Analysis of breast cancer next-generation sequencing datasets for identifying fusion genes responsible for the cancer progression

Abstract

Structural Variants (SVs) and Copy Number Variations (CNVs) at the genomic level are responsible for forming chimeric genes at the transcriptome level, posing significant implications in cancers. In this study, we performed an integrated analysis of the SVs, CNVs and fusion genes in breast cancer sequencing datasets to predict driver fusion genes using paired RNA and WGS (Whole Genome Sequencing) datasets. The combined effect of genomic fusions, chromosomal rearrangements and CNV can help analyze the highly perturbed cancer genome and how they work together, leading to cancer progression. Our analysis yielded chimeric fusion genes supported by multiple events, such as trans-splicing at the transcriptome level, formed due to CNV variation and SVs at the genomic level. We discovered novel gene fusions, such as RARA-PKIA in the SK-BR3 cell line and PLA2R1-RBMS1 in the HCC1395BL cell line. The analysis pipeline of the study can be utilized to explore the other less investigated cancer genomes, which may also help develop diagnostics and better cancer therapeutics, especially for personalized treatments.