplant. 2011;17:923-929.

2. Lee SJ, Klein J, Haagenson M, et al. High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood. 2007;110:4576-4583.

3. Fleischhauer K, Shaw BE, Gooley T, et al. Effect of T-cell-epitope matching at HLA-DPB1 in recipients of unrelated-donor haemopoietic-cell transplantation: a retrospective study. Lancet Oncol;13:366-374.

4. Parmar S, Del Lima M, Zou Y, et al. Donor-recipient mismatches in MHC class I chain-related gene A in unrelated donor transplantation lead to increased incidence of acute graft-versus-host disease. Blood. 2009;114:2884-2887.

5. Askar M, Rybicki L, Zhang A, et al. The Impact of The Major Histocompatibility Complex Class I-Related Chain A (MICA) and Human Leukocyte Antigen (HLA) -DP Mismatches On Severe Acute GVHD in Patients Receiving Allogeneic Hematopoietic Progenitor Cell Transplants (AHPCT) From Adult Unrelated Donors. BBMT. 2012;18:S223 [Abstract].

6. Anderson E, Grzywacz B, Wang H, et al. Limited role of MHC class I chain-related gene A (MICA) typing in assessing graft-versus-host disease risk after fully human leukocyte antigen-matched unrelated donor transplantation. Blood. 2009;114:4753-4754; author reply 4754-4755.

7. Boukouaci W, Busson M, Peffault de Latour R, et al. MICA-129 genotype, soluble MICA, and anti-MICA antibodies as biomarkers of chronic graft-versus-host disease. Blood. 2009;114:5216-5224.

8. Sobecks R, Zhang A, Rybicki L, et al. Acute GVHD in AML and MDS Patients Receiving Matched Related Donor (MRD) Reduced-Intensity Conditioning Allogeneic Hematopoietic Progenitor Cell Transplant (RIC-AHPCT) Correlates with Major Histocompatibility Complex Class I-related Molecule A (MICA) Gene Polymorphisms. Blood. 2011;118:2543 [Abstract].

9. Zhang A, Askar M, DeVecchio J, et al. Racial Differences In MICA-129 Genotype Distribution Among Caucasians And African Americans. Tissue Antigens. 2012;79:465 [Abstract].

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Characteristics of recipients receiving first allogeneic unrelated donor transplants for AML, ALL, CML or MDS with high-resolution HLA typing for HLA-A, -B, -C, –DRB1, -DQB1 and

–DPB1 and have samples available for donor and recipienta

9/10 10/10Characteristics of patients N (%) N (%)Number of patients 171 2956 Number of centers 73 161 Recipient age at transplant, median (range), years 38 (2-74) 39 (<1-78) ≤ 9 y 11 ( 6) 186 ( 6) 10-19 y 23 (13) 278 ( 9) 20-29 y 24 (14) 473 (16) 30-39 y 40 (23) 585 (20) 40-49 y 38 (22) 694 (23) 50 y and older 35 (20) 740 (25) Recipient race/ethnicity

Caucasian, Non-hispanic 143 (84) 2759 (94)African American, Non-hispanic 7 ( 4) 42 ( 1)Asian, Non-hispanic 1 ( 1) 31 ( 1)Pacific Islander, Non-hispanic 0 2 (<1)Native American, Non-hispanic 1 ( 1) 6 (<1)Hispanic, Caucasian race 5 ( 3) 44 ( 1)Hispanic, African American race 1 ( 1) 1 (<1)Hispanic, Asian race 0 2 (<1)Hispanic, Unknown race 13 ( 8) 45 ( 2)Other 0 2 (<1)

Male sex 82 (48) 1702 (58) Karnofsky prior to transplant > 90 110 (68) 2010 (72) Disease at transplant

AML 58 (34) 1072 (36)ALL 39 (23) 536 (18)CML 51 (30) 903 (31)MDS 23 (13) 445 (15)

Disease stage at transplant Early 120 (70) 2090 (71)Intermediate 9 ( 5) 197 ( 7)Advanced/Late 39 (23) 639 (22)Other 3 ( 2) 30 ( 1)

Stem cell source

Bone marrow 128 (75) 1961 (66)PBSC 43 (25) 995 (34)


53


Continued. 9/10 10/10Characteristics of patients N (%) N (%)Number of patients 171 2956 Conditioning regimen

Myeloablative 150 (88) 2483 (84)Reduced intensity 13 ( 8) 296 (10)Nonmyeloablative 7 ( 4) 138 ( 5)Other 1 ( 1) 39 ( 1)

GVHD prophylaxis

Ex vivo T-cell depletion alone 9 ( 5) 110 ( 4)Ex vivo T-cell depletion + post-tx immune suppression

16 ( 9)

205 ( 7)

CD34 selection alone 0 2 (<1)CD34 selection + post-tx immune suppression 0 10 (<1)Cyclophosphamide alone 0 15 ( 1)Cyclophosphamide + others 0 6 (<1)FK506 + MMF +- others 7 ( 4) 187 ( 6)FK506 + MTX +- others (except MMF) 50 (29) 903 (31)FK506 + others (except MTX, MMF) 6 ( 4) 94 ( 3)FK506 alone 1 ( 1) 33 ( 1)CSA + MMF +- others (except FK506) 4 ( 2) 105 ( 4)CSA + MTX +- others (except FK506, MMF) 78 (46) 1152 (39)CSA + others (except FK506, MTX, MMF) 0 67 ( 2)CSA alone 0 25 ( 1)Other GVHD prophylaxis 0 42 ( 1)

In vivo T-cell depleted 41 (24) 627 (21)

HLA-DPB1 Matching Allele matched 28 (16) 457 (15)Single mismatch 95 (56) 1598 (54)Double mismatch 48 (28) 901 (30)

Donor/recipient sex match Male/Male 50 (34) 981 (41) Male/Female 41 (28) 583 (24) Female/Male 20 (14) 413 (17) Female/Female 34 (23) 427 (18)

Donor sex TBD 26 552

Donor/recipient CMV status Negative/Negative 47 (27) 1026 (35) Negative/Positive 62 (36) 916 (31) Positive/Negative 17 (10) 341 (12) Positive/Positive 26 (15) 410 (14) Unknown 19 (11) 263 ( 9)

54


Continued. 9/10 10/10Characteristics of patients N (%) N (%)Number of patients 171 2956

Donor race/ethnicity

Caucasian, Non-hispanic 110 (83) 2123 (94)African American, Non-hispanic 5 ( 4) 26 ( 1)Asian, Non-hispanic 0 7 (<1)Pacific Islander, Non-hispanic 0 1 (<1)Native American, Non-hispanic 4 ( 3) 24 ( 1)Hispanic, Caucasian race 0 11 (<1)Hispanic, Native American race 0 1 (<1)Hispanic, Unknown race 13 (10) 52 ( 2)Other 1 ( 1) 7 (<1)

Donor age in years, median (range) 36 (19-59) 36 (18-60) 18-19 y 2 ( 1) 19 ( 1) 20-29 y 34 (20) 670 (23) 30-39 y 58 (34) 893 (30) 40-49 y 37 (22) 631 (21) 50 y and older 12 ( 7) 165 ( 6)

Donor age TBD 28 578

Year of transplant 1988 1 ( 1) 7 (<1)1989 1 ( 1) 34 ( 1)1990 1 ( 1) 50 ( 2)1991 2 ( 1) 64 ( 2)1992 5 ( 3) 84 ( 3)1993 8 ( 5) 84 ( 3)1994 5 ( 3) 123 ( 4)1995 11 ( 6) 123 ( 4)1996 5 ( 3) 127 ( 4)1997 9 ( 5) 156 ( 5)1998 11 ( 6) 154 ( 5)1999 14 ( 8) 177 ( 6)2000 23 (13) 195 ( 7)2001 26 (15) 184 ( 6)2002 10 ( 6) 174 ( 6)2003 3 ( 2) 81 ( 3)2004 1 ( 1) 112 ( 4)2005 3 ( 2) 155 ( 5)2006 4 ( 2) 166 ( 6)2007 0 147 ( 5)2008 8 ( 5) 202 ( 7)2009 18 (11) 302 (10)2010 2 ( 1) 33 ( 1)2011 0 18 ( 1)2012 0 4 (<1)

Median follow-up of survivors, mo (range) 83 (6-203) 76 (3-250)

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Study Proposal 0712-03 Study Title: The development of Machine Learning based classifiers to define the alloreactivity of HLA mismatches in unrelated donor hematopoietic stem cell transplantation. Yoram Louzoun, PhD, Bar Ilan University, Tel Aviv, Israel Study Objectives: The interaction of CD8 T cells (CTLs) with target cells occurs through the contact of the T cell receptor (TCR), the appropriate MHC-I molecule and the peptides within this molecule. T cells with a receptor recognizing with a high enough affinity combinations of self MHC and self epitopes are believed to be deleted1 or edited 2 during T cells’ thymic education (see Palmer3 for a review) or regulated by regulatory CD4 T cells in the periphery.4 T cells reacting with an intermediate affinity are positively selected.5 When a cell presenting a foreign MHC class I is introduced to a host T cells, they may recognize it and react to it for two main reasons:

– The parts of the TCR CDR3 that interacts with the peptide bound within the MHC interact with an epitope that is presented by the foreign allele, and by none of the similar self alleles.

– The parts of the TCR that directly bind the MHC molecule interact strongly enough with the foreign MHC to induce a T cell response.

Note that an antibody response to the parts of the MHC that do not bind the TCR at all may also be possible that can lead to antibody mediated rejection. We plan to use bioinformatics based estimates of the epitope repertoire and the binding properties of the MHC and the TCR to translate HLA mismatches between donor and recipient to a pair of distances, and then use these distances to produce machine learning bases classifiers for one mismatch transplant results obtained from the NMDP (National Marrow Donor Program). The goal of the proposal is to produce a tool that will predict significantly better than random the outcome of different HLA mismatches. This will allow a better choice among single mismatch donors. Note that while the validation of the methodology will be performed on bone marrow transplantations, it may also be applicable to solid organs. We here propose to develop multiple “distance matrices” based on the difference between HLA class-I alleles in these two aspects.

– Differences in epitopes. We have previously developed multiple methods to estimate epitope repertoires.6-12 The basic concept is to take all proteins, compute peptide that are cleaved, bind TAP and bind MHC for the appropriate allele to produce a “peptidome”. We do this analysis in collaboration with the Admon lab at the technion that elute epitopes. We now propose to apply these algorithms to all highly expressed human proteins (proteins with a high enough expression level to present a reasonable amount of epitopes) and compare the repertoire of different HLA alleles. This will allow us to define the difference between the repertoires of different alleles and produce a distance matrix between alleles as a function of the difference in the repertoire

– Differences in side chains presented to the TCR. We plan to compute the structure of all HLA alleles (using homology based structure modeling) and compute the differences in exposed side chains in residues binding the TCR. We will score each position in the MHC molecule for the probability that differences in this residue would affect the interaction with the TCR. We will then sum the effect of the differences between every pair of HLA alleles on the amino acids where they differ.

56


These distances will then be applied to a large number of bone marrow transplant results to see if Machine learning tools can be used with those to produce a good classifier for historical transplant outcomes. Scientific Justification: Even a single of HLA mismatch (transplants where the donor and recipient have different HLA alleles in some of the alleles) increases the short and long term transplant failure probability.13 14,15 Such mismatches are quite frequent in solid organ as well as in hematopoietic cell transplants. Only 30% of patients in need of a hematopoietic cell transplant will have an HLA identical sibling to serve as the donor,16 and even less in solid organ transplantation. Thus, there is an urgent need to better estimate the effect of different HLA mismatches. The effect of mismatches is not limited to broad difference between alleles. Even limited genetic differences between HLA alleles and MHC linked SNPs affect the outcome of transplants.17,18 We here propose to develop an algorithm to predict different outcomes of transplantations based on the effect of specific single HLA allele mismatches on the T cell response. There are currently multiple scores to estimate the success rate of transplantation. The European Group for Blood and Marrow Transplantation (EBMT) risk score provides a simple tool to assess instantly chances and risks of hematopoietic SCT (HSCT) for an individual patient pre-transplant. This score has five factors: the age of the patient, stage of the disease, time from diagnosis, donor type and donor recipient gender combination.19 More advanced algorithms, such as HLAMATCHMAKER20 or HistoCheck 21 predict the failure probability based on a structural basis or on the structure of the MHC molecule, or on properties of antibodies binding the MHC molecule. We here propose a novel approach to determine the best mismatch (given that no perfect match was found). We first propose to define the distance between HLA alleles based on their structure and repertoire. Then we propose to develop a machine learning framework to use these distances to predict the outcome of transplantations. The fraction of transplantation candidates that do receive donations is limited (over 110,000 patients waiting, most of them for kidneys, with less than 20,000 receiving them in 2011 (http://optn.transplant.hrsa.gov). A major limiting factor in transplantations is the detection of the optimal matching between donors and recipients. The proposed strategy can help increase the number of transplantation candidates that end up finding a similar enough donor, and improve the survival rate following a better choice of a single mismatch donor. The main goal of the current proposal is to provide new criteria for the selection of optimal donors that could increase the transplant success probability and enlarge the pool of candidate donors for each recipient (candidate). Donor-recipient mismatch occurs at two main levels: the binding of T cell receptors to the MHC molecule itself, and the binding of the same receptors to epitopes within the binding groove of the MHC molecule (Figure 1). The difference between (HLA) MHC molecules is either in the cleft of the peptide binding site22 or in the region directly exposed to the T cell. The first type of allo-reaction occurs since the HLA alleles differ in the part exposed to the TCR, while the second type is the results of differences in the binding groove that affect the presented epitope repertoire. Therefore, translating the genetic information of the difference in HLAs into a phenotypic difference between the donor and the recipient requires the computation of the changes in the epitope repertoire, and interactions between the MHC molecule and the TCR. We have now tools to compute in detail the predicted MHC class I epitope repertoire.9,23 These tools will be used to estimate in-silico the image presented to the immune system. In parallel, we plan to define more clearly the MHC residues exposed to

57


the T cell receptor and the probability that differences in the exposed residues will affect the TCR-MHC interaction. The combination of these two measures with advanced machine learning can provide an improved estimate of the effect of an HLA mismatch during transplantation. Study Population: UNKNOWN Data Requirements: In order to test whether the proposed approach can be used to understand the outcome of a mismatch, we would appreciate receiving transplant results at multiple levels for people with a single HLA-I mismatch and if possible no HLA-II mismatch. The outcomes needed would be at the first stage one year survival. However other outcomes such as time to relapse and five year survival would be welcome. The information that we would need will be the genotyping of the donor and recipient HLA (or serotyping if genotyping is not available) and the outcome. Sample Requirements: None Study Design: The research plan consists of four stages:

– Definition of distance matrix for the interaction between the TCR and the MHC molecule. – Definition of the distance matrix for the difference in the epitope repertoire. – Development of Machine Learning based classifiers using large datasets of experimental bone

marrow, using these two matrices to define the effect of the mismatch. – Testing the results of the classification on external test sets.

Differences in Epitope Repertoire: We plan to compute the expected epitope repertoire presented on each allele. This description is obviously statistical and represents only the peptides with the highest MHC-binding affinity and highest cleavage and TAP binding probability. However, these peptides are probably the ones inducing most of the T cell induced immune response. We will compute for most highly frequent HLA class I alleles (40 alleles at the first stage), all the 8-10mer peptides that are cleaved, bind TAP and bind MHC from proteins with a high mRNA expression level to produce a “peptidome.” We plan to apply these algorithms to all highly expressed human proteins (proteins with a high enough mRNA expression level in the appropriate organ to present a reasonable amount of epitopes). We focus on the mRNA expression level, since CTL epitopes are mainly produced from DRIPs 24, which are affected by the total protein production and not by the steady state protein copy number. We have a long term experience in the analysis of presented peptide repertoires, and have validated the algorithms used in multiple different studies.7-12,25 Given the peptidome of each HLA allele, we will compute the overlap between repertoires, in the peptide positions that point their side chains toward the TCR (mainly position 4-7 26,27). These are the positions that the TCR can recognize specifically, and differences in these positions can induce an allo-reaction. The resulting matrix will represent the distance between the repertoires of each allele pairs as defined by the fraction of peptide in the repertoire of A that are not in the repertoire of B. Note that this matrix is not symmetric. We will develop two versions of this matrix. In the first version, we will only compare alleles within of HLA –A with A, B with B and C with C, and in the second version we will compare the repertoire of all HLA alleles for which we have classifiers.

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Differences in MHC binding: The T cell reaction can also result from the interaction of the TCR directly with the MHC molecule, even in absence of differences in the epitope repertoire (say in alleles differing mainly outside the binding groove). In order to compute the TCR-MHC (in contrast with the TCR-peptide) interaction, we plan to take all measured MHC-TCR structures and define for each position in the MHC, the number of structures where this position binds a TCR. We will then apply a smoothing algorithm to give nearby positions (on the surface of the MHC structure not in the sequence) similar scores. This will produce a map of a potential interaction surface. We will assume that this general map is approximately conserved among HLA alleles, and only consider differences between alleles in positions within these maps. We will then compute the structure of all HLA alleles (using homology based structure modeling, with the most similar alleles with measured structures as templates) and compute the difference in exposed side chains in residues within this map. Specifically, we will test for each amino acid difference if it affects an exposed or a buried side chain. We will weight that with the probability that residues should affect the interaction with the TCR, and sum the differences. This will lead to a second distance matrix representing the effect of amino acid differences on the MHC-TCR binding. A parallel yet simpler approach would be to use the standard Collier de perles notation of the IMGT, 28 which has a definition of contact sites for the TCR-MHC and the TCR-peptide positions. This will be used, if the detailed three dimensional approach will fail. Machine Learning: In the current proposal, we plan to analyze only single class I mismatch alleles. Given the distance between the mismatching allele and six alleles of the host, as obtained from the distance matrices, we will describe each mismatch as a 12 vector. We ask for a set of bone marrow transplantations results with a definition of the mismatches, and multiple outcome of the transplantation in short and long term survival, and the occurrence of a relapse within different time periods. Note that the different outcomes may be related in different ways to the mismatch, such as: mortality, non-relapse mortality, relapse, level III-IV GVHD. We will test, which of these outcomes can be related to the mismatch matrices. We will divide this set into three sub-sets: A) An internal learning set, B) and internal test set, and C) an external test set. We will then apply a large battery of machine learning algorithms to relate the 12 dimensional vectors describing the distance and the outcome of the transplantation using only the internal learning set. Each family of classifiers has multiple parameters, and use produce multiple classifiers. We will use the parameter sets producing the best classification of the internal test set. The classifiers providing the best accuracy will be tested on the external test set. The accuracy, sensitivity and specificity obtained on the external test set will be reported as the precision of the classifier. A list of machine learning classifiers to be used as described in the methods.

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References: 1. Nemazee D, Russell D, Arnold B, et al: Clonal deletion of autospecific B lymphocytes. Immunol

Rev 122:117-32, 1991 2. Prak EL, Weigert M: Light chain replacement: a new model for antibody gene rearrangement. J

Exp Med 182:541-8, 1995 3. Palmer E: Negative selection—clearing out the bad apples from the T-cell repertoire. Nature

Reviews Immunology 3:383-391, 2003 4. Sakaguchi S: Naturally arising CD4+ regulatory T cells for immunologic self-tolerance and

negative control of immune responses. Annu. Rev. Immunol. 22:531-562, 2004 5. Nikoli, cacute J, Bevan MJ: Role of self-peptides in positively selecting the T-cell repertoire.

1990 6. Louzoun Y, Vider T: Score for proteasomal peptide production probability. Immunology 1:45-50,

2004 7. Louzoun Y, Vider T, Weigert M: T-cell epitope repertoire as predicted from human and viral

genomes. Molecular Immunology 43:559-569, 2006 8. Vider-Shalit T, Fishbain V, Raffaeli S, et al: Phase-dependent immune evasion of herpesviruses.

Journal of virology 81:9536-9545, 2007 9. Vider-Shalit T, Almani M, Sarid R, et al: The HIV hide and seek game: an immunogenomic

analysis of the HIV epitope repertoire. Aids 23:1311, 2009 10. Vider-Shalit T, Sarid R, Maman K, et al: Viruses selectively mutate their CD8+ T-cell epitopes—

a large-scale immunomic analysis. Bioinformatics 25:i39-i44, 2009 11. Maman Y, Blancher A, Benichou J, et al: Immune-induced evolutionary selection focused on a

single reading frame in overlapping hepatitis B virus proteins. Journal of virology 85:4558-4566, 2011

12. Maman Y, Nir-Paz R, Louzoun Y: Bacteria Modulate the CD8+ T Cell Epitope Repertoire of Host Cytosol-Exposed Proteins to Manipulate the Host Immune Response. PLoS computational biology 7:e1002220, 2011

13. Petersdorf EW, Malkki M, Gooley TA, et al: MHC haplotype matching for unrelated hematopoietic cell transplantation. PLoS medicine 4:e8, 2007

14. Opelz G, Döhler B: Effect of human leukocyte antigen compatibility on kidney graft survival: Comparative analysis of two decades. Transplantation 84:137, 2007

15. Coupel S, Giral-Classe M, Karam G, et al: Ten-year survival of second kidney transplants: impact of immunologic factors and renal function at 12 months. Kidney international 64:674-680, 2003

16. Appelbaum FR, Gundacker H, Head DR, et al: Age and acute myeloid leukemia. Blood 107:3481-5, 2006

17. Morishima S, Ogawa S, Matsubara A, et al: Impact of highly conserved HLA haplotype on acute graft-versus-host disease. Blood 115:4664-4670, 2010

18. Bettens F, Passweg J, Schanz U, et al: Impact of HLA-DPB1 haplotypes on outcome of 10/10 matched unrelated hematopoietic stem cell donor transplants depends on MHC-linked microsatellite polymorphisms. Biology of Blood and Marrow Transplantation, 2011

19. Gratwohl A: The EBMT risk score. Bone Marrow Transplant 47:749-756, 2012 20. Duquesnoy RJ, Marrari M: HLAMatchmaker: a molecularly based algorithm for

histocompatibility determination. II. Verification of the algorithm and determination of the relative immunogenicity of amino acid triplet-defined epitopes. Human Immunology 63:353-363, 2002

21. Elsner HA, Blasczyk R: Sequence similarity matching: proposal of a structure‐based rating system for bone marrow transplantation. European journal of immunogenetics 29:229-236, 2002

22. Filipovich AH, Weisdorf D, Pavletic S, et al: National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: I. Diagnosis and staging working group report. Biol Blood Marrow Transplant 11:945-56, 2005

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23. Vider-Shalit T, Sarid R, Maman K, et al: Viruses selectively mutate their CD8+ T-cell epitopes--a large-scale immunomic analysis. Bioinformatics 25:i39-44, 2009

24. Pamer E, Cresswell P: Mechanisms of MHC class I-restricted antigen processing. Annual review of immunology 16:323-358, 1998

25. Vider-Shalit T, Louzoun Y: MHC-I prediction using a combination of T cell epitopes and MHC-I binding peptides. Journal of immunological methods, 2010

26. Garboczi DN, Ghosh P, Utz U, et al: Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature 384:134-141, 1996

27. Wucherpfennig KW, Allen PM, Celada F, et al: Polyspecificity of T cell and B cell receptor recognition, Elsevier, 2007, pp 216-224

28. Kaas Q, Lefranc MP: T cell receptor/peptide/MHC molecular characterization and standardized pMHC contact sites in IMGT/3Dstructure-DB. In silico biology 5:505-528, 2005

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Characteristics of recipients receiving first allogeneic transplants for AML, ALL, CML or MDS with high-resolution HLA typing for HLA-A, -B, -C, –DRB1 and –DQB1 that have a single

class I HLA mismatcha

Characteristics of patients N Eval N (%)Number of patients 2464 Number of centers 177 Recipient age, median (range), years 2464 38 (<1-73)Age at transplant

≤ 9 y 219 ( 9) 10-19 y 328 (13) 20-29 y 360 (15) 30-39 y 387 (16) 40-49 y 505 (20) 50 y and older 665 (27) Recipient race/ethnicity 2418

Caucasian, Non-hispanic 1974 (82)African American, Non-hispanic 150 ( 6)Asian, Non-hispanic 46 ( 2)Pacific Islander, Non-hispanic 4 (<1)Native American, Non-hispanic 10 (<1)Hispanic, Caucasian race 150 ( 6)Hispanic, African American race 10 (<1)Hispanic, Asian race 2 (<1)Hispanic, Native American race 1 (<1)Hispanic, Unknown race 66 ( 3)Other 5 (<1)


AML 1008 (41)ALL 602 (24)CML 449 (18)MDS 405 (16)

Disease stage at transplant Early 1690 (69)Intermediate 128 ( 5)Advanced 551 (22)Other 95 ( 4)

Stem cell source 2464 Bone marrow 1335 (54)PBSC 1129 (46)


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Continued. Characteristics of patients N Eval N (%)HLA matching for HLA-A, -B, -C, –DRB1 and –DQB1 2464

9/10 HLA-A mismatch 886 (36)9/10 HLA-B mismatch 422 (17)9/10 HLA-C mismatch 1156 (47)


Myeloablative 1936 (79)Reduced intensity 352 (14)Nonmyeloablative 114 ( 5)Other 62 ( 3)


Ex vivo T-cell depletion alone 88 ( 4)Ex vivo T-cell depletion + post-tx immune suppression 203 ( 8)CD34 selection alone 12 (<1)CD34 selection + post-tx immune suppression 35 ( 1)Cyclophosphamide + others 4 (<1)FK506 + MMF +- others 242 (10)FK506 + MTX +- others (except MMF) 769 (31)FK506 + others (except MTX, MMF) 74 ( 3)FK506 alone 44 ( 2)CSA + MMF +- others (except FK506) 124 ( 5)CSA + MTX +- others (except FK506, MMF) 784 (32)CSA + others (except FK506, MTX, MMF) 32 ( 1)CSA alone 19 ( 1)Other GVHD prophylaxis 34 ( 1)



Donor sex TBD 476 Donor race/ethnicity 1

Caucasian, Non-hispanic 1495 (83)African American, Non-hispanic 105 ( 6)Asian, Non-hispanic 33 ( 2)Native American, Non-hispanic 30 ( 2)Hispanic, Caucasian race 22 ( 1)Hispanic, African American race 1 (<1)Hispanic, Unknown race 119 ( 7)Other 7 (<1)

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Continued. Characteristics of patients N Eval N (%)In vivo T-cell depleted 2464 758 (31)

Donor/recipient CMV status 2464 Negative/Negative 697 (28) Negative/Positive 770 (31) Positive/Negative 295 (12) Positive/Positive 467 (19) Unknown 235 (10)

Donor age in years, median (range) 1957 36 (18-61) Donor age 2464 18-19 y 13 ( 1) 20-29 y 472 (19) 30-39 y 750 (30) 40-49 y 544 (22) 50 y and older 178 ( 7)

Donor age TBD 507 (21) Year of transplant 2464

1988 2 (<1)1989 9 (<1)1990 19 ( 1)1991 23 ( 1)1992 45 ( 2)1993 47 ( 2)1994 63 ( 3)1995 80 ( 3)1996 69 ( 3)1997 81 ( 3)1998 86 ( 3)1999 123 ( 5)2000 119 ( 5)2001 145 ( 6)2002 108 ( 4)2003 145 ( 6)2004 186 ( 8)2005 213 ( 9)2006 220 ( 9)2007 229 ( 9)2008 183 ( 7)2009 146 ( 6)2010 77 ( 3)2011 36 ( 1)2012 10 (<1)Median follow up of survivors mo (range) 714 73-(3-269)

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Study Proposal 1112-16 Study Title: Effect of HLA-C allele matching in the context of recipient HLA-C-encoded KIR ligand grouping (C1 or C2) on the outcome of unrelated hematopoietic stem cell transplantation (HSCT). Johannes C Fischer, MD, University Hospital, Düsseldorf, Germany Markus Uhrberg, Ph.D., University Hospital, Düsseldorf, Germany Study Objectives:

1. The role of KIR ligands in unrelated HSCT is matter of current debate; common rules for selecting patients according to KIR ligand matching are not established.

2. Investigating the degree of four-digit HLA-C allele matching in the context of recipients HLA-C defined KIR ligand groups (C1C1, C1C2, C2C2) may help to better define immunogenetic low and high risk constellations associated with differential outcome of HSCT.

Scientific Justification:

– HLA-C-encoded KIR ligands have been identified as major risk factors for the outcome of unrelated allogeneic HSCT 1-3

– In a retrospective study by Fischer et al., CML recipients bearing at least one C2 ligand show worse outcome after allogeneic HSCT when compared to C1 homozygous recipients (HR 5.9, p < 0.01). Poorest outcome was seen in C2 homozygous patients.1

– This was especially true when peripheral blood progenitor cells (PBPC) were used for transplantation, and for patients at advanced disease stage.

– These initial findings were confirmed in a recent study by the same group in an AML/CML cohort (but not in MDS and ALL/NHL patients) receiving unmanipulated PBPC4 as well as in an independent recent analysis of a large cohort of AML patients.5

– Notably, Fischer et al.4 could show that group HLA-C allele matching contributed differentially to the transplantation outcome in C1 and C2 recipients: whereas HLA-C allele matching was beneficial in C1 patients, it had a detrimental influence on clinical outcome in C2 recipients (HR 3.5, p < 0.012). Importantly, this observation remained even true when those HLA-C mismatched patients were excluded that had a KIR ligand mismatch according to Ruggeri et al.6 (HR 3.2, p < 0.02). Thus, this effect cannot easily be attributed to NK cells given that HLA class I mismatches without KIR ligand mismatch should be neglected by NK cells and thus not lead to NK cell activation.

– Based on these finding we grouped patients into four groups: a) HLA C allele-matched KIR Ligand C1C1 recipients, and b)HLA C allele-mismatched C2 recipients (patients at a low transplantation risk); c) HLA-C allele-mismatched C1 and d)HLA-C matched C2 patients (patients with at a high transplantation risk).

The differential HLA-C allele matching/ mismatching effect was predominantly seen in AML/CML/MDS patients receiving myeloablative therapy, although the influence of the conditioning regimes (reduced intensity) could not be fully investigated due to the low number (see also demographic table in the appendix). In this cohort, the low risk group showed excellent relapse control (HR 0.092, p < 0.03) whereas the high risk group had worse outcome. This was especially evident for the C2 patients with HLA-C allele level match and was partly due to a strikingly high early TRM (OR 3.5, p < 0.012), which was due to infections (10/18 TRM events). A similar effect could also be seen in the previously investigated cohort of C2 patients with matched HLA-C alleles.1 Effect was strongest in AML patients. When the patients were summarized into “low” and “high” risk and analyzing all AML/sec AML/CML patients according to the table above, striking low HR ( “high risk” =1) were seen for relapse (0.092 (95% CI 0.011 – 0.769)), TRM 0.234 (0.066 - 0.826), with twice as much infection based events in the high risk than in the low risk group, TRM events in the high risk group, EFS 0.174 (0.059 - 0 .515) and OS 0.153

65


(0.044 .533). Taking those numbers into a power calculation (Stata 10) would result in small needed sample size (>14 per arm at ß < 0.01, 1-α > 0.95.). The results of Geibel et al2 may be biased by following factors: 1st, no events occurred in the AML/CML C1C1 HLA-C allele matched group. I assume that that will not be true in larger cohorts/ longer follow up. 2nd, all patients received PBPC products. It would thus be highly desirable to apply our novel concept of low and high risk groups, based on KIR ligand and HLA-C matching status, to a larger cohort.

The observed differential effects of recipients C1 or C2 status on relapse incidence and transplant outcome1-3 might be explained by novel insights into reconstitution of NK cell repertoires. We recently suggested a model based on the sequential acquisition of KIR receptors during the early reconstitution phase post HSCT.1 The KIR repertoire of NK cells during this early phase is dominated by the expression of the KIR2DL2/3 receptor and this effect is independent of the underlying HLA class I matching status and HLA class I type of the patient. This genetically hard-wired process leads to a higher frequency of KIR2DL2/3+ NK cells, which are specific for C1 ligands and thus represents a source of immuno-competent NK cells that are able to perform adequate surveillance of HLA-C expression of leukemic cells in C1, but not C2 patients. The corresponding KIR for C2 ligands, KIR2DL1, is generally lagging behind and is found at low frequency in the early reconstitution phase, which might lead to a shortage of immuno-competent NK cells in C2 patients. Thus the beneficial effect of HLA-C allele matching in C1 patients stresses the importance of immuno-competent NK cells as important factor for control of relapse and TRM-related virus infection. This fits well to the current concept of NK cell education (also referred to as licensing), in which the encounter of self-KIR ligands is necessary to generate fully functional NK cells.7,8

The observed clinical effects of HLA-C allele mismatches within KIR ligand groups might be due to the activation of alloreactive T cells from the T cell-repleted graft. Activation of HLA-C-alloreactive T cells could explain the beneficial effect on relapse control in C2 patients: given the comparatively small number of immuno-competent NK cells in C2 patients (as discussed above), alloreactive T cells from the transplant could play an important role in relapse control. As HLA-C is a generally weaker stimulus for alloreactive T cell responses compared to HLA-A and –B, HLA-C mismatching might favor T cell-mediated GvL effects without causing a major increase in GvH disease. In summary we hypothesize that C1 patients have a high frequency of immuno-competent NK cells that enable eradication of residual disease at an early stage. HLA-C mismatching and the resulting emergence of alloreactive T cells might thus in C1 patients not have a additional beneficial effect and might even be detrimental due to increased GvHD, but serve an important function in relapse control in C2 patients. The lack of disease control in matched C2 patients would thus be explained by a combination of insufficient numbers of immuno-competent NK cells in the early phase and the lack of alloreactive T cells at later stages post transplantation. Consequently, this group exhibited the poorest clinical outcome of all four groups defined by recipients KIR ligands and HLA-C allele matching 4. We thus aim to improve current donor selection strategies by defining low and high risk groups based on recipients` C1C2 KIR ligand status in combination with donor/recipients HLA-C allele matching status. Analysis of the data pool should therefore include the possible combinations of HLA-C Kir ligand (C1C1 vs C1C2 , and when numbers are sufficient C2C2) and HLA-C allele match. . Based on numbers of the “Venstrom data set”, the confirmation and prove of further details (e.g. differentiation into C1C1, C1C2 and C2C2 patients) should be possible. Hopefully, the resulting strategy will harness NK cell alloreactivity, identify permissive HLA-C mismatches, and improve clinical outcomes for URD HSCT.

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Study Population: The study population will consist of patients receiving his/her first bone marrow or peripheral blood stem cell unrelated donor transplant. Since the described effect was strongest in AML/CML patients, focusing on those will result in a more homogenous patient group than analyzing several diseases together. However, it would also be of interest, how secondary AML behave, but this could be addressed in further study approaches, as well as behavior in lymphoid (adult) diseases. Transplant pairs must have high resolution genotyping for HLA-A, B, C, and DRB1 through the NMDP retrospective high resolution typing program. The population will exclude syngeneic transplants, and second transplants. The population will include any age, HLA-matched and mismatched, and myeloablative as non-myeloablative conditioning, Bone Marrow and PBPC as graft source. The dataset of the study CIBMTR IB12-04 (study chair JM Venstrom, description of the dataset variables thankfully provided by S Spellman) would fit perfectly the need for the proposed investigations. Data Requirements: Within the dataset description, thankfully provided by S Spellman the cause of death (COD) beside TRM/ relapse definition was not mentioned. To test the impact of the proposed classifying on early infections causing TRM the COD information for cases with an event other than relapse would be desirable, if available. Sample Requirements: No further samples testing is needed Study Design:

– The aim of this retrospective study is to determine the influence of HLA-C allele matching on the background of HLA-C encoded KIR ligand status of the recipient in unrelated HSCT.

– Patient-donor pairs should be assigned according the recipient HLA-C KIR ligand expression (C1C1, C1C2, C2C2) and the degree of the HLA-C allele match (match, mismatch without KIR-ligand mismatch, KIR ligand mismatch). This will result in four groups: HLA C KIR ligand patients homozygous for C1 with or without HLA C allele match and patients with HLA-C2 KIR ligand with or without HLA C allele match.

– Initial tests will compare the outcomes (see below) for HSCT recipients according to their HLA-C KIR ligands, and the degree of HLA-C allele matching using multivariable and pairwise comparisons.

– To test the hypothesis of differential impact of HLA-C allele mismatch the analysis should also be done restricting the populations to the recipients C1 or C2 KIR ligand status.

– Based on our previous cohort, which exhibited strongest effects in myeloid leukemia, the analysis should focus on this primary disease. . The cohort of our previous (smaller) study contained PBPC transplantations only. The differential effect of HLA-C allele matching based on the HLA-C KIR ligand status may be not as obvious after BM as after PBPC transplantation: in a previous investigated cohort of CML patients the risk factor for “C2” recipients was more prominent in PBPC recipients than BM recipients.1

Overall and disease-free survival will be estimated by using the Kaplan-Meier method. Cases will be analyzed at the time of last follow-up, and for patients surviving in continuous complete remission, patients will be censored at date of last contact. Second transplant is a censoring event. We will use cumulative incidence estimates to summarize the probability of relapse, and we will consider death without relapse as a competing risk. Cox regression models will be used to examine the association of the different genetic groups with the hazard of failure for the time-to-event outcomes (mortality, relapse, NRM). Logistic regression will be used to assess the association of degree of HLA-C allele matching, also with respect to recipients C1 and C2 KIR ligand status with the probability of acute GvHD. Models

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will be adjusted for patient age, disease severity (low, intermediate, or high), patient cytomegalovirus (CMV) serostatus, T-cell depletion, conditioning [ablative with total body irradiation (TBI), ablative without TBI, or non-ablative], HLA mismatch (10/10 vs. 9/10), and cytogenetics (no abnormalities, good risk, intermediate risk, or poor risk), and finally for the graft source (BM, PBSC). The primary outcome for the study is overall survival, defined as time to death from any cause. The secondary outcomes include disease relapse, acute GVHD (grades II-IV and III-IV), chronic GVHD, disease-free survival (time to relapse or death from any cause other than relapse), non-relapse mortality (death in continuous complete remission of primary disease) . If available, TRM cause (infection, GvHD organ failure) would be also accounted. References:

1. Fischer JC, Ottinger H, Ferencik S, et al. Relevance of C1 and C2 epitopes for hemopoietic stem cell transplantation: role for sequential acquisition of HLA-C-specific inhibitory killer Ig-like receptor. J Immunol 2007; 178(6): 3918-3923.

2. Giebel S, Locatelli F, Wojnar J, et al. Homozygosity for human leucocyte antigen-C ligands of KIR2DL1 is associated with increased risk of relapse after human leucocyte antigen-C-matched unrelated donor haematopoietic stem cell transplantation. Br J Haematol 2005; 131(4): 483-486.

3. Hsu KC, Gooley T, Malkki M, et al. KIR ligands and prediction of relapse after unrelated donor hematopoietic cell transplantation for hematologic malignancy. Biol Blood Marrow Transplant 2006; 12(8): 828-836.

4. Fischer JC, Kobbe G, Enczmann J, Haas R, Uhrberg M. The impact of HLA-C matching depends on the C1/C2 KIR ligand status in unrelated hematopoietic stem cell transplantation. Immunogenetics 2012.

5. Venstrom JM, Pittari G, Gooley TA, et al. HLA-C-dependent prevention of leukemia relapse by donor activating KIR2DS1. N Engl J Med 2012; 367(9): 805-816.

6. Ruggeri L, Capanni M, Urbani E, et al. Effectiveness of donor natural killer cell alloreactivity in mismatched hematopoietic transplants. Science 2002; 295(5562): 2097-2100.

7. Anfossi N, Andre P, Guia S, et al. Human NK cell education by inhibitory receptors for MHC class I. Immunity 2006; 25(2): 331-342.

8. Alyea EP, Kim HT, Ho V, et al. Comparative outcome of nonmyeloablative and myeloablative allogeneic hematopoietic cell transplantation for patients older than 50 years of age. Blood 2005; 105(4): 1810-1814.

The PI’s have no Significant Financial Interests, as defined in the National Marrow Donor Program document titled: Description of Significant Financial Interest Disclosure Form (F00664 revision 3 (8/12)), part of document “Financial Conflict of Interest Policy Applicable to Sponsored Research Awards”, Document Number: P00002 rev. 3 Page 1 of 12, active since August 01, 2012.

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Table 1: Characteristics of patients receiving a first T-cell replete transplant for AML, ALL, CML or MDS that are 8/8, 7/8 or 6/8 high-resolution matched for HLA-A, -B, -C and –DRB1 that have recipient HLA-C defined as C1/C1 and HLA-C matched, C1/C1 and HLA-C mismatched, Any C2 and HLA-C matched, and Any C2 and HLA-C mismatcheda

C1/C1 and HLA-C matched

C1/C1 and HLA-C

mismatched

Any C2 and HLA-C matched

Any C2 and HLA-C

mismatched Variable N (%) N (%) N (%) N (%)Number of patients 2709 656 3718 1019 Number of centers 154 122 159 139 Age, median (range), years 40 (<1-74) 35 (<1-70) 39 (<1-74) 34 (<1-72) Age at transplant 0-9 y 203 ( 7) 63 (10) 287 ( 8) 106 (10) 10-19 y 277 (10) 103 (16) 420 (11) 156 (15) 20-29 y 386 (14) 100 (15) 566 (15) 157 (15) 30-39 y 483 (18) 125 (19) 602 (16) 201 (20) 40-49 y 590 (22) 135 (21) 753 (20) 224 (22) 50 and older 770 (28) 130 (20) 1090 (29) 175 (17) Recipient race/ethnicity 0.40

Caucasian 2422 (89) 561 (86) 3208 (86) 873 (86)African American 45 ( 2) 22 ( 3) 194 ( 5) 62 ( 6)Asian/Pacific Islander 50 ( 2) 18 ( 3) 34 ( 1) 12 ( 1)Hispanic 153 ( 6) 44 ( 7) 235 ( 6) 63 ( 6)Native American 7 (<1) 3 (<1) 13 (<1) 1 (<1)Other/Multiple/Decline 32 ( 1) 8 ( 1) 34 ( 1) 8 ( 1)

Male sex 1524 (56) 357 (54) 2074 (56) 577 (57) Karnofsky prior to transplant > 90 1732 (64) 447 (68) 2428 (65) 702 (69) Disease at transplant

AML 1077 (40) 217 (33) 1476 (40) 378 (37)ALL 562 (21) 143 (22) 772 (21) 250 (25)CML 596 (22) 195 (30) 820 (22) 257 (25)MDS 474 (17) 101 (15) 650 (17) 134 (13)

Disease status at transplant Early 1891 (70) 445 (68) 2599 (70) 671 (66) Intermediate 150 ( 6) 46 ( 7) 191 ( 5) 73 ( 7) Advanced 668 (25) 165 (25) 928 (25) 275 (27)a – Data has been CAP-modeled.

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Continued. C1/C1 and

HLA-C matched

C1/C1 and HLA-C

mismatched


Any C2 and HLA-C

mismatched Variable N (%) N (%) N (%) N (%)Number of patients 2709 656 3718 1019 Interval from diagnosis to TX, median (range), months

10 (0.3-325)

12 (0.4-342)

10 (0.3-316)

11 (0.3-309)

Recipient HLA-C KIR Ligand Scoring

Recipient C1/C1 2709 (100) 656 (100) 0 0Recipient C1/C2 0 0 2865 (77) 783 (77)Recipient C2/C2 0 0 853 (23) 236 (23)

Graft type

Bone marrow 1538 (57) 453 (69) 2097 (56) 721 (71)Peripheral blood 1171 (43) 203 (31) 1621 (44) 298 (29)

Conditioning regimen

Myeloablative 2127 (79) 557 (85) 2903 (78) 863 (85)Reduced intensity 423 (16) 67 (10) 606 (16) 124 (12)Non-myeloablative 159 ( 6) 32 ( 5) 209 ( 6) 32 ( 3)

GVHD prophylaxis Tacrolimus other 1357 (50) 248 (38) 1863 (50) 377 (37) Cyclosporine MTX other 1260 (47) 374 (57) 1720 (46) 566 (56)

Ex vivo T-cell depletion 92 ( 3) 34 ( 5) 135 ( 4) 76 ( 7) In vivo T-cell depletion

No 2064 (76) 495 (75) 2769 (74) 751 (74)Yes 645 (24) 161 (25) 949 (26) 268 (26)

Donor/recipient sex match Male/Male 1089 (40) 226 (34) 1407 (38) 333 (33) Male/Female 729 (27) 156 (24) 908 (24) 245 (24) Female/Male 435 (16) 131 (20) 667 (18) 244 (24) Female/Female 456 (17) 143 (22) 736 (20) 197 (19) Donor/recipient CMV match

Negative/Negative 876 (32) 205 (31) 1121 (30) 345 (34)Negative/Positive 877 (32) 203 (31) 1128 (30) 296 (29)Positive/Negative 345 (13) 87 (13) 532 (14) 154 (15)Positive/Positive 497 (18) 146 (22) 737 (20) 198 (19)Unknown 114 ( 4) 15 ( 2) 200 ( 5) 26 ( 3)

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Continued. C1/C1 and

HLA-C matched

C1/C1 and HLA-C

mismatched


Any C2 and HLA-C

mismatched Variable N (%) N (%) N (%) N (%)Number of patients 2709 656 3718 1019

Donor age, median (range), years 34 (18-60) 36 (19-60) 35 (18-61) 36 (18-61) Donor age

< 30 yrs 901 (33) 158 (24) 1139 (31) 257 (25)30-39 yrs 987 (36) 245 (37) 1343 (36) 409 (40)40 -49 yrs 652 (24) 200 (30) 979 (26) 277 (27)50 and older 169 ( 6) 53 ( 8) 257 ( 7) 76 ( 7)

Donor race/ethnicity Caucasian 2334 (86) 531 (81) 3066 (82) 819 (80)African American 42 ( 2) 20 ( 3) 181 ( 5) 45 ( 4)Asian/Pacific Islander 57 ( 2) 27 ( 4) 39 ( 1) 21 ( 2)Hispanic 129 ( 5) 34 ( 5) 218 ( 6) 59 ( 6)Native American 20 ( 1) 11 ( 2) 29 ( 1) 9 ( 1)Other/Multiple/Decline 127 ( 5) 33 ( 5) 185 ( 5) 66 ( 6)

HLA distribution for HLA-A, -B, -C, and –DRB1

8/8 Matched 2224 (82) 0 2861 (77) 07/8 – 1 MM at HLA-A 273 (10) 0 378 (10) 07/8 – 1 MM at HLA-B 75 ( 3) 0 237 ( 6) 07/8 – 1 MM at HLA-C 0 390 (59) 0 565 (55)7/8 – 1 MM at HLA-DRB1 87 ( 3) 0 138 ( 4) 06/8 – 2 MM at HLA-A 9 (<1) 0 8 (<1) 06/8 – 1 MM at HLA-A, 1 MM at -B 15 ( 1) 0 45 ( 1) 06/8 – 1 MM at HLA-A, 1 MM at -C 0 74 (11) 0 109 (11)6/8 – 1 MM at HLA-A, 1 MM at –DRB1 13 (<1) 0 16 (<1) 06/8 – 2 MM at HLA-B 0 0 3 (<1) 06/8 – 1 MM at HLA-B, 1 MM at -C 0 133 (20) 0 254 (25)6/8 – 1 MM at HLA-B, 1 MM at –DRB1 9 (<1) 0 27 ( 1) 06/8 – 2 MM at HLA-C 0 26 ( 4) 0 41 ( 4)6/8 – 1 MM at HLA-C, 1 MM at –DRB1 0 33 ( 5) 0 50 ( 5)6/8 – 2 MM at HLA-DRB1 4 (<1) 0 5 (<1) 0

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Continued. C1/C1 and HLA-C matched

C1/C1 and HLA-C

mismatched


Any C2 and HLA-C

mismatched Variable N (%) N (%) N (%) N (%)Number of patients 2709 656 3718 1019 ABO matching

Matched 1218 (45) 285 (43) 1540 (41) 409 (40)Minor mismatch 684 (25) 149 (23) 935 (25) 263 (26)Major mismatch 593 (22) 162 (25) 943 (25) 270 (27)Bi-directional mismatch 205 ( 8) 56 ( 9) 291 ( 8) 76 ( 7)Unknown/Missing 9 (<1) 4 ( 1) 9 (<1) 1 (<1)

Number of high-resolution matches out of 8 for HLA-A, -B, -C and –DRB1

6/8 50 ( 2) 266 (41) 104 ( 3) 454 (45)7/8 435 (16) 390 (59) 753 (20) 565 (55)8/8 2224 (82) 0 2861 (77) 0

Year of transplant

1988 5 (<1) 1 (<1) 12 (<1) 01989 30 ( 1) 7 ( 1) 16 (<1) 7 ( 1)1990 30 ( 1) 11 ( 2) 33 ( 1) 10 ( 1)1991 45 ( 2) 17 ( 3) 50 ( 1) 32 ( 3)1992 53 ( 2) 24 ( 4) 65 ( 2) 31 ( 3)1993 54 ( 2) 17 ( 3) 71 ( 2) 38 ( 4)1994 76 ( 3) 23 ( 4) 103 ( 3) 48 ( 5)1995 86 ( 3) 31 ( 5) 111 ( 3) 60 ( 6)1996 94 ( 3) 36 ( 5) 115 ( 3) 70 ( 7)1997 103 ( 4) 39 ( 6) 129 ( 3) 47 ( 5)1998 105 ( 4) 42 ( 6) 134 ( 4) 59 ( 6)1999 111 ( 4) 41 ( 6) 159 ( 4) 62 ( 6)2000 148 ( 5) 51 ( 8) 185 ( 5) 76 ( 7)2001 151 ( 6) 41 ( 6) 182 ( 5) 79 ( 8)2002 117 ( 4) 30 ( 5) 186 ( 5) 58 ( 6)2003 159 ( 6) 43 ( 7) 254 ( 7) 59 ( 6)2004 251 ( 9) 60 ( 9) 302 ( 8) 75 ( 7)2005 274 (10) 43 ( 7) 402 (11) 71 ( 7)2006 311 (11) 42 ( 6) 425 (11) 59 ( 6)2007 292 (11) 34 ( 5) 423 (11) 40 ( 4)2008 142 ( 5) 17 ( 3) 221 ( 6) 29 ( 3)2009 72 ( 3) 6 ( 1) 140 ( 4) 9 ( 1)

Median follow-up of survivors, mo (range) 72 (3-263) 85 (4-252) 65 (3-264)

105 (3-238)

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Study Proposal 1112-27 Study Title: Impact of donor signal-regulatory protein alpha (SIRPα) polymorphism on outcome of allogeneic hematopoietic stem cell transplantation (allo-HSCT) Adam Gassas, The Hospital for Sick Children, Toronto, CA Jayne Danska, The Hospital for Sick Children, Toronto, CA Sujeetha Rajakumar, The Hospital for Sick Children, Toronto, CA Study Objectives:

1. To determine whether there is an association between HSCT donor and donor-recipient SIRP alpha polymorphism and allo-transplant related mortality (TRM) in malignant and non-malignant diseases.

2. To test for an association between HSCT donor SIRP alpha polymorphism and donor-recipient association with hematologic malignancy relapse post allo-HSCT.

Data Analysis Purpose: We performed a pilot study of 100 HSCT related and unrelated HLA-matched donor-recipient pairs to treat non-malignant hematologic disease at the Hospital for Sick Children, Toronto, Canada. The objective was to test for association between genetic polymorphism in SIRPA and HSCT clinical outcomes using DNA sequencing and novelsingle-nucleotide polymorphism (SNP)-specific assays to capture variation in the exon of SIRPA that encodes the binding domain for its’ ligand CD47. Despite the small sample size and high frequency of commons exon 2 variants, we observed a trend (p=0.06) suggesting an effect of donor polymorphism on TRM post HSCT. To validate and replicate these findings, we are seeking a larger sample size. The background and rationale for these studies is presented in section IV, Scientific Justification. Scientific Justification: Signal regulatory protein alpha (SIRPα) is an immunoglobulin ‘superfamily’ transmembrane protein receptor expressed on hematopoietic cells, particularly myeloid cells (neutrophils, macrophages, dendritic cells). The polymorphic extra-cellular IgV domain of human SIRPA interacts with its ubiquitously expressed cellular ligand CD47 and delivers an inhibitory signal that represses macrophage phagocytosis and production of inflammatory mediators. We used positional genetics combined with in vitro and in vivo assays of human hematopoiesis to identify the molecular basis for the unique capacity of lymphocyte-deficient, non-obese diabetic (NOD.SCID) mice to support human HSC engraftment. The study identified the molecular basis of support of human HSC engraftment as the signal regulatory protein (SIRP), and revealed it to be a potent regulator of interactions between human hematopoietic cells and the recipient bone marrow microenvironment.1 These findings revealed a novel SIRP-dependent, macrophage-mediated mechanism critical in hematopoietic stem cell transplantation and identified SIRP genetic polymorphism as a new genetic determinant of human HSC engraftment. SIRP is located on Location chromosome 20p13 (NCBI ID 140885; Ensemble ID :ENSG00000198053) CD47 is highly expressed on acute myeloid leukemia leukemia (AML), and treatment with anti-CD47 blocking monoclonal antibody induced phagocytosis of AML cells transplanted into NOD.SCID mice.2 We showed that the interaction between CD47 on primary AML stem cells (AML-SC) SIRPα on macrophages inhibits phagocytosis of AML cells promoting pathogenesis and decreased survival of NOD.SCID xenotranplant recipients.3 Moreover we used a SIRPα-Fc fusion protein to block this interaction, promoting host phagocytosis and eradication of AML and AML-SC.3 Similar to the Sirpa polymorphisms we defined in mouse strains, we have identified four human SIRPA variants (V1-V4) defined by coding polymorphisms in exon 2 that encodes the extra-cellular IgV domain responsible for binding to CD47.4 We have developed cell-based and protein-based CD47-SIRP� binding assays and determined that V2 and V3 have greater affinity for CD47 than V1 and V4 (Prasolava, et al, in

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preparation). Taken together, our data suggest that these human SIRPA variations may have functional effects in vivo including effects on engraftment, graft vs. host and graft vs. leukemia responses in the clinical setting. SIRPA variants consist of different combinations (haplotypes) of non-synonymous coding variations shown in Figure 1. We have developed a high throughput Single Nucleotide Polymorphism (SNP) genotyping assay (TaqMan and Roche Light cycler) to identify human SIRPA polymorphic variants (Figure 2). The assay is efficient, highly specific and rapid allowing 386 genomic DNA samples to be genotyped in several hours. We used the assays, as well as exon 2 sequencing on a pilot study on 100 HSCT donor-recipient pairs (related and unrelated) and on 281 normal human control samples from the Hap-Map project representing ethnically diverse populations. The allelic frequency of SIRPA variants we defined in the Hap-Map and HSCT donor-recipient pairs are shown (Table 4).

In our pilot study of 100 HSCT donor-recipient pairs, we also sequenced SIRPA exon 2 encoding the IgV domain to confirm the genotyping assays and analyzed the data for associations of genotype and clinical outcomes using a logistic regression model. The model is fitted with the single factor donor SNP (Table 1) or the single factor recipient SNP to the same response mortality. The mortality was not significantly

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different between different genotypes (V1V1 homozygous, V1V2 heterozygous and V2V2 homozygous) of the recipient SNP. We performed a Wald test based on logistic regression that produced a P value of 0.0672 (Table 2). It indicates a trend toward association between donor genotype with at least one V2 allele and the probability of recipient survival (Survival) as compared to V1V1 homozygous donors. Based on these results, we performed a power calculation to estimate the sample size needed for a validation study. The censored calculation is shown in Table 3. Table 1: Frequency table of donor SNP and Mortality levels

Mortality Donor V1V1 (X=0) Donor V1V2 & V2V2 (X=1)

Dead=0 11 9

Alive=1 26 54 37 63

Table 2: The logistic regression was fitted with mortality levels (Alive=1, Dead =0) and the donor SNP (V1V1=0, V2=1)

Table 3: Power analysis for the pilot study with 100 donors

Table 4: SIRPA allelic frequencies between different Hap-Map populations and HSCT donor-recipient pairs

Population No of Samples V1 V2 V3 V4 HSCT donor-recipient pairs 100 57% 43% 0% 0%

CEU (CEPH collection) 47 54.4% 43.6% 0% 2% YRI (Yoruba in Ibadan, Nigeria) 53 66% 34% 0% 0%

CHB (Han Chinese in Beijing) 38 39.4% 56.6% 1.4% 2.6% JPT (Japanese in Tokyo) 44 38.6% 60.2% 0% 1.2%

MEX (Mexican ancestry in L.A) 50 56% 44% 0% 0% GIH (Gujarati Indians in Texas) 49 61.2% 38.8% 0% 0%

Estimate Std. Error Z value Pr (>|z|)

(Intercept) -0.07136 0.80443 -0.089 0.9293

Donor SNP 0.93156 0.50892 1.830 0.0672

α = 0.05 Power = 1-β Effect Size

Proportion of the samples with at

least one V2 allele =Pr(x=1)

Event rate with V1V1 donor SNP

=Pr(y=1/x=0)

Sample size

required

1 0.05 0.8 0.93 0.63 0.70 234

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Study Population: Donor SIRP alpha interaction with antigen presenting cells in the host may also affect graft-versus-leukemia (GVL) effect and effect of SIRP alpha donor polymorphism on relapse post HSCT has never been studied. As suggested by our pilot study, donor SIRPA genotype may have an effect on mortality outcomes post-allo HSCT.

Data Analysis: Logistic regression will be performed and fitted with the donor SNP to predict responses of all cause mortality and relapse. Donor vs. recipient genotype association will also be fitted with the logistic regression analysis in response to all cause mortality (ACM) and relapse (Leukemia cases). Requirements for Inclusion:

– Availability of clinical outcome data post HSCT (TRM and relapse). – Donor-Recipient DNA sample from the NMDP suitable for SIRP alpha polymorphism testing.

Data Requirements: Clinical outcome data on allo-HSCT recipients from unrelated donors and donor-recipients that we will examine for potential association with SIRP alpha polymorphism (V1V1 vs. V1V2 or V2V2). Our primary endpoint for leukemia patients and for patients with non-malignant diseases would be all cause mortality. For AML/ALL cases, leukemia relapse will be treated as a secondary endpoint. Sample Requirements: To perform SNP genotyping and sequencing, we will need access to genomic DNA samples from donor-recipient HSCT individuals. The amount of DNA required to perform the reactions would be 2 micrograms. Based on our power calculation, the sample size required to reach statistical significance would be 260 donor-recipient pairs (234 samples according to Table 1 + 26 samples (10% extra for errors). If separate analyses are performed in the AML and aplastic anemia cohort, then the sample size would be doubled.

Study Design: Recipients of allo-HSCT for malignant and non-malignant diagnosis from living unrelated donors of any age. Our pilot study included children (0-18 yrs) with benign conditions. The purpose for choosing only benign conditions was to eliminate the variables of pre HSCT chemotherapy effect or other treatments for cancer on TRM post HSCT. In the proposed study, we would include both malignant (ALL, AML) and also non-malignant hematologic diseases (adults and pediatric) to expand our sample size. The rationale for including both types of disease is to examine the hypothesis that genotype at SIRPA impacts HSCT outcomes in settings of myeloablative and reduced intensity conditioning as these treatments will affect macrophage cell numbers in recipients. We will attempt to assemble a cohort of 12/12 HLA-A, B, C, DRB1, DQ and DP matched pairs. If numbers allow, the analysis would be limited to a homogeneous patient population, such as AML in first complete remission or aplastic anemia. All races would be included because a greater ethnic diversity can increase genotype diversity and improve the frequency of rarer SIRPA alleles in the study. Also, aplastic anemia is more frequent in East Asian populations compared to European-derived individuals and this disease is of particular interest.

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DNA samples from unrelated/related donors and recipients will be genotyped for SIRPA polymorphisms using the established high throughput Taqman SNP assays. The first objective is to determine whether a larger sample size of donors will demonstrate a significant association of SIRPA genotypes (V1 vs V1/V2, V2V2) with all cause mortality (ACM) that was seen as a trend in our pilot study.. In analysis of both donor vs recipient genotypes we will look for association with ACM, and relapse in the leukemia cases. We will seek independent funding from these studies – our applications will be far more competitive if we can demonstrate access to the DNA samples and clinical data required. Donor and recipient samples from cases where patients were engrafted with bone marrow or mobilized peripheral blood HSC would be eligible for the study.

Significance: A better understanding of the innate immune mechanisms impacting HSCT outcomes such as genetic variation in SIRPA will lead to improved diagnostics to match donor and recipient and to new therapeutics to support engraftment. References:

1. Takenaka K., Wang JC., Mortin-Toth SM., Khalouei S., Gan OI., Dick JE., Danska JS. Polymorphism in Sirpa modulates engraftment of human hematopoietic stem cells. Nature Immunology 2007; 8(12), p 1313-23.

2. Majeti R, Chao MP, Alizadeh AA, Pang WW, Jaiswal S, Gibbs KD Jr, van Rooijen N, Weissman IL. CD47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells. Cell 2009; 138, p 286-299.

3. Theocharides AP, Jin L, Cheng PY, Prasolava TK, Malko AV, Ho JM, Poeppl AG, van Rooijen N, Minden MD, Danska JS, Dick JE, Wang JC. (2012). Disruption of SIRPa signaling in macrophages eliminates human acute myeloid leukemia stem cells in Xenografts. The journal of Experimental Medicine 209(10), p 1883-1899.

4. Hatherley D, Graham SC, Turner J, Harlos K, Stuart DI, Barclay N. (2008). Paired receptor specificity explained by structures of signal regulatory proteins alone and complexed with CD47. Cell 31(2), p266-277.

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Characteristics of recipients receiving first allogeneic transplants that are high-resolution HLA-matched for HLA-A, -B, -C, –DRB1 and –DQB1 and have donor and recipient samples

availablea

Characteristics of patients N Eval N (%)Number of patients 3951 Number of centers 168 Recipient age at transplant, median (range), years 3951 37 (<1-78) ≤ 9 y 529 (13) 10-19 y 506 (13) 20-29 y 576 (15) 30-39 y 497 (13) 40-49 y 635 (16) 50 y and older 1208 (31) Recipient race/ethnicity 3872

Caucasian, Non-hispanic 3500 (90)African American, Non-hispanic 84 ( 2)Asian, Non-hispanic 62 ( 2)Pacific Islander, Non-hispanic 2 (<1)Native American, Non-hispanic 12 (<1)Hispanic, Caucasian race 174 ( 4)Hispanic, African American race 7 (<1)Hispanic, Asian race 2 (<1)Hispanic, Native American 2 (<1)Hispanic, Unknown race 22 ( 1)Other 5 (<1)


AML 2403 (61)ALL 967 (24)SAA 258 ( 7)Inherited abnormalities erythrocyte diff fxn 93 ( 2)SCIDs 111 ( 3)Inherited abnormalities of platelets 10 (<1)Inherited disorders of metabolism 62 ( 2)Histiocytic disorders 47 ( 1)

Disease stage at transplant 3951

Early 2506 (63)Intermediate 117 ( 3)Advanced/Late 683 (17)Other 645 (16)


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Continued. Characteristics of patients N Eval N (%)Stem cell source 3951


HLA-DPB1 Matching 2064

Allele matched 307 (15)Single allele mismatch 1097 (53)Double allele mismatch 660 (32)


Myeloablative 2649 (67)Reduced intensity 781 (20)Nonmyeloablative 402 (10)Other 119 ( 3)


Ex vivo T-cell depletion alone 133 ( 3)Ex vivo T-cell depletion + post-tx immune suppression

181 ( 5)

CD34 selection alone 21 ( 1)CD34 selection + post-tx immune suppression 79 ( 2)Cyclophosphamide alone 25 ( 1)Cyclophosphamide + others 6 (<1)FK506 + MMF +- others 440 (11)FK506 + MTX +- others (except MMF) 1503 (38)FK506 + others (except MTX, MMF) 147 ( 4)FK506 alone 81 ( 2)CSA + MMF +- others (except FK506) 232 ( 6)CSA + MTX +- others (except FK506, MMF) 902 (23)CSA + others (except FK506, MTX, MMF) 108 ( 3)CSA alone 37 ( 1)Other GvHD Prophylaxis 56 ( 1)




Donor sex TBD 1138


Negative/Negative 1200 (30) Negative/Positive 1295 (33) Positive/Negative 416 (11) Positive/Positive 679 (17) Unknown 361 ( 9)

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Continued. Characteristics of patients N Eval N (%)Donor age in years, median (range) 3951 34 (18-61) 18-19 y 42 ( 1) 20-29 y 899 (23) 30-39 y 993 (25) 40-49 y 656 (17) 50 y and older 168 ( 4)


Caucasian, Non-hispanic 2368 (91)African American, Non-hispanic 58 ( 2)Asian, Non-hispanic 30 ( 1)Pacific Islander, Non-hispanic 2 (<1)Native American, Non-hispanic 38 ( 1)Hispanic, Caucasian race 28 ( 1)Hispanic, Native American race 1 (<1)Hispanic, Unknown race 78 ( 3)Other 4 (<1)


1988 1 (<1)1989 15 (<1)1990 22 ( 1)1991 28 ( 1)1992 31 ( 1)1993 41 ( 1)1994 51 ( 1)1995 44 ( 1)1996 64 ( 2)1997 81 ( 2)1998 58 ( 1)1999 71 ( 2)2000 122 ( 3)2001 119 ( 3)2002 117 ( 3)2003 197 ( 5)2004 338 ( 9)2005 437 (11)2006 494 (13)2007 540 (14)2008 404 (10)2009 349 ( 9)2010 207 ( 5)2011 96 ( 2)2012 24 ( 1)Median follow up of survivors, mo (range) 1619 60-(3-250)

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Study Proposal 1112-68 Study Title: The effect of allele-level HLA-matching on survival after umbilical cord blood transplantation for non-malignant diseases in children Dr. Paul Veys, Great Ormond Street Hospital for Children, London, United Kingdom Dr. Mary Eapen, Medical College of Wisconsin, Milwaukee, WI, USA Scientific Justification: Non-relapse mortality (NRM) is high after mismatched umbilical cord blood (UCB) transplants for acute leukemia in children. Early reports identified the importance of selecting cord blood units with adequate total nucleated cell dose (> 2. 5 x 107/kg patient body weight) and HLA-match. Unlike the unrelated adult donor transplants, selection of units considers HLA-match between the donor and recipient considers low resolution HLA-matching at HLA-A and HLA-B and high resolution at HLA-DRB1. Matching at the HLA-C locus is not considered. Recent reports suggest matching at the HLA-C locus is important when selecting cord blood units for patients with hematologic malignancy (Eapen et al; Lancet Oncol 2011). For cord blood transplants that are HLA-matched at HLA-A, -B, -C and –DRB1 (high resolution HLA typing), the non-relapse mortality rate is about 10%. Non-relapse mortality rates are higher after mismatched transplants: 26%, 26%, 34% and 37% after 7/8, 6/8, 5/8 and 4/8 HLA-matched transplants (unpublished data). However, the difference in overall survival is negligible and may be explained by leukemia relapse. To-date, there are no studies that have examined for survival differences after cord blood transplants for non-malignant diseases that consider better HLA-match as a strategy to lower mortality risks. A recent report from the CIBMTR (Horan et al; Blood 2012) that examined the effect of high resolution HLA-match including at the HLA-C locus showed higher mortality after HLA-mismatched transplants. Unlike hematologic malignancies, disease recurrence is uncommon after transplantation for non-malignant diseases. Therefore, the primary objective of this proposal is to examine for survival differences after HLA-matched and mismatched cord blood transplants. HLA-matching will consider high resolution matching at HLA-A, -B, -C and –DRB1. Secondary outcomes include the effect of HLA-matching on hematopoietic recovery, acute and chronic graft-versus-host disease. Study Population: – Study population will include transplants reported to the CIBMTR and the European immune disease

registry. Data Requirements: – First allogeneic transplant – Diseases: severe combined immune deficiency (SCID), non-SCID diseases, inborn errors of

metabolism (IEM), Fanconi anemia, severe aplastic anemia and hemoglobinopathy. – Transplant period: 2000 – 2010

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Characteristics of recipients receiving first umbilical cord blood transplants for non-malignant diseases with high-resolution typing for HLA-A, -B, -C and -DRB1a

Characteristics of patients N Eval N (%)Number of patients 552 Number of centers 92 Recipient age, median (range), years 552 2 (<1-51) Age at transplant 552

≤ 10 y 459 (83) 11-20 y 59 (11) 21-30 y 19 ( 3) 31-40 y 10 ( 2) 41-50 y 4 ( 1) Over 50 1 (<1) Recipient race/ethnicity 528

Caucasian, Non-hispanic 313 (59)African American, Non-hispanic 65 (12)Asian, Non-hispanic 19 ( 4)Pacific Islander, Non-hispanic 1 (<1)Native American, Non-hispanic 4 ( 1)Hispanic, Caucasian race 121 (23)Hispanic, African American race 1 (<1)Hispanic, Native American race 2 (<1)Hispanic, Unknown race 2 (<1)


SAA 71 (13)Inherited abnormalities erythrocyte diff fxn 70 (13)SCIDs 139 (25)Inherited abnormalities of platelets 8 ( 1)Inherited disorders of metabolism 173 (31)Histiocytic disorders 86 (16)Autoimmune disorders 5 ( 1)

HLA matching at high-resolution for HLA-A, -B, -C and –DRB1 552 8/8 66 (15)7/8 82 (19)6/8 97 (23)5/8 101 (24)4/8 61 (14)2/8 to 3/8 22 ( 5)Not high-resolution typed 123a – Data has not been CAP-modeled.

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Continued. Characteristics of patients N Eval N (%)In vivo T-cell depleted 552 460 (83) Conditioning regimen 552

Myeloablative 302 (55)Reduced intensity 144 (26)Nonmyeloablative 53 (10)Other 53 (10)


Ex vivo T-cell depletion alone 2 (<1)CD34 selection alone 1 (<1)FK506 + MMF +- others 58 (11)FK506 + MTX +- others (except MMF) 26 ( 5)FK506 + others (except MTX, MMF) 34 ( 6)FK506 alone 10 ( 2)CSA + MMF +- others (except FK506) 207 (38)CSA + MTX +- others (except FK506, MMF) 33 ( 6)CSA + others (except FK506, MTX, MMF) 151 (27)CSA alone 16 ( 3)Other GVHD prophylaxis/TBD 14 ( 3)


Male/Male 162 (30) Male/Female 112 (21) Female/Male 164 (30) Female/Female 101 (19) Donor/recipient CMV match 552

Negative/Negative 84 (15) Negative/Positive 65 (12) Positive/Negative 34 ( 6) Positive/Positive 46 ( 8) Unknown 323 (59)


2000 3 ( 1)2001 10 ( 2)2002 10 ( 2)2003 18 ( 3)2004 27 ( 5)2005 57 (10)2006 59 (11)2007 90 (16)2008 75 (14)2009 108 (20)2010 95 (17)

Median follow-up of recipients, mo (range) 349 39 (3-122)

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Study Proposal 1212-04 Study Title: Validation of the IB07-02 Study “Effects of HLA Class I Amino Acid Mismatches on Stem Cell Transplant Outcomes” Susana R. Marino, MD, PhD, University of Chicago, Chicago, IL Sang Mee Lee, PhD, University of Chicago, Chicago, lL Theodore Karrison, PhD, University of Chicago, Chicago, lL Andrew Artz, MD, MS, University of Chicago, Chicago, lL Study Objectives: Validate the results from the study IB07-02 “Effects of HLA Class I Amino Acid Mismatches on Stem Cell Transplant Outcomes” using a different dataset. The primary outcome of this study is 1 year overall survival (OS) post-hematopoietic stem cell transplantation (HCT). Secondary outcomes include disease-free survival (DFS), treatment-related mortality (TRM), acute GvHD grades III-IV, and relapse. Scientific Justification: We have already identified a number of high risk amino acid substitution positions and types (AASPT) that are associated with adverse outcomes in HCT with a single HLA class I mismatched, DRB1 matched donor using Random Forest and logistic regression models.1-4 These studies were performed using the following patient population: patients with acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML) and myelodysplastic syndromes (MDS) at early or intermediate stage who received HCT facilitated by the National Marrow Donor Program (NMDP) between 1988 and 2003 and that had high resolution HLA class I and class II typing. We are now requesting a new dataset to validate previous findings. This study will focus on validating the previously identified AASPT associated with increased risk of adverse outcomes in the IB07 cohort. These AASPTs were positions/types with an importance score (IS) of ≥ 5 from the random forests analysis and a p-value of ≤0.01 from the logistic regression modeling for either OS, DFS, TRM, or acute GvHD and are listed in Table 1 below. The overarching goal of this study is that the results could be used in the clinic for donor selection when HLA-matched donors are not available. Study Population: The study population will include adult and pediatric patients who underwent a myeloablative or non-myeloablative first unrelated bone marrow or peripheral blood stem cell HCT for good and intermediate risk hematological malignancies (AML, ALL, and MDS; patients with CML will not be included in this study) between 2004 and 2011. Patients and donors will have HLA-A, B, C, and DRB1 high resolution typing. Patients in the study will have received allogeneic HCT from HLA-matched or single HLA class I allele or antigen mismatched unrelated donors. Outcomes: All event times will be recorded from time of graft infusion (day 0) and assessed through 1 year of follow-up, i.e., patients not experiencing the event within 1 year of transplant will be censored at 1 year (unless last follow-up was prior to 1 year).

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Primary outcomes:

– 1 year overall survival (OS): Death from any cause. Secondary outcomes:

– Disease free survival (DFS): Survival without reoccurrence of the primary disease. For this end point, either death or relapse is considered an event.

– Treatment-related mortality (TRM): Death in continuous complete remission of the primary

disease. Events will be summarized by the cumulative incidence estimate with relapse as a competing risk.

– Acute GVHD grades III-IV: Development of grades III-IV acute GvHD using the Glucksberg

scale (5) or consensus system. Event will be summarized by the cumulative incidence estimate. Death is a competing risk. Second transplant is a censoring event.

– Relapse: Development of clinical relapse of the primary disease as defined by the CIBMTR. The

event will be summarized by the cumulative incidence estimate. Death while in remission is a competing risk. (This endpoint complements TRM.)

Study Design and Data Analysis: With the new dataset, a validation of selected high risk AASPT for each outcome will be evaluated by calculating Kaplan-Meier (6) curves for OS and DFS and cumulative incidence curves (7) for TRM, aGvHD, and disease relapse. Cox (8) regression models will be fit to obtain hazard ratios and confidence intervals for the OS and DFS endpoints; sub distribution hazard ratios (9) will be estimated under a competing risks framework for the remaining endpoints. Each previously identified, individual high-risk AASPT will be tested separately in models adjusted for the four clinical variables (recipient age, disease, disease status, and gender match). At the 5% significance level, we will confirm (or not confirm) the identified AASPT. References:

1. Marino SR, Lin S, Maiers M, Haagenson M, Spellman S, Klein JP, Binkowski TA, Lee SJ, van Besien K. Mismatched unrelated donor stem cell transplantation: identification of HLA class I amino acid substitutions associated with lower survival at day 100 using random forest method. Bone Marrow Transplant 2012; 47: 217-226.

2. Marino S, Lin S, Maiers M, Haagenson M, Spellman S, Lee S, Wang T, Klein J, van Besien K. Identifying amino acid substitution positions associated with day 100 survival in unrelated donor stem cell transplant using Random Forest analysis (Abstract). Blood 2008; 112: 3012.

3. Marino SR, Lee SM, Haagenson M, Binkowski TA, Spellman S, Lee SJ, Karrison T. Identification of non-permissive amino acid substitutions associated to 1 year overall survival in stem cell transplantation (Abstract). Tissue Antigens 2012; 79: 439 (P19).

4. Marino SR, Lee SM, Binkowski TA, Haagenson MD, Maiers M, Spellman S, van Besien K, Lee SJ, Karrison T, Artz A. Identification of High Risk HLA Class I Amino Acid Substitutions in Hematopoietic Stem Cell Transplantation (Abstract). Submitted to the American Society of Hematology Annual Meeting 2012.

5. Glucksberg H, Storb R, Fefer A, Buckner CD, Neiman PE, Clift RA, Lerner KG, Thomas ED. Clinical manifestations of graft-versus-host disease in human recipients of marrow from HL-A-matched sibling donors. Transplantation 1974; 18: 295-304.

6. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 1958; 53: 457-481.

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7. Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: New representations of old estimators. Statistics in Medicine 1999; 18: 695-706.

8. Cox DR. Regression models and life tables (with discussion). Journal of the Royal Statistical Society B 1972; 34: 187-220.

9. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 1999; 94: 496-509.

Table 1. Identified higher-risk AASPTs from IB07 Study.

Event Event Event Event

Death within Death or relapse Death without aGVHD

1 yr within 1 yr relapse < 1 yr grade III-IV

AASPT n 1Y OS 1Y DFS 1Y TRM aGvHD III-IV

OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)

C97_WR 69 2.7(1.59,4.59) 2.43(1.4,4.23) 2.07(1.27,3.39) 2.42(1.46,3.99) C156_RW 22 4.07(1.47,11.3) 3.12(1.13,8.66) 4.5(1.73,11.7) 4.66(1.71,12.8) C116_YL 13 5.92(1.26,27.8) 9.99(1.26,78.9) 5.73(1.55,21.2) 3.1(0.94,10.2) C11_SA 69 2(1.2,3.33) 1.72(1.03,2.88) 2.28(1.39,3.73) 2.46(1.49,4.07) C156_QR 11 15.7(1.95,126) 11.7(1.46,94.1) 3.89(1.1,13.8) 1.8(0.54,5.99) C80_NK* 86 2.09(1.32,3.32) 2.07(1.28,3.33) 1.78(1.14,2.77) 1.65(1.06,2.56) C77_SN* 86 2.09(1.32,3.32) 2.07(1.28,3.33) 1.78(1.14,2.77) 1.65(1.06,2.56) C21_RH 68 1.71(1.03,2.83) 1.68(1,2.81) 1.73(1.05,2.84) 2.27(1.38,3.74) A167_WG* 17 5.04(1.59,16.0 3.75(1.19,11.9) 3.28(1.21,8.84) 2.29(0.86,6.09) A166_ED* 17 5.04(1.59,16.0) 3.75(1.19,11.9) 3.28(1.21,8.84) 2.29(0.86,6.09) C116_YS 24 2.11(0.9,4.93) 1.63(0.7,3.8) 3.11(1.34,7.23) 4.42(1.74,11.2) A97_MI 14 2.08(0.71,6.09) 7.04(1.56,31.8) 1.98(0.67,5.83) 6.23(1.72,22.6) A43_RQ 11 3.48(0.9,13.45) 2.63(0.68,10.1) 5.88(1.53,22.6) 2.64(0.76,9.12) C97_RW 79 1.39(0.87,2.22) 1.16(0.73,1.85) 1.64(1.03,2.6) 1.88(1.19,2.98) C14_WR* 37 1.63(0.83,3.19) 1.53(0.77,3.02) 2.05(1.05,4) 2.53(1.28,4.97) C49_EA* 37 1.63(0.83,3.19) 1.53(0.77,3.02) 2.05(1.05,4) 2.53(1.28,4.97) C24_AS 69 1.43(0.87,2.35) 1.15(0.7,1.89) 1.96(1.2,3.22) 1.99(1.22,3.26) C21_HR 65 1.32(0.8,2.2) 1.13(0.68,1.87) 1.64(0.98,2.72) 2.16(1.3,3.61) A9_FY 24 1.89(0.79,4.51) 2.55(1.01,6.43) 3.08(1.31,7.21) 1.58(0.7,3.58) C80_KN* 104 1.25(0.83,1.88) 1.05(0.7,1.59) 1.41(0.94,2.14) 1.89(1.26,2.83) C77_NS* 104 1.25(0.83,1.88) 1.05(0.7,1.59) 1.41(0.94,2.14) 1.89(1.26,2.83) C94_TI 55 1.35(0.77,2.35) 1.39(0.79,2.44) 1.55(0.89,2.71) 2.1(1.21,3.64) C116_SY 28 1.89(0.86,4.13) 1.66(0.75,3.68) 2.16(1,4.64) 3.61(1.57,8.29) C173_EK 36 1.13(0.57,2.23) 1.07(0.54,2.1) 1.19(0.59,2.41) 2.45(1.23,4.85) A76_EV 20 0.96(0.38,2.41) 1.6(0.63,4.08) 1.23(0.48,3.15) 4.96(1.78,13.8) Notes: Red : n>=10 & IS>=5 & p<=0.01 n=no. of patients with mismatch * Same patients as line below/above

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Characteristics of recipients receiving first allogeneic transplants that are myeloablative and T-cell

replete for AML, ALL or MDS with high-resolution HLA typing for HLA-A, -B, -C and –DRB1 and in early or intermediate disease stage not in previous IB07-02 Random Forest analysisa

Characteristics of patients N Eval N (%)Number of patients 591 Number of centers 133 Recipient age, median (range), years 591 33 (<1-69) Age at transplant 591

≤ 9 y 60 (10) 10-19 y 104 (18) 20-29 y 101 (17) 30-39 y 84 (14) 40-49 y 117 (20) 50 y and older 125 (21) Recipient race/ethnicity 579

Caucasian, Non-hispanic 437 (75)African American, Non-hispanic 41 ( 7)Asian, Non-hispanic 13 ( 2)Pacific Islander, Non-hispanic 1 (<1)Native American, Non-hispanic 2 (<1)Hispanic, Caucasian race 82 (14)Hispanic, African American race 3 ( 1)


AML 313 (53)ALL 211 (36)MDS 67 (11)

Disease stage at transplant Early 558 (94)Intermediate 33 ( 6)

HLA matching for HLA-A, -B, -C and –DRB1 591

7/8 HLA-A mismatch 215 (36)7/8 HLA-B mismatch 122 (21)7/8 HLA-C mismatch 254 (43)

Stem cell source 591



87


Continued. Characteristics of patients N Eval N (%)GVHD prophylaxis 591

FK506 + MMF +- others 74 (13)FK506 + MTX +- others (except MMF) 311 (53)FK506 + others (except MTX, MMF) 22 ( 4)FK506 alone 10 ( 2)CSA + MMF +- others (except FK506) 16 ( 3)CSA + MTX +- others (except FK506, MMF) 148 (25)CSA + others (except FK506, MTX, MMF) 8 ( 1)CSA alone 2 (<1)




Donor sex TBD 200


Negative/Negative 159 (27) Negative/Positive 184 (31) Positive/Negative 71 (12) Positive/Positive 127 (21) Unknown 50 ( 8) Donor age in years, median (range) 385 36 (18-61) Donor age 591

18-19 y 4 ( 1) 20-29 y 88 (15) 30-39 y 146 (25) 40-49 y 103 (17) 50 y and older 44 ( 7)


Caucasian, Non-hispanic 263 (75)African American, Non-hispanic 30 ( 9)Asian, Non-hispanic 10 ( 3)Native American, Non-hispanic 3 ( 1)Hispanic, Caucasian race 17 ( 5)Hispanic, Unknown race 28 ( 8)Other 1 (<1)

88


Continued. Characteristics of patients N Eval N (%)Year of transplant 591

2004 89 (15)2005 108 (18)2006 106 (18)2007 104 (18)2008 71 (12)2009 61 (10)2010 45 ( 8)2011 7 ( 1)


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TO: Immunobiology Working Committee Members FROM: Steve Spellman, MBS; Co-Scientific Director for the Immunobiology WC Stephanie Lee, MD, MPH; Co-Scientific Director for the Immunobiology WC RE: Studies in Progress Summary HLA GENES R04-80s/ IB06-13: HLA disparity and impact of HLA matching on outcome among unrelated umbilical cord blood transplants (SR Marino/LA Baxter-Lowe/V Rocha/M Eapen): This study evaluates high resolution HLA typing in umbilical cord blood transplants and the effect on outcome. It is a collaborative study between the CIBMTR and Eurocord. An abstract was submitted to EBMT 2013 and a draft manuscript is underway. IB05-02s: Effect of single Class I mismatching on unrelated donor hematopoietic stem cell transplantation (HCT) (MBA Heemskerk): This analysis will assess whether success of HCT with HLA class I mismatched unrelated donors differs depending on the number of amino acid sequence differences in the -helices and -sheet of the molecule. Results may help identification of potentially permissible mismatches. This is a collaborative study with the International Histocompatibility Working Group (IHWG). Analysis is underway. IB06-02: Impact of mismatches in low expression HLA loci on the outcome of unrelated donor transplantation (HCT) (M Fernandez-Vina): This study investigates the role of incompatibilities in the HLA DRB3/4/5, DQ and DP (low expression) loci (LEL) on the outcome of unrelated HCT. The hypothesis is that the effects of these loci are weak, cumulative and only demonstrable in combination with mismatches in other loci. A final manuscript is underway and it is expected to be submitted in early 2013. IB06-05: Use of high-resolution HLA data from the National Marrow Donor Program for the International Histocompatibility Working Group (IHWG) in hematopoietic cell transplantation (HCT) (E Petersdorf): The goal of the study is to to define the clinical importance of mismatching at specific HLA loci and at class I and II amino acid residues. This is a collaborative study with the International Histocompatibility Working Group (IHWG). Analysis is underway. IB07-04: Employing advanced bioinformatic methods for predicting peptide specificities of HLA molecules in the characterization of permissible mismatches in hematopoietic cell transplantation (HCT) (S Buus): This study proposes to identify a way to use a bioinformatic tool, MHCNetPan (M. Nielsen, et al., PLoS ONE2, e796, 2007) to define the distances for each pairwise donor-recipient HLA class I and II allele mismatch that are most strongly associated with post-transplant risks of acute GVHD and mortality. This is a collaborative study with the IHWG. Analysis is underway. IB07-07: HLA -DR15 and hematopoietic stem cell transplantation (HCT) outcome (A Gratwohl): This study tests the hypothesis that the presence of HLA-DR15 influences clinical outcome after unrelated donor HCT. Results presented at EBMT 2009 suggest that DR15 does not confer a special susceptibility to alloimmune effects. This is a collaborative study with the IHWG. A draft manuscript is underway.

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IB09-01s: Clinical importance of major histocompatibility complex haplotypes in umbilical cord blood transplantation (E Petersdorf): The primary objective of the study is to evaluate the importance of major histocompatibility complex haplotypes in umbilical cord blood transplantation. The study will investigate the role of 2, 3, 4 and 5 locus HLA haplotypes on outcome. Analysis is underway. IB09-02s: Non-permissive HLA-DPB1 disparities based on T-cell alloreactivity (K Fleischhauer): This study will validate the previous finding that HLA-DPB1 disparities in unrelated donor HCT can be classified as permissive and non-permissive according to T cell alloreactivity patterns and determine whether HLA-DPA1*0201 contributes to alloreactivity. Non-permissive HLA-DPB1 disparities were associated with increased risk of NRM and with protection from disease relapse in non-permissive pairs in the GVHD direction. The inclusion of HLA-DPA1 in the scoring scheme did not improve the assignments. An oral presentation was presented at EBMT 2011 as well as a poster presentation at EFI 2011. A draft manuscript is underway. IB11-03: Evaluation of the impact of allele homozygosity at HLA loci on outcome (CK Hurley/A Woolfrey/M Maiers): This study will evaluate the impact of HLA homozygosity at mismatched HLA loci in unrelated donor HCT. Previous studies have found an increased risk of graft failure in HCT where the mismatch is only in the host versus graft direction (recipient homozygous for the mismatched locus). Analysis is completed and a final manuscript will be submitted in early 2013. IB11-04: Impact of amino acid substitution at peptide binding pockets of HLA class I molecules on HCT outcome (J Pidala/C Anasetti): The goal of the study is to examine the impact of specific amino acid substitutions at HLA class I peptide binding pockets on mismatched unrelated donor HCT. An oral abstract was presented at the 2012 ASH meeting. A draft manuscript is underway. IB11-06: Evaluation of the impact of potentially non-immunogenic HLA-C allele level mismatch (M Fernandez-Vina/M Setterholm): This study will analyze the effect of an HLA0-C*03:03/C*03:04 (a putative non-immunogenic HLA mismatch) on the outcome of unrelated donor HCT. Analysis is completed and a draft manuscript is underway. IB12-01: Impact of unrelated donor HLA-mismatch in reduced-intensity conditioning allogeneic hematopoietic stem cell transplantation outcomes (J Koreth): The goal of this study is to evaluate the impact of HLA mismatching in the reduced intensity conditioning setting. The data file is being prepared. IB12-02: Impact of unrelated donor HLA-mismatch in myeloablative conditioning allogeneic hematopoietic stem cell transplantation outcomes (J Pidala/C Anasetti): The primary objective of the study is to re-evaluate the impact of HLA mismatching described in the Lee et al. Blood manuscript from 2007 in larger more modern cohort. The protocol and data file are under development. IB12-03: Effect of genetic ancestry matching on HSCT outcomes (A Madbouly/M Maiers/N Majhail): The goal of the study is to evaluate the genetic ancestry disparity between donor-recipient pairs and analyze the impact of the disparity on transplant outcome. The protocol is complete and a pilot cohort of transplant pairs is being typed. CYTOKINE/CHEMOKINE R04-75s: Functional significance of cytokine gene polymorphisms in modulating risk of post-transplant complications (E Petersdorf): This study is designed to identify immune response gene variants that are associated with risks of acute GVHD, relapse and mortality after unrelated donor HCT. This is a collaborative study with the IHWG. A draft manuscript is underway.

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IB05-03s: Genetic polymorphisms in the genes encoding human IL-7 Receptor-a: Prognostic significance in allogeneic stem cell transplantation (HCT) (K Muller): This study is designed to validate previous results from a single center analysis that suggested that donors carrying the +1237G variant in the alpha chain of IL-7R are associated with increased treatment related mortality and acute GVHD. The manuscript has been submitted to the International Journal of Immunogenetics. IB09-03s: Clinical relevance of cytokine/immune response genes in umbilical cord blood transplant (E Petersdorf): The overall objective of the study is to understand the relevance of cytokine/immune response gene polymorphism diversity and disparity on the outcome of single and multiple umbilical cord blood tranplantation. Testing is underway. NK/KIR R02-40s/R03-63s: Choosing donors with favorable KIR B genotypes for unrelated hematopoietic cell transplantation (HCT) results in superior relapse protection and better relapse-free survival for patients with acute myeloid leukemia (AML) (J Miller): This is an ongoing study in support of Dr. Miller’s NK Biology program project grant. R04-74s: Functional significance of Killer-IG-ligand genes in HLA-matched and mismatched unrelated hematopoietic cell transplantation (HCT) (K Hsu / B DuPont): The purpose of this study is to determine the influence of donor KIR genotypes and haplotypes on HCT for leukemia. This is a collaborative study with the IHWG and is an ongoing effort. The manuscript was published in New England Journal of Medicine. IB07-03: Analysis of KIR ligands in reduced intensity conditioning allogeneic hematopoietic stem cell transplantation (HCT) (R Sobecks): The objectives of this study are to evaluate the clinical effects of KIR ligand absence in recipients of HLA matched and mismatched unrelated donor RIC HCT for myeloid malignancies. Retrospective donor KIR genotyping to determine the relevance of recipient KIR ligand absence was recently completed. Revisions to the protocol are underway. IB08-06: KIR ligands in umbilical cord blood hematopoietic cell transplantation (HCT) (R Sobecks/V Rocha/M Eapen): The goal fo the study is evaluate the role of HLA-defined KIR ligands on the outcome of umbilical cord blood transplantation for acute leukemia. The study is a collaborative effort between the CIBMTR and Eurocord. Datafile preparation is underway. IB11-05: KIR genotyping and Immune function in MDS patients prior to unrelated donor transplantation (E Warlick/J Miller): The goal of this study is assess the correlation of NK cell function with disease progression in MDS and potentially develop therapeutic immune strategies to control MDS progression. The retrospective cohort provided by the CIBMTR will be compared to a prospective cohort of newly diagnosed MDS patients from the University of Minnesota. This is an ongoing study in support of Dr. Miller’s NK Biology program project grant. IB12-04: Determining the Effects of HLA-C KIR Ligand Expression on Outcomes of Unrelated Hematopoietic Stem Cell Transplantation (J Venstrom): The objective of the study is to determine the influence of high and low expression HLA-C encoded KIR ligands on the transplant outcome and define a donor selection strategy that will optimize NK cell alloreactivity. Revisions to the protocol are underway. IB12-06s: Natural killer cell genomics and outcomes after allogeneic transplantation for lymphoma (V Bachanova/J Miller/D Weisdorf/L Burns): The goal of the study is to evaluate the influence of KIR genotype and ligands on the outcome of allogeneic transplantation for Non-Hodgkin lymphoma. The study will KIR genotype a cohort of unrelated and related donor recipient pairs and test for correlation with relapse and survival. A draft protocol is complete.

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OTHER GENES R04-76s: Identification of functional single nucleotide polymorphisms (SNPs) in unrelated hematopoietic cell transplantation (HCT) (E Petersdorf): This study proposes to identify novel major histocompatibility complex resident SNPs of clinical importance. HLA matched pairs were genotyped for 1120 MHC SNPs and correlated with outcomes. This is a collaborative study with the IHWG. The study is in press at Blood. IB08-08: Genome-wide association in unrelated donor hematopoietic cell transplant (HCT) recipients and donors (R Goyal): This study hypothesizes that an unbiased recipient-donor genome-wide association (GWA) study will identify genes associated with risk of acute graft versus host disease (aGvHD) after HLA-matched unrelated donor HCT. Preliminary results suggested an association with specific HLA-DPB1 alleles and aGVHD. A validation dataset is being prepared to confirm the preliminary results.. IB09-04s: Donor/recipient gene polymorphisms of drug metabolism and in innate immune response post allele-matched unrelated donor hematopoietic stem cell transplantation (HCT) (V Rocha): This study is designed to validate associations between polymorphisms in drug metabolism and innate immune response genes and outcomes previously identified in matched sibling donor HCT in the HLA-matched unrelated donor HCT setting. Analysis is underway. IB09-05s: Identification of functional single nucleotide polymorphisms (SNPs) in umbilical cord blood transplant (E Petersdorf): The primary hypothesis of the study is that umbilical cord blood units and recipients differ for genome-wide single nucleotide polymorphism and gene copy number variation and that these differences may define putative transplant outcome determinants. Testing is underway. IB09-06s/RT09-04*: Genetic susceptibility to transplant-related mortality after matched unrelated stem cell transplant (T Hahn): This is a joint study with the Regimen Related Toxicity working committee and is supported by an R01 grant to Drs. Hahn and Sucheston. This study will test for a genetic association with transplant-related and overall mortality in recipients of myeloablative and reduced intensity conditioning matched unrelated donor HCT. Testing of a preliminary test cohort was completed in 2012 and analysis is underway. The validation cohort was selected and testing will be completed in 2013. IB09-07s: Clinical significance of genome-wide variation in unrelated donor hematopoietic stem cell transplantation (HCT) (E Petersdorf): This study is designed to assess the impact of genome-wide variation between donors and recepients in HLA matched unrelated donor HCT. This is a collaborative study with the IHWG. IB10-01s: Donor and recipient telomere length as predictors of outcomes after hematopoietic stem cell transplant in patients with acquired severe aplastic anemia (S Gadalla): More than one-third of patients with acquired severe aplastic anemia (SAA) have short telomeres. Telomere shortening in peripheral blood is associated with increased risk of malignancies, pulmonary and liver fibrosis, and other complications. This study will explore the role that telomere length plays in outcomes after HCT for SAA. Testing is complete and data file preparation is underway. IB10-03: TLR and HMGB1 gene polymorphisms in unrelated haematopoietic stem cell transplantation (K Müller/ B Kornblit): The objective of this study is to validate single center findings of a correlation between High Mobility Group Box 1 (HMGB1) polymorphism in the recipient and relapse rates following HLA matched unrelated donor HCT. Analysis is underway. IB10-04: A validation study of the role of base excision repair pathway as a predictor of outcome after hematopoietic stem cell transplant (M Arora): This study is designed to validate findings from a single

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center that noted a correlation between SNPs in the DNA repair genes and relapse and TRM after HLA matched unrelated donor HCT. Analysis is underway. IB11-02s: Impact of CTLA4 single nucleotide polymorphisms on outcome after unrelated donor transplant (M Jagasia/W Clark/B Savani/S Sengsayadeth): The aim of the study is to validate findings from a single center analysis that found an association between CTLA4 single nucleotide polymorphisms and relapse, acute and chronic GVHD in unrelated donor HCT for hematological malignancies. Testing is underway. IB12-05/RT10-01: Plasma YKL-40 and CHI3L1 genotype to predict mortality after allogeneic hematopoietic cell transplantation (HCT) (B Kornblit): The goals of the study are to describe the variation in YKL-40 (inflammatory marker) plasma levels in recipients pre-transplant and donors pre-donation and assess the prognostic value in predicting transplantation outcomes. In addition, the plasma levels will be correlated to polymorphisms in the CHI3L1 promoter genotype. Plasma testing is complete and data analysis is underway. SENSITIZATION and TOLERANCE R03-65s: Detection of H-Y antibodies in healthy female donors: Does H-Y presensitization predict male hematopoietic stem cell transplantation (HCT) outcome (D Miklos): The testing methodology has been refined and sample testing will be completed in 2013. IB06-09s: Detection of HLA antibody to the mismatched antigen in single antigen HLA-mismatched unrelated donor transplantation (HCT): Is it associated with GVHD outcome? (S Arai): Draft manuscript is underway and expected to be submitted in 2013. IB06-10: Evaluation of the impact of exposure to non-inherited maternal antigens (NIMA) during fetal life and breast feeding and to the inherited paternal antigens during pregnancy on the clinical outcome of hematopoietic cell transplantation (HCT) from haploidentical family members (J Van Rood): This study will evaluate the impact of NIMA on haploidentical related HCT for hematological malignancies. The study requires HLA typing on the patient, donor and patient parents to assign the presence or absence of NIMA matches. A comprehensive review of the available HLA data was completed August 2010. Planning is underway for a combined study with EBMT. IB09-08: Donor/recipient birth order in matched sibling hematopoietic stem cell transplantation (HCT) (C Dobbelstein): The study proposes to validate findings from a single center study that found matched sibling HCT utilizing donors born after the recipient yielded superior survival with lower incidences of acute GVHD and relapse. The results of the analysis did not support the hypothesis. The manuscript was submitted to BBMT. IB11-01: Analysis of the NIMA effect on the outcome of unrelated PBSC/BM transplantation (G Ehninger/J van Rood/A Schmidt): The goal of this study is to determine whether matching for NIMA in the selection of unrelated donors can lead to better outcomes. The study will be restricted to transplants where the donors were supplied by the DKMS. The DKMS team will attempt to collect samples from each donor’s mother for HLA typing and assignment of NIMA matching. Sample collection and testing are underway. IB11-07: Effect of Rituximab and ABO mismatch (D Miklos/A Logan): The aim of the study is to validate the finding of a single center analysis that found an association between ABO minor mismatch and increased acute GVHD. The study did not validate the acute GVHD findings, but did find a negative impact of minor ABO mismatch on overall survival, treatment related mortality and treatment failure compared to ABO matched transplantation. A draft manuscript is underway.

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MINOR HISTOCOMPATIBILITY ANTIGENS IB11-08: Synergism between minor and major histocompatibility antigens (E Spierings): The goal of the study is to evaluate the interaction between HLA-DPB1 mismatching and female into male (HY mismatched) transplantation to determine whether there is a synergistic effect on transplant outcome. Multivariate analysis revealed a synergistic interaction between HLA-DP mismatching and H-Y mismatching on acute GVHD grades III-IV. A draft manuscript is underway. Approved studies not yet initiated: HLA GENES CYTOKINE/CHEMOKINE IB08-04: Immune response gene polymorphisms in unrelated donor hematopoietic cell transplantation (HCT) in children (K Muller): Study deferred pending completion of an EBMT preliminary analysis. NK/KIR OTHER GENES MINOR HISTOCOMPATIBILITY ANTIGENS IB12-07: Telomeres and incidence of leukemia recurrence and survival after hematopoietic stem cell transplantation (M Eapen): Study deferred pending data and sample collection.

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2012 INTERNATIONAL HISTOCOMPATIBILITY AND IMMUNOGENETICS WORKSHOP (IHIW) MEETINGS ABSTRACT

Impact of HLA Unidirectional Mismatches on the Outcome of Unrelated Donor Hematopoietic Stem Cell Transplantation Carolyn Hurley, Tao Wang, Stephen R. Spellman, John Umejiego, Mahmoud Aljurf, Medhat Askar, Minoo Battiwalla, Jason Dehn, John Horan, Susana Marino, Steven G. E. Marsh, David Maurer, Machteld Oudshorn, Joseph Pidala, Wael Saber, Kirk R Schultz, Victoria Turner, Stephanie J. Lee, Ann Woolfrey The impact of HLA homozygosity at mismatched (MM) loci on the outcome of unrelated donor (URD) hematopoietic stem cell transplantation (HCT) was evaluated. 3,369 myeloablative HCT facilitated by the NMDP between 1988-2009 for acute lymphoid or myeloid leukemia, chronic myeloid leukemia or myelodysplastic syndrome were included. Recipients received predominately bone marrow (BM) (69%); all pairs were high resolution typed for HLA-A,-B,-C,-DRB1. Outcomes were compared among 5 groups: 7/8 bidirectional MM transplants (donor (D) and recipient (R) heterozygous at the mismatched locus, n=1497), 7/8 HVG MM (R homozygous for the MM locus, n=121), 7/8 GVH MM (D homozygous for the MM locus, n=125), 6/8 bidirectional mismatches (n=500), and 8/8 matches homozygous at one or more of the HLA loci (n=1126). Multivariate analyses found no significant differences for overall survival between the 7/8 bidirectional group (reference), HVG MM (HR 1.046, p=0.732), and 7/8 GVH MM (HR 1.228, p=0.114); disease free survival, transplant related mortality showed similar results. Differences were observed among the three 7/8 groups only for grades 3-4 acute GVHD (p=0.002) with HVG MM (HR 0.509, p=0.001) and GVH MM (HR 1.191, p=0.245) compared to 7/8 bidirectional group. There were no differences among the 7/8 groups for relapse, chronic GVHD, neutrophil engraftment or secondary graft failure. These results differ from a previous, smaller report that found a risk of graft failure in HVG MM URD HCT. In summary for patients receiving URD grafts following myeloablative conditioning for malignant diseases, unidirectional mismatches in the GVH vector should be considered at the same level of risk as 7/8 bidirectional mismatches. For HLA homozygous recipients, there is no need to avoid a mismatch at the homozygous locus. In fact, these recipients will have a reduced risk of acute GVHD with a 7/8 HVG MM without an increased risk of disease relapse or graft failure.

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2012 AMERICAN SOCIETY OF HEMATOLOGY (ASH) MEETINGS ABSTRACT Amino Acid Substitution at Peptide-Binding Pockets of HLA Class I Molecules Adversely Impacts Hematopoietic Cell Transplantation Outcomes Joseph Pidala, Tao Wang, Michael D Haagenson, Stephen Spellman, Medhat Askar, Minoo Battiwalla, Lee Ann Baxter-Lowe, Menachem Bitan, Marcelo Fernandez-Vina, Manish Gandhi, Ann A Jakubowski, Martin Maiers, Susana R Marino, Steven GE Marsh, Machteld Oudshoorn, Jeanne Palmer, Vinod K. Prasad, Vijay Reddy, Olle Ringden, Wael Saber, Stella Santarone, Kirk R. Schultz, Michelle Setterholm, Elizabeth Trachtenberg, Vicky Turner, Ann Woolfrey, Stephanie J Lee, and Claudio Anasetti Background: While donor-recipient disparity at HLA loci is associated with greater risk for severe acute graft vs. host disease (GVHD) and inferior survival after unrelated donor allogeneic hematopoietic cell transplantation (HCT), the impact of amino acid substitution (AAS) at peptide binding pockets of the HLA molecule is incompletely understood. Methods: Adult and pediatric patients who received myeloablative or reduced intensity/non-myeloablative first unrelated bone marrow or peripheral blood stem cell transplantation for AML, ALL, CML or MDS between 1988 and 2009 were included. Donors and patients were fully high resolution matched for HLA-A, B, C, and DRB1 (8/8) or had single mismatch (7/8) at one HLA class I locus. Among 7/8 donor-recipient pairs, cases were categorized based on the presence or absence of the AAS of interest at positions 9, 77, 99, 116, or 156 of the class I molecule. In multivariable analysis accounting for patient, disease, and transplantation variables, we studied the independent impact of AAS at these residues on risk for grade III-IV acute GVHD, chronic GVHD, treatment-related mortality, primary malignancy relapse, and overall survival. We compared 7/8 donor-recipient pairs with AAS of interest to 7/8 pairs without these AAS in the primary analyses. Additionally, we performed this analysis restricted to each HLA class I locus. Results: Donor-recipient pairs were 8/8 matched (n=5282), 7/8 with AAS of interest (n=1713), or 7/8 without AAS of interest (n=318). In multivariable analysis, AAS at position 116 was associated with increased risk for grade III-IV acute GVHD (HR 1.21, 1.04-1.42, p=0.0165). No other significant association was detected between AAS studied and clinical outcomes. In multivariable analysis restricted to each class I HLA locus, we detected the following: Among 7/8 matched pairs with mismatch at HLA-C, AAS at position 116 was associated with increased risk for severe acute GVHD (HR 1.42, 1.13-1.79, p=0.0031) and inferior OS (HR 1.2, 1.01-1.41, p=0.0343). AAS at position 99 was associated with increased TRM (HR 1.37, 1.11-1.69, p=0.0037). Of 7/8 pairs with mismatch at HLA-B, AAS at position 9 was associated with increased chronic GVHD (HR 2.19, 1.31-3.66, p=0.0029). Specific amino acid substitution pairs with frequency > 30 were tested for association with HCT outcomes. None met the significance level of 0.00125, pre-specified for multiple comparisons. Conclusions: These results support the concept that AAS at key peptide-binding residues in the HLA class I molecule are associated with increased risk for severe acute GVHD and lower survival.

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CIBMTR IB12-03

EFFECT OF GENETIC ANCESTRY MATCHING ON HSCT OUTCOMES

REVISED PROTOCOL

Study Chair: Abeer Madbouly, PhD

National Marrow Donor Program 3001 Broadway St. NE, Ste. 100 Minneapolis, MN 55412 Telephone: 612-884-8272 Fax: 612-884-8677 Email: [email protected]

Study Co-Chairs: Martin Maiers, BS

National Marrow Donor Program 3001 Broadway St. NE, Ste. 100 Minneapolis, MN 55412 Telephone: 612-627-5892 Fax: 612-884-8677 Email: [email protected]

Navneet Majhail, MD National Marrow Donor Program 3001 Broadway St. NE, Ste. 100 Minneapolis, MN 55412 Telephone: 612-884-867 Fax: 612-884-8677 Email: [email protected]

Study Statistician: Michael Haagenson, MS CIBMTR – Minneapolis 3001 Broadway Street NE, Suite 100 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612-884-8661 Email: [email protected]

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Scientific Directors: Stephen Spellman, MBS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 100 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected] Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center P.O. Box 19024, D5-290 1100 Fairview Avenue North Seattle, WA 98109 USA Telephone: 206-667-5160 Fax: 206-667-1034 Email: [email protected] Working Committee Chairs: David Miklos, MD, PhD

Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182

E-mail: [email protected] Marcelo Fernandez-Vina, PhD

Professor Department of Pathology/Blood Center Stanford University School of Medicine 3373 Hillview Avenue Palo Alto, CA 94304 USA Telephone: 650-723-7968 Fax: 650-725-4470

E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected]

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1.0 OBJECTIVES

1. Study the effect of differences in genetic ancestry, as detected by ancestry informative Single Nucleotide Polymorphisms (SNPs) for HLA matched unrelated donors and recipients, on HSCT transplantation outcomes.

2. Study the effect of recipient non-HLA genetic ancestry on HSCT transplant outcomes.

3. Evaluate the correlation of self-identified race with information derived from genetic ancestry studies.

2.0 SCIENTIFIC JUSTIFICATION:

Survival after unrelated donor (URD) hematopoietic stem cell transplantation (HSCT) is dependent on HLA matching between donor and recipient, donor and recipient ethnicity and numerous other factors, most of which are still unknown. Disparities exist between the outcomes of HSCTs using URDs and those with identical HLA matched sibling, implying the potential effect of factors other than HLA-match.

The search for an HLA matched unrelated donor typically begins among individuals with the same self-identified race/ethnicity (SIRE) as the recipient (due to increased likelihood of an HLA match within the same SIRE group). However it is not clear whether matched race/ancestral background specifically results in better survival. Additionally, some ethnic groups are defined at a very general level: for example, the majority of HSCTs in the US are performed with patients and donors with European ancestry mostly classified as White. Previous studies have addressed racial disparities in outcome for HLA matched related and unrelated donor or CBU HSCT [1-9]Error! Reference source not found.. However, there are no studies to date analyzing outcome with respect to differences in ancestral groups between a stem cell donor and recipient as defined by ancestry informative markers.

Ancestry Informative Markers (AIMs) are a set of genetic markers that differ in allele frequencies across different populations, whether within or across world continents. Most variation is shared among populations, so for most loci the most common allele is the same in each population. AIMs may be used to categorize individuals into populations sharing similar allele frequency distributions and perhaps phenotypes (such as self-identified ethnicity). While an individual’s genetic composition does not change during his or her lifetime, self-identified ethnic affiliation is a result of self-perception and can change over time [10-11]. This can introduce inconsistency in the process of matching stem cell recipients with potential unrelated donors and can potentially affect the outcome of HSCT. Using AIMs to define the ethnic background of volunteer stem-cell donors in unrelated registries, guided by SIRE information can potentially decrease some of the errors associated with the self-reporting of ethnic background.

This study will test the hypothesis that for donor-recipient pairs that are matched at the five primary HLA loci (HLA-A, -C, -B, -DRB1 and -DQB1), genetic ancestry affects transplant outcome. Given that the majority of the data available on HSCT outcome are for donors and recipients of European ancestral origin, we will test several published AIMs panels [12-14] to infer continental ancestry as well as European substructure in the donor-recipient study cohort and will select the panel that would provide the best performance.

The main goal of the study is to investigate the effect of the genetic co-ancestry of donor-recipient pairs on transplant outcome. Additionally, we will investigate potential permissive ancestral mismatches as well as the individual effect of the recipient’s and/or donor’s genetic ancestry. In our

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cohort of approximately 1500 donor-recipient pairs, over 90% of individuals self-identified as White. Several studies examining European genetic substructure have identified significant patterns of structure within Europe along a north to south axis [12-19]. Given the high degree of heterogeneity and admixture within the US European-American population, we anticipate that we will detect similar patterns of European substructure and will investigate whether different patterns have dissimilar associations with transplant outcomes.

3.0 STUDY POPULATION:

Patients receiving a first 10/10 matched (verified through retrospective typing) unrelated donor myeloablative transplantation for hematological malignancy (AML, ALL, CML and MDS). HLA-DPB1 data and samples available.

4.0 OUTCOMES:

Primary outcomes: - Overall survival – Time to death from any cause. Events will be summarized by a survival

curve. - Cases will be analyzed at the time of last follow-up.

- Treatment-related mortality - Death in continuous remission of primary disease. Events will

be - summarized by the cumulative incidence estimate with relapse as a competing risk.

Secondary outcome:

- Acute and chronic GVHD - Relapse - Disease-free survival

5.0 VARIABLES TO BE ANALYZED:

Main effect to be tested:

Association of genetic ancestry with HSCT outcomes:

- Impact of genetic ancestry ‘distance’ between donor and recipient - Evaluating the correlation with self-identified race - Impact of genetic admixture proportion on outcome

Patient-related (at time of transplant):

- Age: in decades (0-9, 10-19, 20-29, 30-39, 40-49, 50 and older). - Gender: female vs. male - Karnofsky score at transplant: < 90 vs. 90-100

Disease-related:

- Disease at transplant: ALL, AML, CML and MDS - Disease status prior to transplant: early vs. intermediate vs. advanced

Transplant-related:

- Source of stem cells: marrow (BM) vs. peripheral blood stem cells (PB) - Donor age: in decades (18-29, 30-39, 40-49, 50 and older) - Year of transplant: (1988-2003)

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- Gender match: M-M vs. M-F vs. F-M vs. F-F

- Donor/recipient CMV status: -/- vs. -/+ vs. +/- vs. +/+ vs. Unknown

- Conditioning regimen: Myeloablative vs. reduced intensity or non-myeloablative

- GvHD prophylaxis: Tacrolimus +/-others vs. CSA +/-others vs. TCD vs. others [Note: Consider CSA/Tac prophylaxis as one group]

- ATG use: Yes vs. no

- HLA-DP match vs. mismatched

6.0 STUDY DESIGN:

The study will proceed as follows:

1. Pilot study and preliminary analysis. 2. SNP AIMs typing of CIBMTR samples. 3. Genetic ancestry analysis on study cohort. 4. Data analysis with respect to outcome.

1. Pilot study

We will test multiple published AIMs panels [12-14] to identify continental as well as sub-European ancestral structures. Two hundred to three hundred randomly chosen donor-recipient pairs will be genotyped using the chosen AIMs panel. All donor-recipient pairs will be fully (10/10) HLA matched. A preliminary genetic ancestry analysis (see details in phase 3) will be conducted and several distance measures assessed in order to identify the most effective measures of patient/donor ancestry proximity. The primary purpose of the pilot study is to allow an estimate of necessary study sample size based on the likely proportions of genetic distances in the study cohort.

2. SNP typing of full cohort

The results from the pilot study will inform the necessary cohort size to attain adequate statistical power as well as the AIMs panel used for the remainder of the study to define donor/recipient genetic ancestry.

3. Analysis of genetic ancestry in study cohort

Unsupervised clustering methods [20] will be applied to individuals in the study cohort according to AIMs genotypes. Clustering will be augmented by principal components analysis (PCA) as implemented in the software EIGNESTRAT [21]. Self-identified race and ethnicity will be examined with respect to the principal clusters. The initial aim is to confirm clear separation among the main continental groups: European, African, Asian and Hispanic. Since the majority of individuals will be of European descent, we will attempt to delineate two primary substructures within this group of European-American individuals: North-Western Europe and South-Eastern Europe, as well as finer structures as the data permits. The donor-recipient ancestry proximity measures identified in the pilot study will be applied to each donor-recipient pair.

4. Data analysis and association with outcomes

Donor-recipient pairs that are ‘matched’ for genetic ancestry will be compared to pairs with ‘mismatched’ genetic ancestry. The definition of genetic ancestry matching will be determined with the assistance of the CIBMTR Statistical Center. Comparisons will also be made based on the genetic ancestry clustering of the recipients alone without regard for the donor’s ancestry to investigate differences in outcome related to the recipient genetic background.

To summarize the characteristics of the dataset, descriptive tables of patient-, disease- and transplant-related factors will be reported. For discrete factors, the number of cases and their

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respective percentages will be calculated. Chi-Square tests will be used to compare discrete factors between the HLA matched vs. mismatched groups. For continuous factors, the median and ranges will be calculated. The Kruskal-Wallis test will be used to compare the continuous factors between the genetic ancestry matched vs. disparate groups. Probabilities for overall survival and disease-free survival will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood's formula. Comparison of survival curves will be done using the log-rank test. Values for other listed outcomes will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance.

Multivariate analyses will be performed using the proportional hazards model to compare the genetic ancestry matched and mismatched groups. Models will be fitted to determine which risk factors may be related to a given outcome. All variables will be tested for the affirmation of the proportional hazards assumption. Factors violating the proportional hazards assumption will be adjusted first before the stepwise model building approach is used in developing models for the primary and secondary outcomes.

7.0 REFERENCES:

1. Davies SM, Kollman C, Anasetti C, et al. (2000) Engraftment and survival after unrelated-donor bone marrow transplantation: a report from the National Marrow Donor Program. Blood.;96:4096-4102.

2. Serna DS, Lee SJ, Zhang MJ, et al. (2003) Trends in survival rates after allogeneic hematopoietic stem-cell transplantation for acute and chronic leukemia by ethnicity in the United States and Canada. J. Clin Oncol.;21:3756-3760.

3. Oh H, Loberiza FR Jr, Zhang MJ, et al. (2005) Comparison of graft versus-host-disease and survival after HLA-identical sibling bone marrow transplantation in ethnic populations. Blood.;105:1408-1416.

4. Baker KS, Davies SM, Majhail NS, et al. (2009) Race and socioeconomic status influence outcomes of unrelated donor hematopoietic cell transplantation. Biol Blood Marrow Transplant.;15:1543-1554.

5. Mielcarek M, Gooley T, Martin PJ, et al. (2005) Effects of race on survival after stem cell transplantation. Biol Blood Marrow Transplant.;11(3):231-9.

6. Baker KS, Loberiza FR Jr., Yu H, et al. (2005) Outcome of ethnic minorities with acute or chronic leukemia treated with hematopoietic stem-cell transplantation in the United States. J Clin Oncol.;23(28):7032-7042.

7. Bhatia S, Sather HN, Heerema NA, et al (2002) . Racial and ethnic differences in survival of children with acute lymphoblastic leukemia. Blood.;100:1957–1964

8. Sekeres MA , Peterson B , Dodge RK, et al.(2004) Differences in prognostic factors and outcomes in African Americans and whites with acute myeloid leukemia. Blood.;103:4036–4042

9. Ballen KK, Klein JP, Pedersen TL, et al. (2012) Relationship of Race/Ethnicity and Survival after Single Umbilical Cord Blood Transplantation for Adults and Children with Leukemia and Myelodysplastic Syndromes. Biol Blood Marrow Transplant.;18(6):903-12

10. Carter KN, Hayward M, Blakely T, et al. (2009) How much and for whom does self-identified ethnicity change over time in New Zealand? Results from a longitudinal study. Social Policy Journal of New Zealand, 36

11. Ford ME, and Kelly PA (2005) Conceptualizing and categorizing race and ethnicity in health services research. Health Services Research:40(5):1658-1675.

12. Tian C, Plenge RM, Ransom M, et al. (2008) Analysis and Application of European Genetic Substructure Using 300 K SNP Information. PLoS Genet 4(1): e4.

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13. Price AL, Butler J, Patterson N, et al. (2008) Discerning the ancestry of European Americans in genetic association studies. PLoS Genet 4(1): e236.

14. Paschou P, Drineas P, Lewis J, et al. (2008) Tracing Sub-Structure in the European American Population with PCA-Informative Markers. PLoS Genet 4(7): e1000114.

15. Seldin M, Shigeta R, Villoslada P, Selmi C, Tuomilehto J, et al. (2006) European population substructure: clustering of northern and southern populations. PLoS Genet 2: e143.

16. Bauchet M, McEvoy B, Pearson L, Quillen E, Sarkisian T, et al. (2007) Measuring European Population Stratification with Microarray Genotype Data. Am J Hum Genet 80: 948–956.

17. Menozzi P, Piazza A, Cavalli-Sforza L (1978) Synthetic maps of human gene frequencies in Europeans. Science; 201: 786–792.

18. McEvoy B, Richards M, Forster P, Bradley DG (2004) The Longue Duree of genetic ancestry: multiple genetic marker systems and Celtic origins on the Atlantic facade of Europe. Am J Hum Genet 75: 693–702.

19. Richards M, Macaulay V, Torroni A, Bandelt HJ (2002) In search of geographical patterns in European mitochondrial DNA. Am J Hum Genet 71: 1168–1174.

20. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics;155: 945–959.

21. Price A, Patterson N, Plenge R, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet.;38:904-909.

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Table 1: Characteristics of myeloablative first transplants for patients with samples available for donor/recipient pairs and with disease of AML, ALL, CML or MDS and are 10/10 high-resolution matched using unrelated NMDP donors from 1995 to 2008a,b

Characteristics of patients N Eval N (%)Number of patients 1335 Number of centers 132 Age, median (range), years 1335 38 (<1-69) Age at transplant 1335

0 - 9 y 88 ( 7) 10-19 y 129 (10) 20-29 y 242 (18) 30-39 y 257 (19) 40-49 y 324 (24) 50 and older 295 (22) Recipient race 1335

Caucasian 1247 (93)African American 20 ( 1)Asian/Pacific Islander 9 ( 1)Hispanic ethnicity 47 ( 3)Native American 5 (<1)Other/Multiple/Declined/Unknown 7 ( 1)

Race matching - Yes 1282 1095 (85) Male sex 1335 784 (59) Karnofsky prior to transplant > 90 1235 919 (74) Disease at transplant 1335

AML 430 (32)ALL 270 (20)CML 384 (29)MDS 251 (19)

Disease status at transplant 1335

Early 971 (73) Intermediate 67 ( 5) Advanced 297 (22) Graft type 1335

Bone marrow 858 (64)Peripheral blood 477 (36)

a - Data has been CAP-modeled. b – Sample availability has been checked.

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Table 1: Continued. Characteristics of patients N Eval N (%)GVHD prophylaxis 1335

FK506 + MTX other 449 (34) FK506 + (MMF or Steroids) other 48 ( 4) FK506 other 46 ( 3) CsA + MTX other 525 (39) CsA other (No MTX) 55 ( 4) MTX other (No CsA) 25 ( 2) T-cell depletion 97 ( 7) Other 90 ( 7) ATG given 1335

No 1101 (82)Yes 234 (18)

In vivo T-cell depletion 1335

No 1089 (82)Yes 246 (18)

HLA-DPB1 typing 1335

Matched 216 (16)Single mismatch 756 (57)Double mismatch 363 (27)


Male/Male 579 (44) Male/Female 331 (25) Female/Male 203 (15) Female/Female 218 (16)Donor/recipient CMV match 1335

Negative/Negative 456 (34) Negative/Positive 358 (27) Positive/Negative 173 (13) Positive/Positive 220 (16) Unknown 128 (10) Donor age, median (range), years 1335 34 (18-60) Donor age 1335

18-29 406 (30) 30-39 460 (34) 40-49 283 (21) 50 and older 87 ( 7)To Be Determined from NMDP Donor database 99 ( 7)

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Table 1 Continued Characteristics of patients N Eval N (%)Year of transplant 1335

1995 78 ( 6)1996 67 ( 5)1997 94 ( 7)1998 76 ( 6)1999 95 ( 7)2000 95 ( 7)2001 96 ( 7)2002 91 ( 7)2003 70 ( 5)2004 94 ( 7)2005 145 (11)2006 147 (11)2007 96 ( 7)2008 91 ( 7)


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CIBMTR IB07-03 ANALYSIS OF KILLER IMMUNOGLOBULIN-LIKE RECEPTOR (KIR) LIGANDS IN

REDUCED INTENSITY CONDITIONING ALLOGENEIC HEMATOPOIETIC STEM CELL TRANSPLANTATION

REVISED PROTOCOL

Study Chairs: Ronald M. Sobecks, MD The Cleveland Clinic 9500 Euclid Avenue Mail Code R35 Cleveland, OH 44195 USA Telephone: 216-445-2626 Fax: 216-445-7444 E-mail: [email protected] Katharine Hsu, MD, PhD Allogeneic Marrow Transplantation Service Memorial Sloan-Kettering Cancer Center 1275 York Avenue New York, NY 10021 USA Telephone: 646-888-2667 Fax: 646-422-0298 E-mail: [email protected] Medhat Askar, MD, PhD The Cleveland Clinic 9500 Euclid Avenue, Mail Code C100 Cleveland, OH 44195 USA Telephone: 216-444-5918 Fax: 216-444-8261 E-mail: [email protected] Study Statistician: Michael Haagenson, MS CIBMTR

3001 Broadway Street, N.E., Suite 100 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612- 884-8661

E-mail: [email protected]

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Scientific Directors: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center 1100 Fairview Ave. North, D5-290 PO Box 19024 Seattle, WA 98109 Phone : 206-667-6190 Fax : 206-667-1034

Email : [email protected]

Stephen Spellman, MS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 100 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected] Working Committee Chairs: David Miklos, MD, PhD

Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 Phone: 650-725-4626 Fax: 650-724-6182 Email: [email protected]

Marcelo Fernandez-Vina, PhD

Professor Department of Pathology/Blood Center Stanford University School of Medicine 3373 Hillview Avenue Palo Alto, CA 94304 USA Telephone: 650-723-7968 Fax: 650-725-4470

E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected]

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1.0 OBJECTIVES: 1.1 To evaluate the clinical effects of donor KIR genotype and donor-recipient HLA

genotypes among matched and mismatched unrelated donor RIC allogeneic HSCT for myeloid malignancies. Clinical outcomes will include CMV reactivation (if data available) graft-versus-host disease (GVHD), relapse, disease-free survival, transplant-related mortality and overall survival.

1.2 To assess the effects of donor KIR ligand absence in matched and mismatched unrelated

donor RIC allogeneic HSCT for myeloid malignancies including graft rejection and for those patients with data available the achievement of T-cell complete donor chimerism (CDC).

2.0 SCIENTIFIC JUSTIFICATION:

Disease relapse continues to be a significant cause of treatment failure after allogeneic HSCT. In particular with RIC approaches, the graft-versus-leukemia (GVL) effect is critical for successful outcomes in patients with advanced myeloid malignancies. As such, further strategies to optimize conditions for achievement of a GVL effect are needed. The GVL effect has been attributed to donor-derived alloreactive immune cells including T-lymphocytes and natural killer (NK) cells 1-4. The reactivity of NK cells and some T-lymphocyte subsets is regulated by the interaction of KIRs with target cell HLA-class I molecules 4. KIR interactions have been suggested to influence outcomes of haploidentical 3, 5, matched unrelated donor 6-8 and HLA-matched related donor (MRD) allogeneic HSCT 9, 10, particularly for AML patients. However, in RIC allogeneic HSCT when both donor and recipient hematopoiesis may coexist, the effect of KIR interactions on outcomes is not well known. Donor activating KIR genotype has been implicated as a contributory factor for CMV reactivation after myeloablative and reduced-intensity conditioning allogeneic HSCT 11-14. Activating KIR genotype has also been reported to influence other posttransplant outcomes including grade 2-4 acute GVHD, transplant-related mortality, relapse-free survival and overall survival 15-18. Donor KIR2DS1 has been demonstrated to have a protective effect against AML relapse after allogeneic HSCT in an HLA-C- dependent manner 18. Group A and B KIR haplotypes have distinctive centromeric (cen) and telomeric (tel) gene-content motifs. Cen and tel B motifs have been reported to have less relapse and improved survival compared to A haplotype motifs in AML patients undergoing unrelated donor HSCT 19. This effect was most notable for cen-B homozygosity. We have analyzed 51 patients who received related donor RIC allogeneic HSCT and have observed that KIR matching influenced the achievement of T-cell (CD3+) CDC 20, 21. Recipient KIR genotype and donor HLA KIR ligands (HLA-A3/11, -Bw4, -C group 1 and –C group 2) were used to generate an inhibitory KIR score for patients from 1 to 4 corresponding to the potential number of recipient inhibitory KIRs available for engagement with donor HLA KIR ligands. As compared to patients with an inhibitory KIR score of greater than 1, those with a score of 1 were less likely to achieve CDC. With increasing inhibitory KIR score, patients tended to be more likely to achieve CDC. This difference could not be attributed to distinct diagnostic subsets or to CD3+ or CD34+ cell doses infused. Thus, patients with lower inhibitory KIR scores may have more active anti-donor effector cells (NK cells and T cell subsets) that may reduce donor cell chimerism. Conversely, those with higher inhibitory KIR scores may have less active populations and be more likely to achieve CDC.

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3.0 STUDY POPULATION: The study population will include all patients reported to the CIBMTR who had received a RIC allogeneic HSCT for AML and MDS from 1999 to 2007 from an unrelated donor. Subjects who received campath were excluded from the population.

4.0 OUTCOMES:

4.1 Overall survival: Time to death from any cause. Event will be summarized by Kaplan-Meier estimate. Cases will be analyzed at the time of last follow-up. There are no competing risks.

4.2 Disease-free survival: Time to disease relapse/progression or death. Event will be

summarized by Kaplan-Meier estimate. Cases will be analyzed at the time of last follow-up. There are no competing risks.

4.3 Transplant-related mortality: Death in continuous remission of primary disease. Event

will be summarized by the cumulative incidence estimate with relapse as a competing risk and second transplant as a censoring event.

4.4 Acute GVHD: Development of Grades II-IV acute GVHD using the Consensus Criteria

system which grades GVHD based on the pattern and severity of abnormalities in skin, gastrointestinal and liver. Event will be summarized by the cumulative incidence estimate. Cases will be analyzed at time of last follow-up. Death is a competing risk. Second transplant is a censoring event.

4.5 Chronic GVHD: Development of symptoms in any organ system fulfilling the criteria of

extensive chronic GVHD. The event will be summarized by the cumulative incidence estimate. Patients will be analyzed at last follow-up. Death is a competing risk and second transplant is a censoring event.

4.6 Disease relapse: Development of clinical relapse of the primary disease as defined by the

CIBMTR. The event will be summarized by the cumulative incidence estimate and patients analyzed at last follow-up. Death is a competing risk and second transplant is a censoring event.

4.7 Graft rejection: Primary graft rejection is defined as failure to ever achieve any donor-

derived hematopoiesis defined as > 5% donor chimerism before day 100. This endpoint will be expressed as a binary outcome at day 100. Patients who die before a chimerism test is performed or who do not have a chimerism result recorded before day 100 are excluded from the analysis of primary graft rejection. Secondary graft rejection is defined as the loss of donor-derived hematopoiesis (donor chimerism < 5%) after complete or mixed chimerism was achieved (documentation of > 5% donor chimerism). The event will be summarized by the cumulative incidence estimate. Patients will be analyzed at last follow-up. Death is a competing risk and second transplant is a censoring event. Patients who do not ever achieve chimerism > 5% are excluded from this analysis.

4.8 T-cell complete donor chimerism: Defined as achievement of >95% donor chimerism.

The event will be summarized by the cumulative incidence estimate and patients analyzed at last follow-up. Death and second transplants are competing risks. Patients who die before a chimerism test is performed are excluded from the analysis.

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5.0 VARIABLES TO BE ANALYZED:

Main effects: The main effects will be assessed separately for patients with AML (n=624) and for patients with AML or MDS (n= 929). The four main mechanisms of NK alloreactivity to be analyzed are:

1) Missing KIR Ligand Model: This model will be examined by assessing the presence or

absence of recipient HLA ligands for cognate donor inhibitory KIR. The presence/absence of HLA-Bw4, HLA-C1, and HLA-C2 KIR ligands will be segregated into the following groups: a. all ligands present (baseline) b. missing C1 ligand only c. missing C2 ligand only d. missing Bw4 ligand only e. missing both Bw4 and C1 or C2.

A two-group comparison between presence of any missing ligand in the patient versus the presence of all ligands will also be evaluated. T-cell chimerism: Patient chimerism data will be evaluated to determine the achievement of complete donor chimerism at days 30, 100, 183 and 365 post-transplant. The analysis will compare those with all donor KIR ligands present (n=218) to those missing 1-2 donor KIR ligands (n=390).

2) Specific Missing KIR Ligand Model: This model assesses the presence or absence of

specific HLA-Bw4, HLA-C1, and HLA-C2 ligands in the recipient irrespective of any other KIR ligand. Each ligand will be evaluated separately from the others, with pairwise comparisons between presence vs absence of the KIR ligand.

3) Effects of KIR2DS1 and HLA-C background: KIR2DS1 presence vs absence in the donor

will be assessed, followed by segregation of KIR2DS1+ donors by their C1 vs C2C2 backgrounds.

4) Donor centromeric KIR content: This model will assess the presence of particular

centromeric KIR combinations which have been previously reported to improve survival. We will assess the following groups: a. homozygous centromeric B (presence of KIR2DS2, KIR2DL2, and absence of KIR2DL3

and KIR2DL1) b. homozygous centromeric A (presence of KIR2DL3 and KIR2DL1, and absence of

KIR2DL2 and KIR2DS2) c. heterozygous centromeric AB (presence of KIR2DS2, KIR2DL2, KIR2DL3, and KIR

2DL1). These three groups will be compared to assess a cumulative dose effect of centromeric B (centromeric AA versus centromeric AB versus centromeric BB). Telomeric B will not be assessed in this initial analysis.

Patient related:

– Age: 20-50 vs. >50 yrs – Gender – Karnofsky score at transplant: <70 vs. 80-90 vs. 90-100

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Disease related: – Disease at transplant: AML and MDS

– Group assessments: – AML + MDS – AML – MDS

Transplant related: – HLA Matching (HLA disparities: HLA-A, -B, -C, -DR, -DQ, -DP or none) – Conditioning regimen (Reduced intensity, non-myeloablative) – Low-dose TBI (200 cGy), Flu/TBI, Flu/Cy, Flu/Cy/idarubicin, Mel/Flu Thiotepa/Cy, Thiotepa/Mel/ATG, Bu/Flu/ATG. - Subset analysis of those who had ATG/T-cell depletion vs. no ATG/TCD – GvHD prophylaxis (calcineurin inhibitor + MMF, calcineurin inhibitor + MTX,

calcineurin inhibitor ± other) – Stem cell source (BM vs. PBSC) – Hematopoietic stem cell dose: CD3, CD34 and TNC – T-cell chimerism analysis: molecular methods (e.g., PCR-based STR, VNTR, RFLP) vs.

other (e.g., FISH, standard cytogenetics) – Donor-recipient sex: M-M vs. M-F vs. F-M vs. F-F – ABO mismatch between donor and recipient – Year of transplant

6.0 STUDY DESIGN:

The presence or absence of HLA KIR ligands (Bw4, C1 and C2) will be determined for each recipient from high resolution HLA class I typing. Patients will then be grouped according to homozygosity for HLA-Bw6, C1 and C2, corresponding to a lack of ligand for donor inhibitory KIR3DL1, KIR2DLI and KIR2DL2/3, respectively. HLA-A antigens/alleles sharing a Bw4 epitope (A23, A24 and A32) will be assessed for each recipient and will be included as KIR3DL1 ligands in a separate analysis. Patients with all KIR ligands present will then be compared with those missing 1 or more KIR ligands and examined for impact on CMV reactivation (if data available), acute and chronic GVHD, relapse, disease-free survival and overall survival. HLA-matched patients will be analyzed independently from the HLA mismatched patients. A separate analysis will also assess the presence or absence of recipient HLA-A3/A11 KIR3DL2 ligands. For the subset of patients with T-cell chimerism data available the number of patients who achieved CDC will be determined at the following post-transplant time points: days +30, +100, +183, + 365. A windowing algorithm will be used to capture T-cell chimerism data for those patients whose data is available but not specifically on one of these defined time points. This will allow the data to be categorized within the nearest defined post-transplant time point. At each of these time points the median percentage of donor T-cell chimerism will be assessed as well as the number of patients with graft failure (< 5% donor chimerism). The patients whose T-cell chimerism was based upon molecular methods will be analyzed independently from those performed with other methods. To summarize the characteristics of the dataset, descriptive tables of patient-, disease- and transplant-related factors will be reported. For discrete factors, the number of cases and their respective percentages will be calculated.

Chi-Square tests will be used to compare discrete factors between the KIR groups.

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– For continuous factors, the median and ranges will be calculated. The Kruskal-Wallis test

will be used to compare the continuous factors between the KIR groups. Univariate probabilities for overall survival will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood's formula. Comparison of survival curves will be done using the log-rank test. Values for other outcomes (except failure to engraft/graft failure) will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance. Multivariate analyses will be performed using the proportional hazards model to compare the KIR groups. Firstly, preliminary models will be built based on only the non-KIR variables to determine which risk factors may be related to a given outcome. All variables will be tested for the affirmation of the proportional hazards assumption. Factors violating the proportional hazards assumption will be adjusted for first before the stepwise model building approach will be used in developing models for the primary and secondary outcomes. For graft rejection and chimerism assessment, logistic regression models will be constructed. Center effect will be tested. Secondly, we test various KIR effects by forcing the KIR variables into the preliminary models. Each KIR variable will be evaluated separately. The KIR models defined in section 5 are the main effects of interest, and overall survival is the main outcome of interest. Similar analyses will then be conducted for the secondary endpoints.

7.0 REFERENCES:

1. Molldrem JJ, Lee PP, Wang C, et al. Evidence that specific T lymphocytes may participate in the elimination of chronic myelogenous leukemia. Nat Med 2000; 6: 1018-1023.

2. Hercend T, Takvorian T, Nowill A, et al. Characterization of natural killer cells with antileukemia activity following allogeneic bone marrow transplantation. Blood 1986; 67: 722-728.

3. Ruggeri L, Capanni M, Urbani E, et al. Effectiveness of donor natural killer cell alloreactivity in mismatched hematopoietic transplants. Science 2002; 295: 2097-3100.

4. Farag S, Fehniger T, Ruggeri L, et al. Natural killer cell receptors: new biology and insights into the graft-versus-leukemia effect. Blood 2002; 100: 1935-1947.

5. Ruggeri L, Capanni M, Casucci M, et al. Role of natural killer cell alloreactivity in HLA-mismatched hematopoietic stem cell transplantation. Blood 1999; 94: 333-339.

6. Giebel S, Locatelli FW, Lamparelli T, et al. Survival advantage with KIR ligand incompatibility in hematopoietic stem cell transplantation from unrelated donors. Blood 2003; 102: 814-819.

7. Hsu K, Gooley T, Malkki M, et al. KIR ligands and prediction of relapse after unrelated donor hematopoietic cell transplantation for hematologic malignancy. Biol Blood Marrow Transplant 2006; 12:828-836.

8. Miller J, Cooley S, Parham P, et al. KIR ligand absence in recipients of unrelated donor (URD) allogeneic hematopoietic cell transplantation (HCT) is associated with less relapse and increased graft versus host disease (GVHD). Blood 2006; 108:55a (abstr # 171).

9. Cook MA, Milligan DW, Fegan CD, et al. The impact of donor KIR and patient HLA-C genotypes on outcome following HLA-identical sibling hematopoietic stem cell transplantation for myeloid leukemia. Blood 2004; 103: 1521-1526.

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10. Hsu KC, Keever-Taylor CA, Wilton A, et al. Improved outcome in HLA-identical sibling hematopoietic stem cell transplantation for acute myelogenous leukemia (AML) predicted by KIR and HLA genotypes. Blood 2005; 105: 4878-4884.

11. Cook M, Briggs D, Craddock C, et al. Donor KIR genotype has a major influence on the rate of cytomegalovirus reactivation following T-cell replete stem cell transplantation. Blood 2006 Feb 1;107(3):1230-2.

12. Chen C, Busson M, Rocha V, et al. Activating KIR genes are associated with CMV reactivation and survival after non-T-cell depleted HLA-identical sibling bone marrow transplantation for malignant disorders. Bone Marrow Transplant 2006 Sep;38(6):437-44.

13. Zaia JA, Sun JY, Gallez-Hawkins GM, et al. The effect of single and combined activating killer immunoglobulin-like receptor genotypes on cytomegalovirus infection and immunity after hematopoietic cell transplantation. Biol Blood Marrow Transplant 2009 Mar;15(3):315-25.

14. Sobecks RM, Askar M, Thomas D, et al. Cytomegalovirus reactivation after matched sibling donor reduced-intensity conditioning allogeneic hematopoietic stem cell transplant correlates with donor killer immunoglobulin-like receptor genotype. Exper Clin Transplant 2011 Feb; 9(1):7-13.

15. Venstrom JM, Gooley TA, Spellman S et al. Donor activating KIR3DS1 is associated with decreased acute GVHD in unrelated allogeneic hematopoietic stem cell transplantation. Blood 2010 Apr 15;115(15):3162-5. Epub 2010 Feb 1.

16. Cooley S, Trachtenberg E, Bergemann TL et al. Donors with group B KIR haplotypes improve relapse-free survival after unrelated hematopoietic cell transplantation for acute myelogenous leukemia. Blood. 2009 Jan 15;113(3):726-32. Epub 2008 Oct 22.

17. Pende D, Marcenaro S, Falco M, et al. Anti-leukemia activity of alloreactive NK cells in KIR ligand-mismatched haploidentical HSCT for pediatric patients: evaluation of the functional role of activating KIR and redefinition of inhibitory KIR specificity. Blood. 2009 Mar 26;113(13):3119-29. Epub 2008 Oct 22.

18. Venstrom JM, Pittari G, Gooley TA, et al. HLA-C-Dependent prevention of leukemia relapse by donor activating KIR2DS1. N Engl J Med 2012;367:805-816.

19. Cooley S, Weisdorf DJ, Guethlein LA, et al. Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia. Blood 2010 Oct 7;116(14):2411-9. Epub 2010 Jun 25.

20. Sobecks R, Ball E, Askar M, et al. Influence of Killer Immunoglobulin-like Receptor (KIR) matching on achieving T cell (CD3+) complete donor chimerism (CDC) in related donor nonmyeloablative allogeneic hematopoietic stem cell transplantation (NMHSCT). Blood 2006; 108:854a (abstr # 3012).

21. Sobecks RM, Ball EJ, Askar M, et al. Influence of Killer Immunoglobulin-Like Receptor/HLA Ligand Matching on Achievement of T-cell Complete Donor Chimerism in Related Donor Nonmyeloablative Allogeneic Hematopoietic Stem Cell Transplantation. Bone Marrow Transplant 2008 Apr;41(8):709-14. Epub 2008 Jan 14.

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Table 1. Characteristics of subjects reported to CIBMTR receiving a RIC allogeneic HSCT for AML or MDS from 1999 to 2007 from an unrelated donor, stratified by recipient ligands

All Ligands

Present

C1 Ligand Missing

Only

C2 Ligand Missing

Only

Bw4 Ligand Missing

Only

Bw4 and (C1 or C2)

Ligands Missing

Variable N (%) N (%) N (%) N (%) N (%) P-valueNumber of Patients 339 107 199 92 189 Number of centers 75 46 65 43 59 Age, median (range), years

57 (20-73)

56 (20-74)

56 (20-72)

55 (20-69)

57 (20-74)

0.44

Age at transplant 20-29 17 ( 5) 7 ( 7) 7 ( 4) 7 ( 8) 8 ( 4) 0.50 30-39 19 ( 6) 5 ( 5) 16 ( 8) 9 (10) 18 (10) 40-49 53 (16) 21 (20) 31 (16) 10 (11) 35 (19) 50+ 250 (74) 74 (69) 145 (73) 66 (72) 128 (68) Donor age, median (range), years

34

(19-59)

35

(19-61)

36

(19-57)

35

(18-59)

33

(18-59)

0.29

Donor age 18-19 4 ( 1) 2 ( 2) 2 ( 1) 3 ( 3) 7 ( 4) 0.09 20-29 105 (31) 28 (26) 55 (28) 24 (26) 62 (33) 30-39 113 (33) 42 (39) 75 (38) 40 (43) 70 (37) 40-49 79 (23) 25 (23) 58 (29) 21 (23) 39 (21) 50+ 38 (11) 10 ( 9) 9 ( 5) 4 ( 4) 11 ( 6) Male Sex 192 (57) 61 (57) 116 (58) 49 (53) 108 (57) 0.96 Karnofsky score prior to transplant

< 90 108 (32) 35 (33) 67 (34) 27 (29) 70 (37) 0.47 >= 90 193 (57) 67 (63) 109 (55) 53 (58) 97 (51) Missing 38 (11) 5 ( 5) 23 (12) 12 (13) 22 (12) HLA matching

9/10 98 (29) 38 (36) 64 (32) 23 (25) 42 (22) 0.09 10/10 241 (71) 69 (64) 135 (68) 69 (75) 147 (78) Disease at transplant

AML 220 (65) 83 (78) 138 (69) 60 (65) 122 (65) 0.12 MDS 119 (35) 24 (22) 61 (31) 32 (35) 67 (35) Disease status at transplant Early 196 (58) 78 (73) 127 (64) 55 (60) 121 (64) 0.21 Intermediate 4 ( 1) 1 ( 1) 4 ( 2) 1 ( 1) 2 ( 1) Advanced 121 (36) 27 (25) 61 (31) 34 (37) 63 (33) Other MDS 18 ( 5) 1 ( 1) 7 ( 4) 2 ( 2) 3 ( 2)

116


Table 1. Continued.

All Ligands Present

C1 Ligand Missing

Only

C2 Ligand Missing

Only

Bw4 Ligand Missing

Only

Bw4 and (C1 or

C2) Ligands Missing

Variable N (%) N (%) N (%) N (%) N (%) P-valueNumber of Patients 339 107 199 92 189 Conditioning regimen TBI 200 cGy 3 ( 1) 0 3 ( 2) 1 ( 1) 4 ( 2) 0.53 Fludarabine + TBI 63 (19) 19 (18) 36 (18) 16 (17) 39 (21) Fludarabine + Cy 27 ( 8) 13 (12) 18 ( 9) 13 (14) 18 (10) Fludarabine + Melphalan 83 (24) 29 (27) 49 (25) 26 (28) 54 (29) Fludarabine + Busulfan + ATG 76 (22) 24 (22) 40 (20) 23 (25) 44 (23) Fludarabine + Busulfan (No ATG) 83 (24) 22 (21) 52 (26) 12 (13) 27 (14) Thiotepa + Cy + ATG 4 ( 1) 0 1 ( 1) 1 ( 1) 3 ( 2) ATG Use

No 220 (65) 68 (64) 138 (69) 52 (57) 126 (67) 0.30 Yes 119 (35) 39 (36) 61 (31) 40 (43) 63 (33) GvHD prophylaxis

CsA + MTX 29 ( 9) 10 ( 9) 18 ( 9) 8 ( 9) 15 ( 8) 0.39 CsA + MMF 70 (21) 22 (21) 43 (22) 18 (20) 37 (20) CsA ± other (No MTX and no MMF) 9 ( 3) 1 ( 1) 7 ( 4) 6 ( 7) 9 ( 5) FK506 + MTX 120 (35) 33 (31) 71 (36) 38 (41) 74 (39) FK506 + MMF 68 (20) 22 (21) 32 (16) 11 (12) 32 (17) FK506 ± other (No MTX/no MMF) 25 ( 7) 11 (10) 15 ( 8) 7 ( 8) 9 ( 5) MMF ± other (No CsA/no FK506) 2 ( 1) 0 2 ( 1) 1 ( 1) 3 ( 2) MTX ± other (No CsA/ no FK506) 0 1 ( 1) 0 0 0 T-cell depletion 9 ( 3) 2 ( 2) 1 ( 1) 3 ( 3) 6 ( 3) Other 7 ( 2) 5 ( 5) 10 ( 5) 0 4 ( 2) Stem cell source

Bone marrow 57 (17) 16 (15) 34 (17) 24 (26) 38 (20) 0.23 PBSC 282 (83) 91 (85) 165 (83) 68 (74) 151 (80) Donor/recipient sex match

Male -> Male 135 (40) 38 (36) 89 (45) 39 (42) 82 (43) 0.14 Male -> Female 84 (25) 20 (19) 47 (24) 25 (27) 58 (31) Female -> Male 57 (17) 23 (21) 27 (14) 10 (11) 26 (14) Female -> Female 63 (19) 26 (24) 36 (18) 18 (20) 23 (12) Donor/recipient CMV match Negative/Negative 91 (27) 26 (24) 51 (26) 30 (33) 47 (25) 0.49 Negative/Positive 121 (36) 33 (31) 80 (40) 32 (35) 80 (42) Positive/Negative 39 (12) 14 (13) 19 (10) 4 ( 4) 21 (11) Positive/Positive 80 (24) 31 (29) 47 (24) 24 (26) 35 (19) Unknown 8 ( 2) 3 ( 3) 2 ( 1) 2 ( 2) 6 ( 3)

117


Table 1. Continued.

All Ligands Present

C1 Ligand Missing

Only

C2 Ligand Missing

Only

Bw4 Ligand Missing

Only

Bw4 and (C1 or C2)

Ligands Missing

Variable N (%) N (%) N (%) N (%) N (%) P-valueNumber of Patients 339 107 199 92 189 Interval from Dx to Tx – AML (months)

7 (<1-69)

7 (1-61)

6 (<1-90)

9 (2-66)

7 (1-62)

0.05

Interval from Dx to Tx – MDS (months)

9 (1-270)

14 (3-236)

10 (<1-186)

10 (3-248)

10 (<1-110)

0.68

Year of transplant 1999 1 (<1) 2 ( 2) 1 ( 1) 1 ( 1) 3 ( 2) 0.11 2000 7 ( 2) 2 ( 2) 3 ( 2) 1 ( 1) 12 ( 6) 2001 15 ( 4) 7 ( 7) 15 ( 8) 6 ( 7) 13 ( 7) 2002 24 ( 7) 5 ( 5) 11 ( 6) 4 ( 4) 7 ( 4) 2003 34 (10) 6 ( 6) 18 ( 9) 11 (12) 19 (10) 2004 39 (12) 13 (12) 40 (20) 17 (18) 27 (14) 2005 75 (22) 18 (17) 36 (18) 21 (23) 39 (21) 2006 91 (27) 32 (30) 54 (27) 22 (24) 46 (24) 2007 53 (16) 22 (21) 21 (11) 9 (10) 23 (12)Median follow-up of survivors, months 54(6-109) 56(20-120) 62(5-106) 59(4-121) 61(5-133) 0.07b

b – Log-rank p-value.

118


Table 2. Characteristics of subjects reported to CIBMTR receiving a RIC allogeneic HSCT for AML or MDS from 1999 to 2007 from an unrelated donor, stratified by KIR2DS1 absence vs. presence in the donor by segregation of KIR2DS1+ donors by their C1 vs. C2C2 backgrounds.

Donor KIR2DS1 Negative

Donor KIR2DS1 Positive /

HLA C2/C2


HLA any C1 Variable N (%) N (%) N (%) P-valueNumber of Patients 601 42 283 Number of centers 91 27 70 Age, median (range), years

56 (20-74) 58 (22-68) 57 (20-74) 0.26Age at transplant 20-29 28 ( 5) 1 ( 2) 17 ( 6) 0.83 30-39 46 ( 8) 2 ( 5) 19 ( 7) 40-49 98 (16) 9 (21) 43 (15) 50+ 429 (71) 30 (71) 204 (72) Donor age, median (range), years

35 (18-61)

38 (19-60)

34 (19-59)

0.70

Donor age 18-19 10 ( 2) 2 ( 5) 6 ( 2) 0.72 20-29 176 (29) 9 (21) 89 (31) 30-39 221 (37) 17 (40) 102 (36) 40-49 151 (25) 10 (24) 61 (22) 50+ 43 ( 7) 4 (10) 25 ( 9) Male Sex 337 (56) 26 (62) 163 (58)

0.72

Karnofsky score prior to transplant

< 90 204 (34) 11 (26) 92 (33) 0.46 >= 90 331 (55) 29 (69) 159 (56) Missing 66 (11) 2 ( 5) 32 (11) HLA matching

9/10 169 (28) 15 (36) 81 (29) 0.57 10/10 432 (72) 27 (64) 202 (71) Disease at transplant

AML 399 (66) 33 (79) 191 (67) 0.27 MDS 202 (34) 9 (21) 92 (33) Disease status at transplant Early 369 (61) 29 (69) 179 (63) 0.70 Intermediate 9 ( 1) 0 3 ( 1) Advanced 204 (34) 13 (31) 89 (31) Other MDS 19 ( 3) 0 12 ( 4)

119


Table 2. Continued.

Variable


N (%)


HLA C2/C2 N (%)


HLA any C1 N (%) P-value

Number of Patients 601 42 283 Conditioning regimen TBI 200 cGy 9 ( 1) 0 2 ( 1) 0.86 Fludarabine + TBI 112 (19) 8 (19) 53 (19) Fludarabine + Cy 51 ( 8) 4 (10) 34 (12) Fludarabine + Melphalan 167 (28) 10 (24) 64 (23) Fludarabine + Busulfan + ATG 133 (22) 10 (24) 64 (23) Fludarabine + Busulfan (No ATG) 123 (20) 10 (24) 63 (22) Thiotepa + Cy + ATG 6 ( 1) 0 3 ( 1) ATG Use No 394 (66) 26 (62) 184 (65) 0.89 Yes 207 (34) 16 (38) 99 (35) GvHD prophylaxis CsA + MTX 57 ( 9) 3 ( 7) 20 ( 7) 0.87 CsA + MMF 112 (19) 13 (31) 65 (23) CsA ± other (No MTX and no MMF) 21 ( 3) 2 ( 5) 9 ( 3) FK506 + MTX 215 (36) 13 (31) 108 (38) FK506 + MMF 112 (19) 6 (14) 47 (17) FK506 ± other (No MTX and no MMF) 48 ( 8) 3 ( 7) 16 ( 6) MMF ± other (No CsA and no FK506) 6 ( 1) 0 2 ( 1) MTX ± other (No CsA and no FK506) 1 (<1) 0 0 T-cell depletion 11 ( 2) 1 ( 2) 9 ( 3) Other 18 ( 3) 1 ( 2) 7 ( 2) Stem cell source

Bone marrow 110 (18) 7 (17) 52 (18) 0.96 PBSC 491 (82) 35 (83) 231 (82) Donor/recipient sex match

Male -> Male 240 (40) 17 (40) 126 (45) 0.53 Male -> Female 154 (26) 7 (17) 73 (26) Female -> Male 97 (16) 9 (21) 37 (13) Female -> Female 110 (18) 9 (21) 47 (17) Donor/recipient CMV match Negative/Negative 166 (28) 9 (21) 70 (25) 0.22 Negative/Positive 228 (38) 10 (24) 108 (38) Positive/Negative 64 (11) 7 (17) 26 ( 9) Positive/Positive 133 (22) 14 (33) 70 (25) Unknown 10 ( 2) 2 ( 5) 9 ( 3)

120


Table 2.Continued.

Variable


N (%)


HLA C2/C2 N (%)

Donor KIR2DS1 Positive / HLA any

C1 N (%) P-value

Number of Patients 601 42 283 Interval from Dx to Tx – AML (months) 7 (<1-69) 6 (3-61) 7 (<1-90) 0.78 Interval from Dx to Tx – MDS (months) 10 (<1-269) 11 (3-23) 9 (2-270) 0.19 Year of transplant 1999 6 ( 1) 1 ( 2) 1 (<1) 0.24 2000 16 ( 3) 2 ( 5) 7 ( 2) 2001 36 ( 6) 2 ( 5) 18 ( 6) 2002 32 ( 5) 3 ( 7) 16 ( 6) 2003 54 ( 9) 2 ( 5) 32 (11) 2004 87 (14) 5 (12) 44 (16) 2005 137 (23) 4 (10) 48 (17) 2006 163 (27) 13 (31) 69 (24) 2007 70 (12) 10 (24) 48 (17) Median follow-up of survivors, months 59 (5-133) 71 (23-120) 59 (4-100) 0.43b


121


Table 3. Characteristics of subjects reported to CIBMTR receiving a RIC allogeneic HSCT for AML or MDS from 1999 to 2007 from an unrelated donor, stratified by centromeric AA, AB and BB.

cen AA cen AB cen BB Variable N (%) N (%) N (%) P-valueNumber of Patients 454 390 80 Number of centers 83 79 42 Age, median (range), years

56 (20-74) 57 (20-74) 55 (20-72) 0.60Age at transplant 20-29 18 ( 4) 20 ( 5) 8 (10) 0.37 30-39 34 ( 7) 27 ( 7) 6 ( 8) 40-49 78 (17) 58 (15) 14 (18) 50+ 324 (71) 285 (73) 52 (65) Donor age, median (range), years

34 (19-61)

35 (18-59)

35 (21-57)

0.76

Donor age 18-19 9 ( 2) 9 ( 2) 0 0.53 20-29 128 (28) 122 (31) 24 (30) 30-39 181 (40) 130 (33) 29 (36) 40-49 106 (23) 93 (24) 21 (26) 50+ 30 ( 7) 36 ( 9) 6 ( 8) Male Sex 251 (55) 228 (58) 46 (58)

0.64

Karnofsky score prior to transplant

< 90 158 (35) 125 (32) 23 (29) 0.20 >= 90 248 (55) 217 (56) 53 (66) Missing 48 (11) 48 (12) 4 ( 5) HLA matching

9/10 129 (28) 113 (29) 23 (29) 0.98 10/10 325 (72) 277 (71) 57 (71) Disease at transplant

AML 304 (67) 260 (67) 57 (71) 0.72 MDS 150 (33) 130 (33) 23 (29) Disease status at transplant Early 290 (64) 233 (60) 52 (65) 0.77 Intermediate 7 ( 2) 5 ( 1) 0 Advanced 142 (31) 139 (36) 25 (31) Other MDS 15 ( 3) 13 ( 3) 3 ( 4)

122


Table 3. Continued. Variable

cen AA N (%)

cen AB N (%)

cen BB N (%) P-value

Number of Patients 454 390 80 Conditioning regimen TBI 200 cGy 9 ( 2) 1 (<1) 1 ( 1) 0.10 Fludarabine + TBI 84 (19) 80 (21) 9 (11) Fludarabine + Cy 39 ( 9) 46 (12) 4 ( 5) Fludarabine + Melphalan 128 (28) 91 (23) 22 (28) Fludarabine + Busulfan + ATG 99 (22) 83 (21) 23 (29) Fludarabine + Busulfan (No ATG) 91 (20) 84 (22) 21 (26) Thiotepa + Cy + ATG 4 ( 1) 5 ( 1) 0 ATG Use No 297 (65) 256 (66) 51 (64) 0.95 Yes 157 (35) 134 (34) 29 (36) GvHD prophylaxis CsA + MTX 44 (10) 27 ( 7) 9 (11) 0.69 CsA + MMF 90 (20) 88 (23) 12 (15) CsA ± other (No MTX and no MMF) 16 ( 4) 13 ( 3) 3 ( 4) FK506 + MTX 158 (35) 148 (38) 28 (35) FK506 + MMF 77 (17) 70 (18) 18 (23) FK506 ± other (No MTX and no MMF) 39 ( 9) 21 ( 5) 7 ( 9) MMF ± other (No CsA and no FK506) 5 ( 1) 3 ( 1) 0 MTX ± other (No CsA and no FK506) 0 1 (<1) 0 T-cell depletion 12 ( 3) 9 ( 2) 0 Other 13 ( 3) 10 ( 3) 3 ( 4) Stem cell source

Bone marrow 83 (18) 74 (19) 12 (15) 0.70 PBSC 371 (82) 316 (81) 68 (85) Donor/recipient sex match

Male -> Male 179 (39) 164 (42) 39 (49) 0.48 Male -> Female 122 (27) 90 (23) 21 (26) Female -> Male 72 (16) 64 (16) 7 ( 9) Female -> Female 81 (18) 72 (18) 13 (16) Donor/recipient CMV match Negative/Negative 116 (26) 110 (28) 19 (24) 0.84 Negative/Positive 169 (37) 146 (37) 30 (38) Positive/Negative 52 (11) 36 ( 9) 9 (11) Positive/Positive 104 (23) 92 (24) 21 (26) Unknown 13 ( 3) 6 ( 2) 1 ( 1)

123


Table 3. Continued.

Variable cen AA N (%)

cen AB N (%)

cen BB N (%) P-value

Number of Patients 454 390 80 Interval from Dx to Tx – AML (months) 7 (1-90) 7 (<1-52) 5 (<1-69) 0.03 Interval from Dx to Tx – MDS (months) 10 (<1-269) 10 (<1-270) 7 (3-260) 0.14 Year of transplant 1999 4 ( 1) 4 ( 1) 0 0.45 2000 12 ( 3) 13 ( 3) 0 2001 22 ( 5) 25 ( 6) 8 (10) 2002 24 ( 5) 23 ( 6) 4 ( 5) 2003 50 (11) 30 ( 8) 8 (10) 2004 68 (15) 61 (16) 7 ( 9) 2005 98 (22) 77 (20) 14 (18) 2006 118 (26) 105 (27) 22 (28) 2007 58 (13) 52 (13) 17 (21) Median follow-up of survivors, months 59 (6-121) 59 (5-133) 48 (4-106) 0.16b


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