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

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.