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Multi-omics integration for identifying prognostic biomarkers in complex disease.

Banff International Research Station for Mathematical Innovation and Discovery

Recent studies in cancer and other complex diseases continues to highlight the extensive genetic diversity between and within cohorts. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Here, we will first discuss the concept of â classifiability` observed in multi-omics studies where individual patientsâ samples may be considered as either â hardâ or â easyâ to classify by different platforms, reflected in moderate error rates with large ranges. We demonstrate in a cohort of 45 stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. We propose a novel multi-step procedure to incorporate this information and was able to improved classification accuracy overall as well as identifying the specific clinical attributes that had made classification problematic in each cohort. Finally, we address an essential step towards utilizing these new biomarkers for therapeutic purposes by developing a novel standardization method which tackles this prospective experimental design problem.

Mathematics; Statistics; Computer science

video/mp4

Author affiliation: University of Sydney

2019-05-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70104

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/