Tensor Decomposition for Sub-typing of Complex Diseases based on Clinical and Genomic Data

Abstract

It has long been understood that stratification of patients into fine-grained cohorts is a foundation of accurate diagnosis and effective treatment of complex diseases, such as cancer. Nevertheless, cancer therapies still fail or cause unnecessary suffering to many patients, which suggests that our current understanding of cancer sub-types needs to be refined. In this paper, we propose CLIGEN, a novel computational pipeline for high-throughput data-driven stratification of patients with a complex disease into cohorts corresponding to multi-modal disease sub-types based on clinical and genomic data. We applied CLIGEN to discover breast cancer sub-types based on the clinical and genomic data of 503 patients with breast ductal carcinoma in the Cancer Genome Atlas (TCGA). Quantitative and qualitative evaluation of the breast cancer sub-types discovered by CLIGEN indicate that they are biologically meaningful and correlate with clinical outcomes, such as patient survival time.