Invited Session 3
May 7, 2021
1:45 PM – 3:15 PM Pacific Time
Dr. Yuan Ji graduated from Fudan University with a bachelor in Mathematics, University of Wisconsin – Madison with a PhD in Statistics. He spent 9 years at The University of Texas M. D. Anderson Cancer Center as Assistant and Associate Professor in Biostatistics and Bioinformatics. Currently, Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. He is an NIH-funded PI focusing on innovative computational and statistical methods for translational cancer research. Dr. Ji is author of over 150 publications in peer-reviewed journals, conference papers, book chapters, and abstracts. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and mTPI-2 designs, which have been widely applied in dose-finding clinical trials. His recent work on precision medicine was elected as one of the top 10 ideas of the Precision Trials Challenge hosted by The Harvard Business School in 2015. Dr. Ji is an elected fellow of ASA.
Upon completing her medical degree from Lady Hardinge Medical College in New Delhi, India, Shivaani Kummar moved to the United States to train in Internal Medicine at Emory University in Atlanta, Georgia. Following this Dr. Kummar was selected to pursue fellowship training at the National Institute of Health (NIH) in Medical Oncology and Hematology, which culminated in being offered a faculty position at Yale University, New Haven CT. After spending four years as Assistant Professor of Medicine at Yale Cancer Center, she moved back to the National Cancer Institute (NCI), NIH, where she developed a clinical research program in novel cancer therapeutics. In 2011 she became Head of Early Clinical Trials Development in the Office of the Director, Division of Cancer Treatment and Diagnosis, NCI. Dr. Kummar moved to Stanford University in 2015 as Professor of Medicine and Director of the Phase I Clinical Research and Translational Oncology Programs. In July 2020, she joined Oregon Health & Science University (OHSU) as Division Chief of Hematology and Medical Oncology, co-Director of the Center of Experimental Therapeutics, and Associate Director for Clinical Research, Knight Cancer Institute, OHSU. Her research interests focus on developing novel therapies for cancer. She specializes in conducting pharmacokinetic and pharmacodynamic driven first-in-human trials tailored to make early, informed decisions regarding the suitability of novel molecular agents for further clinical investigation. Dr. Kummar is the principal investigator of numerous early phase trials, member of NIH grant review committees and national and international scientific committees.
Common cances are becoming a collection of orphan diseases. Wide spread use of genomic sequencing is subdividing cancer into subtypes managed based on presence of druggable drivers. However, most common cancers lack a single driver mutation, instead carry multiple genetic aberrations which may or may not be true drivers. Patient selection, better understanding of prognostic and predictive biomarkers, incorporating assessment of biomarkers for proof-of-mechanism and proof-of-concept, and trial designs that adapt to emerging trial data are some of the considerations in developing novel anticancer therapeutics. In this talk I will discuss the current challenges in designing proof-of-concept early phase trials.
Dr. Rui (Sammi) Tang is a leading expert of biostatistics/bioinformatics in the biotech/pharmaceutical industry and she is currently the Head of Biostatistics, Programming and Medical Writing Department at Servier Pharmaceuticals US. Prior to join Servier she was the Biostatistics Therapeutic Area head of Oncology, Transplants, Ophthalmology and prematurity neonates programs at Shire pharmaceutical. Sammi’s research interests are primarily in the area of adaptive clinical trial design and statistical issues in precision medicine. She has authored more than 35 articles in peer-reviewed scientific journals on methodology, study design, data analysis and reporting and is a co-inventor of several patents. Sammi is co-founder of DahShu which is a 501(c)(3) non-profit organization, founded to promote research and education of 5000 members. She is leading teams in the DIA(Drug Information Association) Innovative design scientific working group of oncology drug development and small population working group for rare disease statistical methodology development. She is also an active member in ASA(American Statistics Association) and ICSA(International Chinese Statistics Association) to serve the biostatistics and data science professional community.
Sammi graduated from the University of Michigan Technology University with a PhD in statistics Genetics.
In the era of precision medicine, biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. Adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. In this session, I will review innovative trial designs proposed to include biomarker hypothesis testing that may require a companion diagnostic device. These include platform, basket, umbrella trials for learning phase trials, various statistical testing strategies in biomarker stratified designs and adaptive biomarker designs for confirmatory trials. I will discuss the statistical challenge of application of biomarker subgroup selection in trial design perspective and how to optimize design to support the clinical trial to maximize the overall probability of success.
Jared earned his B.S. and M.S. degrees in molecular biology from Brigham Young University and his Ph.D. in statistics in 2001 from North Carolina State University. He has worked in both late and early clinical development statistics groups while at Merck. Since 2011 Jared has served as the statistical lead for biomarker discovery and development for pembrolizumab, a cancer immunotherapy targeting the PD1/PD-L1 axis. As Merck’s translational oncology program has grown, Jared provides scientific oversight and strategic consultation to a set of statisticians working in translational oncology. Jared was the primary statistician involved in analyses for cut-off selection for the PD-L1 IHC based diagnostic device that led to the initial approvals of pembrolizumab for the treatment of NSCLC patients expressing high levels of PD-L1 on their tumors. Jared worked with a diverse set of researchers to provide some of the earliest evidence that T-cell inflammation was associated with propensity to respond to pembrolizumab with his modeling defining an 18-gene signature capturing key features defining this favorable immune profile. Similarly, he was a part of the scientific team at Merck that established high tumor mutational burden as a biomarker of tumor response to pembrolizumab, and he supported both the drug and diagnostic device filings that led to the recent tumor agnostic approval for pembrolizumab in TMB-H tumors in the advanced setting where no other treatment options exist.
Pembrolizumab monotherapy is approved in patients with advanced metastatic solid tumors whose disease has progressed following prior treatment and who have no satisfactory alternative treatment options whose tumors are tumor mutational burden high (TMB-H), at a cut-off of 10 Mut/Mb. The history of the prospective identification of the TMB-H cut-off via a training data set and subsequent prospective validation in an independent multi-tumor clinical study, KEYNOTE-158, will be reviewed. Findings in a large whole exome sequencing data set that served as a supportive evidentiary package during regulatory approval will also be presented. Distinctions will be drawn between the approach used to establish the clinical value of TMB-H for pembrolizumab monotherapy and other literature reports that have led to lack of clarity in this area.
Ying Lu, Ph.D., is Professor of Biomedical Data Science in the Department of Biomedical Data Science, and by courtesy in the Department of Radiology and Departement of Health Research and Policy, Stanford University School of Medicine. He is the Co-Director of the Stanford Center for Innovative Study Design and the Biostatistics Core of the Stanford Cancer Institute. He was the director of VA Cooperative Studies Program Palo Alto Coordinating Center (2009-2016) and a Professor of Biostatistics and Radiology at the University of California, San Francisco (1994-2009). His research areas are biostatistics methodology and applications in clinical trials, statistical evaluation of medical diagnostic tests, validation of biomarkers, radiology, osteoporosis, oncology, meta-analysis, and medical decision making. He published more than 300 research papers in peer-reviewed journals, edited two books and numerous book chapters. Professor Lu is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA) and Associate Editor (Biostatistics) for the JCO Precision Oncology. Dr. Lu received his BS in Mathematics from Fudan University, MS in Applied Mathematics from Shanghai Jiao Tong University, and Ph.D. in Biostatistics from the University of California, Berkeley.
Dr. Bingshu Chen is a Professor of Biostatistics in the Department of Public Health Sciences and the Department of Mathematics and Statistics, Queen's University. He is also the Senior Biostatistician in the the Canadian Cancer Trials Group. Dr. Chen received his PhD in Biostatistics at the University of Waterloo in 2003. He spent four years at the United Sates National Cancer Institute in the Division of Cancer Epidemiology and Genetics as a Postdoctoral Fellow and a Research Fellow prior to coming to Queen's University. Dr. Chen's research interests mainly focus on survival analysis and statistical methods for cancer clinical trials. He developed biomarker threshold models to predict treatment subset effect in clinical trials. His other research interests include analysis of health economic data, statistics computing and Bayesian statistics.