how to design high impact trials to indentify biomarkers janet dancey, md ontario institute for...
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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