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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. Acknowledgement. Somesh Chattopadhyay Kun He Rajeshwari Sridhara

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review experience in evaluating predictive biomarkers design and analysis considerations

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

acknowledgement
Acknowledgement
  • 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

outline
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)

slide4

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
terminology
Terminology
  • Targeted : selected; enrichment
  • Untargeted: unselected; all-comers
spectrum in study designs incorporating biomarkers
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

(Kumar, et al,2003)

Untargeted

design issues to be considered
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
slide8

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)
slide9

Prospective design –

Enrichment

Randomize

New Rx

Biomarker +

Control

Screen

Biomarker status

Biomarker -

Off study

issues in enrichment design
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)
example of limitation in assay precision from an enrichment design herceptin
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

slide12

Prospective design –

Biomarker Stratified

New Rx

Biomarker +

Control

Screen

Biomarker status

New Rx

Biomarker -

Control

Randomize

analysis issues in the biomarker stratified design
Analysis Issues in the Biomarker Stratified Design

Potential Analysis Plan:

  • Treatment by biomarker interaction

Issues: May not have enough power for interaction and failure to show interaction does not mean no differential subgroup effect

  • 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

adaptation for selecting biomarker subgroup
Adaptation for selecting biomarker subgroup
  • 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

  • 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);

slide15

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)

slide16

Prospective-retrospective –

Criteria for Consideration

  • • A pragmatic approach may be considered if
  • Adequate, well-conducted and well-controlled trial
  • Large sample size if no stratification by biomarker status for randomization (??)
  • Biomarker ascertainment in a large portion of randomized subjects
  • Assay – acceptable analytical performance
  • Acceptable statistical analysis plan

O’Neill, Dec. 2008 ODAC

retrospective analysis issues
Retrospective Analysis Issues
  • 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.

  • Convenience sample – Issues
    • 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
slide18

Trial Designs-

Phase 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
slide19

I-SPY 2

Biomarkers: ER (+,-), PR(+,-), HER2 (IHC/FISH, gene expression, protein microarray), MammaPrint score (higher MP2, other MP1)

i spy 2
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
i spy 2 issues
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
slide22

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
issues in retrospective analysis dec 2008 odac
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
prospective designed with subgroup hypothesis study bo18192 saturn
Prospective Designed, with subgroup hypothesisStudy BO18192 (SATURN)

Multicenter, International, Randomized, Double-blind, Placebo-controlled, 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 :

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

study bo18192 saturn biomarker characteristics
Study BO18192 (SATURN): Biomarker Characteristics

Note: Protocol implemented a hierarchy of biomarker ascertainment

study bo18192 saturn issues of the egfr ihc status
Study BO18192 (SATURN): Issues of the EGFR IHC status
  • ~16% unevaluable EGFR IHC status
  • EGFR (IHC) test was not reliable
slide30

SATURN : Statistical Issues in biomarker subgroup analysis

  • 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.
prospective designed unplanned adaptation study egf 3008
Prospective designed, unplanned adaptationStudy EGF 3008:
  • Randomized, double-blind, placebo-controlled, parallel-group, multi-center, 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

study egf 3008 design
Study EGF 3008 design

10 objective : PFS in HER2+

20 objective : PFS in ITT

study egf 3008 patient populations
Study EGF 3008:Patient populations

HER2 : FISH + or IHC : 3+

slide34

Study EGF 3008: Kaplan-Meier Estimate of Investigator-Evaluated PFS

slide35

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.

summary
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.
references
References
  • 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

3. 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

4. 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

7. S. J. Mandrekar and D. J. Sargent; Predictive biomarker validation in practice: lessons from real trials; Clinical Trials 2010; 7: 567–573

8. 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

9. R. Simon, S. Wang, Use of genomic signatures in therapeutics development in oncology and other diseases, The Pharmacogenomics Journal 2006; 6, 166–173

10. R. Simon, The use of Genomics in Clinical Trial Design, Clin. Cancer Res., 2008; 14(19), 2008, 5984-5993.

11. 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

references1
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/ucm126185.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