{"scope":{"layer":"general","tissue_scope":"unknown","species":"unknown","system":"unknown","evidence_level":"peer_reviewed","importance":"","importance_reason":""},"id":"VC0152","type":"paper","title":"Computational deconvolution of drug sensitivity via single-cell-to-bulk transcriptome mapping","authors_or_org":"Coudray A, Forey R, Bejar Haro B, Martins F, Carlevaro-Fita J, Sheppard S, Offner SE, La Manno G, Obozinski G, Trono D.","date":"2026-10-16","year":"2026","venue":"PLOS Computational Biology","url":"https://doi.org/10.1371/journal.pcbi.1014862","doi":"10.1371/journal.pcbi.1014862","pmid":"42886762","brain_tumor_focus":"no","tags":["deconvolution","drug-sensitivity","bulk-RNA-seq","single-cell","AML"],"tag_slugs":["drug-response","scrna-seq"],"tag_labels":["Drug response","scRNA-seq"],"tag_labels_zh":["药物反应","单细胞 RNA 测序"],"unmapped_tags":["deconvolution","bulk-RNA-seq","AML"],"one_line_summary_en":"Coudray et al. (PLOS Comput Biol 2026): CLIMB and CLIFF deconvolve bulk RNA-seq into cell-type composition and bulk drug-sensitivity data into cell-subtype/single-cell profiles; validated on leukemic cell-line mixtures and pseudo-bulk data, applied to the beatAML ex vivo screen (121 compounds). Focus is acute myeloid leukemia; no brain-tumor application stated in the abstract.","one_line_summary_zh":"Coudray 等（PLOS Comput Biol 2026）：CLIMB 与 CLIFF 将bulk RNA-seq 解卷积为细胞类型组成，并将bulk 药物敏感性数据解析至细胞亚型/单细胞水平；在白血病细胞系混合物和伪bulk 数据上验证，并应用于 beatAML 离体药筛（121 种化合物）。重点为急性髓系白血病；摘要未提及脑肿瘤应用。","verified":"yes","verification_source":"Crossref (title, 10 authors, 2026-10-16, e1014862, abstract) + PubMed PMID 42886762 (title/date/journal match); abstract scanned for brain/glioma terms: none; full text not read","page_url":"https://gliostate.com/library/VC0152/","api_url":"https://gliostate.com/api/items/VC0152.json","sameAs":["https://doi.org/10.1371/journal.pcbi.1014862","https://pubmed.ncbi.nlm.nih.gov/42886762/"],"is_preprint":false,"facets":{"type":"https://gliostate.com/library/type/paper/","year":"https://gliostate.com/library/year/2026/","tags":["https://gliostate.com/library/tag/drug-response/","https://gliostate.com/library/tag/scrna-seq/"]},"cited_by":[],"related":[{"id":"VC0132","title":"Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations","shared_tags":["scrna-seq","drug-response"],"page_url":"https://gliostate.com/library/VC0132/","api_url":"https://gliostate.com/api/items/VC0132.json"},{"id":"VC0042","title":"Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.","shared_tags":["drug-response"],"page_url":"https://gliostate.com/library/VC0042/","api_url":"https://gliostate.com/api/items/VC0042.json"},{"id":"VC0138","title":"PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responses","shared_tags":["drug-response"],"page_url":"https://gliostate.com/library/VC0138/","api_url":"https://gliostate.com/api/items/VC0138.json"},{"id":"VC0147","title":"DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness","shared_tags":["drug-response"],"page_url":"https://gliostate.com/library/VC0147/","api_url":"https://gliostate.com/api/items/VC0147.json"},{"id":"VC0097","title":"Automated and personalized glioblastoma tumor organoid drug screening platform exposes sensitivity to proteasome and HDAC inhibitors.","shared_tags":["drug-response"],"page_url":"https://gliostate.com/library/VC0097/","api_url":"https://gliostate.com/api/items/VC0097.json"},{"id":"VC0129","title":"DELPHAI, AI Agent for Predicting Drug Response and Resistance","shared_tags":["drug-response"],"page_url":"https://gliostate.com/library/VC0129/","api_url":"https://gliostate.com/api/items/VC0129.json"}]}