Pathway Index Models for Construction of Patient-Specific Risk Profiles

• Activity Number: 437
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Kevin H Eng*+ and Sijian Wang and Christina Kendziorski
• Companies: University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison

Abstract

Statistical methods for variable selection, prediction, and/or classification have proven extremely useful in moving personalized genomics medicine forward, in particular leading to a number of genomic based assays now in clinical use for predicting recurrence of breast, colon, and prostate cancer, as well as transplant rejection. Although invaluable in individual cases, the information provided by these assays is limited. In particular, a patient is classified into one of very few groups (e.g. recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on whether or not to treat (e.g. if recurrence is predicted), they provide no information on how to treat. This talk reviews an approach based on modeling a time-to-event outcome as a function of known biological pathways, identifying important genomic aberrations, and providing pathway-based patient-specific assessments of risk. As we discuss, applications of the approach to the Cancer Genome Atlas (TCGA) ovary project provide results that may significantly impact ovarian cancer research as well as patient treatment.