Grant Number:	1R13CA203409-01 Interpret this number
Primary Investigator:	Satagopan, Jaya
Organization:	Sloan-Kettering Inst Can Research
Project Title:	Statistical and Computational Methods for Pharmacogenetic Epidemiology of Cancer
Fiscal Year:	2016

¿ DESCRIPTION (provided by applicant): The recent advances in genomic technology, coupled with increasing investigation of environmental carcinogens, have led to revolutionary progress in identifying genetic and environmental factors for various cancers and cancer-related traits. These investigations have also demonstrated that carriers of specific genetic variants are more likely to benefit from certain preventive and therapeutic interventions than non- carriers. Such findings have ushered in a new era of pharmacogenetics investigations to identify genetic variants that inform therapeutic response in humans, catalyzing a shift towards developing targeted anticancer therapies and preventive intervention strategies. Statistical methods play a pivotal role in identifying genetic factors that inform response to treatment and preventive intervention, and in the design and analysis of studies of targeted treatments and interventions. There is now a surge in the development of novel statistical methods for addressing the scientific needs of the field of pharmacogenetic epidemiology of cancer. These include (but are not limited to): (i) analytic methods for evaluating gene-treatment, gene- environment, and gene-gene interactions; (ii) power transformation methods for modeling cancer risk to understand variation in treatment benefits according to genetic factors; and (iii) data mining methods to identify acquired and inherited genetic variants associated with cancer etiology, progression, and response to anticancer treatment. Novel stratified, two-stage, and adaptive study designs are also being developed for efficient design of pharmacogenetics studies. Therefore, a two-day symposium on "Statistical and Computational Methods for Pharmacogenetic Epidemiology of Cancer" would be important and timely. The Specific Aims of this symposium are: (1) to promote discussions and collaborations among leading and emerging experts in the areas of statistical genetics, data mining methods, pharmacology, genetic and clinical epidemiology, and oncology to identify clinically actionable genetic risk factors associated with the etiology and progression of cancer, and response to treatment and to identify individuals (defined based on their genetic characteristics) who are likely to respond to specific treatment and intervention strategies; (2) t promote a forum for the interchange of ideas and expertise for evaluating data arising from a variety of study designs including cohort studies, case-control studies, and biomarker-selected and biomarker- enriched trials; (3) to encourage the development of user-friendly software packages for implementing these methods; (4) to promote the use of publicly available data to assess these statistical and computational methods; and (5) to publish select papers from this workshop in the journal Genetic Epidemiology. This is an open symposium with an emphasis on diversity, where extensive efforts will be made to include women, under-represented minority, and junior researchers in the program.