Biomarker Gene Identification for Breast Cancer Classification

TL;DR

This paper presents a novel AI-based method to identify small, clinically relevant gene signatures for breast cancer subtypes, achieving high accuracy and revealing pathway insights.

Contribution

It introduces a new algorithm leveraging interpretable AI to discover gene signatures for breast cancer classification from RNA sequencing data.

Findings

Identified 43 gene signatures for breast cancer subtypes

Achieved 0.91 accuracy with neural network classifier

Revealed relevant pathways like ERBB2 and p53 signaling

Abstract

BACKGROUND: Breast cancer has emerged as one of the most prevalent cancers among women leading to a high mortality rate. Due to the heterogeneous nature of breast cancer, there is a need to identify differentially expressed genes associated with breast cancer subtypes for its timely diagnosis and treatment. OBJECTIVE: To identify a small gene set for each of the four breast cancer subtypes that could act as its signature, the paper proposes a novel algorithm for gene signature identification. METHODS: The present work uses interpretable AI methods to investigate the predictions made by the deep neural network employed for subtype classification to identify biomarkers using the TCGA breast cancer RNA Sequence data. RESULTS: The proposed algorithm led to the discovery of a set of 43 differentially expressed gene signatures. We achieved a competitive average 10-fold accuracy of 0.91, using neural network classifier. Further, gene set analysis revealed several relevant pathways, such as GRB7 events in ERBB2 and p53 signaling pathway. Using the Pearson correlation matrix, we noted that the subtype-specific genes are correlated within each subtype. CONCLUSIONS: The proposed technique enables us to find a concise and clinically relevant gene signature set.