0 2 0 4 0 6 0 8 0 1 0 0

A K T 1

K D R

M T O R

B R A F

P IK 3 C A

E G F R

F L T 3

C D K N 2 A

C S F 1 R

C T N N B 1

F L T 1

ID H 2

M E T

M L H 1

M Y C

P D G F R A

N O T C H 1

N O T C H 2

P T E N

T E R T

A T M

P A L B 2

F A N C A

C R E B B P

R B 1

K R A S

T S C 2

G N A S

B R C A 1 /2

T P 5 3

Mut

ated

Gen

es (³

2 P

t.)

% o f P t . w ith M u ta te d G e n e

G e n o m ic a n d P a th o lo g y S e r v ic e

0 10 20 30 40 50 60 70 80 90 100

CDKN2A

NF1

PIK3CA

MYC

KRAS

BRCA1/2 (somatic)

TP53

%  of  Pt.  

TCGA (n=270, n=412)

Ross (n=48)

Rodriguez-Rodriguez (n= 36)

Arend (n = 109)

0 2 0 4 0 6 0 8 0 1 0 0

A P C

A T M

C D H 1

F G F R 2

G N A S

S M A D 4

C T N N B 1

R B 1

V H L

P T E N

P IK 3 C A

K D R

K IT

T P 5 3

% o f P t . w ith M u ta te d G e n e

Mut

ated

Gen

es (

³ 2

Pt.

)

C ir c u lo g e n e

0 2 0 4 0 6 0 8 0 1 0 0

C C N D

C D K N 2 A /B

C R E B B P

D N M T 3 A

F B X W 7

F G F 2 3

F G F 6

F L T 1

F R S 2

T S C 2

A R ID 1 A

B R A F

K D M 5 A /C

P R K C I

F A N C

P T E N

L YN

M YS T 3

N O T C H 3

T E R C

C C N E 1

N F 1

P IK 3 C A

M YC

K R A S

B R C A 1 /2

T P 5 3

% o f P t . w ith M u ta te d G e n e

Mut

ated

Gen

es (³

2 P

t.)

F o u n d a t io n O n e

Introduction Background: Molecular profiling can play an important role in making treatment decisions and will be a critical component in optimizing personalized medicine cancer care. Utilizing personalized medicine in high-volume clinical environments requires interdisciplinary expertise. The lack of an organized infrastructure in performing next generation sequencing (NGS), integrating these results into the electronic medical record (EMR), and guiding clinicians on how to interpret these tests for clinical decision making remains a barrier in the implementation of personalized medicine. Objectives: 1.  To build an infrastructure of molecular profiling through NGS in patients with recurrent ovarian cancer that could

impact clinical care in a Personalized Medicine Initiative (PMI). 2.  To analyze the results of NGS on tumor and plasma cell free DNA (cfDNA) in patients with recurrent ovarian

cancer. 3.  To generate evidence of the feasibility of providing genotype-guided therapy to patients with recurrent ovarian

cancer.

Results

Analysis of Clinical Cancer Gene Panels by Next Generation Sequencing in Tumor and Circulating Cell-Free DNA Samples in Recurrent Ovarian Cancer Patients

Angelina I. Londoño PhD-1, Naveed Q. Farrukh2, Mary Kat Smith2, Cindy Tawfik2, Ronald D. Alvarez MD2, Kerri S. Bevis MD MSPH2, J. Michael Straughn Jr. MD2, Kenneth H. Kim MD2, Eddy S. Yang MD PhD1, Shuko Harada MD3, Charles A. Leath III MD MSPH2, Warner K. Huh MD2, Rebecca C. Arend MD2

University of Alabama at Birmingham 1Radiation Oncology, 2Obstetrics and Gynecology, , 3 Anatomic Pathology, Birmingham, AL, USA

Conclusions

Methods

▼  Table  2.  Summary  of  results  from  each  gene8c  test    

q Table  4.  FDA-­‐approved  targeted  therapy  with  poten8al  benefits  in  the  FO  cohort  (n=54)    

–  Under IRB approval, patients with recurrent ovarian cancer were consented from September 2015 to November 2016.

–  NGS was performed on archival tumor and on cfDNA at the time of enrollment. –  Before March 2016, NGS was performed on tumor using a 65 gene panel from Genomic Pathology Service

(GPS) at Washington University. –  After March 2016, all NGS was performed using a 315 gene FoundationOne (FO) panel at Foundation

Medicine. –  NGS was performed on cfDNA utilizing a 50 gene panel performed at Circulogene Theranostics. –  A Personalized Medicine Letter (PML) summarizing the results of the tumor NGS and recommendations was

placed in the patients’ EMR. –  Treatment for patients enrolled in the PMI ovarian project was determined by the patients’ physician. –  NGS results detailing genomic alterations were stored in the Ovarian Personalized Medicine Initiative (OVPMI)

database.

2. 13/49 (26.5%) patients had a TP53 genomic alteration in both the tumor and cfDNA. Of the 13, none had the same variant in their TP53 mutation. Overall, 36/49 (73.5%) showed no concordant genomic alterations.

3. 56/109 (54.1%) patients had actionable mutations with potential clinical benefit from FDA-approved targeted therapy based on NGS results.

1. TP53 is the most common genomic alteration found in >60% of recurrent ovarian cancer patients; whereas the majority of altered genes are seen in <1%.

- 6/56 (10.7%) have received targeted therapy. - As of November 21, 2016, 2 patients were on targeted therapy based on their NGS results. •  1 patient on Olaparib (parp inhibitor) based on a somatic

BRCA mutation detected (no germline mutation). •  1 patient on Pazopanib (tyrosine kinase inhibitor) for a FGFR

mutation. - Targeted therapy was started and discontinued on 4 patients. •  2 patients received Trametinib (MEK inhibitor) for a KRAS or

BRAF mutation; discontinued due to rash. •  1 patient received Olaparib for a PTEN mutation; discontinued

due to progression of disease. •  1 patient received Olaparib based on somatic BRCA mutation

(no germline mutation); discontinued due to progression of disease.

q Table  3.  Summery  of  pa8ents  that  received  targeted  therapy  based  on  NGS  results  

qFigure  1.  Genomic  Altera8ons  found  in  Tumor  DNA  .  A.  Genomic  Pathology  Services  (n=55).  B.  Founda8onOne  (n=54).    C.  Comparison  between  GPS  and  FO  (%  of  pa8ents  with  either  BRCA1/2  or  TP53  muta8ons).  D.  Comparison  of  results  to  TCGA,  Ross  et  al.,  Rodriguez-­‐Rodriguez  et  al.      

qFigure 2. Genomic alterations found in cfDNA (n=49)

qTable  1.  Pa8ent  Demographics    

64

22

81

22

0 20 40 60 80 100

TP53

BRCA1/2

% of Pt.

FO GPS

q  Summary  of  Treatments  

•  Hospital initiative covered the cost for GPS testing (62 patients).

•  35 patients on Medicare and Medicaid had 100% of the cost covered for FO NGS.

•  19 patients with private insurance plus FO financial assistance program had 100% of cost covered.

•  One patient was uninsured and 100% of cost covered by FO. •  Circulogene covered the cost for NGS on cfDNA. •  Costs for targeted therapy were covered by insurance or the

supplier.

Acknowledgments  

q  Costs  Associated  with  Targeted  Therapy        

•  Ovarian cancer has a diverse genetic landscape and molecular profiling via NGS offers the opportunity to identify genetic alterations that can be utilized to direct therapy.

•  51.4% of patients with recurrent ovarian cancer had a mutation that could be targeted with a commercially available drug. •  Our study highlights the infrastructure and feasibility of implementing NGS into a clinical workflow to expand the potential treatment choices available to patients. •  With regard to clinical trials, 50.5% of patients could potentially be eligible for a group in the Targeted Agent and Profiling Utilization Registry (TAPUR) Study

and 34.9% of patients for a sub-protocol of the Molecular Analysis for Therapy Choice (MATCH) Trial. •  We have been successful in providing patients with NGS-directed therapy. •  Limitations

–  Collection of archival tumor DNA may not represent the current genomic mutations. –  Given the heterogeneous nature of ovarian tumors, one sample of tumor (current or archival) may not show all mutations present in the cancer, but only

mutations in that location. –  cfDNA represents mainly the genome of dying tumor cells, although viable tumor cells are likely the ones that drive cancer progression and cause therapy

resistance –  Tumor and cfDNA were collected at different time points in the course of a patient’s disease. –  NGS companies use different gene panels, DNA extraction methods, bioinformatics platforms, and variant callers. –  There can be diversity in clinical interpretations of actionable mutations. –  Not all patients who receive NGS-directed therapy will respond.

Personalized Medicine Grant through UAB’s Personalized Medicine Institution, Circulogene Theranostics, UAB Cancer Center, T32 5T32CA183926-02 Research Training Program in Basic and Translational Oncology, ABOG Early Career Grant, Norma Livingston Foundation, and Patients that enrolled in our study.

References 1.Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81. 2. Cancer Genome Atlas Research N. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609-615. 3. Rodriguez-Rodriguez L, Hirshfield KM, Rojas V, et al. Use of comprehensive genomic profiling to direct point-of-care management of patients with gynecologic cancers. Gynecol Oncol. 2016;141(1):2-9. 4. Ross JS, Ali SM, Wang K, et al. Comprehensive genomic profiling of epithelial ovarian cancer by next generation sequencing-based diagnostic assay reveals new routes to targeted therapies. Gynecol Oncol. 2013;130(3):554-559.

Tumor DNA cfDNA

GPS FO

Samples Sent for Sequencing 62 54 56

Insufficient Samples 7 0 7

Patients with NGS Results 55 54 49

Average Number of mutated genes per patient (range) 2.8 (0-10) 3.8 (0-15) 1.8 (0-7)

Number of Genes w/ ≥ 1 alteration 56 76 26

Number of Genes in Panel 65 315 50

•  A total of 116 patients were enrolled ‾  62 sent to GPS, 55 patients with results ‾  54 sent to FO

•  56 plasma samples sent to Circulogene •  49 patients with NGS results from both cfDNA

and tumor (13 by GPS; 36 by FO)

A. B.

C.

qFigure 3. Common variants found in both Tumor DNA vs cfDNA

72.5

8.3

30.6 20.4

0 10 20 30 40 50 60 70 80 90

100

TP53 PIK3CA

% o

f Pt.

Tumor DNA cf DNA

FDA-Approved Targeted Therapy Mechanism Mutation Number of Patients with

Potential Benefits

Trametinib Cobimetinib MEK Inhibitor

BRAF NF-1

KRAS NRAS

14

Olaparib PARP inhibitor

BRCA1 BRCA2 BRIP1 PTEN

13

Everolimus Temsirolimus mTOR inhibitors

AKT2 TSC2

FBXW7 PIK3CA

PTEN mTOR

12

Vemurafenib Dabrafenib BRAF inhibitor BRAF 2

Pazopanib Ponatinib Multikinase inhibitors FGFR1

FGFR2 2

Abemaciclib Ribociclib CDK4/6 inhibitors CCND1 1

Dasatinib Kinase inhibitor LYN 1 Pembrolizumab

Nivolumab Anti-PD-1 inhibitor MSH2 1

Pertuzumab ERBB2 inhibitor ERBB2 1

q Table    5.  Summary  of  BRCA1/2  gene8c  altera8ons      

D.

BRCA Detection Total Received PARP inhibitor

Somatic Mutation Only 7 2

Germline and Somatic Mutation 15 3

Germline Mutation Only 2 1

Currently Receiving Targeted Therapy, n= 2

Targeted Treatment NGS Genomic Alteration

Pazopanib FGFR

Olaparib BRCA

Discontinued from Targeted Therapy, n= 4

Trametinib KRAS

Olaparib PTEN

Trametinib BRAF

Olarparib BRCA

Pa8ent  Characteris8cs  Characteris8cs   n=109/n(%)  

Median  Age  at  Diagnosis  (range)   63.5  (17  -­‐  90)  Median  BMI  (range)   27.6    (18.4  -­‐  45.2)  Stage                IIIC   79  (73  %)              IV   14  (13  %)  Histology            Papillary  Serous   68  (62  %)            Endometrioid   5  (5  %)            Mixed   11  (10  %)            Clear  Cell   3  (3  %)            Mucinous   2  (2  %)            UndifferenOated   4  (4  %)            Other   15  (14  %)  PaOents  who  received  NAC   31  (30  %)  Debulking  Status                NRD   6  (6  %)                OpOmal   54  (50  %)                SubopOmal   25  (23  %)                Unknown   24    (22  %)  Median  Time  between  CompleOon  of  Adj  Therapy  and  Recurrence  (range),  mos   9.1  (0  -­‐  145.2)  PlaOnum  Status              Resistant/Refractory   17  (16  %)              SensiOve   76  (70  %)              Unknown   16  (15  %)  Median  Chemotherapy  Regimens  (range)   6    (2  -­‐  9)  Median  CA-­‐125  (range)              Pre-­‐Treatment   428    (2  –  11,290)              Most  Recent   85.7    (3.5  –  16,098)  PaOent  Status              NED   14  (13  %)              AWD   68  (62  %)              DOD   17  (16  %)              Unknown   10  (9  %)  Median  Progression  Free  Survival  (range),  mos   21.1    (4.2  -­‐  149.5)    Median  Overall  Survival  (range),  mos   35.7  (0.7  -­‐  165.7)    

q Figure  4.   Summary  of   genomic  altera8on  based  on  tumor  histology  

B29

Top Genomic Alterations Tumor Circulogene TP53 TP53

BRCA1/2 KIT KRAS KDR MYC PIK3CA NF1 PTEN

PIK3CA

0   10   20   30   40   50   60   70   80   90  

TP53

BRCA1/2

KRAS

MYC

PIK3CA

NF1

CDKN2A

Total # of Pt.

Papillary Serous Endometrioid Mixed

Clear Cell Mucinous Other

Undifferntiated Unknown