How to Design Clinical Trials in Non Non--small C ll Lung Cancer Cell C iin 2012 with i h Omics O i Data D J. Jack Lee, Ph.D. Department of Biostatistics Past – Life was simple 2012: Data Deluge Help!!! Big Data Challenge - How to Make Sense? Miller, Big data analytics in biomedical research Miller research, Biomedical Computation Review Review, Winter 2011/2012 Trelles et al. Big data, but are we ready? Nat Review Genetics, 2011 Shah and Tenenbaum, The coming age of data-driven medicine: Translational bioinformatics' next frontier, Journal of the American Medical Informatics Assoc, 2012. Big Data Initiative:http://www.whitehouse.gov/sites/default/files/microsites/ostp/ big_data_press_release_final_2.pdf Premise Biological knowledge advances in a lightning speed There are many new targeted agents and many more potential combination therapies Only a small fraction of the drugs are tested A small number of patients enrolled in clinical trials Unfortunately, success rate for oncology drug development is very low. Targeted agents do not work for all patients. Search for the predictive markers to guide the choice of treatments How do we find the answers to the above questions? How to treat patients best in the trial? Time is of the essence. Questions How to enroll more patients in clinical trials? How to test more agents and their combinations efficiently? How to best identify markers, gauge treatment efficacy, and treat patients in a clinical trial? Adaptive Designs ! - Use accumulating data to guide study conduct - We learn as we go. 1. Berry DA. A guide to drug discovery: Bayesian clinical trials. Nature Reviews Drug Discovery, 2006. 2. Lee JJ, Gu X, Liu S. Bayesian adaptive randomization designs for targeted agent development. Clinical Trials, 2010. 3. Berry DA. Adaptive clinical trials in oncology. Nature Reviews Clinical Oncology, 2012. Bayesian Paradigm What is the Bayesian method? y – Prior + Data Æ Posterior (Bayesian 11-2-3) Î New Prior + New Data Æ New Posterior – We learn as we go! – Models unknown parameters with statistical distributions – Properly P l addresses dd various i llevels l off uncertainty t i t – Allows more frequent monitoring and decision making Adaptive in nature – Since we don’t know the answers before the trial, we need to continue to learn during g the trial. – Clinical trial is a learning process. It makes sense to adjust the study conduct based on realreal-time learning during the ti l trial. Ideal for clinical trials! Lee and Chu, Bayesian Clinical Trials in Action. Statistics in Medicine, 2012 Traditional Drug Development Phase I I Phase II Phase III II I III II I II R II R III Traditional Drug Development One dose, Schedule A few doses/schedules Multiple doses/schedules Traditional Drug Development We Did It All Wrong ! How to Fix It? D more Ph i l tto d t i th Do Phase I ttrials determine the b bestt d dose, schedule, and route of administration. D more singleDo single i l -arm or randomized d i d Ph Phase IIA ttrials. i l Do more randomized Phase IIB trials to refine the efficacy. ffi Identify prognostic and predictive markers. Do smaller, more focused Phase III trials. Conduct seamless Phase I/II or Phase II/III trials. Do platform trials. Enroll all patients in trials. Apply adaptive randomization & early stopping rules Continue to learn and to adapt. Evolution of Knowledge in NSCLC Pao and Girard, Lancet Oncology 2010 Evolution of Knowledge in NSCLC Pao and Girard, Lancet Oncology 2010 2011 Sequist et al, Annals of Oncology 2011 How to Choose Treatment Based on Biomarkers? erlotinib crizotinib trastuzumab vemurafenib sorafenib? PIK3 Inhibitors? AKT Inhibitors? MAPK inhibitors? MET inhibitor ? CTTNB1, NRAS, IDH1 ??? Ti 1 evidence: Tier id Assign A i pts t to t corresponding di treatments t t t Tier 2 evidence: Randomized trials, Adaptive randomization Tier 3 evidence: Single arm or randomized screening trials Goals of Clinical Trials for Targeted Agents Identify prognostic and predictive markers – Prognostic markers markers that associate with the disease outcome regardless of the g , stage, g , performance p treatment: e.g., status – Predictive markers markers which predict differential treatment efficacy in different marker groups: e.g., In Marker (− (−), tx does not work b t in but i M Marker k ((+), ) tx t works k Test treatment efficacy – Control type I and II error rates – Maximize study power for testing the effectiveness between treatments – Collective ethics Provide better treatment to patients enrolled in the trial – Assign patients into the better treatment arms with higher probabilities M i i ttotal t l number b off successes iin th i l – Maximize the ttrial – Individual ethics Tool #1: Bayesian Outcome Ad ti Randomization Adaptive R d i ti - Provide better treatments to patients in the trials while testing the treatment efficacy Bayesian Outcome Adaptive Randomization Consider two treatments, binary outcome A new patient is assigned to TX2 with probability Pr( p2 > p1 )c / {Pr( p1 > p2 )c + Pr( p2 > p1 )c } Note that the tuning parameter – c = 0, ER Less imbalance – c = n / (2N) – c = 0.5 or 1 0.1 – c = (n / N ) – c = ∞, “play the winner” More imbalance Continue until reaching early stopping criteria or Nmax Demo 1: P1=0.2, P2=0.4 c = (n / N )0.1 A Simulation Study vs H1: p1 < p2 H0: p1 = p2 vs. Take the Bayesian framework, conclude Arm 2 is better than Arm 1 if: Prob Prob(p (p2 > p1 | Data) > θT Calibrate the design parameters (N and θT) – α=0.1 0.1 for p1=p p2=0.2 0.2 and 11- β = 0.9 for p1=0.2, 0.2, p2=0.4 0.4 For ER, N=134, θT=0.9. 0.1 c = ( n / N ) For AR with the tuning parameter N=184, θT=0.905. Extend the study to reach equal sample size for all designs s.t. the overall response rate can be directly compared. p – At the end of the study, allocate all remaining pts to the better arm, if any; or the control arm if not. ER versus AR Korn and Freidlin. Outcome-Adaptive Randomization: Is It Useful? JCO 2011 Berry DA. Adaptive Clinical Trials: The Promise and the Caution. JCO 2011 Lee, Chen, and Yin. Worth adapting? Revisiting the Usefulness of Outcome-Adaptive Randomization. CCR, 2012 c = (n / N )0.1 No. of NonNon-Responders and Overall Response Rate for ER and AR True Response Rate Equal Randomization Adaptive Randomization [N=134] [[N=184]] Cntl Exp No. of NonResp 0.2 0.2 107.2 20.0 Overall Resp % Expd to Expd. N=184 20.0 02 0.2 04 0.4 93 8 93.8 30 0 30.0 0.2 0.6 80.4 0.2 0.8 67.0 Overall Resp % c = (n / N )0.1 No. of % on Exp. Non-Resp Arm Overall Resp % 147.2 50.0 20.0 32 2 32.2 117 5 117.5 80 6 80.6 36 1 36.1 40.0 45.4 84.0 85.9 54.4 50.0 58.2 51.1 87.1 72.2 AR: Randomization probability bounded at 0.9. Compare AR vs ER w/o Early Stopping ER AR ER stop the trial earlier P1 = 0.2 128.8 124.3 10 00 (184,117.5) (134,93.8) 50 All pts received TX 2 0 50 100 P2 = 0.4 α = 0.10 1-β = 0.90 0 Cu umulative Num mber of Non-Re esponders 150 AR stop t th the trial later 150 Number of Patients Accrued 200 Compare AR vs ER w/o Early Stopping P2 = 0.4 20 18.4 15 ER AR 14.4 10 (134,13.4) Gain 5 (184 7 1) (184,7.1) 0 50 α = 0.10 1-β = 0.90 0 Add ditional Non-Re esponders Co ompared to All P Patients Receiv ved The Bette er Treatment 25 2 P1 = 0.2 100 150 Number of Patients Accrued 200 Early Stopping Based on the interim result, if the comparison between the treatment arms and the control arm is highly informative, we can stop the trial early. – Early stopping for efficacy if Pr( pk > p1 | D) > θ H for any k P ( pk > p1 | D) < θ L for – Early E l stopping t i ffor futility f tilit if Pr( f allll k’s ’s,, k ≠ 1 Compare AR vs ER with Early Stopping 27.4 P2 = 0.4 25 α = 0.10 ER AR 15 20 0 1-β = 0.90 10 (190,8.7) 10.5 Gain 5 84 (274,5.9) 110 0 Additional Non-Re esponders Com mpared to All Patients Receiv ved The Bette er Treatment 30 P1 = 0.2 0 50 100 150 200 Number of Patients Accrued 250 300 Compare AR versus ER AR yields a higher overall success ( Ï response rate or Ï survival) than ER at the cost of increasing N. The differences between AR and ER are less pronounced with early stopping for efficacy and futility. AR enhances h iindividual di id l ethics; thi whereas h ER emphasizes h i collective ethics. AR has substantial benefit over ER when – the efficacy difference between treatments is large – Outside trial population is small, e.g., rare disease, and there is an effective treatment The choice of AR versus ER depends on the focus of the trial and the utility function of the clinical trialists trialists.. Lee, Chen, and Yin. Worth adapting? Revisiting the Usefulness of Outcome-Adaptive Randomization. CCR, 2012 Tool #2: Biomarker Stratified Outcome Adaptive Randomization - Stratify patients based on the biomarkers - Assign g more patients to better treatments Biomarker Stratified AR: A Simulation Total number patients N=240 Two or four treatments with a predictive marker for each treatment treatment. Outcome is binary – response or no response True response Arm 1 Arm 2 Arm 3 Arm 4 Arm 1 Arm 2 Marker Group 1 0.8 0.2 Marker Group 2 02 0.2 08 0.8 Marker Group 1 0.8 0.2 0.2 0.2 Marker Group 2 0.2 0.8 0.2 0.2 Marker Group 3 0.2 0.2 0.8 0.2 Marker Group 4 0.2 0.2 0.2 0.8 Equal randomization design (ER) Physician guided design (PG) – Randomization probability to the better arm: r0 = 0.7, 0.8. – Randomization probability to other arms: r1 = (1(1-r0)/(K r0)/(K--1) Adaptive randomization design (AR) – ER for the first 20% patients – Randomization tuning parameter: (n/N)^0.1 – Beta prior for each treatment Non-informative prior Beta(1,1) NonInformative Prior Pr(Beta(f*0.7,f*0.3)>Beta(f*0.5, f*0.5))>0.95 – f is the equivalent number of patients, f=31.4 2-Arm: ER and PG 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Density 0.05 5 0.06 ER PG0.7 PG0.8 60 80 100 120 140 Number of Responders 160 180 200 2-Arm: ER, PG, and AR (non(non-informative prior) 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Density 0.05 5 0.06 ER PG0.7 PG0.8 AR-Noninformative 60 80 100 120 140 Number of Responders 160 180 200 2-Arm: ER, PG, and AR (non--informative & informative p ((non prior)) 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Density 0.05 5 0.06 ER PG0.7 PG0.8 AR-Noninformative AR Informative AR-Informative 60 80 100 120 140 Number of Responders 160 180 200 4-Arm: ER and PG 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Den nsity 0.05 0.06 ER PG0.7 PG0.8 60 80 100 120 140 Number of Responders 160 180 200 4-Arm: ER, PG, and AR (Non(Non-informative prior) 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Den nsity 0.05 0.06 ER PG0.7 PG0.8 AR-Noninformative 60 80 100 120 140 Number of Responders 160 180 200 4-Arm: ER, PG, PG, and AR (Non--informative and Informative p ((Non prior)) 0.0 07 Distribution of Number of Responders 0.04 0.03 0.02 0.01 0.00 0 Den nsity 0.05 0.06 ER PG0.7 PG0.8 AR-Noninformative AR-Informative 60 80 100 120 140 Number of Responders 160 180 200 Bayesian Adaptive Randomization Design A: Standard Therapy Registration Marker− k R d Randm. AR 1:1 AR 2 2:1 1 B: Targeted AR 1:1 Therapy C: Standard Therapy py Testing Markers Marker + Randm. AR 1:1 AR 1:3 D: Targeted AR 1:4 Therapy Lee JJ, Gu X, Liu S. Bayesian adaptive randomization designs for targeted agent development. Clinical Trials, 2010;7:584-596 Tool #3: Platform Design - for f efficient ffi i t drug d screening i - treat patients best in the trial Goals: Platform Design – To efficiently screen as many drugs as possible – To treat patients with the better treatments in the trial Assumptions – Binary endpoints with 88-week response rate p=0.2 for the standard t d d (CNTL) ttreatment. t t – For experimental (E) arms, p=0.2 or p=0.4 – The study runs for 5 years with enrollment = 10/month – Simultaneous compare 5 E E--arms to the CNTL – Maximum N=60/arm. N 60/arm. When sufficient info accumulates, Graduate effective arms; Fail the ineffective arms Control type I error (10%) and type II error (20%) – Compare C with ith ttwo other th d designs: i Two-arm phase II trials with N=60/arm TwoSequential twotwo-arm study with early stopping Statistical Assumptions Demo 2: Platform Design Results for Number of Drugs Screened Hobbs B. and Lee JJ (unpublished) Tool #4: Seamless Phase II/III Design - Start with a multi-arm multi arm Phase II trial - Eliminate inefficacious arms - Roll into Phase III Seamless Phase II/III Design St t with ith a randomized d i d Ph i l with ith an active ti control t l Start Phase II ttrial (standard treatment) and several experimental arms with different treatments and/or dose or schedules Use a shortshort-term endpoint in the Phase II part, e.g., ORR Drop p inefficacious arms If at least one experimental arm is promising, roll into Phase III with one standard treatment and one or more selected experimental treatments. Use longerlonger-term endpoint, e.g. OS. I f Information ti collected ll t d in i th the Ph Phase II partt is i used d in i th the Phase III. No “white space” in trial conduct. Inoue et al, Biometrics 2002; Bretz et al, Biometrical J 2006; Stallard, SIM 2010 Seamless Phase IIII-III Design Berry, Nature Review Clinical Oncology, 2012 Seamless Phase IIII-III Design Berry, Nature Review Clinical Oncology, 2012 Seamless Phase IIII-III Design Berry, Nature Review Clinical Oncology, 2012 STAMPEDE Trial: 6 Arms, 5 Stages Trial Stages Purpose Primary Endpoint 2nd Endpoints 1 Pilot Phase Safety Feasibility 2 Activity Stage I Failure-free Survival OS, Toxicity, 3 Activity Stage II Failure-free Survival OS, Toxicity, 4 Activity Stage III Failure free Survival Failure-free OS Toxicity, OS, Toxicity 5 Activity Stage IV Overall Survival FFS, Toxicity, QoL, Cost-Eff. Sydes et al, Trials 2009 Tool #5: Multi-marker, Multi-arm Bayesian Adaptive Randomization Design - Biopsy required - Biomarker based - AR - Early stopping (suspend randomization) BATTLE (Biomarker(Biomarker-based Approaches of Targeted Therapy for Lung Cancer Elimination) Patient Population: Stage IV recurrent nonnon-small cell lung cancer (NSCLC) Primary Endpoint: 8 8--week disease control rate (DCR) 4 Targeted treatments, 11 Biomarkers 200 evaluable patients Goal: – Test treatment efficacy – Test biomarker effect and their predictive roles to treatment – Treat patients better in the trial based on their biomarkers 1. Zhou X, Liu S, Kim ES, Lee JJ. Bayesian adaptive design for targeted therapy development in lung cancer - A step toward personalized medicine (Clin (Clin Trials Trials,, 2008). 2. Kim ES, Herbst RS, Wistuba II, Lee JJ, et al, Hong WK. The BATTLE Trial: Personalizing Therapy for Lung Cancer. (Cancer (Cancer Discovery, Discovery, 2011) BATTLE Schema Umbrella Protocol Core Biopsy EGFR VEGF KRAS/BRAF RXR/CyclinD1 Biomarker Profile Randomization: Equal Æ Adaptive Erlotinib Vandetanib Erlotinib + Bexarotene Sorafenib Primary end point: 8 week Disease Control (DC) Demo 3: Adaptive Randomization –BATTLE BATTLE Results: Disease Control in % (n) Marker Groups Treatm ments EGFR KRAS VEGF RXR/ CycD1 None Total Erlotinib 35% ((17)) 14% ((7)) 40% ((25)) 0% ((1)) 38% ((8)) 34% ((58)) Vandetanib 41% (27) 0% (3) 38% (16) NA (0) 0% (6) 33% (52) Erlotinib + Bexarotene 55% (20) 33% (3) 0% (3) 100% (1) 56% (9) 50% (36) Sorafenib 39% (23) 79% (14) 64% (39) 25% (4) 61% (18) 58% (98) Total 43% (87) 48% (27) 49% (83) 33% (6) 46% (41) 46% (244) AACR Presentation: http://app2.capitalreach.com/esp1204/servlet/tc?cn=aacr&c=10165&s=20435&e=12587&&m=1&br=80&audio=false 55 Individual Biomarkers for Response and Resistance to Targeted Treatment: Exploratory Analysis Drug Treatment Biomarker P–value DC Erlotinib EGFR mutation 0.04 Improved Vandetanib High VEGFR-2 expression 0.05 Improved Erlotinib + Bexarotene High Cyclin D1 expression 0.001 Improved EGFR FISH Amp p 0.006 Improved p EGFR mutation 0.012 Worse EGFR high polysomy 0.048 Worse Sorafenib Lessons Learned from BATTLE-1? Biomarker-based adaptive design is doable! It is well received by clinicians and patients. Prospective tissues collection & biomarkers analysis provide a wealth of information Treatment effect & predictive markers are efficiently assessed. Pre-selecting markers is not a good idea. We don’t know what are the best predictive markers at get-go. Bundling markers into groups groups, although reducing the total number of marker patterns, is not the best way to use the marker information either. W t h for Watch f time ti drift d ift and d avoid id it it. AR should kick in earlier & be monitored closely. AR works well only when we have good drugs and good predictive markers. BATTLE-2 Schema Principles • • • • Better specific drugs Better specific targets No biomarker grouping Selection, integration and validation of novel predictive biomarkers Open: June 2011 September 2012: 90 randomized Erlotinib Protocol enrollment Biopsy performed Stage 1 (N=200): Adaptive Randomization KRAS mutation Stage 2 (N=200): Refined Adaptive Randomization “Best” discovery markers/signatures Erlotinib+AKTi MEKi+AKTi Sorafenib Primary endpoint: 8-week disease control N = 400 Biomarker Selection Clinical Trial Stage 1 Cli i l T Clinical Trial i l St Stage 2 Initial Adaptive Randomization with Group 1 markers Refined Adaptive Randomization with Group 4 Markers (200 Pts) (Enrollment of 160 of 200 Pts) Group 1 E t bli h d Known Established K Markers M k (N=1) (N 1) Kras mutation Group 2 High Potential, Candidate Markers (N~20) EGFR copy number, number IGF-1R copy number number, EGFR protein, IGF-1R protein, IGFBP-3 protein, pAKT protein, PTEN protein, LKB1 protein, PIK3CA mutation, BRAF mutation, mRNA RAS pathway activation signature, mRNA EGFR pathway activation signature, and miRNA EGFR signature Bayesian Variable Selection & Model Building • Markers with highest posterior probability as prognostic or predictive markers are selected. Choose 1 or 2 markers per treatment. • Identify models with best power for predicting treatment response Group 3 Initial Discovery Markers (N = Many) Microarray, RPPA, SNP, other mutations, proteins, IHC, FISH, miRNA markers, etc. Group 3 Selected Discovery Markers (N = ~20) 20) Preprocessing for selecting putative individual or composite markers Group p 4 Refined Adaptive p Randomization Markers (N=6 to 10) • Biomarker Steering Committee: • R01 Exec Comm; selected IAB / EAB members • Prespecified Guidelines for Selection: • Rank-ordering from Baysian modeling • Prevalence in Clinical Specimens • Biologic Plausibility • CLIA-certifiable Marker selection for f adaptive randomization. Group G 1 markers are used for f initial adaptive randomization (AR) ( ) in Stage S 1 as well as the refined AR in Stage 2. Group 2 markers and the selected Group 3 markers are entered into the Bayesian variable selection and model building process to identify best markers and corresponding models for predicting treatment response. Biomarker Steering Committee evaluates markers/models based on preselected guidelines to make final decision on Group 4 markers to be used for refined AR in Stage 2. Discovery Platform versus Confirmatory Platform Early phase of drug developing is about discovery and learning learning.. Adaptive design provides an ideal platform for learning – “We We learn as we go.” go go...” Due to the large number of tests, the overall false positive rate may be large. Results found in the discovery platform need to be validated in the confirmatory platform – Validation V lid ti off ttreatment t t efficacy ffi – Validation of predictive markers After narrowing down the biomarkers and treatments combination(s), confirmatory trials can be more focused with smaller sample size. My Dream on Curing Cancer… Enroll all patients into clinical trials trials. Pts are followed uniformly. Endpoints are recorded consistently. Tissues are collected for biomarker analysis. y There is something for everyone – – – – Pts with actionable mutations: Treat /w effective agents g Pts with putative predictive markers: marker marker--based AR trials Pts without informative markers: screening trials Pts serve as their own controls; N N--of of--1 trials All relevant information are collected timely and stored i a searchable in h bl kknowledge l d b base. Each center holds a database /w a compatible format. Information is updated and aggregated regularly. Updated info is used for designing trials & treating pts. Summary Life is complex complex. We know less than what we think we know. Adaptive about Ad ti design d i continues ti tto learn l b t the th new agents’ activities, identifying the predictive markers in order to apply this knowledge to better treat patients in real time. – Ideal for discoveryy – Need validation Adaptive p design g can increase study y efficiency, y allow flexibility in study conduct, and provide better treatment to study participants. Need to conduct more innovative trials to learn and to improve so we can turn promise into progress.