The 3rd Stat4Onc Annual Symposium

Precision oncology trials

Speaker Biography and Abstract



Daniel Catenacci, MD


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).


Next-Generation Precision Oncology Trials


The promise of ‘personalized cancer care’ with therapies toward specific molecular aberrations has potential to improve outcomes. However, there is recognized heterogeneity within any given tumor-type from patient to patient (inter-patient heterogeneity), and within an individual (intra-patient heterogeneity) as demonstrated by molecular evolution through space (primary tumor tometastasis) and time (after therapy). These issues have become hurdles to advancing cancer treatment outcomes with novel molecularly targeted agents. Classic trial design paradigms are challenged by heterogeneity, as they are unable to test targeted therapeutics against low frequency genomic ‘oncogenic driver’ aberrations with adequate power. Usual accrual difficulties to clinical trials are exacerbated by lowfrequencies of any givenmolecular driver. To address these challenges, there is need for innovative clinical trial designs and strategies implementing novel diagnostic biomarker technologies to account for interpatient molecular diversity and scarce tissue for analysis. Importantly, there is also need for pre-defined treatment priority algorithms given numerous aberrations commonly observed within any one individual sample. Access to multiple available therapeutic agents simultaneously is crucial. Finally intra-patient heterogeneity through time may be addressed by serial biomarker assessment at the time of tumor progression. Various ‘next-generation’ biomarker-driven trial designs and their potentials and limitations to tackle these recognized molecular heterogeneity challenges are discussed. Regulatory hurdles, with respect to drug and companion diagnostic development and approval, are considered.



Peter F. Thall, Ph.D.

Department of Biostatistics

M.D. Anderson Cancer Center


Peter F. Thall is the Anise J. Sorrell Professor in the Department of Biostatistics at M.D. Anderson Cancer Center, and an adjunct professor in the Department of Statistics at Rice University.  He is a Fellow of the American Statistical Association and the Society for Clinical Trials, and received the Don Owen Award in 2014.  Dr. Thall has published over 250 papers and book chapters in the statistical and medical literature, and co-authored the 2016 book Bayesian Designs for Phase I-II Clinical Trials. His latest book, Statistical Remedies for Medical Researchers, is in press and should become available in the summer of 2019. Dr. Thall’s research areas include clinical trial design, precision medicine, Bayesian nonparametric statistics, incorporating expert opinion into Bayesian inference, and dynamic treatment regimes. He has presented over 200 invited talks and 30 short courses, served as an associate editor for Journal of the National Cancer Institute, Statistics in Medicine, Statistics in Biosciences, Clinical Trials, and Biometrics, and is an ASA media expert.


Bayesian Oncology Clinical Trial Designs with Subgroup-Specific Decisions


This talk will present Bayesian utility-based designs for two cancer clinical trials that make subgroup-specific outcome-adaptive decisions. The first is a randomized trial comparing nutritional prehabilitation to standard of care for controlling post-operative morbidity (POM) after chemoradiation and esophageal resection.  The design uses elicited utilities of POM scored as a five-level ordinal variable, accounts for two prognostic subgroups, and assumes a robust model for POM as a function of treatment and subgroup. The second is an early phase trial to optimize the dose of umbilical cord blood derived natural killer cells for treating advanced hematologic malignancies. The design does sequential dose-finding and safety monitoring within each of six prognostic subgroups, with decisions based on joint utilities of five co-primary time-to-event outcomes monitored over 100 days post cell infusion. Simulation studies establishing each design’s operating characteristics are presented.



Ying Lu, Ph.D.

Professor of Biomedical Data Science

Co-Director, Biostatistics Core of Stanford Cancer Institute (NIH Comprehensive Cancer Center)

Co-Director, Center for Innovative Study Design

Stanford University School of Medicine


Dr. Lu received his Ph.D. in Biostatistics from the University of California, Berkeley and was faculty in the University of Miami School of Medicine and the University of California, San Francisco. He also served as the Director of VA Cooperative Studies Palo Alto Coordinating Center from 2009-2016. His research focuses include clinical trial design, early phase cancer trials, radiology, medical diagnosis and prognostic prediction, medical decision making, and statistical applications. He serves as biostatistical editor of the JCO Precision Oncology, and is an ASA elected fellow, ICSA 2014 President, and President Elect of WNAR 2019.