A Clustering Approach to Integrative Analysis of Multiomic Cancer Data

TL;DR

This paper introduces a Bayesian nonparametric clustering framework for integrating multi-omics cancer data, enabling identification of tumor subtypes and relevant genomic aberrations to improve cancer classification and understanding.

Contribution

It presents a novel scalable probabilistic model that simultaneously clusters samples and probes, incorporating dependencies across multiple omics domains for cancer data analysis.

Findings

Successfully applied to lung cancer data from TCGA

Accurately identified tumor subtypes and genomic drivers

Demonstrated scalability and flexibility of the method

Abstract

Rapid technological advances have allowed for molecular profiling across multiple omics domains from a single sample for clinical decision making in many diseases, especially cancer. As tumor development and progression are dynamic biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and biological entities that are druggable, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative probabilistic frameworks for high-dimensional multiple-domain cancer data that coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalogue of genomic aberrations associated with cancer taxonomy. We propose an innovative, flexible and scalable Bayesian nonparametric framework for simultaneous clustering of both tumor samples and genomic probes. We describe an efficient variable selection procedure to identify relevant genomic aberrations that can potentially reveal underlying drivers of a disease. Although the work is motivated by several investigations related to lung cancer, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.