Edward Chu, MD UPMC Hillman Cancer Center

Precision Medicine Clinical Trial Designs 2018 AACR Annual Meeting April 15, 2018
Edward Chu, MD UPMC Hillman Cancer Center University of Pittsburgh School of Medicine

One Size Fits All Approach
Time (years) Percent Drug “A”

Recognize Diversity (Heterogeneity)

Precision Cancer Medicine
Move from empiric delivery of systemic therapy to individually-tailored treatment. Give the right drug/treatment to the right person at the right time and at the right dose and right drug exposure Goals Maximize clinical efficacy Minimize side effects Maintain/improve quality of life Reduce overall costs

Fingerprints Are All Different
Cancer genomes can now be fingerprinted using massively parallel sequencing

Precision Medicine: Clinical Trial Designs
• Observation, feasibility studies • Randomized studies • Master protocol studies – basket trials – umbrella trials

MDACC: Enrollment on Genotype-Matched Trials
Mutation in Potentially Actionable Gene Underwent Genomic Testing N = 2000 Genotype-matched trial after genomic testing? No (1211) Genotype-Selected Trial N = 54 Genotype-Relevant Trial N = 29 Yes (789) No (706) Yes (83) 54/2000 (3%) of pts who underwent genomic testing received genotype-matched treatment Meric-Bernstam F, et al J Clin Oncol 2015

IMPACT / COMPACT at the Princess Margaret Hospital
(March July 2014) Tumor Type Patients Accrued Patients Profiled % profiled enrolled on trials % profiled enrolled on genotype matched trials Gynecological 430 405 20% 5% Breast 341 319 13% 6% Lung 339 256 16% 7% Colorectal 326 299 Pancreatobiliary 151 104 9% 1% Upper Aerodigestive 115 102 8% 2% Genitourinary 92 74 12% Other 99 81 21% Totals 1893 1640 15% Bedard P et al, AACR Precision Medicine Series: 2015

Best Tumor Shrinkage of Patients Enrolled
in Therapeutic Trials Un-matched Matched 61%** *Overall Response Rate p-value = 0.04 **Any Reduction in Target Lesions p-value <0.0001 RECIST v1.1 ORR 20%* RECIST v1.1 ORR 11% 32% Bedard P et al, AACR Precision Medicine Series 2015

Precision Oncology: UC San Diego Moores Cancer Center
• Observational study of 347 patients with solid tumors and NGS • 25% patients treated with a “matched” therapy • 27% patients treated with an “unmatched” therapy • 48% patients not evaluable • Higher percentage of patients in “matched” group achieved CR/PR/SD > 6 mth when compared to “unmatched” group, 34.5% vs 16.1% Schwaederle M et al, Mol Cancer Ther 2016

Le Tourneau C, et al, Lancet Oncol 2015

SHIVA Trial: Progression-Free Survival
• 741 patients screened • 293 (40%) patients had at least one molecular alteration matching one of the available treatment regimens PFS med – 2.3 (MT) vs 2.0 (c) PFS at 6 mth – 13% (c) vs 11% (MT) Figure 3: Progression-free survival *One patient had a follow-up of zero days so is not shown here. Le Tourneau C et al, Lancet Oncol 2015

SHIVA Trial: Reasons for Negative Results
Heavily pre-treated patients with advanced disease Only single targeted agents used, no combination regimens of targeted agents Only 3 molecular pathways targeted with limited portfolio of targeted agents (11) tested not covering many actionable mutations Multiple treatment groups, aberrations, tumor types and histology – important source of variability Spatial/temporal intra-patient heterogeneity and tumor context not taken into account

IMPACT: An MD Anderson Precision Medicine Study
Mutation in Potentially Actionable Gene Underwent Genomic Testing N = 1,436 Targetable Molecular Alterations No (257) Matched Therapy N = 390 Non-matched Therapy N = 247 Yes (1,179) No (265) Yes (914) IMPACT: Initiative for Molecular Profiling and Advanced Cancer Therapy 390/1,436 (27%) of pts who underwent genomic testing received genotype-matched treatment Tsimberidou AM et al, JCO Precision Oncology 2017

Comprehensive Clinical Genomic Sequencing: MSK IMPACT
Zehir A et al, Nat Med. 2017

Mutations detected by MSK Impact

Clinical Actionability: MSK Impact

Precision Medicine: Clinical Trial Designs
• Master protocol studies – umbrella trials – basket trials

Master Protocol: Goals
To identify and evaluate novel drugs and/or combinations that target specific molecular biomarkers Improve screening Screening large numbers of patients for multiple targets Reduce screen failure rate Provide a sufficient “hit rate” to engage patients & physicians Increase speed of drug evaluation and development Provide an infrastructure to open new investigations into new therapies/regimens Rapid drug/biomarker testing for detection of “large effects Facilitate FDA approval of new drugs and bring safe & effective drugs to patients faster

Platform Design for Biomarker-driven Therapies: The Master Protocol Concept
Biomarker Profiling Screening Component Biomarker/ Assay 1 Biomarker/ Assay 2 …Biomarker/ Assay n Not Biomarker/ Assay 1-n Clinical Trial Component Framework to evaluate multiple biomarkers and drugs/regimens within the same infrastructure. Sub-study 1 (Investigational therapy 1 ) Design 1 Sub-study 2 (Investigational therapy 2) Design 2 Sub-study n (Investigational therapy n Design n ?

Confirmatory Master Protocol Design
Biomarker Profiling* Not Biomarker 1-n Biomarker 1 Biomarker 2 Biomarker 3 …Biomarker n Sub-study 1 Exp1 SoC1 Sub-study 2 Exp2 SoC2 Sub-study 3 Exp3 SoC3 …Sub-study n Expn SoCn Non-match Study NMT SoC5 1:1 1:1 1:1 1:1 1:1 Multi-arm Master Protocol Homogeneous patient populations & consistent eligibility from arm to arm Each arm independent of the others Infrastructure facilitates opening new arms faster Phase II-III design allows rapid drug/biomarker testing for detection of “large effects” Screening large numbers of patients for multiple targets by a broad-based NGS platform reduces the screen failure rate Provides a sufficient “hit rate” to engage patients & physicians Bring safe & effective drugs to patients faster Designed to facilitate FDA approval of new drugs Exp = experimental therapy or therapeutic regimen SoC = Standard of care and could vary across substudies NMT = experimental therapy with expected broad impact not just within subgroup

Master Protocol Designs: Umbrella Trials
• Tests the effect of different drugs on different mutations/molecular alterations in a single cancer type • Goals of this trial design are to – facilitate genomic screening efficiency and patient accrual – increase speed of drug development and evaluation • Assumes that the molecular biomarker and its effects on the tumor are well understood • Adaptive design to include new drugs targeting novel mutations or molecular alterations as they are identified • Multiple sub-studies required to be open at any given time and constant need for new agents and combinations to be tested

Clinical Trial Designs: Umbrella Trial
Histology-dependent, molecular alteration-specific Tumor type A (lung cancer) Tumor molecular analysis Biomarker 4 Biomarker 3 Biomarker 2 Biomarker 1 Need to make this into 3 slides One with text describing 3 bullet points Then take the 2 bottom boxes and make each into a separate slide Drug 4 Drug 3 Drug 2 Drug 1

BATTLE-1 Trial: Personalizing Therapy for NSCLC
Umbrella Protocol Core Needle Biopsy First completed prospective biopsy-mandated, biomarker-based, adaptive designed umbrella study N=255 Biomarker Profile EGFR mutation/copy number KRAS/BRAF mutation VEGF/VEGFR-2 expression RXRs/Cyclin D1 expression and CCND1 copy number Equal Followed by Adaptive Randomization Need to make this into 3 slides One with text describing 3 bullet points Then take the 2 bottom boxes and make each into a separate slide Erlotinib + Bexarotene Sorafenib Erlotinib Vandetanib Kim ES et al, Cancer Discov 2011

BATTLE-2 Study Protocol enrollment Biopsy performed
Main focus on KRAS-mutant platinum-refractory NSCLC EML4-ALK fusion or EGFR mutation exclusion Stage 1: (n = 200) Adaptive random assignment by KRAS mutation status Statistical modeling and biomarker selection Stage 2: (n = 200) Refined adaptive random assignment “Best” discovery markers/signatures Need to make this into 3 slides One with text describing 3 bullet points Then take the 2 bottom boxes and make each into a separate slide Erlotinib Erlotinib + MK-2206 (AKTi) MK AZD6244 (MEKi) Sorafenib Papadimitrakopoulou V et al, J Clin Oncol 2016

Umbrella Trials in NSCLC
BIOMARKER DRIVEN DISEASE SETTING DESIGN DESIGN TYPE ALCHEMIST Yes Adjuvant non-squamous NSCLC Phase 3 Confirmatory FOCUS 4 Metastatic CRC Phase 2 followed by Phase 3 Discovery and confirmatory I-SPY2 No Neo-adjuvant breast cancer Phase 2 Discovery BATTLE Recurrent NSCLC Phase 2/3 Lung-MAP Previously treated squamous lung cancer National Lung MATRIX trial NSCLC Single-arm Need to make this into 3 slides One with text describing 3 bullet points Then take the 2 bottom boxes and make each into a separate slide

Centralized Testing for EGFR & ALK
Alchemist (Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trials) Centralized Testing for EGFR & ALK (Screening study: A151216) EGFR+ ALK+ Not EGFR+ or ALK+ EA4512 EA5143 A081105 Multi-arm Master Protocol Homogeneous patient populations & consistent eligibility from arm to arm Each arm independent of the others Infrastructure facilitates opening new arms faster Phase II-III design allows rapid drug/biomarker testing for detection of “large effects” Screening large numbers of patients for multiple targets by a broad-based NGS platform reduces the screen failure rate Provides a sufficient “hit rate” to engage patients & physicians Bring safe & effective drugs to patients faster Designed to facilitate FDA approval of new drugs Erlotinib vs Observation Crizotinib vs Observation Nivolumab vs Observation Randomized Phase III Design with OS as primary endpoint

Cell cycle gene alternation+
Round One (June 16, 2014) S1400 FMI NGS/MET IHC Biomarker-driven sub-studies Non-match sub-study S1400C Cell cycle gene alternation+ S1400B PI3K+ S1400D FGFR+ S1400E MET IHC+ S1400A (anti-PD-L1) 7% 12% 9% 20% 52% 1:1 1:1 1:1 1:1 1:1 Arm1 Arm2 Arm1 Arm2 Arm1 Arm2 Arm1 Arm2 Arm1 Arm2 1 Rilotumumab + Erlotinib 2 Erlotinib Amgen 1 GDC-0032 2 Docetaxel Genentech 1 Palbociclib 2 Docetaxel Pfizer 1 AZD4547 2 Docetaxel Astrazeneca 1 Medi4736 2 Docetaxel Medimmune/AZ

LUNG-MAP: Current Schema
Biomarker Driven Sub-Studies Non-match Sub-Studies ABBV-399 S1400K C-Met+ Est. Fall 2017 S1400B PI3K+ Closed 12/12/16 GDC-0032 S1400C CCGA+ Closed 09/01/16 Palbociclib S1400C CCGA+ Closed 10/31/16 AZD4547 S1400E c-Met+ Rilotumumab /Erlotinib vs. Erlotinib Closed 11/26/14 Talazoparib S1400G HRRD+ OPEN S1400A Non-match MEDI4736 Closed 12/18/15 Nivolumab/ Ipilimumab vs. Nivolumab S1400I IO-Naive OPEN MEDI4736/ Tremelimumab S1400F IO-exposed June 2017 S1400A Docetaxel arm closed 4/22/15 S1400B-D Docetaxel arms closed 12/18/15 *Biomarker-driven sub-studies (like S1400G) will progress to Phase III if study meets endpoint and Phase III is feasible at which point the standard of care arm will be determined.

FOCUS4: Umbrella Trial in mCRC

The BEAT AML Trial: BMAL-16-001-M1
Phase Ib / II umbrella trial in patients >60 years with newly diagnosed AML Sponsored by Leukemia and Lymphoma Society (LLS) Target of 500 patients Assign treatment by biomarker Starts with 4 treatment arms with plans to expand to arms AG-221, IDH2 inhibitor Entospletinib, spleen TKI Samalizumab, anti-CD200 antibody BI , anti-CD33 antibody Biomarker negative sub-study

Master Protocol Designs: Basket Trials
• Tests the effect of a single drug on a single mutation or molecular alteration in wide range of cancers • Include rare cancers that would be difficult to study in randomized controlled trials • Allows study of multiple molecular subpopulations of different cancer all within one study • Flexibility to open and close arms of the study

Clinical Trial Designs: Basket Trials
Tumor type A (lung cancer) Tumor type B (gastric cancer) Tumor type C (colon cancer) Tumor type D (breast cancer) Histology-independent, molecular alteration-specific Tumor molecular analysis Biomarker 1 Biomarker 2 Need to make this into 3 slides One with text describing 3 bullet points Then take the 2 bottom boxes and make each into a separate slide Drug 1 Drug 2

Vemurafenib Basket Trial
Figure 1. Study Design and Cohorts. The all-others cohort included cervical cancer, brain tumors, head and neck cancer, esophageal and gastric cancers, pancreatic cancer, sarcoma, and carcinoma of unknown primary type. The breast cancer cohort was closed because of insufficient accrual; the single patient with breast cancer was included in the all-others cohort for the purposes of this report. The ovarian cancer and multiple myeloma cohorts did not have sufficient numbers of patients for a stage 1 analysis and therefore did not undergo formal analysis. Preliminary efficacy results for these cohorts are included with the results for the all-others cohort for the purposes of this report. ECD/LCH denotes Erdheim–Chester disease or Langerhans’-cell histiocytosis, and NSCLC non–small-cell lung cancer. Hyman et al, N Engl J Med 2015;373:726-36

Vemurafenib Basket Trial: Tumor Response
Figure 2 (facing page). Maximum Percent Change from Baseline in the Sum of the Diameters of Target Lesions. The change from baseline in the target lesion diameter is shown for patients who had measurable disease at baseline according to Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1, and who underwent at least one post-treatment evaluation; dashed lines indicate −30% change, the minimum necessary to qualify for partial response according to RECIST. Data are shown for 18 patients in the NSCLC cohort (Panel A), 26 patients in the colorectal cancer cohort who were treated with vemurafenib plus cetuximab (Panel B), and patients in the all-others cohort (i.e., patients with tumor types that were not prespecified) plus 1 patient with low-grade serous ovarian cancer (Panel C). The tumor types in the all-others cohort included gliomas, head and neck cancer, pancreatic cancer, pleomorphic xanthoastrocytoma, esophageal and gastric cancers, sarcoma, and carcinoma of unknown primary type. Five patients (1 in the NSCLC cohort and 4 in the all-others cohort) died before evaluation. Asterisks indicate patients in the dose-escalation stage (dose levels 1 and 2). Hyman et al, N Engl J Med 2015;373:726-36

Vemurafenib Basket Trial: Conclusions
BRAF V600 appears to be a targetable oncogene in some non- melanoma cancers. Clinical activity was observed in non-small cell lung cancer and in Erdheim-Chester disease and Langerhans cell histiocytosis. Histologic context (tumor environment) is an important determinant of response in BRAF V600-mutated cancers. Hyman et al, N Engl J Med 2015;373:726-36

NCI-Molecular Analysis for Therapy Choice (NCI-MATCH)
A phase II precision medicine cancer trial Co-developed by the ECOG-ACRIN Cancer Research Group and the National Cancer Institute Version Date: 06/07/2017

NCI-MATCH Hypotheses Primary: Tumors that share common somatic genetic alterations in oncogenes will be variably responsive to therapies targeting the oncogenic pathway based on lineage specific factors Secondary: Concomitant somatic genetic alterations will predict responsiveness or resistance

NCI-MATCH Eligibility Defined Molecularly
Tumor biopsy to identify mutations/amplifications/ translocations Patients can be screened with local NGS but results must be confirmed on NCI-MATCH assay Patient assignment to relevant agent(s)/subprotocol Perform tumor biopsies and sequencing at progression to identify potential resistance mechanisms Submit de-identified samples to central labs Conduct whole-exome, mRNA sequencing (research purposes)

NCI-MATCH Eligibility
Patients with solid tumors or lymphomas whose disease has progressed following at least one line of standard systemic therapy – or with tumors that do not have standard therapy Exclude histologies that had been approved by the FDA or had shown lack of efficacy with an agent Tumor accessible to biopsy and patient willing to undergo biopsy Adults ≥ 18 year of age ECOG performance status ≤ 1 Adequate organ function

NCI-MATCH Patient Population Considerations
Target: at least 25% of total enrollment to be patients who have “rare” tumors “Common” cancers defined as Breast Non-small cell lung Colon Prostate

NCI-MATCH Assay Workflow

NCI-MATCH Trial Milestones
Opened on August 12, 2015, with 10 treatments Original goal to have 3000 patients submit tumor samples for testing Paused enrollment of new patients on November 11, 2015, for planned interim analysis Expanded to 17 treatments on February 25, 2016 Interim analysis presented on April 18, 2016 at annual AACR meeting Expanded to 24 treatments and resumed enrollment of new patients on May 31, 2016

NCI-MATCH: Initial Ten Studies
Agent(s) Molecular Target(s) Estimated Prevalence Crizotinib ALK Rearrangement (non-lung adenocarcinoma) 4% ROS1 Translocations (non-lung adenocarcinoma) 5% Dabrafenib and Trametinib BRAF V600E or V600K Mutations (non-melanoma) 7% Trametinib BRAF Fusions, or Non-V600E, Non-V600K BRAF Mutations (non-melanoma) 2.8% Afatinib EGFR Activating Mutations (non-lung adenoca) 1 – 4% HER2 Activating Mutations (non-lung adenoca) 2 – 5% AZD9291 EGFR T790M Mutations and Rare EGFR Activating Mutations (non-lung adenocarcinoma) 1 – 2% TDM1 HER2 Amplification (non breast cancer) VS6063 NF2 Loss 2% Sunitinib cKIT Mutations (non GIST) ≈ 35%

NCI-MATCH: Clinical Accrual Summary (5/2016)
Activated 8/12/2015; paused on 1/11/15 for 92 days Patient cases registered for screening, Patient cases with samples, Cases with genetic testing, (87%) Cases with mutation matching 1 of 10 arms, / 645 (9%) Patients matching specific eligibility criteria, / 645 (5%) and assigned to a treatment arm

NCI-MATCH Expanded to 24 Arms in Late May 2016
Arm / Target Drugs(s) A EGFR mut Afatinib B HER2 mut C1 MET amp Crizotinib* C2 MET ex 14 sk E EGFR T790M AZD9291 F ALK transloc Crizotinib G ROS1 transloc H BRAF V600 Dabrafenib+trametinib I PIK3CA mut Taselisib N PTEN mut GSK P PTEN loss Q HER 2 amp Ado-trastuzumab emtansine Arm / Target Drug(s) R BRAF nonV600 Trametinib S1 NF1 mut S2 GNAQ/GNA11 T SMO/PTCH1 Vismodegib U NF2 loss Defactinib V cKIT mut Sunitinib W FGFR1/2/3 AZD 4547* X DDR2 mut Dasatinib Y AKT1 mut AZD 5363* Z1A NRAS mut Binimetinib* Z1B CCND1,2,3 amp Palbociclib* Z1D dMMR Nivolumab* *Pending approval

Expected Overall Match Rate = 23%
NCI-MATCH Projected Match Rates and Enrollments for 24 Treatment Arms (N=5,000 Screened) Expected Overall Match Rate = 23% Arm / Target Expected Match Rate % Expected Enrollment Expected Match Rate % I PIK3CA mut 4.0 89 B ERBB2 mut 0.8 20 Z1B CCND1 amp 3.6 79 H BRAF V600 19 W FGFR1/2/3 2.9 65 T SMO/PTCH1 0.6 14 P PTEN loss 2.5 55 R BRAF non V600 0.3 8 Q ERBB2 amp 1.7 44 E EGFR T790M 0.2 4 S1 NF1 mut 1.9 41 F ALK transloc Z1C CDK4/6 amp 38 V cKIT mut 3 Y AKT1 mut 1.2 28 A EGFR mut Z1A NRAS mut G ROS1 transloc U NF2 loss 1.1 26 S2 GNAQ/GNA11 N PTEN mut 24 C2 MET ex 14 sk No Data Not Known C1 MET amp 0.9 21 Z1D dMMR

NCI-MATCH Trial Milestones (cont’d)
Increased patient target goal from 3000 to 6000 in fall 2016 Added myeloma patients to screening eligibility in fall 2016 Required development of a bone marrow assay, along with validation and implementation across 4-lab network Expanded to 30 treatments on March 13, 2017 Expected match rate, 30%

NCI-MATCH Patient Accrual Thru 04/23/2017
Screened % of Total Less Common Cancers 2885 61.5% Common Cancers* 1809 38.5% Total 4694 The trial has far exceeded the goal that 25% of the 6000 patients screened have rare or less common types of cancer Common cancers screened: Colorectal 15.5%, Breast 12.8%, NSCLC 7.6%, Prostate 2.6%

NCI-COG Pediatric MATCH Study
Relapsed and refractory solid tumors and lymphomas Non-histology driven N=20 per arm 5-7 arms to start Estimated 300 patients/year MEK inhibitor – selumetinib BRAF inhibitor – vemurafenib PI3K/mTOR inhibitor – LY NTRK inhibitor – LOXO-101 ALK or ROS1 inhibitor – ensartinib PARP inhibitor – olaparib

Precision Cancer Medicine: Conclusions
• Rapid advances over the past 2-3 years • Move from empiric delivery of systemic therapy to individually-tailored treatment. Up to 20-25% of patients can now be offered matched agent. • NGS results may offer patients additional therapeutic options • Steep learning curve regarding genome-driven clinical trials Considerations for combination therapy Real time sequencing with serial follow-up Issue of tumor heterogeneity Increase in actionable mutations Understanding of multiple pathways of activation Identifying key drivers for tumor growth and survival Earlier treatment of metastatic disease

Liquid Biopsy: cfDNA from Blood
Plasma/ Serum cfDNA Crowley, E. et al, Nat. Rev. Clin. Oncol. 2013

Precision Medicine and Clinical Trials: Challenges
Tissue acquisition Tissue quality Tumor content Tumor heterogeneity Sampling & sampling time points Choice of assay Cross-platform validation CLIA (Clinical Laboratory Improvements Amendment) certification Bioinformatics analysis to mine data

Precision Medicine and Clinical Trials: Challenges
Incidental germline findings Turnaround time Cost / Insurance coverage Identify “best in class” or necessary drug combinations Availability of FDA-approved drugs / Insurance coverage Availability of investigational genome-driven clinical trials

Role of Next-Generation Sequencing in Precision Medicine Cancer Clinical Trials
Unlikely that genomics is sufficient as a stand-alone strategy to fully characterize the complex molecular landscape of cancer – extending beyond genomics “Next-gen” clinical trials need to target the dynamic status of tumors and account for intratumoral spatial and temporal heterogeneity in their design Close interactions between the genome and the immunome – using NGS to inform the selection of patients most likely to benefit from immune- based therapies

Precision Medicine: Comprehensive Omics Analysis
Genome Transcriptome Epigenome Proteome Patient Pharmacogenome Immunome Tumor Metagenome Integrates Shared Facilities with UPCI Programs, Investigators, Administration, Funding Microbiome Metabolome 61

Thank You Affiliated with the University of Pittsburgh School of Medicine

Edward Chu, MD UPMC Hillman Cancer Center

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