Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 Resistance Montreal, November 5-6th 2010 Potential Conflict of Interest • Dr. Janet E. Dancey – None Objectives • Types of biomarker studies • Uses in clinical trials • Methods and design issues 3 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 4 Why do biomarker studies? • To understand cancer biology • To improve treatments • To change medical practice 5 Why is doing biomarker studies so difficult? • Cancer models are not patients and people are not laboratory models • ….and its not just biology Things you don’t hear in the lab By the way…. • My family has been in-bred for generations • Those cancer cells in my flank had been in culture for decades No visit, treatment, biopsy, imaging today, please…. • I’m not well • the insurance won’t cover it • the REB says you can’t 6 Sometimes it’s where the needle went Intratumoral heterogeneity of carbonic anhydrase IX (CAIX) Effect of distributional heterogeneity on the analysis of tumor hypoxia based on carbonic anhydrase IX VV 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). 7 Sometimes it’s the timing pSer473-Akt antibody in human GI tumors and HT-29 colon cancer xenografts measured by immunohistochemical staining. surgically specimens biopsy specimens Baker, A. F. et al. Clin Cancer Res 2005;11:4338-4340 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. Copyright ©2005 American Association for Cancer Research 8 Sometimes it’s how we measure it 9 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 and validation 10 ‘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). 11 Biomarker Validation Laboratory • Biomarker – marker of biology; ◦ Scientific validation ◦ Technology/analytical validation Years • Assay – method/means of measurement; • Test - clinical context ◦ Clinical validation/qualification • Clinical utility ◦ Value of using the test versus alternatives Clinical Uptake Multistep, multi-year, interative process requiring multidisciplinary collaboration 12 Trial Designs and Biomarkers Trial Phase Purpose Biomarkers Modifications 0 Define dose Select agents Target modulation PK Normal Volunteers Pre-surgical I Metastatic Dose/schedule Target Modulation PK Toxicity Activity 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 Predictive Prognostic Subset analyses Type of marker depends on trial phase 13 Phase 1 Trials: Considerations • Primary goal: To identify an appropriate dose/schedule for further evaluation Small patient • Design principles: numbers ◦ Maximize safety ◦ Minimize patients treated at biologically inactive doses ◦ Optimize efficiency • Study population: ◦ Patients for whom no standard therapy Heterogenous Refractory Tumours Expect target modulation but not anti-tumour activity 14 Where/when do biomarkers play a role? Target Versus Toxic Effects Probability of Effect 1.0 Off Target Toxicity Target Effect in Tumour Target Toxicity Target Toxicity Dose/Concentration/Exposure 15 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):687785 • ERK Inhibition after PLX-4032 Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021) 16 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 PK µM*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 AUC024h ~ 126 µM*h PD (4) 500 1000 PR (1) ↓ PET (2) 2 70 5-fold 35-fold Target 4-fold 2 30 -50% 3-5% 10-fold Pathway Tumor 17 Phase I Predictive Markers Target Drug Test Phase I ORR (%) PARP Olaparib (AZD2281; KU- BRCA1/2 0059436) 9/21 (44%) Ovary, breast, prostate Hedgehog SMO 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 Fong et al NEJM, 2009; von Hoff et al NEJM 2009; Kwak et al ECCO/ESMO 2009: Chapman et al ECCO/ESMO 2009; 18 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 mutations Or not EGFR IHC in colon or lung carcinoma ◦ Negative phase III not further evaluated or under evaluation 19 Phase 3 (or 2) Trial: Effects of Biomarker Assay Initial Selection Strata Agent Marker + Histology Stage Randomize Target Tested Marker - Control Agent Outcome Phase 3: Survival (Phase 2: ORR, TTE) Control • Trial is designed to assess treatment effects in Marker+ and Markergroups • Marker assessment ◦ Assay failure increases number of patients screened ◦ False positives will dilute effect ◦ False negatives will increase the number of patients screened 20 Phase 3 (or 2) Trial –Effect of Assay False Positive and Negatives Marker+ Treatment+ Marker- Treatment Marker x Treatment Interaction with False Assay Results Ideal Marker x Treatment Interaction Survival Survival M+/T+ M+/T+ M-/T+ M+/TM-/TM-/T+ Time 21 Suppose we have a new targeted therapy designed to be effective in patients with Marker A. What types of clinical trials should we design? 22 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 Markernegative patients AND We have an assay that can reliably assess the Marker THEN We might design and conduct clinical trials for Markerpositive patients or in subsets of patients with high likelihood of being Marker-positive IPASS-Schema East Asian Never smoker/light former smoker Pulmonary Adenocarcinoma No prior treatment R A N D O M I Z E Mok et al N Engl J Med 2009;361:947-57 Gefitinib 250 mg daily Paclitaxel 200 mg/m2 Carboplatin AUC 5-6 1° Endpoint PFS 2° EGFR Biomarker 25 IPASS-Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma. Mok et al N Engl J Med 2009;361:947-57 26 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 Prospective/Retrospective Design • Well-conducted randomized controlled trial • Prospectively stated hypothesis, analysis techniques, and patient population • Predefined and standardized assay and scoring system Prospective • 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 Retrospective • Results are confirmed by independent RCT(s) 29 Suppose we want to find out if using a biomarker to select treatment is better? 30 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 Marker-based Strategy Design Marker-Guided Randomized Design Randomize To Use Of Marker Versus No Marker Evaluation Control patients may receive standard or be randomized All Patients Randomize Marker Determined Treatment M+ New Drug M− Control New Drug Randomize Treatment OR Standard Treatment Control Control • 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 32 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). 33 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 34 Biomarker Translational Gaps Laboratory Single Centre Trial Multi-Centre Trial Clinical Practice Rapidity of Science Technology Operations Regulation Standardization Impact • • • 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 35 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 36 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 37