Multi-omics Topic Modeling for Breast Cancer Classification

Abstract

The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or ``topics'' that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development, and are associated to the survival probability.

Molecular Classifiers in Skin Cancers: Challenges and Promises

“…Deep learning models for multi-class classification have been found to outperform classification accuracy in cancers when only a single omic data is used [ 87 ]. Integrative analysis of omics data has identified previously unexplored subtypes of complex and heterogenous lesions such as ovarian and breast cancers, that are associated more significantly with the clinical outcomes than the established TCGA classification [ 88 , 89 ].…”