GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data
Jai Chand Patel, Sushil Kumar Shakyawar, Sahil Sethi, Chittibabu Guda
Abstract
Abstract Motivation Contextual integration of multiomic datasets from the same patient could improve the accuracy of subtype prediction algorithms to help with better prognosis and management of breast cancer. Previous machine learning models have underexplored the graph-based integration, hence unable to leverage the biological associations among different omics modalities. Here, we developed a graph-based method, GAIN-BRCA, using the native features from mRNA, DNA methylation (CpG), and miRNA data as well as the synthesized features from their interactions. GAIN-BRCA computes weightage from miRNA-mRNA and CpG-mRNA interactions to derive a new transformed feature vector that captures the essential biological context. Results GAIN-BRCA demonstrates superior performance with an AUROC of 0.98. GAIN-BRCA, with an accuracy of 0.92 also outperformed the existing methods like MOGONET and moBRCA-net with accuracies of 0.72 and 0.86, respectively. Kaplan-Meier survival analysis revealed subtype-specific prognostic genes, including KRAS in Luminal A (P value = 0.041), TOX in Luminal B (P value = 0.008), and MITF and TOB1 in HER2+ (P values = 0.029 and 0.025, respectively). However, no single gene demonstrated a significant survival correlation unique to the Basal subtype. GAIN-BRCA framework, in combination with SHAP, has identified several subtype-specific biomarkers to aid in the development of precision therapeutics for breast cancer subtypes. Availability and implementation GAIN-BRCA code is publicly accessible on https://github.com/GudaLab/GAIN-BRCA.