{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:52:20Z","timestamp":1792799540599,"version":"4.5.0"},"reference-count":76,"publisher":"American Association for Cancer Research (AACR)","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014039","name":"DOD Prostate Cancer Research Program","doi-asserted-by":"publisher","award":["HT94252410551"],"award-info":[{"award-number":["HT94252410551"]}],"id":[{"id":"10.13039\/100014039","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM147365"],"award-info":[{"award-number":["R01GM147365"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01CA283171"],"award-info":[{"award-number":["R01CA283171"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Silver Family Innovation Foundation"},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA253472"],"award-info":[{"award-number":["U01CA253472"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA281902"],"award-info":[{"award-number":["U01CA281902"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U24CA264128"],"award-info":[{"award-number":["U24CA264128"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Susan G. Komen Foundation","id":[{"id":"https:\/\/ror.org\/02nadbe75","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/100001767","name":"The National Foundation for Cancer Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100001767","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["S10OD034224"],"award-info":[{"award-number":["S10OD034224"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aacrjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>Survival analysis is fundamental to cancer research. Advances in technology have enabled an increasing number of cohort-level cancer studies to incorporate single-cell sequencing alongside clinical survival data. However, no effective strategy currently exists for directly modeling survival outcomes from single-cell data. To address this gap, we present scSurvival, an attention-based multiple-instance Cox regression framework that models each tumor sample as an ensemble of cells to predict survival outcomes at both the patient and single-cell levels. To handle high dimensionality, sparsity, and batch effects, scSurvival integrates a variational autoencoder\u2013based feature extraction module with generative modeling to enhance feature robustness and cross-batch generalizability. Comprehensive simulations demonstrate scSurvival\u2019s superior performance and scalability. In melanoma and liver cancer single-cell RNA sequencing (scRNA-seq) cohorts, scSurvival accurately predicts patient outcomes and identifies the cell subpopulations most critical to survival. Overall, scSurvival enables robust prediction of patient survival while uncovering survival-associated cell subpopulations, advancing single-cell survival analysis in cancer research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance:<\/jats:title>\n                    <jats:p>Survival analysis is central to clinical oncology, yet no effective tools currently model survival outcomes directly from single-cell data. scSurvival bridges this gap by predicting patient outcomes and identifying key subpopulations from scRNA-seq with survival information, enabling scalable analyses and promoting broader adoption of cohort-level single-cell profiling in cancer research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1158\/2159-8290.cd-25-0965","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T19:01:59Z","timestamp":1776798119000},"page":"931-952","update-policy":"https:\/\/doi.org\/10.1158\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution"],"prefix":"10.1158","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6486-7338","authenticated-orcid":false,"given":"Tao","family":"Ren","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 1 Biomedical Engineering Department, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8238-3863","authenticated-orcid":false,"given":"Faming","family":"Zhao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 1 Biomedical Engineering Department, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-8592","authenticated-orcid":false,"given":"Canping","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 1 Biomedical Engineering Department, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0043-7848","authenticated-orcid":false,"given":"Le","family":"Zhou","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 1 Biomedical Engineering Department, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9487-0215","authenticated-orcid":false,"given":"Ling-Yun","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/034t30j35","id-type":"ROR","asserted-by":"publisher"}],"name":"Chinese Academy of Sciences 3 Academy of Mathematics and Systems Science, , Beijing, China."},{"name":"4School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0144-9614","authenticated-orcid":false,"given":"Gordon B.","family":"Mills","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 5 Division of Oncological Sciences, Knight Cancer Institute, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2389-1865","authenticated-orcid":false,"given":"Lisa M.","family":"Coussens","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 6 Cell, Development and Cancer Biology Department, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3364-8324","authenticated-orcid":false,"given":"Zheng","family":"Xia","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 1 Biomedical Engineering Department, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 2 Knight Cancer Institute, , Portland, Oregon."},{"id":[{"id":"https:\/\/ror.org\/009avj582","id-type":"ROR","asserted-by":"publisher"}],"name":"Oregon Health & Science University 7 Center for Biomedical Data Science, , Portland, Oregon."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1086","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"2026050103130019800_bib1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ygyno.2019.08.033","article-title":"Identification of a RNA-seq-based signature to improve prognostics for uterine sarcoma","volume":"155","author":"Zhou","year":"2019","journal-title":"Gynecol Oncol"},{"key":"2026050103130019800_bib2","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.3892\/mmr.2017.6194","article-title":"Identification of prognostic genes in kidney renal clear cell carcinoma by RNA-seq data analysis","volume":"15","author":"Gu","year":"2017","journal-title":"Mol Med Rep"},{"key":"2026050103130019800_bib3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cancergen.2019.04.004","article-title":"A comparison of survival analysis methods for cancer gene expression RNA-sequencing data","volume":"235\u2013236","author":"Raman","year":"2019","journal-title":"Cancer Genet"},{"issue":"Suppl 5","key":"2026050103130019800_bib4","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s12920-020-0686-1","article-title":"Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations","volume":"13","author":"Huang","year":"2020","journal-title":"BMC Med Genomics"},{"key":"2026050103130019800_bib5","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"mRNA-Seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"key":"2026050103130019800_bib6","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/nprot.2009.236","article-title":"RNA-Seq analysis to capture the transcriptome landscape of a single cell","volume":"5","author":"Tang","year":"2010","journal-title":"Nat Protoc"},{"key":"2026050103130019800_bib7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41581-018-0021-7","article-title":"Single-cell RNA sequencing for the study of development, physiology and disease","volume":"14","author":"Potter","year":"2018","journal-title":"Nat Rev Nephrol"},{"key":"2026050103130019800_bib8","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.molcel.2015.04.005","article-title":"The technology and biology of single-cell RNA sequencing","volume":"58","author":"Kolodziejczyk","year":"2015","journal-title":"Mol Cell"},{"key":"2026050103130019800_bib9","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41587-021-01091-3","article-title":"Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data","volume":"40","author":"Sun","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2026050103130019800_bib10","doi-asserted-by":"crossref","first-page":"btaf671","DOI":"10.1093\/bioinformatics\/btaf671","article-title":"scSurv: a deep generative model for single-cell survival analysis","volume":"42","author":"Mizukoshi","year":"2026","journal-title":"Bioinformatics"},{"key":"2026050103130019800_bib11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13073-022-01012-2","article-title":"Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease","volume":"14","author":"Johnson","year":"2022","journal-title":"Genome Med"},{"key":"2026050103130019800_bib12","first-page":"2127","article-title":"Attention-based deep multiple instance learning","volume-title":"Proceedings of the 35th International Conference on Machine Learning. Volume 80. Proceedings of Machine Learning Research","author":"Ilse","year":"2018"},{"key":"2026050103130019800_bib13","unstructured":"Maron\n              O\n            , Lozano-P\u00e9rezT. A framework for multiple-instance learning. Advances in Neural Information Processing Systems. 1997. Available from:https:\/\/proceedings.neurips.cc\/paper_files\/paper\/1997\/file\/82965d4ed8150294d4330ace00821d77-Paper.pdf"},{"key":"2026050103130019800_bib14","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.patcog.2017.10.009","article-title":"Multiple instance learning: a survey of problem characteristics and applications","volume":"77","author":"Carbonneau","year":"2018","journal-title":"Pattern Recognition"},{"key":"2026050103130019800_bib15","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat Methods"},{"key":"2026050103130019800_bib16","doi-asserted-by":"crossref","first-page":"e9620","DOI":"10.15252\/msb.20209620","article-title":"Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models","volume":"17","author":"Xu","year":"2021","journal-title":"Mol Syst Biol"},{"key":"2026050103130019800_bib17","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","article-title":"ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis","volume":"16","author":"Pierson","year":"2015","journal-title":"Genome Biol"},{"key":"2026050103130019800_bib18","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1016\/j.jspi.2004.10.008","article-title":"Bayesian analysis of zero-inflated regression models","volume":"136","author":"Ghosh","year":"2006","journal-title":"J Stat Plann Inference"},{"key":"2026050103130019800_bib19","first-page":"394","article-title":"Understanding and improving layer normalization","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Xu","year":"2019"},{"key":"2026050103130019800_bib20","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026050103130019800_bib21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2307\/1403439","article-title":"Use of many covariates in clinical trials","volume":"59","author":"Tukey","year":"1991","journal-title":"Int Stat Rev"},{"key":"2026050103130019800_bib22","doi-asserted-by":"crossref","first-page":"151","DOI":"10.7551\/mitpress\/9780262033589.003.0009","article-title":"Entropy regularization","volume-title":"Semi-Supervised Learning","author":"Yves","year":"2006"},{"key":"2026050103130019800_bib23","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"key":"2026050103130019800_bib24","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.cell.2018.10.038","article-title":"Defining T cell states associated with response to checkpoint immunotherapy in melanoma","volume":"175","author":"Sade-Feldman","year":"2018","journal-title":"Cell"},{"key":"2026050103130019800_bib25","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1126\/science.ade2292","article-title":"CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers","volume":"381","author":"Bill","year":"2023","journal-title":"Science"},{"key":"2026050103130019800_bib26","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1158\/1078-0432.CCR-21-1130","article-title":"B cells and tertiary lymphoid structures: friends or foes in cancer immunotherapy?","volume":"28","author":"Lauss","year":"2022","journal-title":"Clin Cancer Res"},{"key":"2026050103130019800_bib27","doi-asserted-by":"crossref","first-page":"eadp4942","DOI":"10.1126\/sciadv.adp4942","article-title":"SpaTopic: a statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data","volume":"10","author":"Zhang","year":"2024","journal-title":"Sci Adv"},{"key":"2026050103130019800_bib28","doi-asserted-by":"crossref","first-page":"5970","DOI":"10.1158\/0008-5472.CAN-18-0747","article-title":"Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma","volume":"78","author":"Thrane","year":"2018","journal-title":"Cancer Res"},{"key":"2026050103130019800_bib29","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/S1471-4906(02)02302-5","article-title":"Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes","volume":"23","author":"Mantovani","year":"2002","journal-title":"Trends Immunol"},{"key":"2026050103130019800_bib30","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.cell.2021.01.010","article-title":"A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells","volume":"184","author":"Cheng","year":"2021","journal-title":"Cell"},{"key":"2026050103130019800_bib31","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1038\/s41388-024-03086-1","article-title":"Environmental monobutyl phthalate exposure promotes liver cancer via reprogrammed cholesterol metabolism and activation of the IRE1\u03b1-XBP1s pathway","volume":"43","author":"Zhang","year":"2024","journal-title":"Oncogene"},{"key":"2026050103130019800_bib32","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1016\/j.cmet.2022.10.010","article-title":"Cancer cell-intrinsic XBP1 drives immunosuppressive reprogramming of intratumoral myeloid cells by promoting cholesterol production","volume":"34","author":"Yang","year":"2022","journal-title":"Cell Metab"},{"key":"2026050103130019800_bib33","doi-asserted-by":"crossref","first-page":"eadg7995","DOI":"10.1126\/sciimmunol.adg7995","article-title":"Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma","volume":"9","author":"Barras","year":"2024","journal-title":"Sci Immunol"},{"key":"2026050103130019800_bib34","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1038\/s41590-024-01792-2","article-title":"Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy","volume":"25","author":"Chen","year":"2024","journal-title":"Nat Immunol"},{"key":"2026050103130019800_bib35","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1038\/s41591-023-02371-y","article-title":"Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance","volume":"29","author":"Chu","year":"2023","journal-title":"Nat Med"},{"key":"2026050103130019800_bib36","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ccell.2019.01.003","article-title":"Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1\/anti-CTLA-4 combined therapy","volume":"35","author":"Gide","year":"2019","journal-title":"Cancer Cell"},{"key":"2026050103130019800_bib37","doi-asserted-by":"crossref","first-page":"e20200920","DOI":"10.1084\/jem.20200920","article-title":"Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment","volume":"218","author":"Pauken","year":"2021","journal-title":"J Exp Med"},{"key":"2026050103130019800_bib38","doi-asserted-by":"crossref","first-page":"20159","DOI":"10.1073\/pnas.1918986117","article-title":"ALKBH5 regulates anti-PD-1 therapy response by modulating lactate and suppressive immune cell accumulation in tumor microenvironment","volume":"117","author":"Li","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2026050103130019800_bib39","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/s41590-024-02023-4","article-title":"Circulating tumor-reactive KIR+CD8+ T cells suppress anti-tumor immunity in patients with melanoma","volume":"26","author":"Lu","year":"2025","journal-title":"Nat Immunol"},{"key":"2026050103130019800_bib40","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s41586-022-05400-x","article-title":"Liver tumour immune microenvironment subtypes and neutrophil heterogeneity","volume":"612","author":"Xue","year":"2022","journal-title":"Nature"},{"key":"2026050103130019800_bib41","doi-asserted-by":"crossref","first-page":"217199","DOI":"10.1016\/j.canlet.2024.217199","article-title":"Identification of SPP1+ macrophages in promoting cancer stemness via vitronectin and CCL15 signals crosstalk in liver cancer","volume":"604","author":"Wang","year":"2024","journal-title":"Cancer Lett"},{"key":"2026050103130019800_bib42","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1038\/s41698-024-00564-3","article-title":"Single-cell and spatial analyses revealed the co-location of cancer stem cells and SPP1+ macrophage in hypoxic region that determines the poor prognosis in hepatocellular carcinoma","volume":"8","author":"Fan","year":"2024","journal-title":"NPJ Precis Oncol"},{"key":"2026050103130019800_bib43","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","article-title":"The Molecular Signatures Database (MSigDB) hallmark gene set collection","volume":"1","author":"Liberzon","year":"2015","journal-title":"Cell Syst"},{"key":"2026050103130019800_bib44","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.3390\/cancers13112621","article-title":"The endless sources of hepatocellular carcinoma heterogeneity","volume":"13","author":"Barcena-Varela","year":"2021","journal-title":"Cancers (Basel)"},{"key":"2026050103130019800_bib45","doi-asserted-by":"crossref","first-page":"4790","DOI":"10.1016\/j.cell.2024.06.038","article-title":"Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes","volume":"187","author":"Yang","year":"2024","journal-title":"Cell"},{"key":"2026050103130019800_bib46","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1016\/j.neuron.2024.06.028","article-title":"Ketamine alleviates NMDA receptor hypofunction through synaptic trapping","volume":"112","author":"Vill\u00e9ga","year":"2024","journal-title":"Neuron"},{"key":"2026050103130019800_bib47","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2307\/1402659","article-title":"Analysis of survival data under the proportional hazards model","volume":"43","author":"Breslow","year":"1975","journal-title":"Int Stat Rev"},{"key":"2026050103130019800_bib48","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"J R Stat Soc Ser B (Methodol)"},{"key":"2026050103130019800_bib49","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2026050103130019800_bib50","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2026050103130019800_bib51","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/BF02866759","article-title":"All about the Dirac delta function (?)","volume":"8","author":"Balakrishnan","year":"2003","journal-title":"Resonance"},{"key":"2026050103130019800_bib52","unstructured":"Higgins\n              I\n            , MattheyL, PalA, BurgessC, GlorotX, BotvinickM, . beta-VAE: learning basic visual concepts with a constrained variational framework. 2017[cited 2024 Jan 12]. Available from:https:\/\/openreview.net\/forum?id=Sy2fzU9gl."},{"key":"2026050103130019800_bib53","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1214\/aop\/1176996454","article-title":"I-divergence geometry of probability distributions and minimization problems","volume":"3","author":"Csisz\u00e1r","year":"1975","journal-title":"Ann Probab"},{"key":"2026050103130019800_bib54","first-page":"4743","article-title":"Improved variational inference with inverse autoregressive flow","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"Kingma","year":"2016"},{"key":"2026050103130019800_bib55","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1214\/10-BA507","article-title":"Inference with normal-gamma prior distributions in regression problems","volume":"5","author":"Brown","year":"2010","journal-title":"Bayesian Anal"},{"key":"2026050103130019800_bib56","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemometrics Intell Lab Syst"},{"key":"2026050103130019800_bib57","first-page":"6000","article-title":"Attention is all you need","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"key":"2026050103130019800_bib58","first-page":"5824","article-title":"Gradient surgery for multi-task learning","volume":"33","author":"Yu","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026050103130019800_bib59","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat Med"},{"key":"2026050103130019800_bib60","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","article-title":"Nonparametric estimation from incomplete observations","volume":"53","author":"Kaplan","year":"1958","journal-title":"J Am Stat Assoc"},{"key":"2026050103130019800_bib61","first-page":"163","article-title":"Evaluation of survival data and two new rank order statistics arising in its consideration","volume":"50","author":"Mantel","year":"1966","journal-title":"Cancer Chemother Rep"},{"key":"2026050103130019800_bib62","first-page":"1","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int J Data Min Knowl Manag Process"},{"key":"2026050103130019800_bib63","first-page":"311","article-title":"A review on accelerated failure time models","volume":"12","author":"Saikia","year":"2017","journal-title":"Int J Stat Syst"},{"key":"2026050103130019800_bib64","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"2026050103130019800_bib65","first-page":"1","article-title":"Scikit-survival: a library for time-to-event analysis built on top of scikit-learn","volume":"21","author":"P\u00f6lsterl","year":"2020","journal-title":"J Machine Learn Res"},{"key":"2026050103130019800_bib66","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: guaranteeing well-connected communities","volume":"9","author":"Traag","year":"2019","journal-title":"Sci Rep"},{"key":"2026050103130019800_bib67","doi-asserted-by":"crossref","first-page":"105398","DOI":"10.1016\/j.ebiom.2024.105398","article-title":"Integrated single-cell transcriptomic analyses identify a novel lineage plasticity-related cancer cell type involved in prostate cancer progression","volume":"109","author":"Zhao","year":"2024","journal-title":"EBioMedicine"},{"key":"2026050103130019800_bib68","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/msb.2010.31","article-title":"A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers","volume":"6","author":"Lefebvre","year":"2010","journal-title":"Mol Syst Biol"},{"key":"2026050103130019800_bib69","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1186\/s12885-025-13714-y","article-title":"Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis","volume":"25","author":"Liu","year":"2025","journal-title":"BMC Cancer"},{"key":"2026050103130019800_bib70","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cell.2016.02.065","article-title":"Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma","volume":"165","author":"Hugo","year":"2016","journal-title":"Cell"},{"key":"2026050103130019800_bib71","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/nature24473","article-title":"A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy","volume":"551","author":"\u0141uksza","year":"2017","journal-title":"Nature"},{"key":"2026050103130019800_bib72","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1002\/hep.26467","article-title":"Lipocalin-2 negatively modulates the epithelial-to-mesenchymal transition in hepatocellular carcinoma through the epidermal growth factor (TGF-beta1)\/Lcn2\/Twist1 pathway","volume":"58","author":"Wang","year":"2013","journal-title":"Hepatology"},{"key":"2026050103130019800_bib73","doi-asserted-by":"crossref","first-page":"10202","DOI":"10.1158\/0008-5472.CAN-10-2607","article-title":"A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients","volume":"70","author":"Roessler","year":"2010","journal-title":"Cancer Res"},{"key":"2026050103130019800_bib74","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.cell.2017.05.046","article-title":"Electronic address wbe, cancer genome atlas research N. Comprehensive and integrative genomic characterization of hepatocellular carcinoma","volume":"169","author":"Cancer Genome Atlas Research Network","year":"2017","journal-title":"Cell"},{"key":"2026050103130019800_bib75","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1007\/s00535-013-0791-4","article-title":"Mitochondrial metabolism in the noncancerous liver determine the occurrence of hepatocellular carcinoma: a prospective study","volume":"49","author":"Kudo","year":"2014","journal-title":"J Gastroenterol"},{"key":"2026050103130019800_bib76","doi-asserted-by":"crossref","first-page":"101505","DOI":"10.1016\/j.xcrm.2024.101505","article-title":"Macrophage-coated tumor cluster aggravates hepatoma invasion and immunotherapy resistance via generating local immune deprivation","volume":"5","author":"Ning","year":"2024","journal-title":"Cell Rep Med"}],"container-title":["Cancer Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aacrjournals.org\/cancerdiscovery\/article-pdf\/doi\/10.1158\/2159-8290.CD-25-0965\/3765094\/cd-25-0965.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/aacrjournals.org\/cancerdiscovery\/article-pdf\/16\/5\/931\/3785886\/cd-25-0965.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/aacrjournals.org\/cancerdiscovery\/article-pdf\/16\/5\/931\/3785886\/cd-25-0965.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:13:18Z","timestamp":1777619598000},"score":1,"resource":{"primary":{"URL":"https:\/\/aacrjournals.org\/cancerdiscovery\/article\/16\/5\/931\/784405\/scSurvival-Single-Cell-Survival-Analysis-of"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,21]]},"references-count":76,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,4,21]]},"published-print":{"date-parts":[[2026,5,1]]}},"URL":"https:\/\/doi.org\/10.1158\/2159-8290.cd-25-0965","relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.1158\/2159-8290.32728816","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728849","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728828","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728819","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728852","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728831","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728843","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728822","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728855","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728834","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728846","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728858","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728825","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728837","asserted-by":"object"},{"id-type":"doi","id":"10.1158\/2159-8290.32728840","asserted-by":"object"}],"has-preprint":[{"id-type":"doi","id":"10.1158\/2159-8290.c.8545189","asserted-by":"object"}]},"ISSN":["2159-8274","2159-8290"],"issn-type":[{"value":"2159-8274","type":"print"},{"value":"2159-8290","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,21]]}}}