Challenges in Moving Towards Predictive Oncology

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Challenges in Moving Towards Predictive Oncology

• Richard Simon, D.Sc.
• Chief, Biometric Research Branch
• National Cancer Institute
• http//linus.nci.nih.gov

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BRB Websitehttp//linus.nci.nih.gov/brb

• Powerpoint presentations and audio files
• Reprints Technical Reports
• BRB-ArrayTools software
• BRB-ArrayTools Data Archive
• Sample Size Planning for Targeted Clinical Trials

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• Many cancer treatments benefit only a minority of
  patients to whom they are administered
• Particularly true for molecularly targeted drugs
• Being able to predict which patients are likely
  to benefit would
• save patients from unnecessary toxicity, and
  enhance their chance of receiving a drug that
  helps them
• Help control medical costs
• Improve the efficiency of clinical drug
  development

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Biomarkers

• Surrogate endpoints
• A measurement made before and after treatment to
  determine whether the treatment is working
• Prognostic markers
• A measurement made before treatment to indicate
  likely outcome untreated or on standard treatment
• Predictive classifiers
• A measurement made before treatment to select
  good patient candidates for the treatment

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• Most prognostic factors are not used because they
  are not therapeutically relevant
• Most prognostic factor studies use a convenience
  sample of patients for whom tissue is available.
  Often the patients are too heterogeneous to
  support therapeutically relevant conclusions
• Prognostic factors in focused population can be
  therapeutically useful
• Oncotype DX
• Classifiers that predict benefit from specific
  treatments can be more useful that broad
  prognostic factors

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The Roadmap

• Develop a completely specified genomic classifier
  of the patients likely to benefit from a new drug
• Establish reproducibility of measurement of the
  classifier
• Use the completely specified classifier to design
  and analyze a new clinical trial to evaluate
  effectiveness of the new treatment with a
  pre-defined analysis plan.

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Guiding Principle

• The data used to develop the classifier must be
  distinct from the data used to test hypotheses
  about treatment effect in subsets determined by
  the classifier
• Developmental studies are exploratory
• Studies on which treatment effectiveness claims
  are to be based should be definitive studies that
  test a treatment hypothesis in a patient
  population completely pre-specified by the
  classifier

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New Drug Developmental Strategy I

• Restrict entry to the phase III trial based on
  the binary predictive classifier, i.e. targeted
  design

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Develop Predictor of Response to New Drug
Using phase II data, develop predictor of
response to new drug
Patient Predicted Responsive
Patient Predicted Non-Responsive
Off Study
New Drug
Control
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Applicability of Design I

• Primarily for settings where the classifier is
  based on a single gene whose protein product is
  the target of the drug
• eg Herceptin
• With substantial biological basis for the
  classifier, it may be unacceptable ethically to
  expose classifier negative patients to the new
  drug
• Without strong biological rationale, FDA may have
  difficulty approving the test

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• Efficiency relative to trial of unselected
  patients depends on proportion of patients test
  positive, and effectiveness of drug (compared to
  control) for test negative patients
• When less than half of patients are test positive
  and the drug has little or no benefit for test
  negative patients, the targeted design requires
  dramatically fewer randomized patients

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TrastuzumabHerceptin

• Metastatic breast cancer
• 234 randomized patients per arm
• 90 power for 13.5 improvement in 1-year
  survival over 67 baseline at 2-sided .05 level
• If benefit were limited to the 25 assay
  patients, overall improvement in survival would
  have been 3.375
• 4025 patients/arm would have been required

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GefitinibIressa

• Two negative untargeted randomized trials first
  line advanced NSCLC
• 2130 patients
• 10 have EGFR mutations
• If only mutation patients benefit by 20
  increase of 1-year survival, then 12,806
  patients/arm are needed
• For trial targeted to patients with mutations,
  138 are needed

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Developmental Strategy (II)
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Validation of EGFR biomarkers for selection of
EGFR-TK inhibitor therapy for previously treated
NSCLC patients
Outcome
FISH ( 30)
Erlotinib
2nd line NSCLC with specimen
1 PFS 2 OS, ORR
FISH Testing
Pemetrexed
1-2 years minimum additional follow-up
FISH - ( 70)
Erlotinib
Pemetrexed
4 years accrual, 1196 patients
957 patients

• PFS endpoint
• 90 power to detect 50 PFS improvement in FISH
• 90 power to detect 30 PFS improvement in FISH-
• Evaluate EGFR IHC and mutations as predictive
  markers
• Evaluate the role of RAS mutation as a negative
  predictive marker

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Developmental Strategy (II)

• Do not use the diagnostic to restrict
  eligibility, but to structure a prospective
  analysis plan
• Having a prospective analysis plan is essential
  stratifying (balancing) the randomization is
  not except that stratification ensures that all
  randomized patients will have tissue available
• The purpose of the study is to evaluate the new
  treatment overall and for the pre-defined
  subsets not to modify or refine the classifier
• The purpose is not to demonstrate that repeating
  the classifier development process on independent
  data results in the same classifier

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Analysis Plan A

• Compare the new drug to the control for
  classifier positive patients
• If pgt0.05 make no claim of effectiveness
• If p? 0.05 claim effectiveness for the
  classifier positive patients and
• Compare new drug to control for classifier
  negative patients using 0.05 threshold of
  significance

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Sample size for Analysis Plan A

• 88 events in classifier patients needed to
  detect 50 reduction in hazard at 5 two-sided
  significance level with 90 power
• If 25 of patients are positive, then when there
  are 88 events in positive patients there will be
  about 264 events in negative patients
• 264 events provides 90 power for detecting 33
  reduction in hazard at 5 two-sided significance
  level

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• Study-wise false positivity rate is limited to 5
  with analysis plan A
• It is not necessary or appropriate to require
  that the treatment vs control difference be
  significant overall before doing the analysis
  within subsets

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Analysis Plan B

• Test for interaction between treatment effect in
  test positive patients and treatment effect in
  test negative patients
• If interaction is significant at level ?int then
  compare treatments separately for test positive
  patients and test negative patients
• Otherwise, compare treatments overall

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Sample Size Planning for Analysis Plan B

• 88 events in classifier patients needed to
  detect 50 reduction in hazard at 5 two-sided
  significance level with 90 power
• If 25 of patients are positive, then when there
  are 88 events in positive patients there will be
  about 264 events in negative patients
• 264 events provides 90 power for detecting 33
  reduction in hazard at 5 two-sided significance
  level

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Simulation Results for Analysis Plan B

• Using ?int0.10, the interaction test has power
  93.7 when there is a 50 reduction in hazard in
  test positive patients and no treatment effect in
  test negative patients
• A significant interaction and significant
  treatment effect in test positive patients is
  obtained in 88 of cases under the above
  conditions
• If the treatment reduces hazard by 33 uniformly,
  the interaction test is negative and the overall
  test is significant in 87 of cases

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The Roadmap

• Develop a completely specified genomic classifier
  of the patients likely to benefit from a new drug
• Establish reproducibility of measurement of the
  classifier
• Use the completely specified classifier to design
  and analyze a new clinical trial to evaluate
  effectiveness of the new treatment with a
  pre-defined analysis plan.

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Guiding Principle

• The data used to develop the classifier must be
  distinct from the data used to test hypotheses
  about treatment effect in subsets determined by
  the classifier
• Developmental studies are exploratory
• And not closely regulated by FDA
• Studies on which treatment effectiveness claims
  are to be based should be definitive studies that
  test a treatment hypothesis in a patient
  population completely pre-specified by the
  classifier

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Use of Archived Samples

• From a non-targeted negative clinical trial to
  develop a binary classifier of a subset thought
  to benefit from treatment
• Test that subset hypothesis in a separate
  clinical trial
• Prospective targeted type I trial
• Using archived specimens from a second previously
  conducted clinical trial

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Development of Genomic Classifiers

• Before phase III trial initiated
• After phase III trial using archived specimens
• Use classifier in subsequent phase III trial

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Development of Genomic Classifiers

• Single gene or protein based on knowledge of
  therapeutic target
• Empirically determined based on evaluation of a
  set of candidate classifiers
• e.g. EGFR assays
• Empirically determined based on genome-wide
  correlating gene expression or genotype to
  patient outcome after treatment

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Developing Composite Genomic Classifiers

• Composite classifiers incorporate the
  contributions of multiple single-gene features
• The single gene feature are usually selected
  based on their value for distinguishing patients
  likely to respond to the new rx

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DNA Microarray Technology

• Powerful tool for understanding mechanisms and
  enabling predictive medicine
• Challenges ability of biomedical scientists to
  use effectively to produce biological knowledge
  or clinical utility
• Challenges statisticians with new problems for
  which existing analysis paradigms are often
  inapplicable
• Excessive hype and skepticism

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BRB-ArrayTools

• Statistically state-of-the-art integrated
  software for DNA microarray expression and copy
  number data analysis
• Architecture and statistical content by R Simon
• User interface for use and education of
  biomedical scientists
• Extensive built-in gene annotation and linkage to
  genomic websites
• Publicly available for non-commercial use
• Active user list-serve and message board

http//linus.nci.nih.gov/brb
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BRB-ArrayToolsMay 2007

• 7188 Registered users
• 1962 Distinct institutions
• 68 Countries
• 365 Citations
• Registered users
• 3951 in US
• 565 at NIH
• 275 at NCI
• 2014 US EDU
• 754 US Govt (non NIH)
• 3237 Foreign

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Countries With Most BRB ArrayTools Registered
Users

• France 270
• Canada 269
• UK 244
• Germany 239
• Italy 216
• Taiwan 196
• Netherlands 177
• Korea 168
• Japan 153
• China 150

• Spain 146
• Australia 130
• India 107
• Belgium 83
• New Zeland 61
• Sweden 50
• Singapore 46
• Brazil 48
• Israel 41
• Denmark 40

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Developmental vs Validation Studies

• Developmental studies develop predictive
  classifiers and provide internal estimates of
  predictive accuracy
• Validation studies should establish medical
  utility of a classifier previously developed

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Limitations to Developmental Studies

• Sample handling and assay conduct are performed
  under controlled conditions that do not
  incorporate real world sources of variability
• Small study size limits precision of estimates of
  predictive accuracy
• Predictive accuracy may not reflect clinical
  utility

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Validation Studies

• Should establish that the classifier is
  reproducibly measurable and has clinical utility
  relative to practice standards

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Types of Clinical Utility

• Identify patients whose prognosis is good without
  cytotoxic chemotherapy
• Identify patients who are likely or unlikely to
  benefit from a specific therapy
• Multiple therapeutic options available

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Oncotype-Dx

• Predictive index of distant disease-free survival
  for node negative ER patients receiving local
  therapy plus tamoxifen
• Fully specified classifier developed using frozen
  specimens from NSABP B20 patients
• Applied prospectively to frozen specimens from
  NSABP B14 patients who received Tamoxifen for 5
  years
• Good risk patients had very good relapse-free
  survival

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B-14 ResultsRelapse-Free Survival
Paik et al, SABCS 2003
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Mutations Copy number changes Translocations Expre
ssion profile
Treatment
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Conclusions

• New technology and biological knowledge make it
  increasingly feasible to identify which patients
  require systemic treatment are most likely to
  benefit from a specified treatment
• Predictive medicine is feasible based on
  genomic characterization of a patients tumor
• Targeting treatment can greatly improve the
  therapeutic ratio of benefit to adverse effects
• Treated patients benefit
• Economic benefit for society

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Conclusions

• There are many obstacles to achieving the
  potential of new technology
• Changes are required in the methods of
  translational medicine
• Changes are required in the methods of
  biostatistics
• New models for interdisciplinary collaboration
  are needed
• Leadership is needed to ensure that FDA provides
  adequate incentives to industry for development
  and validation of companion diagnostics.

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Acknowledgements

• Kevin Dobbin
• Alain Dupuy
• Boris Freidlin
• Aboubakar Maitournam
• Yingdong Zhao
• BRB-ArrayTools Development Team