Online Program Home
My Program

Abstract Details

Activity Number: 302 - Omics I
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #329502
Title: A Multi-Step Classifier Addressing Cohort Heterogeneity Improves Performance of Prognostic Biomarkers in Three Cancer Types
Author(s): Ellis Patrick* and Samuel Mueller and Jean Yee Hwa Yang
Companies: University of Sydney and The University of Sydney and University of Sydney, Australia
Keywords: Classification; Prognosis; Cancer; Biomarker; Gene expression; Clinical data

The genetic diversity that exists both between and within tumors poses one of the central challenges in predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual cancer types, for many it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in multiple cancer cohorts that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. A multi-step procedure incorporating this information not only improves classification accuracy but also indicates the specific clinical attributes that had made classification problematic in each cohort. These findings show that including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program