Pathway index models for construction of patient-specific risk profiles. Stat Med 2013 Apr 30;32(9):1524-35
Date
10/18/2012Pubmed ID
23074142Pubmed Central ID
PMC3593986DOI
10.1002/sim.5641Scopus ID
2-s2.0-84876323512 (requires institutional sign-in at Scopus site) 15 CitationsAbstract
Statistical methods for variable selection, prediction, and 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 cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, 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 which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival.
Author List
Eng KH, Wang S, Bradley WH, Rader JS, Kendziorski CAuthors
William H. Bradley MD Professor in the Obstetrics and Gynecology department at Medical College of WisconsinJanet Sue Rader MD Chair, Professor in the Obstetrics and Gynecology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Computer SimulationFemale
Genomics
Humans
Models, Genetic
Models, Statistical
Neoplasm Recurrence, Local
Ovarian Neoplasms
Risk Assessment
Signal Transduction
