{"doi":"10.1101/2022.01.11.475728","title":"Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors","abstract":"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 PER sonalized single- C ell E xpression-based P lanning for T reatments In On cology (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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":299754,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8816,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":325643,"name":"Rahulsimham Vegesna","orcid":"0000-0001-9829-9765","position":1,"is_corresponding":false},{"id":732205,"name":"Saugato Rahman Dhruba","orcid":"0000-0001-5947-6757","position":2,"is_corresponding":false},{"id":3210,"name":"Wei Wu","orcid":"0000-0002-6556-067X","position":3,"is_corresponding":false},{"id":3209,"name":"D. Lucas Kerr","orcid":"0000-0002-0557-8341","position":4,"is_corresponding":false},{"id":662263,"name":"Oleg V. Stroganov","orcid":"0000-0001-6190-823X","position":5,"is_corresponding":false},{"id":320479,"name":"Ivan Grishagin","orcid":"0000-0003-2985-4336","position":6,"is_corresponding":false},{"id":13200,"name":"Kenneth Aldape","orcid":"0000-0001-5119-7550","position":7,"is_corresponding":false},{"id":3220,"name":"Collin M. Blakely","orcid":"0000-0001-8134-3651","position":8,"is_corresponding":false},{"id":43036,"name":"Peng Jiang","orcid":"0000-0002-7828-5486","position":9,"is_corresponding":false},{"id":258191,"name":"Craig J. Thomas","orcid":"0000-0001-9386-9001","position":10,"is_corresponding":false},{"id":3224,"name":"Trever G. Bivona","orcid":"0000-0001-5734-4128","position":11,"is_corresponding":false},{"id":322365,"name":"Alejandro A. Schäffer","orcid":"0000-0002-2147-8033","position":12,"is_corresponding":false},{"id":3227,"name":"Eytan Ruppin","orcid":"0000-0002-7862-3940","position":13,"is_corresponding":false},{"id":3207,"name":"Sanju Sinha","orcid":"0000-0002-2688-0603","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:49.412501Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}