How to Design High Impact Trials to Indentify Biomarkers

Janet Dancey, MD

Ontario Institute for Cancer Research

NCIC Clinical Trials Groups

2nd Quebec Conference on Therapeutic ResistanceMontreal, November 5-6th 2010

Potential Conflict of Interest

• Dr. Janet E. Dancey– None

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Objectives

• Types of biomarker studies

• Uses in clinical trials

• Methods and design issues

4

Why do biomarker studies?

• Biology◦ Understand cancer◦ Identify new targets for therapeutics◦ Identify new markers of diagnosis, prognosis, prediction, monitoring

• Diagnosis◦ To identify site of origin of an undifferentiated tumour,◦ To identify second primary from metastases

• Prognosticate◦ To predict outcome (risk of toxicity, relapse, progression)

• Predication◦ To predict benefit/risk (or lack) from a specific treatment

• Monitor◦ Identify cancer early, monitor response/progression

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Why do biomarker studies?

• To understand cancer biology

• To improve treatments

• To change medical practice

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Why is doing biomarker studies so difficult?

• Cancer models are not patients and people are not laboratory models

• ….and its not just biology

No visit, treatment, biopsy, imaging today, please….

• I’m not well• the insurance won’t cover it• the REB says you can’t

By the way….

• My family has been in-bred for generations

• Those cancer cells in my flank had been in culture for decades

Things you don’t hear in the lab

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Intratumoral heterogeneity of carbonic anhydrase IX (CAIX)

Effect of distributional heterogeneity on the analysis of tumor hypoxia based on carbonic anhydrase IXVV Iakovlev, M Pintilie, A Morrison, et al

Laboratory Investigation (2007) 87, 1206–1217

a) Immunoperoxidase staining for CAIX in a single tissue section. Analysis of the entire section gave a value of 10.8% CAIX labeling. The circles limit the analysis to 0.6 mm simulated tissue microarray (TMA) cores, and show a wide range in CAIX (for publication purpose only, the image was digitally enhanced to better visualize CAIX areas).

Sometimes it’s where the needle went

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Copyright ©2005 American Association for Cancer Research

Baker, A. F. et al. Clin Cancer Res 2005;11:4338-4340

pSer473-Akt antibody in human GI tumors and HT-29 colon cancer xenografts measured by immunohistochemical

staining.

A, patient tumor samples. 1 and 2 are two surgically resected specimens and 3 and 4 are two biopsy specimens. B, HT-29 human tumor xenografts excised from scid mice and kept at room temperature for the times shown. Each section also includes in the upper right-hand quadrant an on-slide control of HT-29 colon cancer cells stained for pSer473-Akt.

Sometimes it’s the timing

surgically specimens biopsy specimens

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Sometimes it’s how we measure it

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Why are successful biomarker studies uncommon?

• Biological heterogeneity◦ Cellular, tumour, patient

• Assay variability◦ Within assay, between assays

• Specimen variability

• Effect size

A lot of “noise” that blur marker and outcome correlation andvalidation

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‘Validation’

Feinstein

• “Validation is one of those words — like health, normal, probability, and disease — that is constantly used and seldom defined.

• We can ... simply say that, in data analysis, validation consists of efforts made to confirm the accuracy, precision, or effectiveness of the results.”

Feinstein, A. R. Multivariable Analysis: An Introduction (Yale University Press, New Haven, 1996).

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Biomarker Validation

• Biomarker – marker of biology; ◦ Scientific validation

• Assay – method/means of measurement; ◦ Technology/analytical validation

• Test - clinical context◦ Clinical validation/qualification

• Clinical utility◦ Value of using the test versus alternatives Clinical Uptake

LaboratoryY

ears

Multistep, multi-year, interative process requiring multi-disciplinary collaboration

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Trial Designs and Biomarkers

Trial Phase Purpose Biomarkers Modifications

0 Define doseSelect agents

Target modulationPK

Normal VolunteersPre-surgical

I Metastatic Dose/schedule Target ModulationPKToxicityActivity

Expanded cohorts to evaluate target , toxicity or screen activity

II Metastatic Activity Predictive markers Randomized

III Metastatic Clinical benefit Predictive markers Subset analyses

III Adjuvant Clinical benefit PredictivePrognostic

Subset analyses

Type of marker depends on trial phase

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Phase 1 Trials: Considerations

• Primary goal: To identify an appropriate dose/schedule for further evaluation

• Design principles:◦ Maximize safety◦ Minimize patients treated at biologically inactive doses◦ Optimize efficiency

• Study population:◦ Patients for whom no standard therapy

Small patient numbers

HeterogenousRefractoryTumours

Expect target modulation but not anti-tumour activity

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Where/when do biomarkers play a role?Target Versus Toxic Effects

Pro

bab

ility

of

Eff

ect

1.0

Dose/Concentration/Exposure

Target Effect in Tumour

Target Toxicity

Target Toxicity

Off Target Toxicity

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Biomarker Studies in Phase 1 Trials

• MGMT activity after O6-benzylguanine Friedman H et al J Clin Oncol 16:3570-5, 1998; Spiro et al. Cancer Res

59:2402-10 1999; Dolan et al Clin Cancer Res 8:2519-23, 2002

• 20S proteosome inhibition after bortezomib Lightcap E et al. Clin Chem 46:673-683, 2000; Adams J, Oncologist 1: 9-

16, 2002;

• DCE-MRI after PTK787/valatanib Galbraith S et al NMR in Biomed 15:132-142, 2002; Morgan, B. et al. J

Clin Oncol; 21:3955-3964 2003;

• S6K inhibition after everolimus Tanaka C et al J Clin Oncol 26:1596-1602, 2008

• PARP Inhibition after ABT-888 Kinders RJ, et al. Clin Cancer Res. 2008 Nov 1;14(21):6877-85

• ERK Inhibition after PLX-4032 Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021)

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PLX4032, a V600EBRAF kinase inhibitor: correlation of activity with PK and PD in a phase I trial.

Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021)

Patients pERK PRE

pERK KI67 PRE

KI67 PKµM*h

Imaging

4 range 50-100, median

60;

range 10-40,

median 11

range 20-60%, median 45%;

range 5-25%, median 12.5%

mean AUC0-24h ~

126 µM*h

PD (4)

2 70 2 30 -50% 3-5% 500 - 1000

PR (1)↓ PET (2)

Target Pathway Tumor

5-fold

35-fold

4-fold

10-fold

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Phase I Predictive Markers

Fong et al NEJM, 2009; von Hoff et al NEJM 2009; Kwak et al ECCO/ESMO 2009: Chapman et al ECCO/ESMO 2009;

Target Drug Test Phase I ORR (%)

PARP Olaparib (AZD2281; KU-0059436)

BRCA1/2 9/21 (44%) Ovary, breast, prostate

HedgehogSMO

GDC-0449 Mutation (PTCH/SMO)

18/33 (56%) Basal Cell

EML4-ALK PF-02341066 Translocation 20/31 (61%) Lung

BRAFV600E PLX4032 (RG7204) Mutation 19/27 (70%) Melanoma

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Biomarkers in Phase 2/3

• Focus on developing predictive markers• Difficult to demonstrate that the absence of

predictive markers contributed to “failure” of drug◦ Known prior to phase III

HER2, ◦ Positive phase III subsequently analyzed for subset and

marker was helpful Cetuximab, panitumumab and KRAS Erlotinib/gefitinib and EGFR FISH and mutationsOr not EGFR IHC in colon or lung carcinoma

◦ Negative phase III not further evaluated or under evaluation

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Phase 3 (or 2) Trial: Effects of Biomarker Assay

HistologyStage

Initial Selection

Target Tested

Marker +

Marker -

Agent

Control

Agent

Control

Strata Randomize Outcome

• Trial is designed to assess treatment effects in Marker+ and Marker- groups

• Marker assessment◦ Assay failure increases number of patients screened◦ False positives will dilute effect◦ False negatives will increase the number of patients screened

Phase 3: Survival (Phase 2:ORR, TTE)

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Phase 3 (or 2) Trial –Effect of Assay False Positive and Negatives

Time

Sur

viva

l

Sur

viva

l

M+/T+

M+/T-

M-/T-M-/T+

M+/T+M-/T+

Marker+

Marker-

Treatment+

Treatment -

Ideal Marker x TreatmentInteraction

Marker x TreatmentInteraction with False Assay Results

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Suppose we have a new targeted therapy designed to be effective in patients with Marker A.

What types of clinical trials should we design?

Biomarker Clinical Trial Designs

• Target Selection or Enrichment Designs

• Unselected or All-comers designs ◦ Marker by treatment interaction designs (biomarker

stratified design)◦ Adaptive analysis designs◦ Sequential testing strategy designs◦ Biomarker-strategy designs

• Hybrid designs

Target Selection/Enrichment Designs

If we are sure that the therapy will not work in Marker-negative patients

AND

We have an assay that can reliably assess the Marker

THEN

We might design and conduct clinical trials for Marker-positive patients or in subsets of patients with high

likelihood of being Marker-positive

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IPASS-Schema

East AsianNever smoker/light former smokerPulmonary AdenocarcinomaNo prior treatment

RANDOM I ZE

Gefitinib 250 mg daily

Paclitaxel 200 mg/m2

Carboplatin AUC 5-6

1° Endpoint PFS2° EGFR BiomarkerMok et al N Engl J Med 2009;361:947-57

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IPASS-Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma.

Mok et al N Engl J Med 2009;361:947-57

Unselected “All Comers” Trial Designs

If we are not sure that the Marker will define groups of patients that will benefit/not benefit from treatment

OR

There isn’t a validated assay that can reliably assess the status of the Marker

THEN

We might design and conduct clinical trials in unselected patients and try to identify predictive markers and robust

assays.

Retrospective and Prospective Analysis Designs

Retrospective Analyses Designs• Hypothesis generation studies

◦ Retrospective analyses based on convenience samples• Prospective/retrospective designs

Prospective Designs • Marker by treatment interaction designs (biomarker stratified

design)• Adaptive analysis designs• Biomarker-strategy designs• Sequential testing strategy designs

• Hybrid designs

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• Well-conducted randomized controlled trial

• Prospectively stated hypothesis, analysis techniques, and patient population

• Predefined and standardized assay and scoring system

• Upfront sample size and power calculation

• Samples collected during trial and available on a large majority of patients to avoid selection bias

• Biomarker status is evaluated after the analysis of clinical outcomes

• Results are confirmed by independent RCT(s)

Prospective/Retrospective Design

Prospective

Retrospective

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Suppose we want to find out if using a biomarker to select treatment is better?

Marker-based Strategy Design

If we think that one therapy will work in Marker-negative and another therapy will work in the Marker-positive patients

AND

We have a validated assay that can reliably assess the Marker status

THEN

We might design and conduct clinical trial to test whether using the biomarker to select treatment for patients is better than not

using the marker to select treatment

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Control

Marker-Guided Randomized DesignRandomize To Use Of Marker Versus No Marker EvaluationControl patients may receive standard or be randomized

All Patients

Marker DeterminedTreatment

Randomize Treatment

New Drug

New Drug

Control

Marker-based Strategy Design

M+

M−

• Provides measure of patient willingness to follow marker-assigned therapy• Marker guided treatment may be attractive to patients or clinicians• Inefficient compared to completely randomized or randomized block design

Ran

dom

ize

Standard Treatment

OR

Control

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Example: ERCC1: Customizing Cisplatin Based on Quantitative Excision Repair Cross-Complementing 1

mRNA Expression

Cobo M et al. J Clin Oncol; 25:2747-2754 2007

• 444 chemotherapy-naïve patients with stage IIIB/IV NSCLC enrolled, • 78 (17.6%) went off study before receiving chemotherapy, due insufficient tumor for

ERCC1 mRNA assessment. • 346 patients assessable for response: Objective response was 39.3% in the control

arm and 50.7% in the genotypic arm (P = .02).

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Predictive Markers Trials: Considerations

• Is the drug/treatment active?

• Do we have a marker/markers?

• What are the treatment effects within patient subsets? ◦ Are there enough patients to assess treatment effects in

Marker+ and/or Marker- groups?

• Does the trial design distinguish predictive and prognostic effects?

• Is there a reliable assay to assess the biomarker?

• Samples requirements

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Biomarker Translational Gaps

• Rapid generation of new science in laboratory• High content single institution trials can address biological questions• Impact requires translation to multi-centre trials and ultimately clinical practice

Laboratory Single Centre Trial

Multi-CentreTrial

ClinicalPractice

OperationsRegulationStandardization

Rapidity of ScienceTechnology

Impact

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Unprecedented Opportunity

• Rapid advances in understanding of cancer biology

• Rapid advances in technology

• An increasing arsenal of active agents available commercially or under clinical development

• Many opportunities for biomarker evaluation

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8 Considerations for Biomarkers in Clinical Trials

• What is the question?

• Biomarker(s) – What we want to measure

• Assay – How we measure it

• Specimen – What we measure it in

• Study/Trial Design – Why, when, how we study it

• Study Execution – Can we get the study done

• Study Outcome – What it tells us

• Likely Impact – Whether we use it