Transcript ASCO_2008_files/Meropol CCS personalized medicine

Predicting Safety and Efficacy
of Treatment for Colon Cancer
Clinical Science Symposium
Towards Personalized Medicine: Trials and
Technologies That Will Lead to Individualized
Therapy for Cancer
Neal J. Meropol, M.D.
Fox Chase Cancer Center
Philadelphia, PA
May 31, 2008
Disclosures
• Consulting or Advisory role
– Amgen
– Astra Zeneca
– Genentech
– Genomic Health
– Imclone
– Saladax
– Sanofi Aventis
– Zealand Pharma
• Stock Ownership
– Saladax
The Context
• Multiple treatment options for patients with
colorectal cancer
• No single “correct” treatment algorithm
• All available treatments are toxic
• All available treatments have modest activity
• No obvious new agents on the horizon
The Treatment Discovery Cycle in
Oncology: Where are we?
Drug
Discovery
Examples
• 5FU modulation
• Newer cytotoxics
• Antibodies
Demonstrate
Clinical Activity
Optimize Use
Personalized Medicine Prerequisites:
Target, Drug, Classifier
Old paradigm: Empirical Medicine
Diagnosis
Select Treatment
New paradigm: Personalized Medicine
Apply
Diagnostic
Select Treatment
Classifier
Diagnosis
Select
Treatment
Apply
Diagnostic
Classifier
Revise
Treatment
It’s all about variability:
“Predictive” vs. “Prognostic”
• Predictive: explains variability in response to
treatment
• Prognostic: explains variability irrespective of
treatment
Variability exists in the host
(germline) and tumor (somatic)
Why weren’t validation studies
undertaken until recently?
• It wouldn’t affect clinical care
– Limited options for alternative therapy
– Results not sufficiently discriminating
• Love for new drugs
• Technical aspects of assay performance
But
The times have changed
What should we expect from a classifier?
• It must assist in decision making
– Must it be perfect as a discriminator?
• Yes - If no competing therapies
• No - If competing therapies
• Possible results
– Patient will definitely benefit – doesn’t tell
who not to treat
– Patient will definitely not benefit – doesn’t
tell who to treat
– Patient will be more or less likely to benefit
Potential Predictive Markers for Colon Cancer
Treatment
Drug
Fluoropyrimidines
General
Marker
TS, DPD*, TP, MSI, MTHFR
expression/polymorphisms
UGT polymorphisms*, MSI, transporter
polymorphisms
ERCC1, GST P1, XPD expression, transporter
polymorphisms
gene amplification/polymorphism, RAS
mutation, BRAF mutation, ligand expression,
PTEN expression, VEGF levels
VEGF polymorphisms, ICAM
polymorphisms/levels, E-selectin levels, HIF1,
Glut-1, VEGFr gene expression
Circulating tumor cells
*FDA-recognized
Yellow = presented at ASCO 2008
Irinotecan
Oxaliplatin
EGFR Antibodies
VEGF inhibitors
Mutant RAS
WT RAS
The
personalized
approach to
treatment of
colorectal
cancer has
arrived
PFS benefit of
panitumumab only
seen in patient with
wild-type KRAS
R. Amado et el. JCO 2008
When added to FOLFIRI, the benefit of
cetuximab is restricted to patients with
WT RAS tumors
Wild type RAS
(N=348)
Mutant RAS
(N=192)
Response
FOLFIRI vs.
Favor cetuximab
FOLFIRI/Cetuximab P = 0.0025
No difference
Progression-Free Survival
FOLFIRI vs.
Favor cetuximab
FOLFIRI/Cetuximab HR = 0.68
P = 0.017
No difference
Van Cutsem et al. ASCO Plenary, 2008
Tumor gene expression and K-Ras
mutations in fixed paraffin-embedded
tissue predict response to cetuximab in
metastatic colon cancer
Authors
J.B. Baker1, D. Dutta1, D. Watson1, T. Maddala1, S.
Shak1, E.K. Rowinsky2, L. Xu3 , E. Clark3 , D.J. Mauro3 ,
S. Khambata-Ford3
1Genomic
Health, Inc. Redwood City, CA
2Imclone Systems, Inc., New York, NY
3Bristol Myers Squibb, Princeton, NJ
Baker et al. Summary
• 226 patients with metastatic colorectal cancer treated
with single-agent cetuximab
• Retrospective analysis of banked tissue from 3
studies (~425 patients in parent studies)
• Association of RAS mutation and quantitative gene
expression with clinical outcomes
• Key findings:
– Gene expression can be reliably measured in
FFPE tissue
– RAS mutation associated with lack of response
– 4-gene model discriminates outcomes (“disease
control” and PFS) in patients with WT RAS
If validated, is this test “good enough” to
assist in treatment decision making?
WT RAS
Response + SD
87 (60%)
Disease
Progression
57 (40%)
Total
144 (100%)
If validated, is this test “good enough” to
assist in treatment decision making?
WT RAS
Low Response
Gene Score
High Response
Gene Score
Response + SD
87 (60%)
16 (27%)
71 (85%)
Disease
Progression
57 (40%)
44 (73%)
13 (15%)
144 (100%)
60 (100%)
84 (100%)
Total
Clinical utility is dependent on other available options
Things I’d like to know more about
• Platform characteristics
– e.g. how frequent are indeterminate results?
• Prediction vs. Prognosis
– Is gene expression profile associated with
response or only “disease control”?
– “Disease control” may be heavily influenced by
natural history rather than treatment
• Will equivalent results be obtained with other
EGFR inhibitors?
• Will the use of this test result in improved patient
outcomes for patients?
• These data are worthy of validation in an
independent patient sample
Cellular transporter pharmacogenetics in
metastatic colorectal cancer: initial analysis of
C80203
H. L. McLeod, K. Owzar, D. Kroetz, F. Innocenti, S. Das, P.
Friedman, K. Giacomini, R. Goldberg, A. Venook, M. J. Ratain
Univ of North Carolina-Chapel Hill, Chapel Hill, NC; Duke,
Durham, NC; UCSF, San Francisco, CA; University of Chicago,
Chicago, IL; CALGB, Chicago, IL
McLeod et al. Summary
• Genomic DNA from 180 of 238 patients on C80203
(FOLFOX vs. FOLFIRI +/- cetuximab)
• Genotyping of transporter genes involved in
irinotecan and oxaliplatin clearance:
– ABCC2, ABCC4, ABCG2, SLCO1B1, SLC22A1,
and SLC22A2
• Association of genotype with response and toxicity
• Key findings:
– ABCG2 34 G>A associated with response to
FOLFOX, resistance to FOLFIRI
– No associations with toxicity
Pharmacogenetics (Genetic Variation) Impacts
Pharmacokinetics and Pharmacodynamics
Dose and
Compliance
Target focused
Tumor
Response
Pharmacokinetics
-Absorption
-Distribution
-Metabolism
-Excretion
Pharmacodynamics
Drug focused
Target focused
Normal
Tissue
Toxicity
The promise and challenge of
pharmacogenetics
• The promise
– Mechanism-based
– Non-invasive
– Response and toxicity prediction
• The challenge
– Low-frequency alleles
– Multiple interrelated systems
– Large sample sizes required to develop
and validate models
What have we learned?
• Germline DNA collection is possible in an Intergroup
clinical trial
• ABCG2 34 G>A polymorphisms are uncommon
• Association with treatment effect requires clinical
validation and mechanistic support (previous published
work suggests no impact on irinotecan PK and
increased in vitro sensitivity)
• Large sample sizes will be required to identify predictive
associations with low frequency SNPs
• Individual SNPs as predictive markers will likely be rare
given the complexity of drug metabolism and clearance,
target expression and function, and mutidrug regimens
• Candidate gene selection based on pathway
understanding complements genome wide screening
efforts
We must be prepared to integrate
new findings
• Patient care
– Recognize that predictive markers will generally
not provide absolute guidance
– Assess and communicate value and comparative
effectiveness of personalized approaches
– Develop streamlined systems for tumor and
germline marker assessment
• Research
– Emphasize prospective tissue acquisition
– Anticipate and react promptly to new data that
impact ongoing research studies and patient care
The impact of personalized medicine for
pharma is uncertain
Advantage?
• Costs
– Development time
– Production
• Risks
– Success rate
• Returns
– Market size
– Duration of treatment
– New treatment market
– New diagnostic market
– Competition
– Pricing (value, novelty, need)
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Conclusions
• We can successfully personalize the
therapy of patients with colorectal cancer
• We must continue to build well-annotated
tissue repositories in prospective
randomized clinical trials
• Now more than ever, industry and
academia must identify shared goals
• With more effective personalized treatment
approaches we have an opportunity to shift
emphasis from the traditional focus on pvalues (and marginal benefit) to focus on
(and demand for higher) value of new
innovation