Download Targeted Therapy in Ovarian Cancer

Cancer Precision Medicine: A Primer
Rebecca C. Arend, MD
Division of Gyn Oncology
OUTLINE
• Background
• Where we have been
• Where we are
• Where we are going
•
•
•
•
Targeted Therapy in Ovarian Cancer
How to Individualized Targeted Therapy
Personalized Medicine Initiative at UAB
The Future of “Personalized or Precision Medicine”
• We have a long way to go
Ovarian Cancer Statistics
• 21,290 new cases, 14,180 deaths
• State of Alabama: 350 cases, 260 deaths
• 5th in cancer deaths among women
• More deaths than any other cancer of the female
reproductive system
• ~75% diagnosed at late stage (stage III/IV)
• Most treated with surgical cytoreduction and adjuvant
platinum- taxane based chemotherapy
• Most patients recur within 2 years and receive multiple
rounds of chemotherapy
Ovarian Cancer - Progress in Outcome
70
IP THERAPY
46
34
22
Survival (mo)
58
CISPLATIN
AGGRESSIVE
SURGERY,
COMBINATION
CHEMOTHERAPY
PACLITAXEL
10
-2
1975
1980
1985
1990
YEAR
1995
PACLITAXEL
CARBO
2000
2005
Modified from David Spriggs
FDA approved drugs for Ovarian Cancer
1978
2016
 1978
 1990
 1991
 1992
 1996
 1999
 2005
 2006
 2014
 2014
 2016
 2016
Cisplatin
Altretamine
Carboplatin
Paclitaxel
Topotecan
Liposomal Doxorubicin (Accelerated)
Liposomal Doxorubicin (Full)
Gemcitabine
Bevacizumab (platinum resistant)
Olaparib (BRCA mutation carriers)
Bevacizumab (platinum sensitive)
Recaparib (somatic+germline BRCA)
New Chemotherapy Approaches
•
Intraperitoneal chemotherapy
•
Neo-adjuvant chemotherapy
•
Dose dense taxanes
•
Anti-angiogenic therapy
•
PARP inhibitors
Anti-angiogenic therapy: Targeted
Therapy
VEGF Inhibition
Biologically-Targeted Drugs (Ovarian Cancer)
45
FDA Approval:
November 2014
Bevacizumab
40
PFS ≥ 6 mos (%)
35
30
Sorafenib
25
20
Dasatinib
Imatinib
15
10
5
A6
Temsirolimus
Gefitinib
Mifepristone
Enzastaurin
Lapatinib
Vorinostat
0
0
5
10
15
Response Rate (%)
20
25
Bevacizumab
• FDA approved for use in combination with chemotherapy
in the treatment of women with platinum-resistant,
recurrent ovarian cancer
• Studied as primary therapy and consolidation with
paclitaxel and carboplatin showed modest improvement in
PFS and OS
• Not FDA approved
PARP inhibitors: Individualized Targeted
Therapy
Poly ADP ribose polymerase inhibitor that blocks enzymes in repairing damaged DNA
BRCA and PARP
Olaparib approved for women with advanced
ovarian cancer with defective germline BRCA genes
Proportion Of Hereditary
Ovarian Cancer
Ovarian Cancer
15%
Sporadic
Hereditary
Recaparib approved for women with advanced ovarian
cancer with defective germline and/or somatic BRCA
genes
Specific Pathways
How do we individualize treatment?
Can we use Genetic Sequencing?
Personalized Medicine Initiative
Division of Gyn Oncology at UAB
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.
Methods
– 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.
Methods
Patient Demographics
Genomic Alterations Found in Tumor DNA
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%.
Results compared to Public databases
TP53
BRCA1/2 (somatic)
KRAS
TCGA (n=270, n=412)
MYC
Ross (n=48)
PIK3CA
Rodriguez-Rodriguez (n= 36)
NF1
Arend (n = 109)
CDKN2A
0
10
20
30
40
50
% of Pt.
60
70
80
90
100
Circulating DNA in the bloodstream
Primary tumor
bloodstream
circulating
normal
DNA
circulating
tumor DNA
Metastatic tumor
Summary of results from each genetic test
• A total of 116 patients were
enrolled
• 62 sent to GPS, 55 patients with
results
• 54 sent to FO
• 6 plasma samples sent to
Circulogene
• 49 patients with NGS results from
both cfDNA and tumor (13 by GPS;
36 by FO)
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.
Summary of genomic alteration based on histology
CDKN2A
NF1
PIK3CA
Papillary Serous
Endometrioid
Mixed
Clear Cell
Mucinous
Other
Undifferntiated
Unknown
MYC
KRAS
BRCA1/2
TP53
0
10
20
30
40
50
Total # of Pt.
60
70
80
90
Summary of pts that received targeted therapy
FDA-approved targeted therapy
potential benefits in the FO cohort (n=54)
Summary of BRCA1/2 genetic alterations
Summary of Treatments
• 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.
56/109 (54.1%) patients had actionable mutations with potential clinical benefit
from FDA-approved targeted therapy based on NGS results
Costs Associated with Targeted Therapy
• 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.
Conclusions
• 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.
• We have been successful in providing patients with NGSdirected 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
 cfDNA represents mainly the genome of dying tumor cells
 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.
Retrospective Study – Tumor Bank
Informed
Consent
(N=37)
Surgery canceled (N=1)
Diagnostic
Laparoscopy
(N=36)
Primary
Debulking (N=3)
cfDNA
Collected at
the time of
recurrence
(N=4)
cfDNA
and
Tumor
DNA
(N=14)
HGPS with Pre
and Post
Treatment
Specimens
Collected
(N=20)
Exclusions:
Liver Cirrhosis
(N=1),
Not High Grade
pap-serous
histology (N=6),
No interval
debulking
performed (N=5),
Complete
pathologic
response at
interval
debulking (N=1)
Quality RNA
extracted for
Nanostring
(N=19)
Study Design
Pre and Post Comparison Overall
Molecules
Exp. Value
NR4A3
17.864
NR4A1
16.294
FOS
4.993
OSM
4.327
KLF4
4.309
DUSP5
4.241
SFRP2
4.064
NFATC1
3.598
WNT16
3.583
RASGRF2
3.215
Molecules
Exp. Value
RAD51
-2.653
HIST1H3B
-2.471
FANCA
-2.395
HIST1H3G
-2.391
CCNA2
-2.143
HELLS
-2.137
CCNB1
-2.111
E2F1
-2.094
TTK
-2.034
HIST1H3H
-2.009
Hereditary Ovarian Cancer Signaling
Hereditary Ovarian Cancer Signaling
Plasma vs. Tumor Variant Overlap
Pre Neoadjuvant DNA Analysis
Post Neoadjuvant DNA Analysis
Top Four Mutations
Pre-NACT
Post-NACT
TP53
Tumor Only
Tumor and Plasma
N=0
N=10
N=4
N=0
N=
9
N=5
PIK3CA
N=3
N=3
N=0
N=0
N=3
N=2
N=2
N=2
N=0
KDR
N=0
N=2
N=4
KIT
N=3
N=2
N=3
N=3
N=3
N=1
Plasma
Only
Frequency of alterations, overall concordance, and potential targeted therapy
TP53
Freq in
Tissue
100
Freq in
Plasma
68
Overall
Concordance
36
KIT
35
39
64
PTEN
7
18
75
PIK3CA
CTNNB1
21
0
36
14
79
86
BRAF
0
11
89
EGFR
APC
ATM
FGFR2
GNAQ
0
0
0
0
0
11
7
7
7
7
KDR
7
RET
Gene
FDA Approved Therapy
Clinical Trials
WEE1 inhibitors
89
93
93
93
93
NA
Tyrosine kinase inhibitors-imatinib, nilotinib, sorafenib, dasatinib,
sunitinib
MTOR inhibitors- everolimus, temsirolimus; Parp inhibitorolaparib
MTOR inhibitors- everolimus, temsirolimus
MTOR inhibitors- everolimus, temsirolimus
BRAF inhibitors-vemurafenib, dabrafenib; MEK inhibitorstametinib, cobimentinib
lapatinib, erlotinib, gefitinib
NA
Parp inhibitor- olaparib
Multikinase inhibitors -pazopanib, ponatinib
MEK inhibitors-tametinib, cobimentinib
7
93
Cabozantinib, axitinib, sorafenib, regorafenib, sunitinib
Anti angiogenics
0
7
93
Multikinase inhibitors – vandetanib, ponatinib, sunitinib
RET inhibitors
KRAS
7
0
93
MEK inhibitors – trametinib, cobimentinib
MEK inhibitors
ABL1
0
4
96
ABL inhibitor-imatinib, nilotinib, dasatinib, bosutinib, ponatinib
Tyrosine kinase inhibitors
ERBB2
0
4
96
ERBB4
FBXW7
GNAS
IDH1
JAK2
MET
0
0
0
0
0
0
4
4
4
4
4
4
96
96
96
96
96
96
NRAS
0
4
96
PTPN11
0
4
SMAD4
0
SMARCB1
VHL
Tyrosine kinase inhibitors
MTOR/Parp inhibitors
MTOR inhibitors
WNT inhibitors
MEK inhibitors
EGFR inhibitors
WNT inhibitors
ATM inhibitors
Multikinase inhibitors
PKC inhibitors
96
lapatinib, afatnib, neratinib, dacomitinib, ado-trastuzumab,
emtansine, pertuzumab
Tyrosine kinase inhibitors – gefitinib, lapatinib
MTOR inhibitors - everolimus , temsirolimus
MEK inhibitors – trametinib, cobimentinib
NA
NA
MET inhibitors- crizotinib, cabozantinib
BRAF inhibitors-vemurafenib, dabrafenib; MEK inhibitorstametinib, cobimentinib
NA
PTPN11/SHP2 inhibitors
4
96
NA
TGFβ inhibitors, anti angiogenics
0
4
96
NA
EZH2 inhibitors
0
4
96
Tyrosine kinase inhibitors – sunitinib, sorafenib, MTOR inhibitors
– everolimus, temsirolimus
VEGF inhibitors
HER2 inhibitors
Pan-Her inhibitors
MTOR inhibitors
ERK inhibitors
IDH1 inhibitors
JAK2 inhibitors
MET inhibitors
MAP/ERK inhibitors
Conclusions
• Mutations are more frequently detected in the plasma
compared to the tumor.
• Tumor heterogeneity is better captured by circulating cfDNA
than from a small biopsy of tumor.
• More mutation changes are picked up in the plasma following
therapy.
• Caution must be taken in interpreting the results for clinical
purposes because much is still unknown about the dynamic
biology of circulating cfDNA.
• Since cfDNA represents mainly the genome of dying tumor cells,
it may not be appropriate to use these mutations to guide
targeted therapy.
• Further research is required to identify the utility of cfDNA as a
dynamic “liquid” tumor biopsy tool
Acknowledgments
Arend Lab
•
Angelina Londoño, Ph.D.
•
Ashwini Katre, M.S.
•
Naveed Farrukh B.S.
•
Mary Kat Smith B.S.
•
Taylor Turner, M.D.
•
Haller Smith, M.D.
•
Allison Montgomery B.S.
•
Cindy Tawfik B.S.
•
Zach Dobbin, M.D.
Don Buchsbaum, PhD
Andres Forero, M.D.
Ronald D. Alvarez, M.D.
Eddy S. Yang, M.D.
Shuko Harada, M.D.
Charles A. Leath III, M.D.
Warner K. Huh, M.D.
Lyse Norian, PhD
Circulogene
•
Chen Yeh, M.D.
•
Andrew Ford,
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.