Invited Session 1
May 7, 2021
10:00 AM – 11:30 AM Pacific Time
Neby Bekele is Vice President, Biostatistics and Clinical Data Management at Exelixis. He is responsible for Biostatistics, Statistical Programming, and Clinical Data Management. Neby joined Exelixis in September of 2020.
Prior to joining Exelixis, Neby was Vice President, Biometrics at Gilead Sciences where he was Head of Biometrics (Biostatistics, Bioinformatics, Statistical Programming, & Clinical Data Management).
Prior to Gilead, he was on the faculty at M. D. Anderson Cancer Center for 10 years where he began working as a research assistant professor in 2001 and rose to the rank of tenured associate professor. During this period, he collaborated with researchers in Leukemia, Neuro-Oncology, Neuro-Surgery, Thoracic/Head and Neck and Thoracic/CV Surgery.
Neby’s research includes applications of novel design and analysis methods to solve questions related to the design and analysis of clinical trial data. Other specific areas of interest include the application of Bayesian methods in clinical trials, adaptive designs, missing data problems and multiple testing procedures. He has 120+ collaborative/methodological peer-reviewed manuscripts and book chapters.
Neby earned his Ph.D. in statistics from Baylor University. Prior to studying statistics, he earned degrees in Economics (BA) Political Science (BA, MA), and Urban Planning (MCRP). In addition, he is fluent in Spanish (having lived in Mexico for 8 yrs). This background has given Neby a unique global perspective which has served him well in a variety of cross-cultural contexts. In addition to this background, Neby has an interest in exploring and understanding the intersection between critical thinking skills, communication skills, and foundations of clinical trial design and practice to improve Biostatistical decision-making.
Daniel Catenacci, MD, Associate Professor of Medicine, is an adult GI medical oncologist, and Director of the gastrointestinal oncology program at the University of Chicago. He serves as the Assistant Director of Translational Research in the Comprehensive Cancer Center.
In addition to his clinical practice, Dr. Catenacci is an active basic and clinical researcher, focusing on the treatment of gastroesophageal (esophagus, gastroesophageal junction, and stomach) cancers. His bench-to-bedside translational research has an overarching goal to validate and improve personalized treatment, immunotherapy, and precision medicine for gastroesophageal cancer and other GI cancers. A major focus of his research is on the quantification of tumor genetic molecular heterogeneity both between individuals with gastroesophageal cancer, but importantly also within a given individual within one tumor site, and from one tumor site to another, and how this impacts personalized targeted therapeutic approaches. Additionally, Dr. Catenacci designs and executes novel clinical trials to implement treatment strategies based on these laboratory and clinical discoveries. Dr. Catenacci serves as Associate Editor for the Journal of American Medical Association Network Open (JAMA Netw Open) and is on the editorial board of the Journal of Clinical Oncology Precision Oncology (J Clin Oncol PO).
Background: Targeted and immunologic therapies have limited benefit in advanced (aGEA) due to baseline (primary vs metastatic tumor) and temporal molecular heterogeneity. We performed a novel expansion platform type II study to evaluate an individualized therapy as determined by a pre-specified treatment strategy.
Methods: PANGEA enrolled newly diagnosed aGEA pts who then received up to 3 standard cytotoxic lines of therapy in sequence (FOLFOX, FOLFIRI, FOLTAX). Baseline tissue biomarker profiling was mandated on the primary tumor at baseline, a metastatic tumor at baseline, and also a progressing lesion at both first and second progression time points (PD1/PD2), as well as circulating tumor DNA (ctDNA) analysis throughout. After initiating first line chemotherapy and upon learning the metastatic biomarkers profiling results, a matched antibody was added by a predefined prioritized treatment algorithm incorporating tissue and blood biomarker profiling results. At PD1, patients went to second line chemotherapy plus continued the initially assigned antibody. Upon obtaining results of PD1 biomarker profiling, patients changed to a new antibody only if indicated, otherwise they continued on the original antibody. The same was done at PD2. The primary endpoint of the study was 1yr overall survival. Assuming exponential survival, enrolling 68 patients provided 80% power to detect a 1yr overall survival of 63% compared to historical 50% 1yr overall survival (HR 0.67), using a 1-sided test (0.10 alpha).
Results: 80 pts were enrolled, and 68 were treated per protocol (based on availability of antibodies). At data cut-off 8/20/20, 13 of 68 patients were still on trial with a median follow up time of 27.6 months. All 68 patients had first line treatment, 87% second line therapy, and 25% had third line therapy. The 1yr survival rate was 66%, meeting the primary efficacy endpoint. The 3yr & 4yr overall survival rates were 12% & 8%, respectively. Tumor molecular heterogeneity led to antibody therapy change by personalized treatment algorithm in 35% of patients at baseline, 49% after first line therapy and 48% after second line therapy. Any grade >3 non-hematologic toxicity through all 3 treatment lines was seen in 25% of pts.
Conclusions: PANGEA showed superior efficacy compared to historical controls, supporting further evaluation of this individualized treatment strategy in a larger prospective randomized study.
Mei-Yin C Polley, Ph.D, holds a doctoral degree in Biostatistics from Columbia University. Currently, she is Associate Professor of Biostatistics at The University of Chicago, and the Head of the Statistics Division and Senior Statistician of the Brain Tumor Committee for NRG Oncology (a National Cancer Institute sponsored member of the national clinical trials network group). Her methodological interests include the design, conduct, analysis and monitoring of all phases of cancer clinical trials, biomarker reproducibility, innovative group sequential methods for biomarker validation, prognostic and predictive modeling, and biomarker-based clinical trial designs. Dr. Polley has been actively involved in a broad spectrum of cancer research with a particular focus on breast cancer and brain cancer. She has served on many scientific governing or advisory bodies including the National Cancer Institute Steering Committees (breast, lymphoma, head and neck cancer, and pediatric and adolescent solid tumor), the Scientific Program Committee of American Society of Clinical Oncology (ASCO), and the US Veterans Affairs (VA) Oncology A Study Section.
Recent advances in biotechnology have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this talk, I will review several commonly used biomarker-based randomized clinical trial designs in oncology, with a particular focus on design efficiency. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Real trial examples in oncology will be used throughout to illustrate these designs. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology.
J. Jack Lee, Ph.D., is Professor of Biostatistics, Kenedy Foundation Chair in Cancer Research, and Associate Vice President in Quantitative Sciences at the University of Texas MD Anderson Cancer Center. His areas of statistical research include design and analysis of clinical trials, Bayesian adaptive designs, master protocols, statistical computation/graphics, drug combination studies, and biomarkers identification and validation. Dr. Lee has also been actively participating in basic, translational, and clinical cancer research in the area of head and neck cancer, lung cancer, melanoma, chemoprevention, immuno-oncology, and precision oncology. He is an elected Fellow of American Statistical Association, Society for Clinical Trials, and American Association for the Advancement of Science. He is a Statistical Editor of Cancer Prevention Research and serves on the Statistical Editorial Board of Journal of the National Cancer Institute.
Bayesian hierarchical models (BHM) have been applied in clinical trials to allow for information sharing across subgroups. Traditional Bayesian hierarchical models do not have subgroup classifications; thus, information is shared across all subgroups. When the subgroups are heterogenous, these subgroups may belong to different clusters. Thus, placing all subgroups in one pool and borrowing information across all subgroups can result in substantial bias for the subgroups with strong borrowing, or a lack of efficiency gain with weak borrowing. To resolve this difficulty, we propose two approaches: (1) hierarchical Bayesian classification and information sharing (BaCIS) model and (2) Bayesian cluster hierarchical model (BCHM) for the design of multi-group phase II clinical trials with binary outcomes. Under BaCIS, subgroups are classified into low- or high-response clusters based on their outcomes, mimicking the hypothesis testing framework. Under BCHM, the number of clusters is determined by the Dirichlet process via the non-parametric Bayes model. Subsequently, information borrowing takes place among similar subgroups. The incorporation of predictive markers in BHM can be found in recent literature. These methods can be applied to the design and analysis of basket or platform trials to increase the study efficiency while remain high accuracy in assessing treatment efficacy.
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.
Dr. Jane Fridlyand is a Senior Director in the Department of Data and Statistical Sciences at Genentech. Jane leads a group of data scientists that focuses on Early Development Oncology development programs and personalized healthcare. Dr. Fridlyand got her Ph.D in Statistics from the University of California at Berkeley and continued on as a faculty at UCSF Cancer Center and Division of Biostatistics working on methods for high dimensional genomic data analyses. Dr. Fridlyand joined Early Clinical Development, Oncology at Genentech in 2007 and since then has worked in both early and late stage development at Roche/Genentech. She is an author of over 50 peer-reviewed publications and several book chapters. Her main interests include early development and bridging exploratory research with clinical development.