American Statistical Association
Advances in cancer research have shown that tumors have heterogeneous genetic events, many of which are targetable by anticancer agents. Tests of treatment-biomarker interactions tend to be underpowered when done as secondary objectives in trials of drug efficacy. Clinical trials that make use of adaptive randomization and Bayesian prediction have been proposed as more efficient for investigating multiple agents and predictive biomarkers. We proposed that clinical studies enrolling patients with multiple cancer modalities (CSMCM) would greatly contribute to statistical learning and accelerate the pace at which new drugs are studied. In silico simulations to evaluate the benefit of CSMCMs in a research portfolio require accurate parameters of (1) accession of patients by disease modality, and (2) joint prevalence of target gene mutations. At the Dana-Farber Cancer Institute (DFCI), a research study was initiated in 2011 to parallelize molecular profiling with routine histopathology at diagnosis or disease progression, and to date has assayed >5000 patients across 11 disease centers. We will present the design and characteristics of in silico studies parameterized from this cohort.
|Date:||Wednesday, October 22, 2014|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.