A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes

TL;DR

This paper introduces an integrative method to identify sparse trans-acting DNA copy-number effects on gene expression in breast cancer, revealing key oncogenes and distinguishing copy-number driven from independent expression changes.

Contribution

It presents a novel approach to learn a sparse regulatory network linking DNA copy-number regions to gene expression, improving detection of trans-effects in cancer genomics.

Findings

Identified key oncogenes like ESR1 and ERBB2 as highly copy-number dependent.

Demonstrated the method's efficiency and comparable goodness of fit to existing models.

Revealed the global influence of DNA hotspots on gene expression in breast cancer.

Abstract

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset. Information from this network helps distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated and real data.