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Updated: May 7, 2026
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
Published on: March 12, 2021
Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics
Yue Wang1, Santiago Haase2,3,4,5, Austin Whitman3,4,5
1Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Abstract:
Understanding how individual cancer cells adapt to drug treatment is a fundamental challenge limiting precision medicine cancer therapy strategies. Here, we present a multimodal framework that integrates bulk and single-cell treated and untreated transcriptomics data to identify drug-responsive cell populations in triple-negative breast cancer (TNBC). Our framework defines seven bulk-level "identities," each representing unique combinations of biologically relevant genes. These trackable identities are further mapped onto single cells and uncover global patterns of how cell populations respond to drug treatment. By capturing the evolving nature of cellular states, we show that a select few identities dominate and drive population-level responses during treatment, which allows us to better predict how entire tumors respond to treatment. This insight is essential for designing precise combination therapies tailored to the unique heterogeneity of patient tumors, addressing the single-cell variations that ultimately determine therapeutic outcomes.
Insights
This study introduces a new framework to track how individual cancer cells respond to drug treatment in triple-negative breast cancer (TNBC). It identifies key cell populations driving treatment response, aiding in the development of precise combination therapies.
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.
