Optimizing the Multi-Omics Data Types and Variant Combinations for Accurate Breast Cancer Molecular Subtypes Classification

Breast cancer is a highly heterogeneous disease with Luminal-A, Luminal-B, HER2-Enriched, Basal-Like, and Normal-Like molecular subtypes. Accurate classification of breast cancer molecular subtypes is essential for effective diagnosis, treatment, and planning. In recent years, multi-omics data has been widely used to improve classification performance. However, most of the existing studies focus on various combinations of multi-omics data types and variants without considering their biological relevance and computational effectiveness. This research study aims to systematically analyze, validate, and optimize the combinations of multi-omics data types and variants for accurate breast cancer molecular subtypes classification. The main goal is to identify the most suitable biologically meaningful combinations for improving classification performance. This research study provides the biological rationale for integrating the multi-omics data types and variants, and analyzes the various combinations used by existing studies for breast cancer subtype classification and the reasons behind their selection. Based on this analysis, possible and best-proposed combinations of multi-omics data types and variants are presented for the accurate classification of breast cancer molecular subtypes, based on both biological and computational perspectives. In addition, this research study identifies and recommends reliable public databases that provide multi-omics datasets with verified PAM50 labels for accurate subtype classification. The findings can help researchers design more accurate and reliable classification models by using the best proposed combination of multi-omics data types and variants, and select appropriate datasets with validated subtype labels.