Classification of triple-negative breast cancers based on Immunogenomic profiling

Area of Science:

• Immunogenomics
• Cancer Biology
• Translational Oncology

Background:

• Triple-negative breast cancer (TNBC) is highly heterogeneous, prompting research into genomic subtyping.
• Current TNBC classification lacks focus on immune signatures crucial for predicting immunotherapy response.
• Identifying immune-based subtypes can optimize patient stratification for targeted therapies.

Purpose of the Study:

• To classify triple-negative breast cancer (TNBC) subtypes using immune signatures.
• To explore the potential of immunogenomic profiling for stratifying TNBC patients for immunotherapy.

Main Methods:

• Utilized four public TNBC genomics datasets for immunogenomic profiling.
• Applied unsupervised and supervised machine learning to classify TNBC based on 29 immune signatures.
• Analyzed gene co-expression subnetworks and associated transcription factors for each subtype.

Main Results:

• Identified three reliable and predictable TNBC subtypes: Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L).
• Immunity_H exhibits increased immune cell infiltration, anti-tumor activity, and superior survival, with activated cancer-associated pathways.
• Immunity_L shows suppressed immune signatures and activated cell cycle, DNA replication, and metabolism pathways, with distinct transcription factor subnetworks.

Conclusions:

• TNBC classification based on immune signatures offers potential clinical utility for treatment selection.
• This immunogenomic approach may improve patient outcomes by enabling tailored immunotherapy strategies.
• Further research into these subtypes can refine therapeutic decisions for TNBC.