Transcript presentation_5-26-2011-6-52-41

Review Experience in Evaluating
Predictive Biomarkers –
Design and Analysis Considerations
Yuan-Li Shen, Dr. P.H.
U.S. Food and Drug Administration
Center for Drug Evaluation and Research
Office of Biostatistics
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Acknowledgement
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Somesh Chattopadhyay
Kun He
Rajeshwari Sridhara
Qiang (Casey) Xu
Sue Jane Wang
The views expressed in this talk are those of the author, not of the FDA
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Outline
• Motivation : HER2, EGFR
• Types of designs
– Prospective (Adaptive, enrichment, all-comer)
– Prospective-Retrospective
– Phase II – biomarker subgroup selection
• Issues in designs and analysis
- Example of HER-2 for MBC (metastatic breast cancer)
- Example of EGFR for NSCLC (non-small cell lung cancer)
- Example of K-ras for mCRC (metastatic colorectal cancer)
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Why targeted in Cancer trials
• Limit drug exposure to those who benefit
• Avoid drug use in those who will be harmed
• Optimize drug dosing
• Improve efficiency of trial design
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Terminology
• Targeted : selected; enrichment
• Untargeted: unselected; all-comers
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Spectrum in study designs incorporating
biomarkers
Targeted
e.g. Hormonal therapy in ER/PR positive breast cancer women;
e.g. Herceptin for HER2+ metastatic breast cancer; Note: HER2–amplified
in 25–30% of all breast cancer patients (Cobleigh et al, 1999)
e.g.Gleevec for KIT+ Gastrointestinal Stromal Tumor (GIST);
e.g. products underdevelopment: BRAF inhibition in melanoma; ALK
inhibition in NSCLC
e.g. K-Ras WT for metastatic colorectal cancer (mCRC)
e.g. EGFR for non-small cell lung cancer (NSCLC) (???)
e.g. EGFR in squamous cell head and neck cancer.
Note: EGFR is overexpressed in 80% of squamous cell carcinoma
Untargeted
(Kumar, et al,2003)
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Design – Issues to be considered
• Strength of the biological evidence
(e.g. relationship between biomarker and treatment;
treatment effectiveness)
• Prevalence of the biomarker
• Validity and reliability of the testing tool
• Feasibility of the assessment
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Type of Trial Designs
• Prospective design
--- Enrichment (e.g. Herceptin in HER2 positive for mBC)
--- Biomarker stratified (with subgroup hypothesis)
--- Planned Adaptation
 Drop a biomarker subgroup, based on a futility analysis
 Adaptive signature design
• Prospective-retrospective
(e.g. anti-EGFR mAb in K-ras WT for patients with mCRC)
• Phase II : Exploratory biomarker subgroup selection (e.g. I-SPY 2)
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Prospective design –
Enrichment
Randomize
New Rx
Biomarker +
Screen
Biomarker status
Control
Biomarker -
Off study
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Issues in Enrichment Design
Requires smaller sample size (relative to all comers
design), however…..
• Can not answer if the biomarker is predictive
• Testing assay may not correctly identify the subgroup
(misclassification)
• Can not be used for clinical validation of the diagnostic
tool
• Slow accrual if only a small proportion of the biomarker
positive subgroup is in the population (low prevalence)
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Example of limitation in assay precision from an
enrichment design (Herceptin)
DFS results in Herceptin for adjuvant breast cancer trial by
HER2 overexpression or amplification
Reference:
Herceptin
package
insert
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Prospective design –
Biomarker Stratified
New Rx
Biomarker +
Screen
Biomarker status
Control
New Rx
Biomarker -
Control
Randomize
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Analysis Issues in the Biomarker Stratified Design
Potential Analysis Plan:
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Treatment by biomarker interaction
Issues: May not have enough power for interaction and failure to
show interaction does not mean no differential subgroup effect
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Hierarchical testing:
Issues: more complicated when interim analyses for efficacy and
futility will be performed and more endpoints (e.g. PFS, OS) will be
involved.
α-splitting:
Issues: Overall treatment effect may be driven by one of the
Biomarker subgroup; when to perform the event driven analysis
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Adaptation for selecting biomarker subgroup
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Planned adaptation:
-- Futility analysis
-- Combining Phase II and III data
-- Adaptive signature design :
Stage 1 data will be used to define a biomarker “sensitive” subgroup and
the rule will be applied to stage 2 to select a biomarker sensitive
subgroup; Analysis will be performed to evaluate treatment effect in
overall population and biomarker subgroup selected at stage 2
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Unplanned adaptation based on independent information – include
subgroup hypothesis
e.g. HER2+ in Lapatinib+Letrozole trial (studied in all comers;
change analysis population to HER2+; not stratified by HER2 status);
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Prospective - Retrospective
• Working Definition : In completed or post-interim-analysis trial
where genomic samples were collected prior to treatment initiation,
whether or not full ascertainment, the genomic hypothesis is
‘prospectively specified’ prior to diagnostic assay testing. However,
the clinical outcome data without genomic information have already
been (partially) collected, unblinded, and analyzed. The genomic
data analysis might be arguably ‘prospectively performed, which is a
retrospective analysis.
* Wang et al (2006 TPJ) ; Wang, et. al. (2010, Clinical Trials)
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Prospective-retrospective –
Criteria for Consideration
• A pragmatic approach may be considered if
1) Adequate, well-conducted and well-controlled trial
2) Large sample size if no stratification by biomarker status for
randomization (??)
3) Biomarker ascertainment in a large portion of randomized subjects
4) Assay – acceptable analytical performance
5) Acceptable statistical analysis plan
O’Neill, Dec. 2008 ODAC
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Retrospective Analysis Issues
1.
Retrospective analysis is for hypothesis generating
and should not be used to salvage a failed trial
Issues:  type I error, if the primary endpoint from the
ITT population is not significant; possible selection
bias; imbalance between treatment arms.
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Convenience sample – Issues
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Studies with smaller the sample size, more likely to have
imbalance of baseline characteristics
Imbalance in biomarker distribution and baseline
characteristics leads to biased treatment effect
Results confounded – depending on ratio of biomarker + vs. –
and distribution of the baseline characteristics
To confirm the results requires replication
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Trial DesignsPhase II dose/biomarker screening
I-SPY (2) : (Investigation of Serial Studies to Predict Your therapeutic
response with imaging and molecular analysis 2) – a process
targeting the rapid, focused clinical development of paired
oncological therapies and biomarkers.
• Framework : Adaptive phase II clinical trial design in the
neoadjuvant setting for women with high risk, locally advanced
breast cancer
• Rationale:
-- identify a biomarker subgroup which is more likely to respond to a
chemotherapy
-- test, analytically validate and qualify biomarkers
-- employ an adaptive trial design to enable efficient learning
-- utilize organizational management principles and bioinformatics to
eliminate current inefficiencies in clinical trial
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I-SPY 2
Biomarkers: ER (+,-), PR(+,-), HER2 (IHC/FISH, gene expression,
protein microarray), MammaPrint score (higher MP2, other MP1)
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I-SPY 2 :
• Standard therapy: weekly paclitaxel(+trastuzumab for HER2 positive)
doxorubicin+cyclophosphamide (AC)
• Drugs: 5 new chemotherapies are tested simultaneously
(each will be tested in a min. of 20 and a max. of 120 patients)
• Biomarkers: 14 out of 256 possible biomarker combinations are of interest
• Endpoint: pathologic complete response
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I-SPY 2 : Issues
• More for hypothesis generating
• Difficulties in interpreting the results
– comparative analysis not interpretable
• Not clear about the robustness of Baysian adaptation
• Products with toxicity, clinical trial hold issues, differential
toxicity and benefit risk can not be identified
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Prospective - Retrospective
K-ras in EGFR inhibitor for mCRC
Background
• The efficacy of EGFR inhibitors is restricted to patients with wild-type
(WT) K-ras status (Amado, JCO 2008, Van Cutsem ASCO 2008 and
others)
• June 2008, NIH/NCI issued an action letter to exclude enrollment to
mutant K-ras subgroup
• Dec. 16, 2008 Oncologic Advisory Committee was held to discuss
the considerations based on retrospective analyses for a biomarker
subgroup
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Retrospective analysis results (Dec. 2008 ODAC)
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Retrospective analysis results (Dec. 2008 ODAC)
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Issues in retrospective analysis (Dec. 2008
ODAC)
Issues :
• If no statistical significance in the primary efficacy
endpoint, all other analyses are exploratory (i.e. can not
be used to salvage a failed trial) :
e.g. EPIC, OPUS and PACCE
• Convenience sample, e.g. EPIC
• Interim results may not be robust and reproducible
• No documented evidence that the treatment groups with
ascertained biomarker status are comparable in baseline
characteristics
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Prospective Designed, with subgroup hypothesis
Study BO18192 (SATURN)
• Multicenter, International, Randomized, Double-blind, Placebocontrolled, Phase III – maintenance therapy after platinum-based
doublet chemotherapy in patients with advanced or recurrent or
metastatic NSCLC
• Patients who had CR, PR or SD were randomized to receive
Tarceva or placebo using adaptive randomization method (Pocock
and Simon, 1975) based on the following stratification factors :
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EGFR protein expression, stage of disease, ECOG performance status,
chemotherapy regimen, smoking status and region.
Tarceva (Erlotinib) : EGFR tyrosine kinase inhibitor (TKI)
Co-primary hypothesis : PFS in all randomized patients
PFS in EGFR IHC positive subset
Alpha – allocation : 0.03 for PFS in all randomized patients;
0.02 for PFS in EGFR IHC positive subset
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Study BO18192 (SATURN) Design (continue…)
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Study BO18192 (SATURN): Biomarker Characteristics
Note: Protocol implemented a hierarchy of biomarker ascertainment
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Study BO18192 (SATURN):
Issues of the EGFR IHC status
• ~16% unevaluable EGFR IHC status
• EGFR (IHC) test was not reliable
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SATURN : Statistical Issues in biomarker
subgroup analysis
(1) More than 16% had missing or indeterminate EGFR IHC status which
may create problems in interpreting the biomarker subgroup results;
(2) The test used for EGFR IHC status is not approved by FDA for NSCLC.
(3) Whether EGFR IHC testing is more likely to accurately assess EGFR
status for NSCLC is uncertain –
Other EGFR status (mutation, FISH) had close to 50% patients with
missing or indeterminate status, so the analyses based on the other
biomarker testing are considered only exploratory.
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Prospective designed, unplanned adaptation
Study EGF 3008:
• Randomized, double-blind, placebo-controlled, parallel-group, multicenter, study to evaluate the efficacy and tolerability of the combined
treatment of lapatinib and letrozole versus the treatment of placebo
and letrozole in 1286 post-menopausal women with hormone
receptor positive (ER positive and/or PgR positive) advanced or
metastatic breast cancer and had not received prior therapy for
advanced or metastatic disease.
• Stratified by site of disease and prior adjuvant endocrine therapy
• Adaptation -- Sample size increased from 760 to 1280
-- Change primary population (from ITT to HER2+)
(after 760 patients were randomized)
-- Hierarchical testing
10 : PFS in HER2+
20 : PFS in ITT
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Study EGF 3008 design
10 objective : PFS in HER2+
20 objective : PFS in ITT
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Study EGF 3008: Patient populations
HER2 : FISH + or IHC : 3+
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Study EGF 3008 : Kaplan-Meier Estimate of
Investigator-Evaluated PFS
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Study EGF 3008 : Statistical Issues
(1) The primary efficacy population was changed from ITT to HER2-positive
after 760 patients were randomized, and sample size was increased to
1286 to have adequate HER2-positive patients;
(2) Study was not stratified at randomization by HER2 status;
(3) Demonstration of OS benefit has generally been the standard for
consideration of regular approval in first-line setting of breast cancer.
Currently, the OS benefit of Lapatinib has not been demonstrated.
Note: Lapatinib was approved under accelerated approval.
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Summary
• The prospective designed trial is preferred.
-- if there is no clear biological evidence of the
biomarker subgroup and diagnostic tool can
reliably identify the biomarker subgroup,
biomarker stratified design may be preferred.
• If the retrospective analysis would be performed, high
biomarker ascertainment rate should be required. It can
not be used to salvage a failed study. Robustness of the
results should be demonstrated and an additional study
that can replicate the results is required as the
supporting information.
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References
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11.
M. Cobleigh, CL Vogel, D. Tripathy, et al.; Multination study of the efficacy and safety of humanized anti-HER2
monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has women who
have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic
disease. J. Clin Oncol. 1999;17(9):2639-2648
AD Barker, CC Sigman, et. al.; I-SPY 2: An Adaptive Breast Cancer Trial Design
in the Setting of Neoadjuvant Chemotherapy; Clinical pharmacology & Therapeutics, vol. 86, No. 1, July 2009
B. Johnson and P. Ja¨nne, Selecting Patients for Epidermal Growth Factor Receptor Inhibitor Treatment: A FISH
Story or a Tale of Mutations? J. of Clin. Oncology, Vol. 23, No. 28, 2005, 6813-6816
V. Kumar, RS Cotran, SL Robbins, et. al, editors: Robbins Basic Pathology. Seventh edition. Saunders,
Philadelphia; 2003.
B. Freidlin, L. M. McShane, E. L. Korn; Randomized Clinical Trials With Biomarkers: Design Issues; J Natl
Cancer Inst 2010;102:152–160
B. Freidlin, R. Simon; Adaptive Signature Design: An Adaptive Clinical Trial Design for Generating and
Prospectively Testing A Gene Expression Signature for Sensitive Patients; Clin Cancer Res, 2005:11(21), 2005,
7872-7878
S. J. Mandrekar and D. J. Sargent; Predictive biomarker validation in practice: lessons from real trials; Clinical
Trials 2010; 7: 567–573
V. Cutsem, I. Lang, et. al., KRAS status and efficacy in the first-line treatment of patients with metastatic
colorectal cancer (mCRC) treated with FOLFIRI with or without cetuximab: The CRYSTAL experience., J. Clin
Oncol 26, No. 15S (May 20 Supplement), 2008:2
R. Simon, S. Wang, Use of genomic signatures in therapeutics development in oncology and other diseases, The
Pharmacogenomics Journal 2006; 6, 166–173
R. Simon, The use of Genomics in Clinical Trial Design, Clin. Cancer Res., 2008; 14(19), 2008, 5984-5993.
R. Simon, S. Paik, D. Hayes, Use of Archived Specimens in Evaluation of Prognostic and Predictive Biomarkers,
Commentaries, J. of National Cancer Ins., V. 101, Issue 21, 2009,1446-1452
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References
12. S. Wang, R. O’Neill and H. M. J.Hung, Approaches to evaluation of treatment effect in randomized clinical trials with
genomic subsets, Pharmaceut. Statist. 2007; 6: 227–244
13. S. Wang, R. O’Neill, J. Hung, Statistical considerations in evaluating pharmacogenomics-based clinical effect for
confirmatory trials, Clinical Trials 2010; 7: 525–536
14. K-Ras Briefing document from Oncologic Drugs Advisory Committee, 12/16/08:
http://www.fda.gov/ohrms/dockets/ac/cder08.html#OncologicDrugs
15. Tarceva Briefing document from Oncologic Drugs Advisory Committee, 12/16/09:
http://www.fda.gov/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/OncologicDrugsAdvisoryCommittee/ucm1
26185.htm
16. Gleevec labeling:
http://www.gleevec.com/prescriptioninformation.jsp?site=PU027801&source=01030&irmasrc=GLIWB0082
17. Herceptin labeling:
http://www.gene.com/gene/products/information/pdf/herceptin-prescribing.pdf
18. Lapatinib labeling:
http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/022059s3s6lbl.pdf
19. G. Amado, M. Wolf, et.al., Wild-Type KRAS Is Required for Panitumumab Efficacy in patients With Metastatic Colorectal
Cancer, J. of Clin. Oncology, Vol. 26, No. 10, 2008, 1626-1634
20. A. S. Crystal, and A. T. Shaw, New Targets in Advanced NSCLC: EML4-ALK, Clinical Advances in Hematology &
Oncology Volume 9, Issue 3, 2011, 207-214
21. A. Vultur, J. Villanueva and M. Herln, Targeting BRAF in Advanced Melanoma: A First Step toward Manageable Disease
Clin Cancer Res April 1, 2011 17:1658
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