Phenotypic Mapping of Dexamethasone Response in Breast Cancer Cells using Single-cell Transcriptomics

Authors

• J Li
• B Anchang

Abstract

Identifying tumor heterogeneity in response to treatment prior to clinical intervention is critical for long-term survival. We’ve developed an AI-based reference mapping strategy to profile tumor subpopulations in response to perturbations using single-cell transcriptomics. This strategy, known as PHENO-DEX, integrates two major algorithms: DSFMix and PHENOSTAMP. We use DSFMix, based on tree models to identify response/non-response cell trajectories from a Dex-treated breast cancer cell dataset. Then, using a feed forward loop neural network algorithm, PHENOSTAMP, we next create a Dex-responding reference map, identifying 9 cell states (4 responsive and 5 non-responsive). Each cell state exhibits unique characteristics which correlates with cell plasticity response to Dex. We projected thirty breast cancer cell lines and multiple clinical breast cancer tumors onto the reference map, effectively revealing their cell state heterogeneity in response to Dex. In summary, we’ve provided a framework to comprehensively characterize both cell lines and clinical samples, which better dissects the responsive states to Dex of tumors prior to any treatment, thereby providing clinical guidance for treatment decisions.

Scientific Focus Area: Cancer Biology

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