Cancer Clinical Trials in the Genomic Era

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Cancer Clinical Trials in the Genomic Era

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

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• Prognostic biomarkers
• Measured before treatment to indicate long-term
  outcome for patients untreated or receiving
  standard treatment
• May reflect both disease aggressiveness and
  effect of standard treatment
• Used to determine who needs more intensive
  treatment
• Predictive biomarkers
• Measured before treatment to identify who will
  benefit from a particular treatment

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• Endpoint
• Measured before, during and after treatment to
  monitor pace of disease and treatment effect
• Pharmacodynamic (phase 0-1)
• Does drug hit target
• Intermediate response (phase 2)
• Does drug have anti-tumor effect
• Surrogate for clinical outcome (phase 3)

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Prognostic Predictive Biomarkers

• Single gene or protein measurement
• Scalar index or classifier that summarizes
  contributions of multiple genes

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Prognostic Predictive Biomarkersin Genomic
Oncology

• Many cancer treatments benefit only a minority of
  patients to whom they are administered
• Being able to predict which patients are likely
  to benefit can
• Help patients get an effective treatment
• Help control medical costs
• Improve the success rate of clinical drug
  development

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Validation Fitness for Intended Use
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Biomarker Validity

• Analytical validity
• Measures what its supposed to
• Reproducible and robust
• Clinical validity (correlation)
• It correlates with something clinically
• Medical utility
• Actionable resulting in patient benefit

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Clinical Utility

• Biomarker informs action that benefits patient by
  improving treatment decisions
• Identify patients who have very good prognosis on
  standard treatment and do not require more
  intensive regimens
• Identify patients who are likely or unlikely to
  benefit from a specific regimen

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ObjectiveUse biomarkers to

• Develop effective treatments
• Know who needs these treatments and who benefits
  from them

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Prognostic markers

• There is an enormous published literature on
  prognostic markers in cancer.
• Very few prognostic markers (factors) are
  recommended for measurement by ASCO, are approved
  by FDA or are reimbursed for by payers. Very few
  play a role in treatment decisions.

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Prognostic Biomarkers Can be Therapeutically
Relevant

• lt10 of node negative ER breast cancer patients
  require or benefit from the cytotoxic
  chemotherapy that they receive

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OncotypeDx Recurrence Score

• Intended use
• Patients with node negative estrogen receptor
  positive breast cancer who are going to receive
  an anti-estrogen drug following local
  surgery/radiotherapy
• Identify patients who have such good prognosis
  that they are unlikely to derive much benefit
  from adjuvant chemotherapy

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• Selected patients relevant for the intended use
• Analyzed the data to see if the recurrence score
  identified a subset with such good prognosis that
  the absolute benefit of chemotherapy would at
  best be very small in absolute terms
• Used an analytically validated test

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Major problems with prognostic studies of gene
expression signatures

• Inadequate focus on intended use
• Reporting highly biased estimates of predictive
  value

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Major problems with prognostic studies of gene
expression signatures

• Inadequate focus on intended use
• Cases selected based on availability of specimens
  rather than for relevance to intended use
• Heterogeneous sample of patients with mixed
  stages and treatments. Attempt to disentangle
  effects using regression modeling
• Too a great a focus on which marker is prognostic
  or independently prognostic, not whether the
  marker is effective for intended use

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• Goodness of fit is not a proper measure of
  predictive accuracy
• Odds ratios and hazards ratios are not proper
  measures of prediction accuracy
• Statistical significance of regression
  coefficients are not proper measures of
  predictive value

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Goodness of Fit vs Prediction Accuracy

• For pgtn problems, fit of a model to the same data
  used to develop it is no evidence of prediction
  accuracy for independent data

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Validation of Prognostic Model

• Completely independent validation dataset
• Splitting dataset into training and testing sets
• Evaluate 1 completely specified model on test set
• Cross-validation

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Leave-one-out Cross Validation for Classifier of
Two Classes

• Full dataset P1,2,,n
• Omit case 1
• V11 T12,3,,n
• Develop classifier using training set T1
• Classify cases in V1 and count whether
  classification is correct or not
• Repeat for case 2,3,
• Total number of mis-classified cases

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Complete cross Validation

• Cross-validation simulates the process of
  separately developing a model on one set of data
  and predicting for a test set of data not used in
  developing the model
• All aspects of the model development process must
  be repeated for each loop of the cross-validation
• Feature selection
• Tuning parameter optimization

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Cross Validation

• The cross-validated estimate of misclassification
  error is an estimate of the prediction error for
  the model fit applying the specified algorithm to
  full dataset

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Prediction on Simulated Null DataSimon et al. J
Nat Cancer Inst 9514, 2003

• Generation of Gene Expression Profiles
• 20 specimens (Pi is the expression profile for
  specimen i)
• Log-ratio measurements on 6000 genes
• Pi MVN(0, I6000)
• Can we distinguish between the first 10
  specimens (Class 1) and the last 10 (Class 2)?
• Prediction Method
• Compound covariate predictor built from the
  log-ratios of the 10 most differentially
  expressed genes.

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Cross-validation Estimate of Prediction Error
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• Partition data set D into K equal parts
  D1,D2,...,DK
• First training set T1D-D1
• Develop completely specified prognostic model M1
  using only data T1
• eg
• Using M1, compute prognostic score for cases in
  D1
• Develop model M2 using only T2 and then score
  cases in D2

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• Repeat for ... TK -gt MK -gt DK
• Group patients into 2 or more risk groups based
  on their cross-validated scores
• Calculate Kaplan-Meier survival curve for each
  risk-group

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• To evaluate significance, the log-rank test
  cannot be used for cross-validated Kaplan-Meier
  curves because the survival times are not
  independent

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• Statistical significance can be properly
  evaluated by approximating the null distribution
  of the cross-validated log-rank statistic
• Permute the survival times and repeat the entire
  cross-validation procedure to generate new
  cross-validated K-M curves for low risk and high
  risk groups
• Compute log-rank statistic for the curves
• Repeat for many sets of permutations

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Predictive Biomarkers

• Cancers of a primary site often represent a
  heterogeneous group of diverse molecular entities
  which vary fundamentally with regard to
• the oncogenic mutations that cause them
• their responsiveness to specific drugs

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• In most positive phase III clinical trials
  comparing a new treatment to control, most of the
  patients treated with the new treatment did not
  benefit.
• Adjuvant breast cancer 70 long-term
  disease-free survival on control. 80
  disease-free survival on new treatment. 70 of
  patients dont need the new treatment. Of the
  remaining 30, only 1/3rd benefit.

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Predictive Biomarkers

• Estrogen receptor over-expression in breast
  cancer
• Anti-estrogens, aromatase inhibitors
• HER2 amplification in breast cancer
• Trastuzumab, Lapatinib
• OncotypeDx gene expression recurrence score in N
  ER breast cancer
• Low score -gt not responsive to chemotherapy
• KRAS in colorectal cancer
• WT KRAS cetuximab or panitumumab
• EGFR mutation in NSCLC
• EGFR inhibitor
• V600E mutation in BRAF of melanoma
• vemurafenib
• ALK translocation in NSCLC
• crizotinib

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Standard Paradigm of Phase III Clinical Trials

• Broad eligibility
• Base primary analysis on ITT eligible population
• Dont size for subset analysis, allocate alpha
  for subset analysis or trust subset analysis
• Only believe subset analysis if overall treatment
  effect is significant and interaction is
  significant

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Standard Paradigm Sometimes Leads to

• Treating many patients with few benefiting
• Small average treatment effects
• Problematic for health care economics
• Inconsistency in results among studies
• False negative studies

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The standard approach to designing phase III
clinical trials is based on two assumptions

• Qualitative treatment by subset interactions are
  unlikely
• Costs of over-treatment are less than costs
  of under-treatment

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Subset Analysis

• In the past generally used as secondary analyses
• Numerous subsets examined
• No control of type I error
• Trial not sized for subset analysis

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• Neither conventional approaches to subset
  analysis nor the broad eligibility paradigm are
  adequate for genomic based oncology clinical
  trials
• We need a prospective approach that includes
• Preserving study-wise type I error
• Sizing the study for the primary analysis that
  includes any subset analysis
• If there are multiple subsets, replacing subset
  analysis with development and internal unbiased
  evaluation of an indication classifier

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• Although the randomized clinical trial remains of
  fundamental importance for predictive genomic
  medicine, some of the conventional wisdom of how
  to design and analyze rcts requires
  re-examination
• The concept of doing an rct of thousands of
  patients to answer a single question about
  average treatment effect for a target population
  presumed homogeneous with regard to the direction
  of treatment efficacy in many cases no longer has
  an adequate scientific basis

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• How can we develop new drugs in a manner more
  consistent with modern tumor biology and obtain
  reliable information about what regimens work for
  what kinds of patients?

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Development is Most Efficient When the Scientific
Basis for the Clinical Trial is Strong

• Having an important molecular target
• Having a drug that is deliverable at a dose and
  schedule that can effectively inhibit the target
• Having a pre-treatment assay that can identify
  the patients for whom the molecular target is
  driving progression of disease

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When the Biology is Clear the Development Path is
Straightforward

• Develop a classifier that identifies the patients
  likely (or unlikely) to benefit from the new drug
• Develop an analytically validated test
• Measures what it should accurately and
  reproducibly
• Design a focused clinical trial to evaluate
  effectiveness of the new treatment in test
  patients

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Using phase II data, develop predictor of
response to new drug
Targeted (Enrichment) Design
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Predictive Biomarkers

• Estrogen receptor over-expression in breast
  cancer
• Anti-estrogens, aromatase inhibitors
• HER2 amplification in breast cancer
• Trastuzumab, Lapatinib
• OncotypeDx gene expression recurrence score in
  breast cancer
• Low score for ER node - -gt no chemotherapy
• KRAS in colorectal cancer
• WT KRAS cetuximab or panitumumab
• EGFR mutation in NSCLC
• EGFR inhibitor
• V600E mutation in BRAF of melanoma
• vemurafenib
• ALK translocation in NSCLC
• crizotinib

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Evaluating the Efficiency of Targeted Design

• Simon R and Maitnourim A. Evaluating the
  efficiency of targeted designs for randomized
  clinical trials. Clinical Cancer Research
  106759-63, 2004 Correction and supplement
  123229, 2006
• Maitnourim A and Simon R. On the efficiency of
  targeted clinical trials. Statistics in Medicine
  24329-339, 2005.

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• Relative efficiency of targeted design depends on
  
• proportion of patients test positive
• specificity of treatment effect for test positive
  patients
• When less than half of patients are test positive
  and the drug has minimal benefit for test
  negative patients, the targeted design requires
  dramatically fewer randomized patients than the
  standard design in which the marker is not used

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Two Clinical Trial Designs

• Standard design
• Randomized comparison of new drug E to control C
  without the test for screening patients
• Targeted design
• Test patients
• Randomize only test patients
• Treatment effect D in test patients
• Treatment effect D- in test patients
• Proportion of patients test is p
• Size each design to have power 0.9 and
  significance level 0.05

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RandRat nuntargeted/ntargeted

• If D-0, RandRat 1/ p2
• if p0.5, RandRat4
• If D- D/2, RandRat 4/(p 1)2
• if p0.5, RandRat16/91.77

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Comparing T vs C on Survival or DFS5 2-sided
Significance and 90 Power
Reduction in Hazard Number of Events Required
25 509
30 332
35 227
40 162
45 118
50 88
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• Hazard ratio 0.60 for test patients
• 40 reduction in hazard
• Hazard ratio 1.0 for test patients
• 0 reduction in hazard
• 33 of patients test positive
• Hazard ratio for unselected population is
• 0.330.60 0.671 0.87
• 13 reduction in hazard

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• To have 90 power for detecting 40 reduction in
  hazard within a biomarker positive subset
• Number of events within subset 162
• To have 90 power for detecting 13 reduction in
  hazard overall
• Number of events 2172

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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 test
  patients, overall improvement in survival would
  have been 3.375
• 4025 patients/arm would have been required

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Web Based Software for Planning Clinical Trials
of Treatments with a Candidate Predictive
Biomarker

• http//brb.nci.nih.gov

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Regulatory Pathway for Test

• Companion diagnostic test with intended use of
  identifying patients who have disease subtype for
  which the drug is proven effective

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Implications for Early Phase Studies

• Need to design and size early phase studies to
  discover an effective predictive biomarker for
  identifying the correct target population
• Need to establish an analytically validated test
  for measuring the predictive marker in the phase
  III pivotal studies

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When the drug is specific for one target and the
biology is well understood

• May need to evaluate several candidate tests
• e.g. protein expression of target or
  amplification of gene
• Phase II trials sized for adequate numbers of
  test positive patients and to determine
  appropriate cut-point of positivity

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When the drug has several targets or the biology
is not well understood

• Should biologically characterize tumors for all
  patients on phase II studies with regard to
  candidate targets and response moderators
• Phase II trials sized for evaluating candidates
• Opportunity for sequential and adaptive designs
  to improve efficiency

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Empirical screening of expression profiles or
mutations to develop predictive marker

• Should re-think whether to develop the drug
• Larger sample size required
• Dobbin, Zhao, Simon, Clinical Ca Res 14108,
  2008.
• Use of archived samples from previous negative
  phase III trial
• Use of large disease specific panel of
  molecularly characterized human tumor cell lines
  to identify predictive marker

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Stratification DesignInteraction Design
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Develop prospective analysis plan for evaluation
of treatment effect and how it relates to
biomarker

• Defined analysis plan that protects type I error
  and permits adequately powered evaluation in test
  patients
• http//brb.nci.nih.gov
• Trial sized for defined analysis plan
• Test negative patients should be adequately
  protected using interim futility analysis

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Fallback Analysis Plan

• Test average treatment effect at reduced level p0
  
• If significant claim broad effectiveness
• If overall effect is not significant, test
  treatment effect in marker subset at level
  .05-p0
• If significant claim effectiveness for marker
  subset
• Claim of significance for marker subset should
  not require either
• Overall significance
• Significant interaction

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Sample size for Analysis Plan

• To have 90 power for detecting uniform 33
  reduction in overall hazard at 1 two-sided
  level requires 370 events.
• If 33 of patients are positive, then when there
  are 370 total events there will be approximately
  123 events in positive patients
• 123 events provides 90 power for detecting a 45
  reduction in hazard at a 4 two-sided
  significance level.

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Strong confidence in test Small r2 and large
r1 Weak confidence in test Small r2 and small
r1 p00 selected to control type I error rates
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Bayesian Two-Stage DesignRCT With Single Binary
Marker
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Adaptive Threshold Design

• Randomized clinical trial of E vs C
• Single candidate biomarker with K candidate
  cut-points
• Entry not restricted by biomarker value
• Adaptive in the sense that no pre-specified
  cut-point is provided. Eligibility is not changed
  during trial based on interim results

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Final Analysis in Two Parts

• Test global null hypothesis that treatment E is
  equivalent to C in efficacy for all biomarker
  values
• If global null hypothesis is rejected, develop
  information about how effectiveness of E depends
  on biomarker value

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Bootstrap Confidence Intervals for Threshold b
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The confidence interval for the cut-point can be
used to inform treatment decisions for future
patients
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Key Points

• It can be beneficial not to define a cut-point
  for the biomarker prior to conducting the phase
  III clinical trial
• The phase II database may be inadequate with
  regard to number of cases, lack of control group,
  different endpoint
• The only thing that stands in the way of a more
  informative phase III trial is the aspirin
  paradigm that the ITT analysis of the eligible
  population is required to serve as a basis for
  approval

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The Biology is Often Not So Clear

• Cancer biology is complex and it is not always
  possible to have the right single predictive
  classifier identified with an appropriate
  cut-point by the time the phase 3 trial of a new
  drug is ready to start accrual

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With a Small Number of Candidate
BiomarkersBiomarker Selection Design

• Based on Adaptive Threshold Design
• W Jiang, B Freidlin R Simon
• JNCI 991036-43, 2007

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Biomarker Selection Design

• Have identified K candidate biomarkers B1 , , BK
  thought to be predictive of patients likely to
  benefit from T relative to C
• Cut-points not necessarily established for each
  biomarker
• Eligibility not restricted by candidate markers

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Marker Selection Design
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Designs When there are Many Candidate Markers and
Multi-marker Classifiers are of Interest
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Adaptive Signature Design
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• The indication classifier is not a binary
  classifier of whether a patient has good
  prognosis or poor prognosis
• It is a two sample classifier of whether the
  prognosis of a patient on E is better than the
  prognosis of the patient on C

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• The indication classifier maps the vector of
  candidate covariates into E,C indicating which
  treatment is predicted superior for that patient
• The classifier need not use all the covariates
  but variable selection must be determined using
  only the training set
• Variable selection may be based on selecting
  variables with apparent interactions with
  treatment, with cut-off for variable selection
  determined by cross-validation within training
  set for optimal classification
• The indication classifier can be a probabilistic
  classifier

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Treatment effect restricted to subset.10 of
patients sensitive, 400 patients.
Test Power
Overall .05 level test 46.7
Overall .04 level test 43.1
Sensitive subset .01 level test (performed only when overall .04 level test is negative) 42.2
Overall adaptive signature design 85.3
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Overall treatment effect, no subset effect. 400
patients
Test Power
Overall .05 level test 74.2
Overall .04 level test 70.9
Sensitive subset .01 level test 1.0
Overall adaptive signature design 70.9
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• This approach can be used with any set of
  candidate predictors
• The approach can also be used to identify the
  subset of patients who dont benefit from the new
  treatment when the overall ITT comparison is
  significant

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Key Idea

• Replace multiple significance testing by
  development of one indication classifier and
  obtain unbiased estimates of the properties of
  that classifier if used on future patients

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• At the conclusion of the trial randomly partition
  the patients into K approximately equally sized
  sets P1 , , PK
• Let D-i denote the full dataset minus data for
  patients in Pi
• Omit patients in P1
• Apply the defined algorithm to analyze the data
  in D-1 to obtain a classifier M-1
• Classify each patient j in P1 using model M-1
• Record the treatment recommendation E or C

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• Repeat the above steps for all K loops of the
  cross-validation (develop classifier from scratch
  in each loop and classify omitted patients)
• When cross-validation is completed, all patients
  have been classified once as what their optimal
  treatment is predicted to be

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• Let S denote the set of patients for whom
  treatment E is predicted optimal
• Compare outcomes for patients in S who actually
  received E to those in S who actually received C
• Compute Kaplan Meier curves of those receiving E
  and those receiving C
• Let z standardized log-rank statistic

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Test of Significance for Effectiveness of E vs C

• Compute statistical significance of z by
  randomly permuting treatment labels and repeating
  the entire cross-validation procedure to obtain a
  new set S and a new logrank statistic z
• Do this 1000 or more times to generate the
  permutation null distribution of treatment effect
  for the patients in S

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• The size of the E vs C treatment effect for the
  indicated population is (conservatively)
  estimated by the Kaplan Meier survival curves of
  E and of C in S

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Cross-Validated Adaptive Signature Design

• Define indication classifier development
  algorithm A
• Apply algorithm to full dataset to develop
  indication classifier for use in future patients
  M(xA,P)
• Using K fold cross validation
• Classify patients in test sets based on
  classifiers developed in training sets e.g.
  yiM(xiA,P-i)
• Si yi E
• Compare E to C in S and estimate size of
  treatment effect
• is an estimate of the size of the
  treatment effect
• for future patients with M(xA,P)E

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Cross-Validated Adaptive Signature Design

• Approximate null distribution of
• Permute treatment labels
• Repeat complete cross-validation procedure
• Generate permutation distribution of the
• values for permuted data
• Test null hypothesis that the treatment effect in
  classifier positive patients is null using as
  test statistic cross-validated estimate of
  treatment effect in positive patients

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70 Response to E in Sensitive Patients25
Response to E Otherwise25 Response to C30
Patients Sensitive
ASD CV-ASD
Overall 0.05 Test 0.830 0.838
Overall 0.04 Test 0.794 0.808
Sensitive Subset 0.01 Test 0.306 0.723
Overall Power 0.825 0.918
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25 Response to T 25 Response to CNo Subset
Effect
ASD CV-ASD
Overall 0.05 Test 0.047 0.056
Overall 0.04 Test 0.04 0.048
Sensitive Subset 0.01 Test 0.001 0
Overall Power 0.041 0.048
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The Objectives of a Phase III Clinical Trial

• Test the global null hypothesis that the new
  treatment E is uniformly ineffective relative to
  a control C for all patients while preserving the
  type I error of the study
• If the global null hypothesis is rejected,
  develop an internally validated labeling
  indication for informing physicians in their
  decisions about which patients they treat with
  the drug.
• Not a hypothesis testing problem

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Prediction Based Clinical Trials

• We can evaluate our methods for analysis of
  clinical trials in terms of their effect on
  patient outcome via informaing therapeutic
  decision making

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• Hence, alternative methods for analyzing RCTs
  can be evaluated in an unbiased manner with
  regard to their value to patients using the
  actual RCT data

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Expected t Year DFS Using Indication Classifier
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Expected t Year DFS With Conventional Analysis
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Prediction Based Clinical Trials

• The resampling approach provides an internally
  validated way of evaluating the effectiveness of
  indication classifiers for informing treatment
  selection to improve patient outcome

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Prediction Based Clinical Trials

• By switching from subset analysis to development
  of indication classifiers and by using
  re-sampling and careful prospective planning, we
  can more adequately evaluate new methods for
  analysis of clinical trials in terms of improving
  patient outcome by informing therapeutic decision
  making

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• By applying the classifier development algorithm
  to the full dataset D, an indication classifier
  is developed for informing how future patients
  should be treated
• M(xA, D) for all x vectors.
• The cross validation merely serves to
• provide an estimate of the treatment effect for
  future patients with M(xA, D)E
• and to provide a significance test of the null
  hypothesis that the treatment effect is zero

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• The stability of the indication classifier
  M(xA,D)can be evaluated by examining the
  consistency of classifications M(xiA, B) for
  bootstrap samples B from D.

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• Although there may be less certainty about
  exactly which types of patient benefit from E
  relative to C, classification may be better than
  for standard clinical trials in which all
  patients are classified based on results of
  testing the single overall null hypothesis

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• This approach can also be used to identify the
  subset of patients who dont benefit from a new
  regimen C in cases where E is superior to C
  overall at the first stage of analysis. The
  patients in SC D S are not predicted to
  benefit from E. Survivals of E vs C can be
  examined for patients in that subset and a
  permutation based confidence interval for the
  hazard ratio calculated.

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506 prostate cancer patients were randomly
allocated to one of four arms Placebo and 0.2 mg
of diethylstilbestrol (DES) were combined as
control arm C 1.0 mg DES, or 5.0 mg DES were
combined as T. The end-point was overall
survival (death from any cause).

• Covariates Age, performance status (pf), tumor
  size (sz), stage/grade index (sg), serum acid
  phosphatase (ap)

Cova
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Figure 1 Overall analysis. The value of the
log-rank statistic is 2.9 and the corresponding
p-value is 0.09. The new treatment thus shows no
benefit overall at the 0.05 level.
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Figure 2 Cross-validated survival curves for
patients predicted to benefit from the new
treatment. log-rank statistic 10.0, permutation
p-value is .002
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Figure 3 Survival curves for cases predicted not
to benefit from the new treatment. The value of
the log-rank statistic is 0.54.
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Marker Strategy Design
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Marker Strategy Design

• Generally very inefficient because some (many)
  patients in both randomization groups receive the
  same treatment
• Often poorly informative
• Not measuring marker in control group means that
  merits of complex marker treatment strategies
  cannot be dissected

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Validation of Predictive BiomarkerStratification
Design
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Prospective-Retrospective Study
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In some cases a trial with optimal structure for
evaluating a new biomarker will have been
previously performed and will have pre-treatment
tumor specimens archived

• Under certain conditions, a focused analysis
  based on specimens from the previously conducted
  clinical trial can provide highly reliable
  evidence for the medical utility of a prognostic
  or predictive biomaker
• In some cases, it may be the only way of
  obtaining high level evidence

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Prospective-Retrospective Design
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Conclusions of Simon, Paik, Hayes

• Claims of medical utility for prognostic and
  predictive biomarkers based on analysis of
  archived tissues can have either a high or low
  level of evidence depending on several key
  factors.
• These factors include the analytical validation
  of the assay, the nature of the study from which
  the specimens were archived, the number and
  condition of the specimens, and the development
  prior to assaying tissue of a focused written
  plan for analysis of a completely specified
  biomarker classifier.
• Studies using archived tissues from prospective
  clinical trials, when conducted under ideal
  conditions and independently confirmed can
  provide the highest level of evidence.
• Traditional analyses of prognostic or predictive
  factors, using non analytically validated assays
  on a convenience sample of tissues and conducted
  in an exploratory and unfocused manner provide a
  very low level of evidence for clinical utility.

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Guidelines Proposed by Simon, Paik,
HayesProspective-retrospective design

• Adequate archived tissue from an appropriately
  designed phase III clinical trial must be
  available on a sufficiently large number of
  patients that the appropriate biomarker analyses
  have adequate statistical power and that the
  patients included in the evaluation are clearly
  representative of the patients in the trial.
• The test should be analytically validated for use
  with archived tissue.
• Testing should be performed blinded to the
  clinical data.
• The analysis plan for the biomarker evaluation
  should be completely specified in writing prior
  to the performance of the biomarker assays on
  archived tissue and should be focused on
  evaluation of a single completely defined
  classifier.
• The results should be validated using specimens
  from a similar, but separate study involving
  archived tissues.

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Acknowledgements

• Kevin Dobbin
• Boris Freidlin
• Wenyu Jiang
• Aboubakar Maitournam
• Shigeyuki Matsui
• Michael Radmacher
• Jyothi Subramanian
• Yingdong Zhao