The 3rd Stat4Onc Annual Symposium
Precision oncology trials
Speaker Biography and Abstract
Daniel Catenacci,
MD
Bio:
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).
Title:
Next-Generation
Precision Oncology Trials
Abstract:
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
Bio:
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.
Title:
Bayesian Oncology Clinical
Trial Designs with Subgroup-Specific Decisions
Abstract:
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
Bio:
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.