Transforming treatment and improving survival for ovarian cancer patients
Applications of genomic profiling for
real time decisions in cancer treatment
Laura Shawver, PhD
Founder
Overview
 Ovarian Cancer statistics and treatment
 Clearity Foundation: mission and process
 Clearity profiling panel and results interpretation
 Incorporating profiling in clinical trial designs
Ovarian cancer statistics and standard of care
22,280 new cases and 15,500 deaths estimated in the US in 2012
~75% diagnosed at stage III/IV
Most treated with surgical cytoreduction followed by adjuvant platinumtaxane based chemotherapy
Most patients recur within 2 years and receive multiple rounds of
subsequent chemotherapy
27% survive >10 years
Multiple choices for recurrent disease
Can we inform the treatment decision?
NCCN Guidelines for Epithelial Ovarian Cancer/Fallopian Tube Cancer/Peritoneal Cancer 3.2012
The Clearity Foundation launched as a nonprofit organization in 2008 to:

Bring molecular profiling to the forefront of ovarian cancer
diagnosis and treatment

Assist doctors in identifying therapy informed by their patient’s
tumor molecular profile

Expedite the clinical development of novel targeted agents for
ovarian cancer

Increase the probability of success by utilizing molecular
profiling to select patients for clinical trials
Leading advisors and scientific presentations
Scientific Advisory Board
Beth Karlan, MD, Chair
Cedars Sinai & UCLA
Medical Center
Doug Levine, MD
Memorial Sloan Kettering
Cancer Center
Johnathan Lancaster, MD
Moffitt Cancer Center
Julie Cherrington, PhD
Pathway Therapeutics
Ursula Matulonis, MD
Dana Farber Cancer Center
& Harvard Medical School
Deb Zajchowski, PhD
Clearity Foundation Scientific
Director
Mol Cancer Ther; 11(2) February 2012:
Treatment-related protein biomarker expression
differs between primary and recurrent ovarian
carcinomas DA Zajchowski, BY Karlan and LK
Shawver
ASCO 2011: Expression Profiles in Matched
Primary and Recurrent Ovarian Carcinomas
DA Zajchowski, BY Karlan and LK Shawver,
AACR 2011: Molecular Profiling in Recurrent
Ovarian Cancer Patients DA Zajchowski, C
Bentley, J Gross, BY Karlan and LK Shawver
AACR 2010: Selecting Patients for Ovarian
Cancer Clinical Trials by Profiling Tumors
against a Broad Panel of Molecular Markers
DA Zajchowski, J Gross, BY Karlan, K Bloom, D
Loesch, A Alarcon and LK. Shawver
6
Ovarian cancers are molecularly heterogeneous
Genomic alterations observed in nearly every chromosome
Serous ovarian cancer
Any one genomic alteration is found in only a small fraction of patient tumors
 One drug will not be effective for the population
 Drugs must be developed for specific molecular tumor types
 Profiling of individual tumors is essential
 A broad profiling panel is necessary
7
Data provided by Dr. D. Levine
How Clearity works
Clearity Profiling Services:
•Physician and patient education
•Testing coordination
•Secure data analysis
•Results reported to patient
Oncologists and
Patients
Patient and medical team
using molecular profiles to
prioritize therapeutic
options
8
Use tumor molecular profiles to inform
choice of chemotherapy and/or clinical trial
Chemotherapy
Marker Panel
Select
chemotherapy
for next
treatment
Targeted Therapy
Marker Panel
Select chemo for
combination with
targeted agent in
clinical trial
Select clinical
trial with
targeted
agent
9
Key take away messages
 Chemotherapy agents are targeted cytotoxic treatments
 Chemotherapy will continue to play an important role but molecular
targeted agents, on an individualized basis, will become
increasingly important
 Molecular profiling is available to help prioritize treatments and will
be increasingly utilized
– for chemotherapy agents
– for molecular targeted agents
– for clinical trials
 Clinical trials should be considered early in the treatment process
Current panel of IHC tests
Growth Factors/ Receptors
EGFR*
Her2*
IGF1R
c-Met*
VEGF
PDGFR
Cytoplasmic Signal Transducers and Apoptosis Regulators
K-ras**
B-raf**
PIK3CA**
PTEN
Bcl-2
Survivin
Cox-2
Nuclear Signaling Proteins
Hormone Receptors/Transcription Factors
ER
AR
PR
Cell Cycle
Ki67
p16
Rb
Chemotherapy Sensitivity Markers
DNA Synthesis/Transcription
TLE3
Topo1
Top2A
ECM
SPARC
Chemotherapy Resistance Markers
Drug Transporters
BCRP
MRP1
* DNA amplification
MDR1/PGP
DNA Repair/ Modification
ERCC1
** DNA mutational analysis
MGMT
DNA Synthesis/Cell Division
RRM1
TS
TUBB3
11
Data stored and analyzed in
Diane Barton Database
UNK
3%
Stage
IV
6%
Platinum Response
I
8%
II
7%
NA
25%
III
76%
Histology
TC UNK Ad
SB
CS
1% 2% 5%
1%
1%
Refractory
7%
Resistant
15%
Sensitive
53%
Specimen Source
CC
6%
Endo
8% GC
2% Mucin
2%
MMMT
1%
Serous
70%
Primary
peritoneal
20%
Mixed
3%
Distant Mets
5%
*200 patients; March 15, 2012 (n=242; Aug 15, 2012)
Ovary
34%
Peritoneal
recurrence
41%
N=244
Data collected as histoscores
Histoscore =
% tumor stained x
Intensity =92
Marker expression in all patients provides basis
for interpretation of individual results
300
250
H Score
200
150
100
50
Box, inter-quartile range; line, median; whiskers, maximum and minimum values
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
EGFR
0
Clinical research evidence for biomarkers is used
to interpret results
http://www.clearityfoundation.org/drugs-and-biomarkers.aspx
Chemotherapy selection uses published evidence and
expression cut-offs derived from current database
300
250
High Topo I  Irinotecan, topotecan
High Topo II  Doxorubicin, etoposide
200
Low RRM1  Gemzar
H Score*
150
Low TS  Fluoropyrimidines
100
High SPARC nab-Paclitaxel
High PGP  No Taxane, no doxil
50
High BCRP  No Topotecan
BCRP
SPARC
PGP
RRM1
TS
TOP2A
TOPO1
0
Low: <25th percentile High: >75th percentile
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor Molecular Profile Informs Therapeutic Decisions
with stage IIIB papillary serous carcinoma
300
3/2005
Carbo/tax x6
250
taxol 10 mos
recurrence 12/2008
carbo/tax x 6
150
carbo/tax/bev x3
100
12/2009
Doxil x3
50
Surgery 1/2010
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
200
Tumor Profiled
Gemzar
17
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor
Molecular
Profile
Informs Therapeutic Decisions
th percentile)
High
EGFR
(98
with stage IIIB papillary serous carcinoma
 EGFR inhibitors have been
ineffective in clinical trials
although benefit seen in
individual patients
 Mutations can predict
sensitivity and resistance to
EGFR inhibitors in lung
cancer
 Follow up mutation analysis
conducted; patient was wt
300
250
150
100
50
taxol 10 mos
recurrence 12/2008
carbo/tax x 6
carbo/tax/bev x3
12/2009
Doxil x3
Surgery 1/2010
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
200
3/2005
Carbo/tax x6
Tumor Profiled
Gemzar
18
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor Molecular Profile
Therapeutic Decisions
High Informs
ER
with stage IIIB papillary serous carcinoma
 Anti-estrogens and aromatase
inhibitors utilized in ovarian 3/2005
Carbo/tax x6
cancer patients but not approved
taxol 10 mos
due to lack of efficacy in clinical
studies
recurrence 12/2008
300
250
carbo/tax x 6
150
carbo/tax/bev x3
100
12/2009
Doxil x3
50
Surgery 1/2010
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
200
Tumor Profiled
Gemzar
19
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor Molecular Profile Informs Therapeutic Decisions
with
Highstage
SPARCIIIB papillary serous carcinoma
 Clinical trial for nab-paclitaxel
(none at the time) or off-label
use
 patient had long history of
taxane treatment
300
250
taxol 10 mos
recurrence 12/2008
carbo/tax x 6
150
carbo/tax/bev x3
100
12/2009
Doxil x3
50
Surgery 1/2010
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
200
3/2005
Carbo/tax x6
Tumor Profiled
Gemzar
20
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor Molecular Profile Informs Therapeutic Decisions
with stage IIIB papillary serous carcinoma
300
3/2005
Carbo/tax x6
250
taxol 10 mos
200
150
100
50
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
recurrence 12/2008
Low TS
carbo/tax x 6
 Fluoropyrimidinescarbo/tax/bev x3
and pemetrexed
12/2009
considered as a
Doxil x3
reasonable optionSurgery 1/2010
Tumor Profiled
Gemzar
21
Case Study: profile for patient diagnosed in 3/2005
Figure 5. Tumor Molecular Profile Informs Therapeutic Decisions
with stage IIIB papillary serous carcinoma
300
3/2005
Carbo/tax x6
250
200
Low
RRM
150 High RRM1 associated with
resistance to gemcitabine
100 Gemcitabine is an
approved agent in recurrent
50
ovarian cancer
recurrence 12/2008
carbo/tax x 6
carbo/tax/bev x3
12/2009
Doxil x3
Surgery 1/2010
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
c-MET
IGF1Rb
HER2
0
EGFR
H Score*
taxol 10 mos
Tumor Profiled
Gemzar
22
RRM1 is gemcitabine’s target and efficacy biomarker
Drug
Mechanism of Action
Gemcitabine
Inhibits cell division by blocking DNA
synthesis
Gem
3
Gem   GemPP-> GemPPP
4
DCK
ENT1
5
2
HuR
RRM2
RRM1
1 Blocks
 DNA 
synthesis
Growth
inhibition
dCDP
CDP
Gemcitabine Resistance Markers
Marker
Name
Biological Role
Evidence
References
RRM1
ribonucleotide
reductase,
regulatory subunit
M1
Enzyme synthesizes
deoxyribonuceosides from
ribonucleoside precursors
High protein levels associated with
poor response and outcome in
pancreatic, biliary, and NSCLC
patients after gemcitabine-based
therapy
Akita, Zheng et al. 2009;
Reynolds, Obasaju et al.
2009; Nakamura, Kohya et
al. 2010
RRM2
For
ribonucleotide
reductase,
information
on each
regulatory subunit
Enzyme synthesizes
High mRNA expression correlated
Itoi, Sofuni et al. 2007;
deoxyribonuceosides
from
with
poor
outcome
following
Boukovinas,
Papadaki et al.
marker, visit http://www.clearityfoundation.org/drugs-and-biomarkers.aspx
ribonucleoside precursors
gemcitabine treatment in
2008; Souglakos,
23
Case5.Study:
profile Profile
for patient
in 3/2005
Figure
Tumor Molecular
Informsdiagnosed
Therapeutic Decisions
with stage IIIB papillary serous carcinoma
300
3/2005
Carbo/tax x6
250
200
recurrence 12/2008
carbo/tax x 6
150
carbo/tax/bev x3
12/2009
Doxil x3
100
Ge,zar
MGMT
MRP1
BCRP
SPARC
PGP/MDR1
TLE3
TUBB3
ERCC1
RRM1
TS
TOP2A
TOPO1
Ki-67
PR
AR
ER
COX-2
VEGF
PDGFR Beta
PDGFR Alpha
Tumor Profiled
c-MET
0
IGF1Rb
Surgery 1/2010
HER2
50
EGFR
H Score*
taxol 10 mos
Gemzar
Gemzar (3/2010)
Gemcitabine
9/2011: NED
24
Often, only one of the commonly used agents to treat
recurrent ovarian cancer is prioritized by the profile
Pemetrexed,
capecitabine
Gemcitabine
Topo I
inhibitors
Topo II
inhibitors
Teal, tumor marker expression met quartile cutpoint criterion: RRM1, TS <25th percentile; TOP2A, TOP1 >75th percentile.
150/196 (76%) can be assigned to one of these agents.
25
Biopsy of recurrent disease is needed to obtain
relevant profiling information
Mol Cancer Ther; 11(2) February 2012
Primary
Recurrence
EGFR
5% 2+; 10
68% 3+; 255
26
Marker expression differences in patient-matched
primary and recurrent samples
H score
160
35SE-M-S
40
35SE-O-S
10
EGFR
HER2 IGF1Rb c-MET
VEGF
COX-2
ER
Ki-67
TOPO1 TOP2A
TS
RRM1 ERCC1
PGP
SPARC
BCRP
MRP1 MGMT
42S-MD-S
H score
160
42S-M-S
40
42S-P-S
10
EGFR
HER2 IGF1Rb c-MET
VEGF
COX-2
ER
Ki-67
TOPO1 TOP2A
TS
RRM1 ERCC1
PGP
SPARC
BCRP
MRP1 MGMT
Our knowledge and ability to rationally target
ovarian cancer has evolved since 2008
Molecular characterization of ovarian tumors reveals new
means for targeting and stratifying patients
Advances in methods for genomic characterization of tumor
samples (next-gen sequencing in CLIA setting)
Increase in the number of molecularly targeted agents
entering trials for ovarian cancer
28
Low frequency of specific genetic aberrations in
primary high grade serous ovarian carcinoma
—> extensive genomic interrogation necessary to characterize tumors
29
From TCGA study: Nature 474, 609- 615 (2011)
Genomic markers can be used to
assign patients to clinical trial agents
PARP inhibitors
RB
PI3K/RAS
HR Alterations/BRCAness
CDK inhibitors
AURK inhibitors PI3K/AKT/mTOR inhibitors
MEK inhibitors
Notch
Notch inhibitors
From TCGA study: Nature 474, 609- 615 (2011)
30
New emphasis for profile: clinical trial selection
Chemotherapy
Marker Panel
Select
chemotherapy
for next
treatment
Targeted Therapy Marker
Panel (IHC and DNA SEQ)
Select chemo for
combination with
targeted agent in
clinical trial
Select clinical
trial with
targeted
agent
31
Expanded panel provides more options for Clearity
patients
ChemoTx panel
Chemosensitivity Markers
DNA Synthesis/Cell Cycle
Topo1
Top2A
Transcription/Translation Regulators
TLE3
HuR
Drug Transporters/Metabolism
ENT1
DCK
Ki67
IHC
Chemoresistance Markers
Apoptosis Regulators
Survivin
DNA Repair/ Modification
ERCC1
MGMT
DNA Synthesis/Cell Division
RRM1
RRM2
TS
Drug Transporters
MDR1/PGP
BCRP
MRP1
TUBB3
Taxanes
Gemcitabine
Doxil
Topo I inh
(Pemetrexed)
Clinical trial
options
Targeted Tx panel
GF / Receptors
EGFR*
Her2*
IGF1R*
c-Met*
Genotype Analysis: Mut/Fusions/Transloc/Amp-Del
ABL1
ATM
ATR
BRCA1
BRCA2
BAP1
PRKDC
TOP1
ERCC2
FANCA
MLH1
DNA Repair
MRE11A
MUTYH
MSH2
MSH6
TP53
MDM2
MDM4
ARAF
RAF1
BRAF
GNA11
NF1
GNAQ
NRAS
GUCY1A2
HRAS
KRAS
MAP2K1
MAP2K2
MAP2K4
PTCH1
PTCH2
SMO
SUFU
BCL2
BCL2A1
BCL2L1
BCL2L2
MCL1
BCL6
VEGF
NOTCH1
NPM1
HOXA3
NTRK1
NTRK2
NTRK3
RAS-MAPK
TNKS
TNKS2
SOX10
SOX2
ALK
TGFBR2
RUNX1
SMAD2
SMAD3
SMAD4
IHC + DNA Seq*
PDGFR*
ER*
PHLPP2
AKT1 PIK3CA
AKT2 PIK3CG
AKT3 PIK3R1
PTEN
PTPN11
STK11
RICTOR
RPTOR
MTOR
PI3K-AKT-MTOR
TSC1
TSC2
JUN
ERG
PAX5 NF2
PAK3
PTEN*
Bcl-2*
PKHD1
CD79A
CARD11
CD79B
TNFAIP3
IKBKE
PTPRD
AURKB
AURKA
MYCL1
MYCN
MYC
Cell Cycle
GATA1
CEBPA
RARA
ESR1
AR
NKX2-1
Cell Cycle/ Survival
CDKN2A
CDKN2B
CDKN2C
RB1
CCND1
CCND2
CCND3
CCNE1
CDK4
CDK6
CDH1
CDH2
CDH20
CDH5
EZH2
ARID1A
SMARCA4
SMARCB1
KDM6A
DNMT3A
DOT1L
CHEK1
CHEK2
HSP90AA1
USP9X
WT1
LRP1B
LRP6
DDR2
CTNNB1
CDK8
APC
CRKL
CRLF2
IKZF1
Plus
more…….
GNAS
IDH1
IDH2
ARFRP1
TBX22
TET2
MLL
MEN1
SRC
PLCG1
FOXP4
VHL
FBXW7
MPL
STAT3
JAK1
JAK2
JAK3
INHBA
MITF
ERBB2
EPHA3
EPHA5 ERBB3
EPHA6 ERBB4
EPHA7
EPHB1 FGFR1
EPHB4 FGFR2
EPHB6
FGFR3
FGFR4
ABL2
FLT1
FLT3
FLT4
KDR
MET
EGFR
IGF1R
Membrane
INSR
IRS2
Receptors
PDGFRA
PDGFRB
RET
CBL
KIT
IGF2R
GPR124
LTK
Cox-2
Survivin
Rb*
BRCAnalysis (DNA Repair/HR Pathway Marker Panel)
DNA repair inhibitors
Cell cycle inhibitors
Proliferation/
survival signaling
inhibitors
P16*
CCND1*
* Next-gen exon sequencing of 182+ genes
32
Frequency of hotspot mutations in PIK3CA,
KRAS, BRAF, and EGFR is histologydependent
Clear cell: PIK3CA
Mucinous and carcinosarcoma: KRAS
PIK3CA
KRAS
BRAF
EGFR
WT
Mut
WT
Mut
WT
Mut
WT
Mut
Serous
82
1
66
1
11
0
10
0
Endo
13
2
6
1
2
0
1
0
CC
4
5
2
0
1
0
1
0
Muc
2
0
2
1
1
0
3
0
Ad
3
1
4
0
1
0
1
0
C (MM)
2
0
1
2
1
0
0
0
SB
1
0
1
0
1
0
0
0
Examples of gene alterations in recurrent
ovarian cancer
Histology TP53 MDM2 KRAS
MUT
S
S
S
S
S
S
S
S
S
S
S
MUT
AMP
MUT
MUT
MUT
SS
MUT
TR
NF1 CCND1CCND2 CCNE1 MYC MYCL1 MYCN BRCA1 BRCA2 ATM IGF1R PATCH CDK4 MCL1 PIK3CA
FS
AMP
FS
AMP
AMP
FS
AMP
AMP
AMP
AMP
AMP
FS
FS
AMP
AMP
TR
AMP
AMP
AMP
AMP
CC
CC*
MUT
MX-ECCM
MX-ECC
AMP
>5-fold
AMP
≤5-fold
AMP
AMP
MUT
MUT
MUT
MUT
SS, splice site mutation; FS, frameshift; TR, truncation
MUT
Case Study: profile for patient diagnosed in 2009 with
stage IIIC clear cell carcinoma
300
4/2009
CDDP/tax (ip) x 3
Carbo/tax (iv) x3
250
12/2009
recurrence
150
Tumor profiled
100
1/2010
Topotecan +
AMG 386*
(clinical trial)
50
Topotecan
PGP
MGMT
MRP1
BCRP
ERCC1
RRM1
TS
TOP2A
TOPO1
AR
PR
ER
Ki-67
COX-2
EGFR
0
HER2
H Score*
200
5/2011
Stable disease
Case Study: profile for patient diagnosed in 2009 with stage IIIC
clear cell carcinoma
300
4/2009
CDDP/tax (ip) x 3
Carbo/tax (iv) x3
250
12/2009 recurrence
200
Tumor profiled
H Score*
IHC Results
Irinotecan
150
100
5/2011 Stable disease
50
6/2011 Surgery -residual disease
Tumor profiled
PGP
MGMT
MRP1
BCRP
TUBB3
RRM1
ERCC1
TS
TLE3
SPARC
SPARC
TOP2A
TOPO1
PR
AR
ER
Ki-67
COX-2
c-MET
IGF1Rb
EGFR
HER2
0
DNA Sequencing Results
RxIrinotecan
+ Everolimus
Gene Mutation Approved Drugs and Clinical Trial Agents
PIK3CA
1/2010 Topotecan +
AMG 386*
(clinical trial)
H1047R
PI3K inhibitors, mTOR inhibitors
(rapamycin, temsirolimus, everolimus)
Everolimus
9/2012 NED
Protein and DNA results are integrated into one
report that highlights therapy options
SUMMARY of AGENTS
IHC Results
IHC results—Drug correlations
DNA Sequencing Results
Clearity report summarizes results from multiple
labs and provides consensus interpretation
Summary of
relevant patient
medical history
Summary of agents
(approved and in clinical
trials) associated with
clinical benefit extracted
from pg 2
Compilation of data from all
labs with interpretation
(percentile rank, potential
drugs)
Individual profile compared to
ovarian cancer population
Contact information for help
with clinical trials, lab reports.
Number of patients whose
data are included in Diane
Barton Database
38
New emphasis for profile: clinical trial selection
Chemotherapy
Marker Panel
Select
chemotherapy
for next
treatment
Targeted Therapy Marker
Panel (IHC and DNA SEQ)
Select chemo for
combination with
targeted agent in
clinical trial
Select clinical
trial with
targeted
agent
39
Vision of an Ovarian Cancer Clinical Trials
Coalition (OCCTC)
 Increase the probability of drug development success by utilizing
molecular profiling to select patients for clinical trials
 Facilitate access to eligible patients through patient advocate-driven
clinical trials education, outreach, and established web portal for
clinical trials identification and matching
 Remove barriers for screening large number of patients for
enrollment by testing for all drug biomarkers in every patient
 Reduce costs of profiling assays and make efficient use of biopsy
samples
 Make more treatment options available for ovarian cancer patients
40
Summary
• Ovarian cancer is heterogeneous and a
broad profiling panel is needed to capture
data relevant to each individual
• Commonly utilized agents for treatment of
recurrent ovarian cancer can be prioritized
using molecular markers
• Molecular profiling can help prioritize drugs
being tested in clinical trials
41
Thank you!
• To everyone who has participated in the
Clearity process
• To our physician partners
• To the Clearity team
•
•
•
•
Dr. Deb Zajchowski
Hillary Theakston
Kathleen Zajchowski
And our volunteers!
42
www.clearityfoundation.org