Biomarker identification of triple negative breast cancer subtypes using machine learning

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Triple Negative Breast Cancer (TNBC) is an aggressive breast cancer subtype with limited targeted treatment options.
• Understanding its molecular heterogeneity is key for improving patient outcomes.
• Current diagnostic and prognostic tools require enhancement for TNBC.

Purpose of the Study:

• To develop a comprehensive analytical framework for identifying robust molecular subtypes of TNBC.
• To elucidate the underlying biological mechanisms associated with each subtype.
• To identify clinically relevant biomarkers for improved diagnosis, prognosis, and therapeutic strategies.

Main Methods:

• Integrated analysis combining unsupervised clustering, differential gene expression, pathway enrichment, and explainable machine learning.
• Consensus clustering applied to public gene expression datasets for patient subgrouping.
• Model-agnostic explainable AI used to assess the contribution of genes and pathways to subtype classification.

Main Results:

• Successfully delineated distinct molecular subtypes of TNBC with associated biological mechanisms.
• Identified subtype-specific gene signatures and pathways.
• Demonstrated the prognostic significance of identified genes, highlighting clinical applicability.
• Validated the framework's compatibility with various machine learning models.

Conclusions:

• The study elucidates the molecular heterogeneity of TNBC, crucial for advancing personalized medicine.
• The developed framework facilitates the discovery of interpretable, biologically meaningful biomarkers.
• Findings support the development of more accurate, biomarker-driven clinical strategies for TNBC treatment and management.