S-Score: A Scoring System for the Identification and Prioritization of Predicted Cancer Genes

Jorge E S De Souza,Renan Valieris,Dirce M Carraro,André F Fonseca,Sandro J De Souza,Richard D Kolodner,Jean Y J Wang

doi:10.1371/journal.pone.0094147

Abstract

A new method, which allows for the identification and prioritization of predicted cancer genes for future analysis, is presented. This method generates a gene-specific score called the “S-Score” by incorporating data from different types of analysis including mutation screening, methylation status, copy-number variation and expression profiling. The method was applied to the data from The Cancer Genome Atlas and allowed the identification of known and potentially new oncogenes and tumor suppressors associated with different clinical features including shortest term of survival in ovarian cancer patients and hormonal subtypes in breast cancer patients. Furthermore, for the first time a genome-wide search for genes that behave as oncogenes and tumor suppressors in different tumor types was performed. We envisage that the S-score can be used as a standard method for the identification and prioritization of cancer genes for follow-up studies.

Highlights

The availability of different ‘‘omics’’ technologies and the recent development of generation sequencing have brought new perspectives to the field of cancer research [1]
The The Cancer Genome Atlas (TCGA) data include somatic mutations, gene expression, methylation and copy number variation, which together with clinical information from the patients represent an important resource for the development of new strategies for diagnostic and therapeutic interventions as well as providing baseline data for more detailed studies of specific genes and pathways [2,3,4,5]
These genome-wide data have been used to identify genes that are altered in cancer. These alterations typically occur in tumor suppressor genes like p53 or oncogenes like KRAS

Summary

Introduction

The availability of different ‘‘omics’’ technologies and the recent development of generation sequencing have brought new perspectives to the field of cancer research [1]. Previous attempts to generate scores for cancer genes have used a single type of data, either mutation frequency or expression pattern [6,8]. We propose the S-score, which integrates information on mutation status, expression pattern, methylation status and copy number to produce a unique value directly proportional to the frequency in which a given gene is altered in a cancer type.

Results

Conclusion