A Multi-Omic Characterization of Multiple Oncogenic Interactions and its Therapeutics in Invasive Breast Carcinoma applying Bioinformatics Tools

Abstract

Invasive Breast Carcinoma is a complex heterogeneous disease in terms of diagnosis, clinical course, and pathology categorization. The World Health Organization (WHO) categorization does include more than a dozen variants, that are less prevalent, but are nonetheless extremely well described. In order to administer the appropriate therapy for each tumor, and to move from large, randomized research to a targeted one, it is crucial to understand the cancer type. This has now been made easier thanks to bioinformatics, the massive amounts of data produced, and the availability of tools to evaluate, understand, and then use this data in cancer therapy approaches. In this study, 12 genes associated with invasive breast carcinoma were mined in the cBioPortal for Cancer Genomics platform. Data was analyzed via bioinformatic tools such as Cytoscape, and MutationTaster. Further analysis regarding the oncogenic status of detected mutations allowed us to predict drugs that can be used in targeted treatment.TP53 was found to have the highest alteration percentage followed by ERBB2 and CDH1. Most of the gene combinations were co-occurrent with couple of mutually exclusive ones. The overall survival rate for patients harboring our 12 studied genes was lower than the unaltered group. Our study seeks to provide novel insights into invasive breast carcinoma’s mutational signature, which would help improve therapeutic approaches, chemoresistance, autophagy, as well as the pathways disrupted in breast cancer.