Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.

Area of Science:

• Oncology
• Genomics
• Computational Biology

Background:

• Understanding cellular adaptation to cancer therapies is crucial for advancing precision medicine.
• Triple-negative breast cancer (TNBC) presents significant therapeutic challenges due to its heterogeneity.
• Current strategies often overlook dynamic single-cell responses to drug treatment.

Purpose of the Study:

• To develop a multimodal framework integrating bulk and single-cell transcriptomics data.
• To identify drug-responsive cell populations and their contribution to treatment outcomes in TNBC.
• To predict tumor response based on evolving cellular states during therapy.

Main Methods:

• Integration of bulk and single-cell RNA sequencing data from treated and untreated TNBC samples.
• Definition and tracking of seven bulk-level 'identities' based on gene expression patterns.
• Mapping of bulk identities onto single cells to analyze population-level drug responses.

Main Results:

• The framework successfully identified distinct drug-responsive cell populations within TNBC.
• A few dominant 'identities' were found to drive the overall population response to treatment.
• The study demonstrated the ability to predict tumor response by analyzing these evolving cellular states.

Conclusions:

• The developed framework offers a novel approach to understanding cancer cell adaptation during drug treatment.
• Identifying key cell populations and their dynamics can improve predictions of therapeutic efficacy.
• This work provides essential insights for designing personalized combination therapies for TNBC patients.