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Use of Biomarkers and Translational Science to Accelerate and Improve Oncology Drug Development J. Carl Barrett, VP and Global Head, Oncology Biomarkers and Imaging, Oncology Translational Medicine, Novartis RAD001 N N N N N N N N N N N N N N N N NNN NN N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N NNN NN N Conceptual Challenges in Drug ug Development e e op e t for o O Oncology co ogy Cancer is a heterogeneous disease • How to select patients for a targeted therapy? Ca Cancer ce ce cells s acqu acquire e mutations utat o s in multiple utpe targets/pathways. • How to test rationally based combination therapies Cancer cells deregulate growth controls used in normal cells. cells • How to ensure safety of targeted therapies 2 Challenges g in Developing p g Targeted g Therapies p Identification of right patient Optimization of dose and schedule Detection of tumor responses rapidly for proof of concept trials Development of rationally based combination therapies Qualification of surrogate endpoints for disease monitoring Assuring safety of drug therapy Changing pattern of therapy Understanding population differences in response 3 Challenges g in Developing p g Targeted g Therapies p Identification of right patient Optimization of dose and schedule Detection of tumor responses rapidly for proof of concept trials Development of rationally based combination therapies Qualification of surrogate endpoints for disease monitoring Assuring safety of drug therapy Changing pattern of therapy Understanding population differences in response 4 BIOMARKERS ARE PIVOTAL IN MEETING THESE CHALLENGES Assay Validation and Qualification for Clinical Use Objective Characteristics Assay can reproducibly Standard Validation Analytical procedure is •Trueness/Accuracy, at least 3 validation runs Determine criteria before •Overall Precision •Reagents and reference material (standards) stability and inventory control •Intra run, inter run, analyst change, equipment, lot # •Relative accuracy/recovery from multiple donors, including patient samples Parallelism, dilution •Sensitivityy Assay working range •Assay Range •Assay performance evaluation f from more validation lid ti runs measure the analyte suitable for its intended use development of assay Requirements li linearity it established •Parallelism Specificity Stability, including bench •Dilution linearityy Precision and Accuracy •Specificity/selectivity top stability data from 3 validation runs Sample Collection integrity 5 •Stability Advanced Validation •Long term stability (6 months-5 years) Extensive testing of interference •Extensive and risk recommendation •Robustness (reagent and change control) •Use of in-study validation data from pilot study The Pyramid of Biomarker Clinical Assay Development Analytical y validation is the basis for clinical utilityy Steps needed for clinical interpretation Clinical interpretation Data in normal and disease •Assay characteristics •Sample type, timing •Sample handling 6 •preclinical proof of principle •define differences between disease and normal •estimate variability •assay interference testing •define association of marker with clinical endpoints or therapies Accelerate and Improve Drug Development p Challenges in accelerating and improving drug development: Selection of dose and schedule Selection of right g patients mTOR Is a Novel Cancer Target g Growth Factors Nutrients glucose, amino acids, etc IGF, EGF, VEGF etc M t ti Mutations in cancer PI3K mTOR is an intracellular serinethreonine kinase activated by mutations t ti in i cancer mTOR is downstream of growth AKT g g factor and nutrient signalling mTOR is a central regulator of RAD001 protein synthesis S6k eif--4e eif Protein Synthesis RAD001 is a multifunctional inhibitor of: • cell growth and proliferation • angiogenesis Growth & Proliferation 8 Angiogenesis Bioenergetics • cancer cell metabolism (bioenergetics) Phase 1 trials: PD effect after RAD001 in patients TUMOR Bas seline SKIN p-S6 Ser235/6 RAD 10 d RAD001 treatment resulted in an almost complete inhibition of pS6 in tumor and skin p-S6 Ser235/6 300 100 N=27 N 27 pS6235/236 N=38 N 38 pS6 S6235/236 80 All pts 200 60 100 40 20 0 0 -100 N= 9 Baseline RAD001 28 28 Hscore Ps6 pre tumor Hscore pS6on1 tumor Tabernero J, et al.; JCO 2008 Feb 25 -20 N= Baseline RAD001 39 39 Hscore pS6 pre basal Hscore pS6 on1 basal Better Inhibition of p70S6 Kinase With Daily Schedule Tumor Inhibitiion of p70S S6 Kinase Activity, % 100 Daily dosing, mg 10 5 Weekly dosing, mg 70 50 30 20 50 10 0 0 1 2 3 4 5 Time, days 6 7 Continuous target inhibition is predicted to be achievable through the use of daily dosing schedules . 10 RAD001(Everolimus/Afinitor) Clinical Overview Daily dose of 10 mg inhibits mTOR pathway and is generally well tolerated Clinical evidence for activity in multiple tumor types • RCC • NET • Lymphoma y p • Breast cancer • Gastric cancer 11 mTOR Pathway Activation: Predictive Biomarkers? Growth Factors VEGF IGF EGF mTOR is downstream in g gp pathway y frequently q y signaling deregulated in cancer1,2 Regulators of mTOR activity RAS PTEN PI3K LKB1 RAS Protein ABL Synthesis ER TSC2 AKT AMPK mTOR Bioenergetics Cell Growth & Proliferation 1. 122. 3. mTOR deactivating Activation of mTOR can result in loss of cell growth control and enhanced cell metabolism ((bioenergetics) g ) in cancer cells1,3 TSC1 RAD001 mTOR activating Angiogenesis Averous and Proud. Oncogene. 2006 Oct 16;25(48):6423-6435 Mamane et al. Oncogene. 2006;25(48):6416-6422 Ellisen. Cell Cycle. 2005;4(11):1500-1502 Study design and biomarker analysis Sample recovery • Newly diagnosed, untreated patients with ER+ localized breast cancer likely to benefit from hormonal therapy • Palpable tumor: > 2 cm diameter S C R E E N R A N D O M I Z E Tumor biopsies (pretreatment) 13 N = 138 Letrozole 2.5 mg/d RAD001 10 mg/d Surgery N = 132 Letrozole 2.5 mg/d Placebo 16 weeks Tumor biopsies (day 15) Tumor samples (surgery) The mTor pathway Relevant tested biomarkers are in orange Growth factors, Her2, Insulin IRS PI3K Stress Glucose PIP2 PIP3 PDK1 PTEN P473 P308 AKT Rictor mTORC2 GBL mSin1 Actin AA TSC1TSC2 LKB1 AMPK RAD001 Rheb Raptor mTORC1 GBL eIF4G p70S6K1 eIF4EBP1 Autophagy P235 pS6 14 P240 Translation initiation Synthesis targets: Cyclin D1 Summary Sample Statistics and demographics 270 Patients 212 (79%) ( %) Baseline biomarker evaluable ((adequate baseline biopsy)) 207 (77%) Primary biomarker evaluable and clinical outcome evaluable Of those, 182 (67%) had an evaluable second biopsy, and 161 (60%) had an evaluable final biopsy percentage tumor <1 <5 5 10 20 30 40 50 >50 50 15 proportion of baseline samples 6(3%) 14(6%) 27(12%) 38(17%) 39(17%) 44(19%) 23(10%) 17(7%) 22(10%) Feature Lobular Amplified for Her2 Mutant for PIK3CA Mutant for TP53 TP53 high by IHC ER negative Incidence 6% 12% 39% 17% 18% 0.5% Major pharmacodynamic changes at day 15 Reduction in PS6240 and 235 reveals RAD treated cases 40 20 Change in H Score (%+ ffor Ki67) 0 Cycd1_3-1 ER_3-1 PR_3-1 ki67_3-1 pAkt_3-1 -20 PS6235_3- PS6240_31 1 TP53_3-1 -40 -60 -80 -100 -120 RAD+LET LET -140 Marked downregulations in progesterone receptor and cyclinD1 are seen in response to letrozole A slight bias towards mild upregulation of pAkt is evident in both arms 16 Outcome measures in neoadjuvant trials Clinical radiographic and pathologic Clinical, We used a clinical primary endpoint (palpation), which is used widely, widely but is also regarded as “soft” soft . Mammographic and ultrasound endpoints have also been used, but there appears to be a great deal of controversy used and operator dependence in their interpretation. Unlike clinical and mammographic changes changes, changes in tumor proliferation index during neoadjuvant therapy have been linked with long term outcome in the context of aromatase inhibitor therapy 17 Indicators of drug response Cell cycle response (Ki67) at day 15 after initiation of neoadjuvant therapy is predictive of long term response to aromatase inhibitors Baseline Ki67 Data: Dowsett 2006; cutoffs are in ln (%Ki67+) Day 15 Ki67 18 Indicators of drug response Clinical response versus cell cycle RAD001 treated population shows enhanced response by cell cycle as well as clinical criteria All evaluable patients Responder evaluation CR+PR palpation d15 Ki67<=1 d15 Ki67<=2 d15 Ki67<=3 19 RAD001+LET Responder 73(70%) 36(40%) 52(57%) 57(63%) LET Responder 64(62%) 17(21%) 25(30%) 30(37%) Baseline Ki67 values are equally distributed in RAD001+LET and LET arms but Ki67 at d15 shows a large difference between RAD001 + LET and LET treated arms 100 90 % cas ses in category y 80 70 RAD+LET baseline 60 RAD+LET d15 50 LET baseline 40 LET d15 30 20 10 0 <0.5 <1 <1.5 <2 <2.5 <3 ln (%Ki67+) 20 <3.5 <4 <4.5 <5 Ki67 drops and absolute values as indicators of cellcycle response This graph illustrates the change in Ki67 from baseline (green lines) and the final value at d15 for each evaluable patient in the trial. In generall d drops ffrom b baseline li are greater t iin th the RAD001 arm. 21 RAD+LET LET Ln (%Ki67+ @d15)<1 Ln (%Ki67+ @d15)<1 Clinical response versus cell cycle analysis Clinical evaluation of response (on palpation in 2222) Ki67d15 K Ki67d15 5-Ki67 baseline e correlates moderately with extent of reduction in Ki67 and designation of progressive disease correlates well with high proliferation; however, clinical categorizations are poor predictors of low Ki67 values values. CR 22 PR NC PD CR PR NC PD Exploratory survey of candidate markers does not reveal a solitary marker which can be used to predict RAD001 cellcycle y response p ROC curves for the baseline biomarkers in RAD001 and RAD001+LET arms, using Ki67<=2 at day 15 as the indicator of response. (Large deviations from the diagonal are indicative of a potential cutoff distinguishing responders from non responders) RAD LET RAD+LET LET RAD LET RAD+LET LET AIB1 PAKT473 Ki67 PS6240 PTEN 23 CyclinD1 Mutational analysis in CRAD001C2222 A more robust source of data? Mutation analysis for p53 and PIK3CA was performed in both the archival tissue sample set and in the 2222 study Mutation analysis in archival blocks has had mixed reviews in the past; however the technology has moved rapidly We found that small changes in sample recovery methods greatly improved sequencing success in archival tissue Mutation incidence in PIK3CA was very similar for our archival and fresh tissue sample sets. 24 PIK3CA exon 9 mutants in the 2222 trial are less responsive to LET alone but are as sensitive as the overall population to RAD+LET Percentage of cases with Ki67<=2 at day 15 Percent reduction in Ki67 from baseline at day 15 0 70 PIK3CA e9 mutant PIK3CA e20 mutant only only 60 PIK3CA wt onlyy -20 50 -40 40 RAD+LET LET 30 RAD+LET RAD LET -60 LET 20 -80 10 -100 0 PIK3CA e9 mutant only PIK3CA e20 mutant only PIK3CA w t only (n >= 8 in all subsets) 25 -120 <0.05 <0.05 PIK3CA mutation as an outcome predictor in breast cancer Based on recent studies in the literature Exon 9 (Helical Domain) Mutations are associated with worse natural history (Barbareschi et al 2007) 26 Conclusions Execution of a large biomarker study is feasible within the context of a medium sized neoadjuvant j trial, and tissue q quality y is acceptable p for analysis of phosphoepitopes and mutation. Addition of RAD001 to letrozole caused a significant increase in efficacy by clinical response and a near doubling of response rate by cell cycle criteria. The increased cell cycle response rate in the RAD001 + letrozole arm was found in all “marker negative” subpopulations including PTEN positive, PIK3CA wild type tumors RAD001 with daily dose is active in multiple molecular subtypes of breast cancer Exon 9 mutant PI3K tumors may be a particularly worthwhile target for RAD001 RAD001. 27 Predictive Biomarkers: Issues to consider Clinical endpoint chosen for correlations Prognostic and predictive value of a biomarker need to be evaluated Negative and positive predictive biomarkers are important to define Molecular definition of a given cancer may vary during progression which may require interrogation of metastasis vs archival diagnostic specimen 28 Biomarkers in Drug Development Pharmacodynamics Biomarkers • Target g or downstream indicators Mechanism of Action Biomarkers Predictive Biomarkers • Positive and negative Safety Biomarkers Response Biomarkers/Efficacy Outcome Biomarkers Surrogate Endpoint • Accepted clinical outcome 29 Imatinib Targets the Cause of CML Imatinib—a specific inhibitor of a small family of tyrosine kinases, including Bcr-Abl 30 Residual disease detection in CML is changing therapy Ph+ Residual disease detection for CML is a commonly used method for assessing disease burden Detection 1012 of response by cytogenetics is less sensitive than Nu umber of leukem mic cells PCR for CML 31 1010 CHR Cytogenetic Cy oge e c MCR response CCR 108 3 log reduction 4 log reduction 106 104 102 1 Limits of detection RQ-PCR RQresponse 32 Lo og reducction of BCR BCR--ABL BCR-ABL increase when imatinib stopped Restarted International scale Base line 100% 10 1.0 10% 2.0 1% Imatinib ceased 3.0 0.1% 4.0 0 01% 0.01% Pre 3 6 9 12 15 18 Months from the start of Imatinib 33 Issues with Current Bcr-Abl Transcript Testing: es g Assay ssay Validation a da o Needed eeded No standard platform used. No standard reagents used used. No standard assay conditions used. Assay used for different purposes (MMR or g in transcript p levels following g therapy) py) changes Availability of standards to measure MMR limited to a few labs labs. 34 Personalized Medicine: Chronic management of cancer patient Development of safe and effective targeted therapies for the treatment of molecular subtypes of cancer Ability to monitor disease burden in real time allows physician to follow disease course and therapeutic effectiveness, to detect disease progression and to make best therapeutic decisions for patients Development of new therapies for resistant cancers and combination treatments for long term survival of solid cancers 35 Challenges in the implementation of biomarkers in innovative clinical trials Many molecular technologies are not sufficiently robust for routine clinical use Lack of access to tumor specimens in the clinical trial (availability, logistics, quantity, quality, and stability of clinical material are issues) Novel endpoints need assay validation and biomarker qualification in clinical trials to link test result with clinical outcome Such efforts can be long and expensive and delay drug development if a co diagnostic is required co-diagnostic Reimbursement climate not encouraging for molecular testing STILL, BIOMARKERS AND NEW CLINICAL ENDPOINTS ARE STILL ESSENTIAL FOR DEVELOPMENT OF TARGETED THERAPIES AND PERSONALIZED MEDICINE 36 Novartis Translational Medicine Strategy Accelerate drug development -use use of biomarkers in early clinical trials to select patients, inform dose and schedule selection, demonstrate early clinical responses, assure safety, and develop rationally based combination therapies Improve utility of biomarkers in drug development - sample collection excellence - build biomarker toolkit and experience - development p of minimally y and non-invasive methods Facilitate the development of personalized medicine 37 p of CML where p patient specific p adjustment j of -example therapy is based on drug levels, monitoring of disease progression, and molecular definition of a patient’s leukemia “If it were not for the great variability among individuals Medicine might be a science not an individuals, art. Sir William Osler, The Principles and Practice of Medicine 1892 38
