Clinical Trials in the Era of Personalised Medicine and Biomarkers. Chris Karapetis New Zealand Society of Oncology Conference 2 nd July 2012

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1 Clinical Trials in the Era of Personalised Medicine and Biomarkers Chris Karapetis New Zealand Society of Oncology Conference 2 nd July 2012

2 The EGFR Signaling Network is Vast and Complicated

3 EGFR activation involves downstream signalling pathways that include KRAS and other kinases

4 Personalised Medicine Personalised medicine is a now a universally embraced medical treatment concept. Translating improved understanding of tumour biology to clinical benefit Represents a treatment strategy that enables optimal delivery of individualised therapy Select those that benefit (according to a biomarker) Exclude those that will not Improve efficacy, maximise chance of benefit Improve cost effectiveness, reduce expense Reduce unnecessary toxicity

5 Personalised Medicine Drivers of personalised medicine in oncology include: improved understanding of tumour biology discovery of molecular targets predictive biomarker appreciation and cost effectiveness considerations The transition from scientific discovery to demonstrable clinical benefit is a complicated, resource intensive and challenging process.

6 Biomarkers (= Biological Marker) Molecular, genetic or phenotypic characteristics of cancer cells, or a disease phenotype, which aids in predicting cancer development, clinical behavior, or prognosis Hinestrosa et al, Nat Rev Cancer 7:309-15, 2007 A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention NIH Biomarkers Definitions Working Group, 2001

7 Biomarkers (= Biological Marker) We need a reliable biomarker(s): to provide an accurate prediction of who will respond and benefit from a therapy which may improve the therapeutic index to improve cost effectiveness of therapy Allow subgroup selection The predictive value of the biomarker would need to be differentiated from its prognostic implications To select (if may be useful) or exclude (if futile) patients from therapeutic intervention

8 Critical Phases in Biomarker Therapy Research Basic Science Research (Phase I) Small, biology driven, technology and skill dependent Usually university focus, but increasing industry influence Increasing translational research relevance, cross talk between lab and clinician researchers Early Clinical Phase (Phase II) Small number of patients, phase II One arm, predictive v prognostic significance difficult to determine Multiple reports, results often not uniform Need validation Phases based on paper by Pepe MS et al, J Nat Cancer Inst 93: , 2001

9 Critical Phases in Biomarker Therapy Research Larger Clinical Trials (Phase IIIa) Usually retrospective analyses of selected biomarkers within defined populations Correlation with outcome measures Initial studies are usually small, from single centres, and the outcome measure is usually a clinical surrogate such as tumour response. Subsequent studies utilise clinical data from large number of patients, either from large single centre or pooled multi-centre databases or through large clinical trials. The outcome measure then is usually overall survival or progression free survival. Issue of bias, multiplicity, no interaction tests, prognostic v predictive significance Phases based on paper by Pepe MS et al, J Nat Cancer Inst 93: , 2001

10 Critical Phases in Biomarker Therapy Research Prospective Biomarker Driven Clinical Trials (Phase IIIb) Biomarker analysis part of the study protocol Design and biomarker selection justified through biologic plausibility and previous findings Limitation; How can we allow for what we do not yet know? How can we take into account advancing technology (e.g. NGS) Still have issue of bias and multiplicity Will still explore retrospectively, but allow for this through patient information and consent process Engagement of ethics committees Upfront engagement of lab scientists, pathologists, other diagnostic services such as radiology, hematology and others Large collaborative research efforts for rare or uncommon biomarkers Phases based on paper by Pepe MS et al, J Nat Cancer Inst 93: , 2001

11 Critical Phases in Biomarker Therapy Research Population Impact Accuracy of results in the population (Phase 4) Are we seeing what we expected when the biomarker goes national? Is the test being applied properly Is the technology practical? Is the specimen obtainable? Are we getting results in the appropriate timeframe? Cancer Control impact overall (Phase 5) Has the biomarker directed therapy had an effect on the overall burden of the disease in the population? Phases based on paper by Pepe MS et al, J Nat Cancer Inst 93: , 2001

12 Challenges in Biomarker Driven Clinical Research in Oncology 1. Pathology Department 2. Statistics 3. Prognostic v Predictive 4. Corroboration 5. Multiple testing and bias 6. Making sense of multiple biomarkers 7. Optimal Clinical Application of biomarkers 8. Collaboration

13 Challenge No. 1 The Pathology Department Take into account the keepers of the tissue Usually pathologists, but may be other craft group The handling of bio-specimens is work for them Limited resources Costs financial, time Collaboration on research projects Blurred lines of research v clinical application Appropriate processing of tissue The right tissue? (well preserved, well- annotated, relevant material) Enough tissue? Delays Ethics Biobanks need to be adequately resourced

14 A Personal Journey into Biomarker Research: the CO.17 Experience AGITG/NCIC study an idea in 2002 At the concept stage looking for clinical research leadership I was asked to lead opportunity Worked on the protocol with NCIC investigators Set up Trial Management Committee Collaboration Support Contribution

15 A Personal Journey into Biomarker Research: the CO.17 Experience Not an easy ride Never will work patients will not accept a BSC arm These patients are too sick to go into trials Senior member of TMC resigns before we really got started AGITG considered pulling plug, but agreed to proceed

16 NCIC CTG CO.17: Randomized Phase III Trial in mcrc Failed or intolerant to all recommended therapies R E G I S T E R EGFR testing by IHC R A N D O M I Z E Cetuximab* + BSC BSC alone Disease Progression * Cetuximab 400 mg/m 2 IV week 1 then 250 mg/m 2 IV weekly or Unacceptable Toxicity 1:1 Stratification: Centre ECOG PS (0 or 1 vs. 2)

17 2007 Cetuximab prolonged survival compared to best supportive care in the setting of pretreated metastatic colorectal cancer

18 NCIC CTG CO.17: Overall Survival Study arm MS (months) 95% CI Cetuximab + BSC BSC alone HR 0.77 (95% CI = ) Stratified log rank p-value = CETUXIMAB + BSC CENSORED BSC CENSORED Jonker et al, NEJM 2007

19 From 2007, single-arm retrospective studies suggested a role for KRAS as a predictive biomarker for EGFR MoAb Reference Treatment N (MT %) MT ORR % WT Lièvre et al 1 cmab ± CT 114 (32) 0 44 Benvenuti et al 2 pmab or cmab or cmab + CT 48 (33) 6 31 Finocchiaro et al 3 cmab ± CT 81 (40) 6 26 Di Fiore et al 4 cmab + CT 59 (27) 0 28 Khambata-Ford et al 5 cmab 80 (38) 0 10 Freeman et al 6 pmab 62 (39) 0 11 De Roock et al 6 cmab or cmab + irinotecan 108 (41) 0 41 cmab, cetuximab; CT, chemotherapy; pmab, panitumumab 1. Lièvre et al. J Clin Oncol 2008;26:374 9; 2. Benvenuti et al. Cancer Res 2007;67:2643 8; 3. Finocchiaro et al. J Clin Oncol 2007;25: Di Fiore et al. Br J Cancer 2007;96:1166 9; 5. Khambata-Ford et al. J Clin Oncol 2007;25: Freeman et al. Eur J Cancer 2007;5(Suppl):239; 7. De Roock et al. Ann Oncol 2007;19:

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21 Proportion Alive NCIC CTG C0.17: Overall survival in K-ras Mutant patients 1 Study arm MS (months) 95% CI 0.8 Cetuximab + BSC BSC alone HR % CI (0.70,1.37) Log rank p-value: Cetuximab BSC Cetuximab BSC Time from Randomisation (Months)

22 Proportion Alive NCIC CTG C0.17: Overall survival in K-ras Wild- Type patients Study arm MS (months) 95% CI Cetuximab + BSC BSC alone HR % CI (0.41,0.74) Log rank p-value: < Cetuximab BSC Time from Randomisation (Months) Cetuximab BSC

23 Kaplan-Meier Curves for Overall Survival According to Treatment Hazard ratio 0.98, p=.9 Is K-ras mutation status predictive of benefit with cetuximab? Does cetuximab s effect differ according to K-ras mutation status? Hazard ratio 0.55, p<.001 Interaction p-value 0.01, ie hazard ratios of 0.98 and 0.55 are significantly different Karapetis CS et al. N Engl J Med 2008;359:

24 Challenge 2: Statistics The biomarker needs to be clinically relevant and we wish to confirm statistically significance To understand the impact of a biomarker you must understand the principle of the test for interaction The test for interaction is a statistical method to test for an interaction between treatment effect and subgroup as defined by the biomarker

25 What is an interaction test?

26 Interaction Test The interaction term is a ratio of 2 HRs: HR for cetuximab over BSC (for Kras WT) HR for cetuximab over BSC (for Kras Mutant) The test for interaction has the null hypothesis that the interaction HR = 1.00 As the p value for the interaction term was significant we reject the null hypothesis and claim that there was a significant treatment by Kras mutation status interaction. In other words the treatment effect of cetuximab over BSC differs significantly according to the Kras mutation status of the tumour Can then investigate effect size and direction but if p was not significant, we should not do this. Avoid comparing p values across subgroups.

27 Challenge 3 Prognostic v Predictive So we see that: K-ras wild type patients receiving cetuximab live longer K-ras predicts for benefit associated with cetuximab BUT Is K-ras prognostic? Many say that if there is a difference in outcome by biomarker when the treatment is the same then this biomarker is prognostic, but this not always correct (as evidenced by the CO.17 result) Prognostic significance is truly established when we see a differential effect without treatment or intervention. Can the biomarker tell who will have a better outlook without treatment?

28 Is K-ras mutation status prognostic? Is it associated with survival regardless of treatment? KRAS status MS (months) 95% CI Mutated Wild-Type HR 1.01 Log rank p-value 0.97 Karapetis NEJM 2008; 359:1757

29 IS IT PREDICTIVE OR PROGNOSTIC? The Importance of Semantics: Variation in definitions and reporting practice Cause for confusion PREDICTIVE DEFINITION: A thing predicted; a forecast The action of predicting something From Latin praedicere make known beforehand PROGNOSIS DEFINITION: The likely course of a disease or ailment The forecast of a likely outcome of a situation From Greek pro before gnosis know

30 Why does it matter? Did this group of patients do better because they were going to anyway? Do they have a better prognosis? Did the intervention make the difference? Are we sure that the drugs work? Can we select out the group that will benefit and give the expensive new drug to this subset only? Are we confident enough to exclude a group of patients from what has been proven to be effective in the overall studied population How best should we apply treatments to maximise benefit and minimise wasted effort and harm?

31 Predictive factor Disease, patient, or pharmacodynamic feature which is predictive of biological response to a specific therapy May or may not have prognostic value in untreated disease or in patients treated with other therapies

32 Examples of predictive factors ER expression and tamoxifen therapy in breast cancer Cr 17p deletion and fludarabine-based therapies in CLL C-kit mutations and Imatinib in GIST Cr 5q deletion and revlimid in MDS Presence of H Pylori in gastric marginal zone lymphoma & antibiotic therapy Her2 over-expression and Herceptin in breast cancer Bcr-abl expression and TKIs in CML Mutation or amplification of EGFR and EGFR inhibitors in lung cancer Activating K-ras mutations and EGFR inhibitors in colorectal cancer ABCG2 SNP s and irinotecan-associated myelosuppression

33 PREDICTIVE FACTORS predict an outcome, related to the effect of an intervention Predict Response Predict PFS Predict OS Predict QoL improvement Predict Symptom Improvement Predict Toxicity In the absence of the applied intervention, predictive factors may not be related to prognosis

34 PROGNOSTIC FACTORS PREDICT OUTCOME INDEPENDENT OF INTERVENTION CRITICAL POINT - PROGNOSTIC FACTORS will predict an outcome that is not related to an intervention PROGNOSTIC FACTORS are often predictive of survival in Oncology Often used in early stage cancer to describe parameters that predict for relapse e.g. number of lymph nodes involved Can be used in advanced stage disease eg LDH in NHL, HCG in testis Good and Bad Prognostic Factors You may wish to view this as a particular type of predictive factor predicts an outcome, usually survival, but the prediction direction will not be influenced by an intervention

35 Examples of poor prognostic factors Poor PS in most advanced cancers High LDH Low albumin Weight Loss Stage of Cancer Poor tumour differentiation In early stage disease vascular invasion, lymph node involvement, tumour size

36 Di Nicolantonio F. et al. J Clin Oncol 2008 BRAF in K-ras Wild Type Tumours BRAF mut (N=10) Median OS : 6.0 mos BRAF wt (N=40) Median OS : 13.0 mos Logrank Test: p=0.003 HR= % CI: Loupakis et al ASCO 2009 BRAF V600E mutation has been retrospectively suggested to predict resistance to anti-egfr monoclonal antibodies in patients with KRAS wild type tumors.

37 BRAF in K-ras Wild Type Tumours BUT IS THIS OBSERVATION PROGNOSTIC OR PREDICTIVE? BRAF mut (N=10) Median OS : 6.0 mos BRAF wt (N=40) Median OS : 13.0 mos Logrank Test: p=0.003 HR= % CI: Loupakis et al ASCO 2009 BRAF V600E mutation has been retrospectively suggested to predict resistance to anti-egfr monoclonal antibodies in patients with KRAS wild type tumors. (Di Nicolantonio F. et al. J Clin Oncol 2008)

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39 PROGNOSTIC VERSUS PREDICTIVE FACTOR PROGNOSTIC Inherent, part of the disease or the patient Unrelated to intervention Predicts outcome of survival PREDICTIVE Can be part of the disease, the patient or a reaction May be related to intervention Can predictive various outcomes

40 Challenge 4 Corroboration Your results need to be reproduced by others Need some sort of biological plausibility Scientific rationale Beware small studies - inconsistent conclusions (you can find corroboration if you look hard enough) Consistent finding, but not 100% consistent

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42 K-ras in mcrc Corroboration Consistent across trials Consistent across lines of therapy Class effect Direction of effect is positive vs. nil or negative

43 Challenge 5: Multiple Testing and Bias If you go fishing, you will eventually catch a fish RISKS of MULTIPLE TESTING Inflated type 1 error (false positive) arising from multiple subgroup or biomarkers testing REPORTING BIAS Report your positives, hide you negatives Rush to report when you get a desired result PUBLICATION BIAS Positive results more likely to be published Results showing no correlation with outcome may never see the light of day

44 Pre-specified v Exploratory Pre-specified subgroup analyses are planned and documented BEFORE any examination of the data, preferably in the protocol Provides reassurance against data dredging Exploratory (or post-hoc) subgroup analyses DO NOT specify the hypothesis being tested before any examination of the data Rationale Biologic plausibility Previous results Expected direction of the difference

45 What is the chance of getting NO statistically significant results from a sample with nothing going on (p=.05)? None If you test it once? If you test it twice? If you test it 10 times? If you test in 20 times? If you test it 50 times? If you test it 100 times?.95 n Stockler NHMRC Clinical Trials Centre 45

46 What is the chance of getting at least 1 statistically significant result from a sample with nothing going on (p=.05)? If you test it once? If you test it twice? If you test it 10 times? If you test in 20 times? If you test it 50 times? If you test it 100 times? None At least 1.95 n 1 (.95) n Stockler NHMRC Clinical Trials Centre 46

47 Challenge No. 6 Making sense of multiple biomarker results together Examine a biomarker within a biomarker subgroup and this could go on, a biomarker within a biomarker within a biomarker subgroup The permutations could be complicated, and decision making not straightforward E.g. In Kras wild type advanced CRC: Should we avoid cetuximab/panitumumab if BRAF mutation is also present? Should we avoid cetuximab/panitumumab if NRAS mutation present?

48 High epiregulin gene expression plus K-ras wild-type status predict for cetuximab benefit Extraction of tumour-derived genomic RNA EREG gene expression detected by quantitative real time-pcr Cox model was used to test interaction between epiregulin level and treatment to assess the predictive effect among patients with K-ras wildtype Epiregulin level was first analyzed as a continuous variable Then as a binary variable based on a pre-specified threshold derived from a prior study 1 as primary analysis a threshold which had minimum p-value in the interaction test as exploratory sensitivity analysis without adjustment for multiple comparisons

49 Proportion alive Proportion alive Overall survival by treatment: Patients with Combimarker positive (K-ras wild-type and high EREG) v (K-ras wild-type and low EREG expression) Low EREG by pre-specified threshold Cetuximab + BSC High EREG by pre-specified threshold Cetuximab + BSC BSC alone 40 BSC alone # At Risk CET+BSC: BSC alone: HR 0.77 [ ], p= Time from randomization (months) # At Risk 4 CET+BSC: BSC alone: HR 0.43 [ ], p< Time from randomization (months) In the K-ras wild-type subset: OS was better for cetuximab than BSC among patients with high EREG but not for low EREG patients The p-value of interaction was 0.13 for pre-specified threshold

50 Application of biomarkers for optimal EGFR antibody based therapy Who will not benefit: - Kras mutant but MAYBE patients with G13D mutations do benefit - *Kras wild type but NRAS mutant should not receive EGFR antibody combined with oxaliplatin based chemotherapy (recent AMGEN announcement) Still uncertain: - K-ras wild type and low EREG (suggestive data) - K-ras wild type and PTEN loss (inadequate data) - K-ras wild type and PIK3CA over-expression (inadequate data) Most likely to benefit - K-ras wild-type, BRAF wild-type, EREG high, PTEN in tact BRAF is a powerful prognostic marker, but predictive value contentious For Now only K-ras WT v Mutant has practical and accepted application

51 Challenge No. 7 Determine Optimal Clinical Application of Biomarker Will this biomarker affect clinical practice? When should the biomarker be measured? Multiple biomarker application Access to intervention and therapy Cost of therapy or intervention

52 Challenge No. 8 Collaboration Research involves many parties and strongest and fastest results are obtained when these parties work together Drivers support the small single-centre study, but we need to embrace collaborative studies Politics, Opinions, Biases, Kudos, Finances, Resources differences can make collaboration challenging Facilitate Translational Research Effective bi-directional communication Shared physical & intellectual interactions Ideally some strategic cross-appointments Protected research time for clinicians Mutual appreciation & respect Recognition (including authorship) Tissue bank with clinically annotated samples Money

53 Challenges Ahead Measurable Target(s) Reliable measurements Feasible measurements technology, expertise, time, cost, tissue, resources Biopsy v blood, primary v secondary, repeat biopsies Changing marker significance with stage of disease, type of measurement, over time Patient Benefit targeted therapy for them, not for the drug or the cancer s sake Patient benefit often variable and hard to define conflicting data, different settings, multiple small series, old studies, often there is no comparison or no treatment arm (prognostic v predictive impact) How do we incorporate multiple biomarkers into decision making? Treatment decision algorithms Accelerated development and approval strategies Affordable New Therapy Who pays for the test (predictive biomarker) and the therapy? How much? For how long? Benchmark HR for approval FDA stating that they need to see HR of 0.6 or less

54 Thank You