Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Sinha, S.; vegesna, R.; Dhruba, S. R.; Wu, W.; Kerr, D. L.; Stroganov, O. V.; Grishagin, I.; Aldape, K. D.; Blakely, C. M.; Jiang, P.; Thomas, C. J.; Bivona, T. G.; Schaffer, A. A.; Ruppin, E.

doi:10.1101/2022.01.11.475728

bioRxiv · 10.1101/2022.01.11.475728

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Sinha, S.·vegesna, R.·Dhruba, S. R.·Wu, W.·Kerr, D. L.·Stroganov, O. V.·Grishagin, I.·Aldape, K. D.·Blakely, C. M.·Jiang, P.·Thomas, C. J.·Bivona, T. G.·Schaffer, A. A.·Ruppin, E.

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Abstract

Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients single-cell (SC) tumor transcriptomics. First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients tumors SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

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Sinha, S., vegesna, R., Dhruba, S. R., Wu, W., Kerr, D. L., Stroganov, O. V., Grishagin, I., Aldape, K. D., Blakely, C. M., Jiang, P., Thomas, C. J., Bivona, T. G., Schaffer, A. A., Ruppin, E.. 2022-01-12. Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors. https://doi.org/10.1101/2022.01.11.475728

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