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SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment
Ziyang Tang1, Xiang Liu2, Zuotian Li2,3
1Department of Computer and Information Technology, Purdue University, Indiana, USA.
Abstract:
Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations.
Key Points:
We have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data.SpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance.SpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance.
Insights
Spatial cellular heterogeneity drives varied drug responses within tumors. SpaRx, a new model, maps this spatial drug resistance, aiding personalized cancer treatments.
Area of Science:
- Computational biology
- Genomics
- Cancer research
Background:
- Spatial cellular heterogeneity influences drug response and resistance within tumors.
- Emerging spatial technologies offer single-cell resolution of gene expression patterns.
- Understanding spatial drug resistance is crucial for optimizing cancer therapy.
Approach:
- Developed SpaRx, a graph-based domain adaptation model for spatial drug response analysis.
- SpaRx integrates pharmacogenomics data with single-cell spatial transcriptomics.
- Employed hybrid learning with dynamic adversarial adaptation for knowledge transfer.
Key Points:
- SpaRx reveals heterogeneous drug sensitivity and resistance across tumor locations.
- Identifies drug-resistant tumor cells forming ecosystems with their microenvironment.
- SpaRx is an open-source software for spatial transcriptomics data analysis.
Conclusions:
- SpaRx characterizes spatial therapeutic variability and uncovers drug resistance mechanisms.
- Enables identification of personalized drug targets and effective combination therapies.
- Provides insights into the spatial tumor microenvironment's role in drug resistance.
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