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

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’ tumor’s 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.