# Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks

## TL;DR

This paper introduces COMBO, a method that uses multi-layer networks to identify chromosomal aberrations as cancer drivers by integrating gene and DNA data.

## Contribution

COMBO introduces a novel approach to assess chromosomal aberrations as cancer drivers using multi-layer Boolean implication networks.

## Key findings

- COMBO successfully identifies chromosome 20 amplification as a cancer driver across multiple TCGA datasets.
- The method reveals complex relationships between transcriptome and epigenome in cancer histotypes.
- Chromosome 8q amplification effects were also analyzed in TCGA datasets using COMBO.

## Abstract

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.