individualized systems medicine strategy to tailor ... · chemorefractory acute myeloid leukemia...
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RESEARCH ARTICLE
1416 | CANCER DISCOVERY�DECEMBER 2013 www.aacrjournals.org
Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia Tea Pemovska 1 , Mika Kontro 2 , Bhagwan Yadav 1 , Henrik Edgren 1 , Samuli Eldfors 1 , Agnieszka Szwajda 1 , Henrikki Almusa 1 , Maxim M. Bespalov 1 , Pekka Ellonen 1 , Erkki Elonen 2 , Bjørn T. Gjertsen 5 , 6 , Riikka Karjalainen 1 , Evgeny Kulesskiy 1 , Sonja Lagström 1 , Anna Lehto 1 , Maija Lepistö 1 , Tuija Lundán 3 , Muntasir Mamun Majumder 1 , Jesus M. Lopez Marti 1 , Pirkko Mattila 1 , Astrid Murumägi 1 , Satu Mustjoki 2 , Aino Palva 1 , Alun Parsons 1 , Tero Pirttinen 4 , Maria E. Rämet 4 , Minna Suvela 1 , Laura Turunen 1 , Imre Västrik 1 , Maija Wolf 1 , Jonathan Knowles 1 , Tero Aittokallio 1 , Caroline A. Heckman 1 , Kimmo Porkka 2 , Olli Kallioniemi 1 , and Krister Wennerberg 1
ABSTRACT We present an individualized systems medicine (ISM) approach to optimize cancer
drug therapies one patient at a time. ISM is based on (i) molecular profi ling and
ex vivo drug sensitivity and resistance testing (DSRT) of patients’ cancer cells to 187 oncology drugs,
(ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples
from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples
from patients with acute myeloid leukemia (AML) uncovered fi ve major taxonomic drug-response sub-
types based on DSRT profi les, some with distinct genomic features (e.g., MLL gene fusions in subgroup
IV and FLT3 -ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses.
After progression under DSRT-guided therapies, AML cells displayed signifi cant clonal evolution and
novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance
to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.
SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized
cancer therapies for individual patients as well as to understand and predict disease evolution and the
next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted
drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. Cancer Discov; 3(12);
1416–29. ©2013 AACR.
See related commentary by Hourigan and Karp, p. 1336.
RESEARCH ARTICLE
Authors’ Affi liations: 1 Institute for Molecular Medicine Finland, FIMM; 2 Hematology Research Unit Helsinki, Helsinki University Central Hospital, University of Helsinki, Helsinki; 3 Department of Clinical Chemistry and TYKSLAB, Turku University Central Hospital, University of Turku, Turku; 4 Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; 5 Department of Clinical Science, Hematology Section, University of Bergen; and 6 Department of Internal Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway
Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).
T. Pemovska and M. Kontro shared fi rst authorship of this article.
T. Aittokallio, C.A. Heckman, K. Porkka, O. Kallioniemi, and K. Wennerberg shared senior authorship of this article.
Current address for M.M. Bespalov: Stem cells and Neurogenesis Unit, Division of Neuroscience, San Raffaele Scientifi c Institute, Milan, Italy.
Corresponding Author: Krister Wennerberg, Institute for Molecular Medi-cine Finland, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland. Phone: 358-9-191-25764; Fax: 358-9-191-25737; E-mail: krister.wennerberg@fi mm.fi
doi: 10.1158/2159-8290.CD-13-0350
©2013 American Association for Cancer Research.
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DECEMBER 2013�CANCER DISCOVERY | 1417
INTRODUCTION
Adult acute myeloid leukemia (AML) is a prototype exam-
ple of the challenges of modern cancer drug discovery, devel-
opment, and patient therapy. With the exception of the
retinoic acid–sensitive acute promyelocytic leukemia subtype,
molecularly targeted therapeutic approaches for AML are yet
to be translated into clinical advances. The disease has tra-
ditionally been subdivided into different subtypes (M0–M7)
based on cellular lineage and biomarkers ( 1 ). Current World
Health Organization classifi cation refl ects the fact that a grow-
ing number of AML cases can be categorized on the basis of
their underlying genetic abnormalities that defi ne distinct clin-
icopathologic entities ( 2 ). Genomic changes in AML are now
relatively well understood, with each AML sample containing
roughly 400 genomic variants, of which an average of 13 reside
in coding regions ( 3, 4 ). Recurrent changes have highlighted
potential driver genes, including NPM1 , CEBPA , DNMT3A ,
TET2 , RUNX1 , ASXL1 , IDH2 , and MLL , with mutations in FLT3 ,
IDH1 , KIT , and RAS genes modifying the disease phenotype ( 5 ).
Although several of the recurrent genetic alterations link to trac-
table drug targets, genetic testing of patients with AML has yet
to result in effective personalized or stratifi ed therapies.
Patients with AML have a poor outcome, with a 5-year
survival of 30% to 40% ( 6, 7 ). The standard therapy for most
adult patients with AML is conventional chemotherapy con-
sisting of the nucleoside analog cytarabine combined with
a topoisomerase II inhibitor ( 8, 9 ). A number of second-line
treatment options have been applied in patients with AML after
relapse, but the response rates have remained low. Furthermore,
patients with AML at relapse exhibit an increased number of
genetic alterations, which can be attributed to disease progres-
sion and/or DNA-damaging agents used for routine chemo-
therapy ( 10 ). In light of the genomic and molecular diversity of
AML, and its continuous evolution in response to chemother-
apy, it is important to better understand the potential utility of
all targeted cancer drugs that are already available in the clinic.
These drugs could be systematically repurposed as off-label
indications to responding subgroups of AML. Furthermore,
comparative information on the effi cacy of the hundreds of
emerging targeted anticancer agents, as well as their potential
combinations, in patient-derived ex vivo samples could dramati-
cally help prioritize clinical development of such agents.
To facilitate testing of already clinically available drugs as
well as emerging targeted inhibitors for patients with AML,
we undertook a comprehensive functional strategy to directly
determine the drug dependency of cancer cells based on ex vivo
drug sensitivity and resistance testing (DSRT). First, we applied
a systematic large panel of drugs covering both cancer chemo-
therapeutics and many clinically available and emerging molec-
ularly targeted drugs. Second, we developed a new way to score
for differential drug response in AML cells as compared with
the effi cacy of these drugs in normal bone marrow cells. Third,
we verifi ed DSRT predictions in vivo by treating patients with
AML with off-label drugs. Fourth, we assessed the molecular
mechanisms underlying development of cancer progression
and drug resistance by repeat sampling from relapsed patients,
followed by genomic and transcriptomic profi ling as well as
a new DSRT analysis to understand both coresistance as well
as new vulnerabilities. Taken together, we term this approach
individualized systems medicine (ISM; Fig. 1 ).
Here, we demonstrate that the ISM strategy made it possible to
(i) create a taxonomy of comprehensive drug responses in AML,
(ii) identify clinically actionable AML-selective targeted drugs,
(iii) clinically apply such therapies for individual chemorefrac-
tory AML patients predicted to be sensitive to targeted drugs,
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Pemovska et al.RESEARCH ARTICLE
and (iv) follow individually optimized therapies in patients by
analysis of the clonal evolution of leukemic cells and molecular
profi ling to understand mechanisms of drug resistance.
RESULTS ISM Strategy for Personalized AML Therapy
To uncover the mechanisms of drug response and resistance
as well as to monitor therapy response at the level of individual
AML subclones, we combined DSRT and deep molecular profi l-
ing data. DSRT was implemented to AML blast cells ex vivo using
a comprehensive set of 187 drugs, consisting of conventional
chemotherapeutics and a broad range of targeted oncology
compounds ( Table 1 and Supplementary Table S1). Each drug
was tested over a 10,000-fold concentration range, allowing for
the establishment of accurate dose–response curves for each
drug in each patient and control sample with identifi cation of
half-maximal effective concentrations and maximal responses
(Supplementary Table S2). Although these responses refl ect the
drug–sample interaction, the detailed interpretation of curve
parameters is diffi cult. To overcome this issue, we found that the
most informative way to assess quantitative drug sensitivities
was to convert the information to a Drug Sensitivity Score (DSS),
a metric used to determine the area under the dose–response
curves. Importantly, we developed a new scoring system for
assessing leukemia-selective effects by comparing the DSS in the
AML cells with those of healthy donors (selective DSS: sDSS).
Analysis of the drug-response data revealed that DSRT is highly
reproducible ( r = 0.98; Supplementary Fig. S1) and that all con-
trol samples exhibited similar response profi les (Supplementary
Fig. S1). The analysis of DSRT results was completed in 4 days,
rapid enough for clinical implementation. Indeed, we carried
out a pilot test for the implementation of optimized therapies
for 8 patients with chemorefractory AML. Patient treatment
outcome was assessed by clinical criteria, but also by genomic
profi ling to understand the clonal architecture underlying drug
response and emerging resistance. Data on recurrent, paired
samples identifi ed drugs that showed effi cacy after the develop-
ment of drug resistance and highlighted drugs that could be
applied in combination. This approach provided the framework
for a real-time continuous cycle of learning and optimization of
therapies, one patient at a time, thereby creating an individual-
ized systems medicine process for improving cancer care.
DSRT Identifi ed Signal Transduction Inhibitors as Putative AML-Selective Drugs
Ex vivo DSRT was performed on 28 samples obtained from
18 patients with AML (Supplementary Table S3). Eighteen sam-
ples were collected at relapse and 10 at diagnosis, mainly from
patients with adverse or intermediate cytogenetic risk (according
to European LeukemiaNet; ref. 9 ). Out of the 187 drugs tested,
sDSS indicated the most selective ex vivo effective drugs for each
individual patient, with a focus on leukemia-specifi c effi cacies by
comparing responses with normal bone marrow mononuclear
cells. The results were expressed as a patient-specifi c water-
fall plot ( Fig. 2A ). Several targeted drugs exhibited a selective
response in a subset of the AML samples, with only a minimal
response in the control samples ( Fig. 2B and C and Supplemen-
tary Fig. S2), suggesting that these AML samples were addicted
to the signaling pathways inhibited by the drugs. In contrast,
the average sensitivity to conventional chemotherapeutics did
not signifi cantly differ between the patient samples and controls
( Fig. 2B and D ). This refl ects the known limited therapeutic
window for these drugs and the diffi culty in predicting their
Figure 1. Functional ISM platform for improved AML therapy. The platform involves (i) comprehensive direct DSRT of 187 approved and investigational oncology compounds in ex vivo primary cells from serial AML samples; (ii) clinical implementation of testing results in individual patients with relapsed and refractory disease; (iii) deep molecular and genomic profi ling of the patients with AML from consecutive samples before and after relapse and drug resistance for monitoring disease progression and clonal evolution; and (iv) integrating drug sensitivity, next-generation sequencing, and clinical follow-up data to understand the biology of disease, drug sensitivity, and resistance that can lead to rapid introduction of novel therapies to the clinic. DSS, Drug Sensitivity Score.
DSRT
Molecular profiling
Effective drugs
Resistant drugs
–9 –8 –7 –6 –5
0
20
40
60
80
100
Log conc (mol/L)
% S
urv
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Se
lective
DS
S
Genome Transcriptome Signalome
Individualized drug treatment selection
Result
Understanding
biology of disease
Understanding
drug sensitivity
and resistance
Rapid introduction
of therapies
Diagnosis
Relapse 1
Relapse 2
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DECEMBER 2013�CANCER DISCOVERY | 1419
ISM Approach to Therapy Selection RESEARCH ARTICLE
clinical effi cacy based on ex vivo testing. Interestingly, cytarabine,
an established and effective AML drug, showed higher selective
effi cacy against AML cells than other cytotoxic agents ( Fig. 2B
and Supplementary Fig. S2), suggesting the feasibility of com-
bining cytarabine with promising molecularly targeted drugs in
the future clinical testing of relapsed or primary AML.
Signifi cant anticancer-selective effects were observed for the
tyrosine kinase inhibitors (TKI) dasatinib in 10 of the AML
patient samples (36%) and sunitinib in 10 (36%), MAP–ERK
kinase (MEK) inhibitors such as trametinib in 10 (36%), rapalogs
such as temsirolimus in 9 (32%), foretinib in 9 (32%), ponatinib in
7 (25%), ruxolitinib in 7 (25%), dactolisib in 7 (25%), MK-2206 in
6 (21%), sorafenib in 6 (21%), and quizartinib in 5 (18%; Fig. 2E ).
Thus, we identifi ed several highly ( ex vivo ) AML-selective drugs
that are not currently approved for AML but that are approved
for other cancer indications, and would therefore be available
in the clinic. Furthermore, a number of effective investiga-
tional drug classes were seen, such as AKT inhibitors and ATP-
competitive mTOR inhibitors, which could be prioritized for
future clinical studies of chemorefractory AML.
Table 1. Drug classes and drugs represented in the DSRT screening platform
Classes of drugs Drugs
Alkylating agents Altretamine, azacitidine, busulfan, carboplatin, carmustine, chlorambucil, cyclophosphamide, dacarbazine,
ifosfamide, lomustine, pipobroman, procarbazine, streptozocin, temozolomide, thioTEPA, uracil mustard
Antimetabolites Allopurinol, capecitabine, cladribine, clofarabine, cytarabine, decitabine, fl oxuridine, fl udarabine,
fl uorouracil, gemcitabine, mercaptopurine, methotrexate, nelarabine, pentostatin, thioguanine
Antimitotics ABT-751, docetaxel, indibulin, ixabepilone, paclitaxel, patupilone, S-trityl-L-cysteine, vinblastine,
vincristine, vinorelbine
Antitumor antibiotics Bleomycin, dactinomycin, mitomycin C, plicamycin
BCL-2 inhibitors Navitoclax, obatoclax
HDAC inhibitors Belinostat, CUDC-101, entinostat, panobinostat, tacedinaline, vorinostat
Hormone inhibitors Abiraterone, aminoglutethimide, anastrozole, exemestane, fi nasteride, fl utamide, fulvestrant, goserelin,
letrozole, megestrol acetate, nilutamide, raloxifene, tamoxifen
HSP90 inhibitors Alvespimycin, BIIB021, NVP-AUY922, tanespimycin
Immunomodulators Celecoxib, dexamethasone, fi ngolimod, imiquimod, lenalidomide, levamisole, methylprednisolone,
plerixafor, prednisolone, prednisone, tacrolimus, thalidomide
Kinase inhibitors, AGC Alisertib, AT9283, AZD1152-HQPA, BI2536, bryostatin 1, danusertib, enzastaurin, fasudil, midostaurin,
MK-2206, ruboxistaurin, sotrastaurin, UCN-01
Kinase inhibitors, CAMK AZD7762, PF-00477736
Kinase inhibitors, CK1 MK-1775
Kinase inhibitors, CMGC Alvocidib, AZ 3146, doramapimod, palbociclib, seliciclib, SNS-032
Kinase inhibitors, PIKL AZD8055, dactolisib, idelalisib, OSI-027, PF-04691502, pictilisib, XL147, XL765
Kinase inhibitors, STE Pimasertib, refametinib, selumetinib, trametinib
Kinase inhibitors, TK Afatinib, axitinib, BMS-754807, canertinib, cediranib, crizotinib, dasatinib, dovitinib, EMD1214063,
erlotinib, foretinib, gandotinib, gefi tinib, imatinib, lapatinib, lestaurtinib, linsitinib, masitinib, MGCD-265,
motesanib, nilotinib, pazopanib, ponatinib, quizartinib, regorafenib, ruxolitinib, saracatinib, sorafenib,
sunitinib, tandutinib, tivozanib, tofacitinib, vandetanib, vatalanib
Kinase inhibitors, TKL Vemurafenib
PARP inhibitors Iniparib, olaparib, rucaparib, veliparib
Proteasome inhibitors Bortezomib, carfi lzomib
Rapalogs Everolimus, sirolimus, temsirolimus
Smoothened (Hh) inhibitors Erismodegib, vismodegib
Topoisomerase I/II inhibitors Amonafi de, camptothecin, daunorubicin, doxorubicin, etoposide, idarubicin, irinotecan, mitoxantrone,
teniposide, topotecan, valrubicin
Miscellaneous antineoplastics Bexarotene, hydroxyurea, mitotane, tretinoin
Other 2-Methoxyestradiol, anagrelide, bimatoprost, pilocarpine, Prima-1 Met, serdemetan, tarenfl urbil,
tipifarnib, XAV-939, YM155
Abbreviations : AGC: PKA, PKG, and PKC kinase group; CAMK: calcium/calmodulin–dependent protein kinase group; CK1: casein kinase group; CMGC: CDK, MAPK, GSK3, and CLK kinase group; PIKL: phosphoinositide 3-kinase (PI3K)-like (PI3K inhibitors and inhibitors of related atypical kinases: mTOR, DNA-PK, ATM, ATR); STE: sterile kinase group; TK: tyrosine kinase group; TKL: tyrosine kinase-like group.
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Pemovska et al.RESEARCH ARTICLE
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Taxonomy of AML Based on the Comprehensive Drug-Response Profi les
Overall drug-response patterns of the patient samples
were visualized with unsupervised hierarchical clustering.
Although each individual sample showed a unique leukemia-
selective drug-response profi le, the overall drug-response
profi les segregated the AML patient samples into fi ve robust
functional subgroups ( Fig. 3 ; Supplementary Figs. S3 and
S4). Thus, despite the underlying genomic and phenotypic
variability in AML, similar drug sensitivity patterns were
observed among the AML patient samples for certain drug
classes. Compared with controls, all fi ve groups showed
increased sensitivity to navitoclax, a BCL-2/BCL-XL inhibitor,
HSP90 inhibitors, and histone deacetylase (HDAC) inhibi-
tors. Group I exhibited a strong selective response to navito-
clax and lack of sensitivity to the remainder of the tested
compounds. Group II AMLs were largely nonresponsive to
receptor TKIs, but instead showed a potential infl ammatory
signal-driven phenotype as seen by selective responses to a
group of immunosuppressive drugs (e.g., dexamethasone or
prednisolone), Janus-activated kinase (JAK) family kinase
inhibitors, and MEK inhibitors. Group III, IV, and V AMLs
were selectively sensitive to a broad range of TKIs, indicating
that they were driven or addicted to receptor tyrosine kinase
signaling pathways. Group III AMLs displayed a similar
sensitivity pattern to HSP90, HDAC, and phosphoinositide
3-kinase (PI3K)/mTOR inhibitors as group IV and V, albeit
with lower selectivity. Group IV AMLs were especially sensi-
tive to MEK and PI3K/mTOR inhibitors, whereas group V
AMLs showed selective responses to receptor TKIs target-
ing ABL, VEGF receptor (VEGFR), platelet derived growth
factor receptor (PDGFR), FLT3, KIT, PI3K/mTOR as well
as topoisomerase II inhibitors ( Fig. 3 ). Overall, 19 of
28 samples were sensitive to tyrosine kinase inhibition,
correlating well with the fi ndings in the recent study by
Tyner and colleagues ( 11 ).
Figure 2. Targeted compounds exhibit cancer-selective ex vivo drug responses in AML. A, waterfall plot that highlights the most potent cancer-selective drugs for each individual patient as well as drugs that are most likely to exhibit resistance/no sensitivity. B, comparison of cancer selectivity of ex vivo drug responses (DSS) of four clinically approved drugs, conventional chemotherapeutic agents (left), and four signal transduction inhibitors (right). The DSSs are compared between healthy bone marrow samples (controls; n = 7) and AML patient samples ( n = 28) with the range and median DSS value depicted. C and D, the distribution of the sensitivity to trametinib (MEK inhibitor) and idarubicin (topoisomerase II inhibitor) in 28 AML patient and 7 control samples expressed as Z -score (SD from the average control DSS drug response). E, percentages of AML patient samples selectively respond-ing ex vivo to selected signal transduction inhibitors as assessed by sDSS, represented as a bar graph.
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DECEMBER 2013�CANCER DISCOVERY | 1421
ISM Approach to Therapy Selection RESEARCH ARTICLE
Taxonomy of Cancer Drugs Based on the Comprehensive Drug-Response Profi les in AML
The hierarchical clustering also stratifi ed the drugs based
on the variability of responses among the patients with
AML (Supplementary Fig. S3). In this unsupervised analysis,
drugs with similar modes of action clustered together, such
as the PI3K/mTOR inhibitors, MEK inhibitors, HSP90 and
HDAC inhibitors, VEGFR-type TKIs, PDGFR inhibitors, and
ABL-like kinase inhibitors, antimitotics, and topoisomerase
II inhibitors. Thus, the unsupervised clustering of drugs
into subgroups defi ned by their intended targets strongly
supports the consistency and reproducibility of the DSRT
analysis as well as its ability to acquire biologically and medi-
cally relevant information. However, there were also notable
deviations from the expected patterns. Importantly, the FLT3
inhibitor quizartinib clustered with the topoisomerase II
inhibitors but not with other TKIs. Furthermore, the recently
approved BCR–ABL1 and FLT3 inhibitor ponatinib clustered
with cytarabine, HSP90, and HDAC inhibitors and not with
other TKIs. These unexpected links may represent underlying
key molecular mechanisms of these drugs in the AML con-
text, including unexpected off-target effects. Furthermore,
these drug-clustering patterns from human AML patient
specimens ex vivo may in the future be critically important in
designing novel therapeutic combination strategies for clini-
cal trials in AML. Therefore, this provides new combinatorial
possibilities that could not have easily been discovered with-
out the unbiased DSRT data.
Genomic and Molecular Findings Underlying the Drug-Response Profi les
To test whether the molecular profi les of the patient sam-
ples correlated with the overall drug responses, we compared
the distribution of signifi cant AML mutations and recurrent
gene fusions ( 4 ) with the DSRT-driven clustering ( Fig. 3 ).
FLT3 -mutated samples appeared in several different functional
groups, but all patient samples in group V, the most tyrosine
kinase–dependent group, carried these mutations. Hence, the
group V drug-response pattern is a strong indicator of driver
FLT3 mutations, and, as expected, an FLT3 mutation is an
indicator that the cells are likely to respond to TKI treatment.
Several FLT3 inhibitors, such as quizartinib, sunitinib, and
foretinib, were among the most selective drugs for group V.
Importantly, TKIs without FLT3 inhibitory activity, such as
dasatinib, were highly effective in this group ( P = 0.03), suggest-
ing that FLT3-driven AMLs are also dependent on other tyrosine
kinase signals. Furthermore, mutations in RAS genes correlated
signifi cantly with ex vivo sensitivity to MEK inhibitors ( P =
0.001). Samples from two patients with MLL fusions clustered
together in group IV, possibly linking MLL fusions with sensitiv-
ity to MEK inhibitor sensitivity. In addition, an enrichment of
TP53 mutations in groups I and II was observed (3 of 4 cases),
and all patient samples in taxonomic group II were associated
Figure 3. Functional taxonomy of AML based on comprehensive drug response and mutation profi les. A dendrogram of the DSRT responses shows clustering of the AML patient samples in fi ve functional groups (group I, II, III, IV, and V). Samples were clustered with the complete linkage method using Euclidean distance meas-ures. This approach provides a data-driven way to classify samples based on drug effi cacies and drugs based on their differential bioac-tivity across patient samples. Sensitivity or nonsensitivity to either navitoclax, ruxolitinib, MEK inhibitors, dasatinib, quizartinib, sunitinib, PI3K/mTOR inhibitors, and topoisomerase II inhibitors drives the sample groupings. The molecular profi les (signifi cant AML mutations and recurrent gene fusions), disease stage, and adverse karyotype status of the patient sam-ples are also shown to illustrate the correlation between functional drug sensitivity and somatic mutation and cytogenetics data. D, diagnosis; D*, secondary AML diagnosis; R, relapsed and/or refractory.
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M1M2FAB subtypes M2 M2M2M2 M5M5 M5M5 M5M5M5M5M5M5M5 M1M1
KIT
DasatinibSunitinib
IDH1/IDH2
Adverse karyotype
D D* R R D* D R D* R R D R R R D R R R R R R R R R D RDisease stage
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Pemovska et al.RESEARCH ARTICLE
with adverse karyotypes. Beyond these examples, the majority
of the clustering of drug sensitivities could not be attributed to
obvious alterations in the AML tumor genomes.
DSRT Was Predictive of Clinical Responses and Recapitulates Acquisition of Resistance In Vivo
The results of DSRT were considered to be therapeutically
actionable if (i) a distinct leukemia-selective response pattern
was seen, (ii) drugs showing sensitivity were available for com-
passionate or off-label use without signifi cantly delaying the
treatment, and (iii) no standard therapy was available. Accord-
ing to European LeukemiaNet ( 9 ) response criteria, 3 of 8
evaluable patients had a response to DSRT-guided therapy
[Supplementary Table S4; patient 600 (dasatinib–sunitinib–
temsirolimus): complete remission with incomplete platelet
recovery (CRi); patients 718 (sorafenib–clofarabine) and 800
(dasatinib–clofarabine–vinblastine): morphologic leukemia-
free state]. Four other patients had responses that did not
meet the European LeukemiaNet criteria. Patient 560 showed
a rapid clearance of blasts in peripheral blood after 5 days
of treatment (dasatinib–sunitinib), after which therapy was
discontinued because of gastrointestinal toxicity. Patient 252
(AML with three prior relapses) had an 8-week progression-free
period during dasatinib monotherapy (bone marrow blasts
65%–40%–70%). Patient 784 achieved a transient response with
dasatinib–sunitinib–temsirolimus therapy: bone marrow blasts
decreased from 70% to 35%, but the treatment response was lost
due to selection of a resistant clone. Patient 1145 had hemato-
logic improvement during ruxolitinib–dexamethasone therapy.
Even patients with partial or transitional clinical responses had
a profound effect on the clonal composition of the tumors,
including selection of potential drug resistance–associated
mutations. Therefore, detailed genomic analysis of such cases is
important to measure the impact of therapy and to understand
the potential mechanisms of response and resistance.
Here, we present in detail two clinical examples on the
implementation of DSRT results in patients with AML,
including the consecutive sampling and serial monitoring of
drug-sensitivity profi les and clonal evolution in vivo . In the
fi rst case, the bone marrow cells from a relapsed and refractory
54-year-old patient (sample 600_2) with a normal-karyotype
AML FAB M5 were subjected to DSRT and deep molecular
profi ling. The patient had previously failed three consecutive
induction therapies ( Fig. 4A ). The DSRT results highlighted
dasatinib, sunitinib, and temsirolimus among the top fi ve
most selective approved drugs. In an off-label compassion-
ate use setting, the patient received a combination of these
targeted drugs, resulting in rapid reduction of the bone
marrow blast count and marked improvement in the poor
performance status. Concomitantly, the blood counts rapidly
normalized resulting in CRi. However, 30 days after achieving
the CRi response, resistance emerged. A new DSRT analysis
from the relapsed sample (600_3) showed that the drugs used
in patient treatment exhibited remarkably reduced anticancer
activity as compared with the pretreatment sample ( Fig. 4B ),
demonstrating a match between ex vivo and in vivo responses.
In this patient, the ex vivo responses to many other drugs were
also strongly reduced in the relapsed sample ( Fig. 4C ).
A fusion transcript joining NUP98 exon 12 and NSD1 exon
6 resulting from a cryptic chromosome translocation t(5;11)
(q35;p15.5) was detected by RNA sequencing in sample 600_2.
This oncogenic fusion ( 12 ) is relatively common in cytogeneti-
cally normal pediatric AML ( 13, 14 ), but relatively rare in adult
AML ( 15 ). The NUP98 – NSD1 fusion was also detected in the
diagnostic sample (600_0) and all follow-up samples, suggest-
ing that this fusion was the initiating event in the development
of the patient’s disease. Exome sequencing revealed a diverse
subclonal architecture highlighted by two FLT3 -ITD (Supple-
mentary Fig. S5A and S5B) and four different WT1 mutations
(Supplementary Fig. S6A and S6B; Supplementary Table S5;
and Supplementary Methods). After induction chemotherapy,
the predominant FLT3 -ITD harboring subclone was no longer
detectable. Instead, subclones containing WT1 mutations and
a second FLT3 -ITD emerged ( Fig. 4D and E ). The sensitivity
to quizartinib, sunitinib, and other FLT3 inhibitors indicated
FLT3 as a disease driver in the 600_2 sample. In the DSRT-
relapsed 600_3 sample, the dasatinib, sunitinib, and tem-
sirolimus therapy had further selected a specifi c subclone still
containing the second FLT3 -ITD even though the response
to FLT3 inhibitors and other TKIs was lost. The broad loss
in drug response in the 600_3 sample was also accompanied
with decreased phosphorylation of AKT (S473), CHK2 (T68),
CREB (S133), ERK1/2 (T202/Y204, T185/Y187), FAK (Y397),
p38α (T180/Y182), and STAT1 (Y701; data not shown).
DSRT and Molecular Profi ling Defi ned Key Oncogenic Signals and Mechanisms of Drug Resistance
A second clinical case was a 37-year-old patient (784_1) who
was diagnosed with a recurrent t(11;19)(q23;p13.1) transloca-
tion and corresponding MLL – ELL fusion gene. The patient had
relapsed from three previous rounds of conventional therapy.
Initial DSRT results (784_1) showed selective responses to
MEK inhibitors, rapalogs, and several TKIs, including dasat-
inib ( Fig. 5A ). This patient was also treated with dasatinib,
sunitinib, and temsirolimus, which led to a rapid decrease in
both peripheral leukocytosis and bone marrow blast counts,
but the effect was short-lived. The ex vivo drug sensitivity of
the resistant sample (784_2) revealed that the cells had lost
their response to dasatinib and rapalogs, but preserved their
response to ATP-competitive mTOR inhibitors (such as dac-
tolisib and AZD8055). Interestingly, the resistant sample gained
sensitivity to a TKI that was previously ineffective in the DSRT,
BMS-754807 [insulin-like growth factor-I receptor (IGF-IR)/
Trk inhibitor], as well as crizotinib (TKI) and tipifarnib (farnesyl-
transferase inhibitor), several topoisomerase II inhibitors, and
immunomodulatory and differentiating compounds ( Fig. 5B ).
Using the DSRT data on kinase inhibitors with published
comprehensive biochemical profi ling data ( 16 ), we identifi ed
putative kinases to which the cells were addicted. Impor-
tantly, a comparison between the fi rst and the relapsed sam-
ple identifi ed a major switch in kinase addiction with a loss
of addiction to Src family kinases, PI3K and p38 mitogen-
activated protein kinases (MAPK), and a gain of addiction to
ALK and Trk family receptor tyrosine kinases ( Fig. 5C ).
The resistance to the dasatinib, sunitinib, and temsirolimus
treatment in this patient was not associated with any novel
detected mutations or other genetic alterations, but coincided
with more than 1,000-fold enrichment of two fusion tran-
scripts, ETV6–NTRK3 and STRN-ALK (Supplementary Fig. S7),
suggesting that resistance emerged from the selection of
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ISM Approach to Therapy Selection RESEARCH ARTICLE
Figure 4. Clinical implementation of DSRT predictions and clonal evolution analysis in a heavily refractory AML patient. A, clinical follow-up of patient 600 from diagnosis through relapse, depicting percentage of bone marrow blasts and number of neutrophils. B, initial DSRT results from relapsed and refractory patient 600 showed that the patient cells ex vivo exhibited sensitivity to several kinase inhibitors (including dasatinib and sunitinib) and rapa-logs, and as a result, the patient was treated with a combination of dasatinib, sunitinib, and temsirolimus. The patient achieved complete remission under this drug regimen, but relapsed 5 weeks later. Bar graphs show the before and after relapse sDSS for sensitivity to dasatinib, sunitinib, and temsirolimus. C, comparison of DSRT responses of the initial (600_2) and relapsed sample (600_3), revealing the loss of sensitivity to the majority of tested com-pounds. Drugs for which the DSS decreased greater than 10 from 600_2 to 600_3 are marked in blue, other drugs with an sDSS greater than 10 in 600_2 are marked in pale blue, and drugs with an sDSS greater than 10 in both 600_2 and 600_3 are marked in green. D, clonal progression of the disease in the patient from diagnosis to relapse; further information is included in the Supplementary Data and Supplementary Table S5. E, summary heatmap illustrat-ing the key putative oncogenic genetic alterations in the clones depicted in D.
A
C D
0 50 100 150 180 200 2200
20
40
60
80
100
0.0
2.5
5.0
Chemotherapy Das–Sun–Tem
D600_0 600_2 600_3
%
10E
9
Chemotherapy Das–Sun–Tem Das–Tem
Bone marrow blasts (%)
Neutrophils (x10E9)
Temsirolimus
sD
SS
NUP98–NSD1
600_0 600_1 600_3600_2
74 %
6%
20%
33%
37%
15%
15%
90%
Clone1FLT3-ITD #1
Clone 3WT1 #2
FLT3-ITD #2WT1 #1Clone 2
WT1 #4Clone 5
WT1 #3Clone 4
<10%
<10%
B Dasatinib
sD
SS
600_2 600_30
5
10
15Sunitinib
sD
SS
600_2 600_30
5
10
15
20
600_2 600_3–5
0
5
10
15
0 5 10 15 200
5
10
15
20
Plicamycin
Cytarabine
Mitoxantrone
Navitoclax
Alvespimycin
BIIB021
Ponatinib
PF-04691502
Tivozanib
AZD7762
NVP-AUY922
Lestaurtinib
DasatinibAxitinib
Temsirolimus
Entinostat
Sorafenib
Sirolimus
Etoposide Sunitinib
Quizartinib
600_2 sDSS
600_3 s
DS
S
Tanespimycin
E
NU
P98–
NSD
1 fu
ssio
n
IST1
(MP2
43-)
FLT
3-IT
D #
1FL
T3-IT
D #
2W
T1(-3
79?)
(#1)
H2A
FZ(V
110G
)
SDR
42E1
(R30
3Q)
WT1
(-376
R?)
(#2)
WT1
(-370
?) (#
3)
WT1
(-380
?) (#
4)
Clone 1
Clone 2
Clone 3
Clone 4
Clone 5
preexisting small subclones. The ETV6–NTRK3 encodes for
the oncogenic fusion protein TEL–TrkC, whereas the STRN-
ALK fusion was out of frame and therefore did not result
in a functional fusion protein ( Fig. 6A and Supplementary
Fig. S8). These genetic events were accompanied by increases
in p70S6 kinase (T389) and CREB (S133) phosphorylation
( Fig. 6B ), suggesting that mTORC1 was hyperactivated, a
known mechanism of resistance toward rapalogs ( 17 ). The
fusion gene MLL – ELL was detected from the diagnostic and
subsequent samples, suggesting that this was an initiating
driver event in this leukemia. Similar to patient 600, patient
784 initially also had FLT3 -ITD mutations (Supplementary
Fig. S9A and S9B) that were lost after induction chemo-
therapy, and a mutation to WT1 augmented by LOH that
persisted throughout the course of the disease ( Fig. 6C and
D and Supplementary Table S6). The gained sensitivity to
the dual IGF-IR/TrkC inhibitor BMS-754807 fi ts with the
model of the TEL–TrkC fusion protein as a new driver,
because the oncogenic potential of this fusion has been shown
to be dependent on the activity of IGF-IR ( 18, 19 ) and lead
to hyperactivation of mTORC1 ( 20, 21 ). Thus, we predict
that in this patient, the mechanism of resistance involved
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Pemovska et al.RESEARCH ARTICLE
Figure 5. Functional DSRT and kinase inhibitor sensitivity defi ned key oncologic signals and mechanisms of resistance. A, initial DSRT results from refractory patient 784, highlighting selective sensitivity to MEK inhibitors, rapalogs, and several TKIs. On the basis of these results, the patient was treated with dasatinib, sunitinib, and temsirolimus (marked with asterisks). B, correlation of the DSRT results of the initial (784_1) and resistant (784_2) sample, illustrating that the relapsed cells had lost sensitivity to dasatinib and rapalogs but retained sensitivity to ATP-competitive mTOR inhibitors and gained sensitivity to BMS-754807, crizotinib, danusertib, and tipifarnib. Drugs for which the DSS decreased greater than 10 from 784_1 to 784_2 are marked in blue, other drugs with an sDSS greater than 10 in 784_1 are marked in pale blue, drugs for which the DSS increased greater than 10 from 784_1 to 784_2 are marked in red, other drugs with an sDSS greater than 10 in 784_2 are marked in pink, and drugs with an sDSS greater than 10 in both 600_2 and 600_3 are marked in green. C, kinase inhibitor sDSS responses matched with target profi les described by Davis and colleagues ( 16 ) yield putative kinase addiction subnetworks in the two patient samples.
A B
C
Top selective drugs in the 784_1 sample
0 5 10 15 20
CUDC-101 - HDACiPictilisib - PI3Ki
Idelalisib - PI3KiDactolisib - mTOR/PI3Ki
Doramapimod - p38iTanespimycin - HSP90i
Tivozanib - TKI (VEGFR)Lestaurtinib - Broad TKI
Dasatinib - Broad TKI*BIIB021 - HSP90i
Selumetinib - MEKiPlicamycin - RNA synth.i
AZD8055 - mTORiTrametinib - MEKi
PF-04691502 - mTOR/PI3KiEverolimus - Rapalog
Temsirolimus - Rapalog*Sirolimus - Rapalog
Pimasertib - MEKiRefametinib - MEKi
sDSS
784_1 784_2
CK1ε
JNK2
CK1δBTK
EPHB4
p38γ
p110α
p110γp110β
p110δ
CDK2
ULK2
CDK16
MEK2
YES1
MEKK2
TrkA
DAPK1
MEK1
LTK
CHK2
PhKγ2
ALK
MER
PhKγ1
TrkC
IRAK3
p38β
HCKCSK
EPHA4
TXK
FYNBMX
p38α
EPHB2
CDK4CDK9 CDK18
CDK13CDK11B
GSK3A CDK11A
CK1ε
JNK2
CK1δBTK
EPHB4
p38γ
p110α
p110γ
CDK17
p110δ
CDK2
ULK2
CDK16
MEK2
YES1
MEKK2
TrkA
DAPK1
ITK
MEK1
LTK
CHK2
BIKE
PhKγ2
ALK
MER
PhKγ1
AAK1
TrkC
IRAK3
p38β
HCKCSK
EPHA4
TXK
FYNBMX
p38α
EPHB2
CDK4CDK9 CDK18
CDK13CDK11B
GSK3A CDK11A
AAK1
ITK
BIKE
p110β
78
4_
2 s
DS
S
784_1 sDSS
Paclitaxel
0 5 10 15 200
5
10
15
20
Sirolimus
TemsirolimusEverolimus
Dasatinib
PF-04691502
AZD8055
Lestaurtinib
Docetaxel
Tipifarnib
Dactinomycin
BMS-754807
Navitoclax
Danusertib
Dexamethasone
RefametinibPimasertib
TrametinibSelumetinib
BIIB021
Plicamycin
Vinblastine
Dactolisib
VincristineTopotecan
Mitoxantrone
CDK17
TEL–TrkC-mediated activation of IGF-IR signaling, which pro-
moted hyperactivation of mTORC1. Supporting this hypothe-
sis, we observed synergistic activities between the BMS-754807
IGF-IR/TrkC inhibitor and dactolisib, an ATP-competitive
mTOR inhibitor ( Fig. 6E ). The combination of BMS-754807
and the MEK inhibitor trametinib, on the other hand, did not
result in synergism, indicating that the TEL–TrkC/IGF-IR/
mTORC1 dependency represents a separate signal than the
one leading to addiction to MEK signaling ( Fig. 6F ). Taken
together, these results indicate how ISM strategy helps to iden-
tify not only the mechanisms of resistance, but also potential
ways to counteract it with combinatorial therapies.
DISCUSSION
We present here an ISM strategy based on the systematic func-
tional testing of patient-derived primary cancer cell sensitivity to
targeted anticancer agents coupled with genomic and molecu-
lar profi ling. Importantly, the intent is to guide treatment
decisions for individual cancer patients coupled with monitor-
ing of subsequent responses in patients to measure and under-
stand the effi cacy and mechanism of action of the drugs. The
ISM strategy allows for an iterative adjustment of therapies for
patients with cancer, one patient at a time, with repeated sam-
pling playing a major role in understanding and learning from
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DECEMBER 2013�CANCER DISCOVERY | 1425
ISM Approach to Therapy Selection RESEARCH ARTICLE
each success and failure. Furthermore, ISM facilitates learning
by discovering molecular or functional patterns from past
patient cases to help therapeutic assessment of new patients.
Application of the DSRT technology to 28 AML patient
samples identifi ed effective molecularly targeted compounds
inducing selective toxic or inhibitory responses in AML cells
over normal bone marrow mononuclear cells. Our data suggest
the intriguing possibility that anticancer agents already in clini-
cal use for other diseases, such as dasatinib (current approved
indications are chronic myeloid leukemia and Ph+ acute lym-
phoblastic leukemia), sunitinib (renal cell cancer and gastroin-
testinal stromal tumors), and temsirolimus (renal cell cancer),
could be repositioned for subsets of AML patients. Although
we did not achieve long-term cures in patients with AML receiv-
ing DSRT-guided therapies, the clinical responses seen are
encouraging, given the fact that most of the patients with AML
studied here had complex chemorefractory, end-stage disease.
Obviously, the clinical results arise from a nonrandomized
setting and need to be verifi ed. However, the clinical implemen-
tation of ISM in individual patients is a powerful way to create
hypotheses to be tested in systematic clinical studies, both for
existing and emerging drugs. Indeed, we also identifi ed several
investigational oncology drug classes, such as ATP-competitive
PI3K/AKT/mTOR pathway inhibitors, MEK inhibitors, and
JAK inhibitors, which deserve attention as drugs to priori-
tize for future clinical trials in AML. Furthermore, the ISM
approach could also help to identify effective drug combina-
tions based on associating drug sensitivities.
Unsupervised clustering of the patient samples identifi ed
fi ve functional taxonomic groups in AML based on ex vivo
drug responses. FLT3 mutations were highly enriched among
the most TKI-sensitive functional group (group V), with FLT3
inhibitors being the most selective class of drugs for this group.
However, these samples were also selectively sensitive to other
kinase inhibitors, such as dasatinib, that lack FLT3 activity, sug-
gesting that oncogenic FLT3 signaling may be dependent on the
Figure 6. IGF-IR and mTOR inhibition has a synergistic effect in ETV6 – NTRK3 -driven AMLs. A, validation sequencing and resulting predicted protein structure of two fusions identifi ed in this patient with RNA sequencing. The MLL – ELL fusion was present throughout the disease, whereas the ETV6–NTRK3 (TEL–TrkC) fusion was detected in the dasatinib–temsirolimus resistant sample. B, phosphoproteomic profi ling of the initial and resistant samples displayed a signaling switch in the leukemic cells. C, clonal evolution of the AML from diagnosis to relapse. D, summary heatmap illustrating the key putative oncogenic genetic alterations in the clones depicted in C. E, combinatorial treatment of the patient cells with the IGF-IR/TrkC inhibitor BMS-754807 and either dactolisib (ATP-competitive mTOR inhibitor) or trametinib (MEK inhibitor) revealed that there is a synergistic effect between mTOR and IGF-IR/TrkC inhibition. F, a model of kinase driver switch and drug resistance in this patient.
A
MLLELL
ETV6NTRK3
Zinc finger, CXXC-type(IPR002857)
RNA pol II elongationfactor ELL (IPR019464)
Occludin/RNA pol II elongationfactor ELL domain (IPR010844)
Pointed doman(IPR003118)
Tyr kinase catalyticdomain (IPR020635)
MLL ELL
TEL TrkC
1 140646
1 336 466
622
825
BM
S-7
54807
Das–Sun–Tem
TEL-TrkC/IGF-IR
HyperactivatedTORC1IG
F-I
R/T
rKC
i
0 0.025 0.25 2.5 25 250
Trametinib
MEKi
Nonsynergistic
Dasatinib
target
TORC1
784_1 784_2
D
0 0.1 1 10 100 1,000
Dactolisib
mTOR/PI3Ki
Synergistic
0
1
10
100
1,000
10,000
B
pp38α
pGSK-3
α/β
pMEK1/
2
pCREB
pp70
S6K
0
1
2
3
4
5
Fo
ld c
ha
ng
e
784_1784_2
F
MLL
–ELL
fusi
onW
T1
PD
CD
10(R
157H
)TP
63(I4
32T)
FLT3
-ITD
#1
FLT3
-ITD
#2
WT1
LO
HE
TV6–
NTR
K3
fusi
onS
TRN
–ALK
fusi
on
Clone 1
Clone 2
Clone 3
Clone 4
Clone 5
E
C
Chemotherapy Das–Sun–Tem
784_1784_0 784_2
MLL–ELLWT1
Clone 1FLT3-ITD #1
Clone 2FLT3-ITD #2
Clone 3WT1 LOH
Clone 4ETV6–NTRK3
Clone 5STRN–ALK
Resistance
therapy
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Pemovska et al.RESEARCH ARTICLE
signaling of other tyrosine kinases whose inhibition may syner-
gize with the therapeutic effects of FLT3 inhibitors. Given that
the clinical implementation of FLT3 inhibitors has proven very
challenging, the identifi cation of druggable synergistic kinase
signals or drugs could be extremely important. Furthermore, we
observed a signifi cant association between activating mutations
in RAS genes and sensitivity to MEK inhibitors, in line with pre-
vious results showing that trametinib exhibited favorable clini-
cal responses in patients with RAS -mutated refractory AML ( 22 ).
Finally, we identifi ed a tendency of clustering of TP53 mutations
in the two least-responsive functional groups (groups I and II)
and mutations and fusions linking to epigenetic modulation in
groups III and IV. However, most of the drug-response classifi ca-
tions and variabilities could not yet be attributed to the main
mutations detected in the patient samples. Such drug responses
may arise from nongenomic causes and complex combinatorial
molecular pathways, and these fi ndings therefore highlight the
value of DSRT in (i) functionally validating suggestions aris-
ing from the genomic profi les and (ii) discovering other drug
dependencies that have yet to be deduced from genomic or
transcriptomic data. The relationship between genomic changes
and drug response may also be more complicated in the chem-
orefractory patient samples studied here.
Despite multiple efforts over several decades, the direct pre-
diction of cancer cell chemosensitivity in the clinical setting has
remained an elusive goal. Compared with previously published
approaches, the ISM approach presented here has distinct differ-
ences. First, our studies focused on targeted drugs, whereas most
of the previous efforts of ex vivo drug testing have focused on
conventional chemotherapeutics ( 23–27 ). The ex vivo responses
to these agents are often nonselective and more diffi cult to inter-
pret and translate to clinical patient care. Second, we focused
on leukemias, whereas many previous studies have focused on
solid tumors ( 28–33 ), where representative samples are diffi cult
to acquire and consecutive samples from recurrent disease are
typically not available. Third, we measured selective anticancer
effects as compared with normal bone marrow mononuclear
cells, which makes it possible to identify cancer-selective drugs
with potential for less systemic toxicity. Fourth, we performed a
rapid analysis of the in vivo effects of the drugs from consecutive
samples. Our novel endpoint for therapy effi cacy in patients was
an impact on the clonal composition of the cancer sample.
The different types of responses in our clinically translated
patient cases highlight the diffi culty in predicting mecha-
nisms of resistance and support the importance of repeated
functional testing such as DSRT during disease progression
to identify changes in drug sensitivities. In patient 600, the
leukemic cells carried a FLT3 -ITD mutation and showed
addiction to this oncogene based on highly selective responses
to FLT3 inhibitors, such as quizartinib and sunitinib. Inter-
estingly, the same FLT3 -ITD variant remained in the resistant
cells, but the response to quizartinib, sunitinib, and other
FLT3 inhibitors was lost, and the cells acquired pan-resistance
to almost all agents tested. In contrast, in patient 784, resist-
ance to dasatinib, sunitinib, and temsirolimus treatment was
linked to enrichment of clonal populations carrying a tyro-
sine kinase fusion gene that mediated resistance. The ETV6–
NTRK3 fusion and the resulting TEL–TrkC fusion protein is
a known oncogenic driver in AML and other cancers ( 34–36 )
and is dependent on IGF-IR kinase activity ( 18–20 , 37, 38 ).
This hypothesis is supported by the acquisition of sensitivity,
based on ex vivo testing, to the dual IGF-IR/TrkC inhibitor
BMS-754807 exclusively in the relapsed sample.
In conclusion, we present an individual-centric, functional
systems medicine strategy to systematically identify drugs to
which individual patients with AML are sensitive and resistant,
implement such strategies in the clinic, and learn from the inte-
grated genomic, molecular, and functional analysis of drug sen-
sitivity and resistance in paired samples. ISM strategy provides
a powerful way to create hypotheses to be tested in formal clini-
cal trials, for existing drugs, emerging compounds, and their
combinations. In the future, ISM may pave a path for routine
individualized optimization of patient therapies in the clinic.
METHODS Study Patients and Material
Twenty-eight bone marrow aspirates and peripheral blood samples
(leukemic cells) and skin biopsies (noncancerous cells for germline
genomic information) from 18 AML and high-risk [according to the
WHO classifi cation-based Prognostic Scoring System (WPSS); ref. 39 ]
patients with myelodysplastic syndromes (MDS) and 7 samples from
different healthy donors (controls) were obtained after informed con-
sent with approval (No. 239/13/03/00/2010, 303/13/03/01/2011).
Patient characteristics are summarized in Supplementary Table S3.
Mononuclear cells were isolated by Ficoll density gradient (Ficoll-
Paque PREMIUM; GE Healthcare), washed, counted, and suspended
in Mononuclear Cell Medium (MCM; PromoCell) supplemented with
0.5 μg/mL gentamicin and 2.5 μg/mL amphotericin B. One sample
from patient 393, a secondary AML after MDS with 20% myeloblasts,
was enriched for the CD34 + cell population (sample 393_3, cor-
responding to the blast cell population) using paramagnetic beads
according to the manufacturer’s instructions (Miltenyi Biotech).
Development of the Compound Collection The oncology compound collection covers the active substances from
the majority of U.S. Food and Drug Administration/European Medi-
cines Agency ( FDA/EMA)–approved anticancer drugs ( n = 123), as well
as emerging investigational and preclinical compounds ( n = 64) covering
a wide range of molecular targets (Supplementary Table S1). The com-
pounds were obtained from the National Cancer Institute Drug Testing
Program (NCI DTP) and commercial chemical vendors: Active Biochem,
Axon Medchem, Cayman Chemical Company, ChemieTek, Enzo Life
Sciences, LC Laboratories, Santa Cruz Biotechnology, Selleck, Sequoia
Research Products, Sigma-Aldrich, and Tocris Biosciences.
DSRT Ex vivo DSRT was performed on freshly isolated primary AML cells
derived from patient samples as well as mononuclear cells derived
from healthy donors. The compounds were dissolved in 100% dimethyl
sulfoxide (DMSO) and dispensed on tissue culture–treated 384-well
plates (Corning) using an acoustic liquid handling device, Echo 550
(Labcyte Inc.). The compounds were plated in fi ve different concentra-
tions in 10-fold dilutions covering a 10,000-fold concentration range
(e.g., 1–10,000 nmol/L). The predrugged plates were kept in pressu-
rized StoragePods (Roylan Developments Ltd.) under inert nitrogen
gas until needed. The compounds were dissolved with 5 μL of MCM
while shaking for 30 minutes. Twenty microliters of single-cell sus-
pension (10,000 cells) was transferred to each well using a Multi Drop
Combi (Thermo Scientifi c) peristaltic dispenser. The plates were incu-
bated in a humidifi ed environment at 37°C and 5% CO 2 , and after
72 hours cell viability was measured using CellTiter-Glo luminescent
assay (Promega) according to the manufacturer’s instructions with a
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Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
DECEMBER 2013�CANCER DISCOVERY | 1427
ISM Approach to Therapy Selection RESEARCH ARTICLE
Molecular Devices Paradigm plate reader. The data were normalized to
negative control (DMSO only) and positive control wells (containing
100 μmol/L benzethonium chloride, effectively killing all cells).
Generation of Dose–Response Curves and Analysis of Data The plate reader data were uploaded to Dotmatics Browser/Studies
software (Dotmatics Ltd.) for a normalized calculation of percentage
survival for each data point and generation of dose–response curves
for each of the drugs tested. The dose–response curves were fi tted on
the basis of a four-parameter logistic fi t function defi ned by the top
and bottom asymptote, the slope, and the infl ection point (EC 50 ).
In the curve fi tting, the top asymptote of the curve was fi xed to 100%
viability, whereas the bottom asymptote was allowed to fl oat between 0%
and 75% (i.e., drugs causing < 25% inhibition were considered inactive).
Scoring and Clustering of DSRT Data To quantitatively profi le individual patient samples in terms
of their DSRT-wide drug responses, as well as to compare drug
responses across various AML patient samples, a single measure was
developed: DSS. The curve fi tting parameters were used to calculate
the area under the dose–response curve (AUC) relative to the total
area (TA) between 10% threshold and 100% inhibition . Furthermore,
the integrated response was divided by a logarithm of the top asymp-
tote ( a ). Formally, the DSS was calculated by:
DSSAUC
TA log=
××
100
a
We scored for differential activity of the drugs in AML blast cells
in comparison with control cells, sDSS. Clustering of the drug sen-
sitivity profi les across the AML patient and control samples was per-
formed using unsupervised hierarchical complete-linkage clustering
using Spearman and Euclidean distance measures of the drug and
sample profi les, respectively. Reproducibility of the clustering and
the resulting drug-response subtypes detected was evaluated using
the bootstrap resampling method with the Pvclust R-package ( 40 ).
Prediction of Kinase Addictions sDSSs of kinase inhibitors were further used to predict sample-
specifi c kinase addictions. Sample-specifi c sDSS responses were
compared with target profi les for 35 kinase inhibitors overlapping
between our compound panel and the panel profi led by Davies
and colleagues ( 16 ). More specifi cally, for each kinase target, we
calculated a Kinase Inhibition Sensitivity Score (KISS) by averaging
the sDSS values among those compounds that selectively target
the kinase. These putative selective kinases were compared with
gene expression to exclude nonexpressed targets, and the remaining
kinases defi ned a putative “kinaddictome” for each patient sample.
For displaying purposes, the resulting kinases were depicted in a tar-
get similarity network, in which edges connect kinases with similar
inhibitor specifi city profi les (ref. 16 ; Spearman rank-based correla-
tion > 0.5; Szwajda and colleagues, unpublished data).
DNA Sequencing Genomic DNA was isolated using the DNeasy Blood & Tissue Kit
(Qiagen). Exome sequencing was performed on the patient samples
highlighted in Fig. 3 . In addition, whole-genome sequencing was
performed using DNA from the skin and AML cells from sample
784_2. DNA (3 μg) was fragmented and processed according to the
NEBNext DNA Sample Prep Master Mix protocol. Exome capture
was performed using the Nimblegen SeqCap EZ v2 capture Kit
(Roche NimbleGen). Sequencing of exomes and genomes was done
using HiSeq1500, 2000, or 2500 instruments (Illumina). For germ-
line control samples 4 × 10 7 and for tumor samples 10 × 10 7 2 × 100
bp paired-end reads were sequenced per sample. The leukemia DNA
sample from patient 1497 was sequenced using the Illumina TruSeq
Amplicon Cancer Panel and the MiSeq sequencer (Illumina).
Somatic Mutation Calling and Annotation from Exome-Sequencing Data
Sequence reads were processed and aligned to the reference genome
as described previously ( 41, 42 ). Somatic mutation calls were made
for exome capture target regions of the NimbleGen SeqCap EZ v2
Capture Kit (Roche NimbleGen) and the fl anking 500 bps. High con-
fi dence somatic mutations were called for each tumor sample using
the VarScan2 somatic algorithm ( 43 ) with the following parameters:
– strand-fi lter 1
– min-coverage-normal 8
– min-coverage-tumor 1
– somatic- P value 0.01
– normal-purity 0.95
– min-var-freq 0
Mutations were annotated with SnpEff ( 44 ) using the Ensembl v68
annotation database. To fi lter out false-positive calls due to genomic
repeats, somatic mutation calls in regions defi ned as repeats in the
RepeatMasker track obtained from the University of California, Santa
Cruz (UCSC) Genome Browser were removed from the analysis. To fi l-
ter out misclassifi ed germline variants, population variants included
in dbSNP (Single Nucleotide Polymorphism database) version 130
were removed. Remaining mutations were visually validated using the
Integrated Genomics Viewer (Broad Institute).
Analysis of Mutation Frequencies in Serial Samples To examine frequencies of the identifi ed mutations in samples
where the mutations did not pass the criteria for high confi dence
mutations, variant frequencies and read counts for each mutation
were retrieved from a set of unfi ltered variant calls generated by
VarScan2 with the following parameters:
– strand-fi lter 0
– min-coverage-normal 8
– min-coverage-tumor 1
– somatic- P value 1
– normal-purity 1
– min-var-freq 0
In addition, we used variant allele frequencies from control–leuke-
mia pairs to identify regions of LOH.
FLT3-ITD Detection by Capillary Sequencing and qPCR For determination of patients’ FLT3 -ITD status, genomic DNA was
extracted from the bone marrow mononuclear cell fraction. Qualita-
tive PCR was performed as described by Kottaridis and colleagues ( 45 )
by using a 6-carboxyfl uorescein (FAM)-labeled forward primer. The
PCR products were separated on an agarose gel and in capillary elec-
trophoresis with an ABI3500D× Genetic Analyzer and sequenced using
M13-tailed direct sequencing. Assessment of minimal residual disease
(MRD) level was performed with real-time quantitative PCR (qPCR). A
patient ITD-specifi c ASO (allele-specifi c oligonucleotide) primer was
designed at the ITD junction region and used together with a down-
stream TaqMan probe and reverse primer (primer sequences available
upon request). Albumin gene qPCR was additionally performed to
normalize the variability in DNA quality in the follow-up samples.
Amplicon Sequencing Amplicons were amplifi ed using locus-specifi c PCR primers carrying
Illumina compatible adapter sequences, grafting sequences (P5 and
P7), and an amplicon-specifi c 6-bp index sequence (Supplementary
Table S7). The PCR reaction contained 10 ng of sample DNA, 10 μL
of 2× Phusion High-Fidelity PCR Master Mix (Thermo Scientifi c),
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Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
1428 | CANCER DISCOVERY�DECEMBER 2013 www.aacrjournals.org
Pemovska et al.RESEARCH ARTICLE
and 0.5 μmol/L of each primer. Following PCR amplifi cation, samples
were purifi ed using Performa V3 96-Well Short Plate and QuickStep2
SOPE Resin (EdgeBio). Sequencing of PCR amplicons was performed
using the Illumina HiSeq2000 instrument (Illumina). Samples were
sequenced as 101-bp paired-end reads and one 7-bp index read.
Library Preparation, Sequencing, and Data Analysis of Transcriptomes
Total RNA (2.5 to 5 μg) was used for depletion of rRNA (Ribo-Zero
rRNA Removal Kit; Epicentre), purifi ed (RNeasy clean-up-kit; Qia-
gen), and reverse transcribed to double-stranded cDNA (SuperScript
Double-Stranded cDNA Synthesis Kit; Life Technologies). Random
hexamers (New England BioLabs) were used for priming the fi rst-
strand synthesis reaction.
Illumina-compatible Nextera Technology (Epicentre) was used for
preparation of RNAseq Libraries. High Molecular Weight (HMW)
buffer and 50 ng of cDNA were used for tagmentation as recommended
by the manufacturer. After the tagmentation reaction, the fragmented
cDNA was purifi ed with SPRI beads (Agencourt AMPure XP, Beckman
Coulter). The RNAseq libraries were size selected (350–700 bp frag-
ments) in 2% agarose gel followed by purifi cation with QIAquick gel
extraction kit (Qiagen).
Each transcriptome was loaded to occupy one third of the lane
capacity in the fl ow cell. C-Bot (TruSeq PE Cluster Kit v3, Illumina)
was used for cluster generation, and an Illumina HiSeq2000 platform
(TruSeq SBS Kit v3-HS reagent kit) was used for paired-end sequenc-
ing with 100-bp read length. Nextera Read Primers 1 and 2 as well
as Nextera Index Read Primer were used for paired-end sequencing
and index read sequencing, respectively. RNA-seq data analysis, such
as fusion gene identifi cation, mutation calling, and gene expression
quantitation (Tophat and Cuffl inks), was done as described previ-
ously ( 46 ). Primers used to validate as well as quantify the fusion
genes detected are listed in Supplementary Tables S8 and S9.
Proteomic Analysis Phosphoproteomic analysis of the AML patient samples was per-
formed using Proteome Profi ler antibody arrays (R&D Systems)
according to the manufacturer’s instructions. Lysates containing 300
μg of protein were applied to the arrays, and fl uorescently labeled
streptavidin (IRDye 800 CW streptavidin; LI-COR) and an Odyssey
imaging system (LI-COR) were used for detection.
Other Statistical Analyses Statistical analysis was performed using GraphPad Prism 5. A
Pearson correlation test was used to determine the correlations between
drug sensitivity profi les of healthy donor samples. A two-tailed Student
t test was used to assess the correlation between RAS or FLT3 mutations
and MEK inhibitor or dasatinib sensitivity, respectively. A correlation
in sensitivity was considered statistically signifi cant when P < 0.05.
Disclosure of Potential Confl icts of Interest S. Mustjoki has honoraria from the Speakers Bureaus of Novartis
and Bristol-Myers Squibb and is a consul tant/advisory board member
of the same. O. Kallioniemi has received commercial research support
from IMI-Project Predect (a consortium of pharmaceutical compa-
nies) and is a consultant/advisory board member of Medisapiens. No
potential confl icts of interest were disclosed by the other authors.
Authors’ Contributions Conception and design: T. Pemovska, M. Kontro, E. Elonen, I. Västrik,
J. Knowles, C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg
Development of methodology: T. Pemovska, M. Kontro, B. Yadav,
H. Edgren, S. Eldfors, M.M. Bespalov, R. Karjalainen, E. Kulesskiy,
S. Lagström, M. Lepistö, M.M. Majumder, P. Mattila, L. Turunen, M. Wolf,
T. Aittokallio, C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg
Acquisition of data (provided animals, acquired and managed
patients, provided facilities, etc.): M. Kontro, M.M. Bespalov, P. Ellonen,
E. Elonen, B.T. Gjertsen, R. Karjalainen, E. Kulesskiy, A. Lehto, M. Lepistö,
T. Lundán, P. Mattila, A. Murumägi, S. Mustjoki, A. Parsons, M.E. Rämet,
M. Suvela, L. Turunen, M. Wolf, C.A. Heckman, K. Porkka
Analysis and interpretation of data (e.g., statistical analysis,
biostatistics, computational analysis): T. Pemovska, B. Yadav,
H. Edgren, S. Eldfors, A. Szwajda, H. Almusa, E. Elonen, T. Lundán,
M.M. Majumder, J.M.L. Marti, S. Mustjoki, I. Västrik, T. Aittokallio,
C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg
Writing, review, and/or revision of the manuscript: T. Pemovska,
M. Kontro, H. Edgren, S. Eldfors, E. Elonen, B.T. Gjertsen, T. Lundán,
A. Murumägi, S. Mustjoki, J. Knowles, T. Aittokallio, C.A. Heckman,
K. Porkka, O. Kallioniemi, K. Wennerberg
Administrative, technical, or material support (i.e., report-
ing or organizing data, constructing databases): T. Pemovska,
R. Karjalainen, A. Lehto, A. Palva, A. Parsons, T. Pirttinen, M. Suvela,
L. Turunen, I. Västrik, C.A. Heckman, K. Porkka
Study supervision: E. Elonen, J. Knowles, T. Aittokallio, C.A. Heck-
man, K. Porkka, O. Kallioniemi, K. Wennerberg
Acknowledgments The authors thank the patients for donating their samples for
research, Jani Saarela and Ida Lindenschmidt (FIMM Technology
Centre, High Throughput Biomedicine Unit) for technical assist-
ance, Biocenter Finland research infrastructures (Drug Discovery
and Chemical Biology platform as well as the Genome-Wide Methods
network), EATRIS and BBMRI ESFRI infrastructures for technical
and infrastructural support, and Labcyte Inc. for technical assistance
in developing the DSRT pipeline as part of an ongoing collaboration
with FIMM.
Grant Support This study was fi nancially supported by the Academy of Finland (to
T. Aittokallio and O. Kallioniemi); Biocenter Finland (to O. Kallioniemi
and K. Wennerberg); ERDF: the European Regional Development
Fund (to I. Västrik); EU FP7 BioMedBridges (to O. Kallioniemi);
Finnish Cancer Societies (to O. Kallioniemi and K. Porkka); the Jane
and Aatos Erkko Foundation (to K. Wennerberg); the Sigrid Jusélius
Foundation (to O. Kallioniemi); and Tekes: Finnish Funding Agency
for Technology and Innovation (to O. Kallioniemi and J. Knowles).
Received July 8, 2013; revised September 12, 2013; accepted Sep-
tember 13, 2013; published OnlineFirst September 20, 2013.
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2013;3:1416-1429. Published OnlineFirst September 20, 2013.Cancer Discovery Tea Pemovska, Mika Kontro, Bhagwan Yadav, et al. Patients with Chemorefractory Acute Myeloid LeukemiaIndividualized Systems Medicine Strategy to Tailor Treatments for
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