Academic Journal
CDState Resolves Malignant Cell Heterogeneity from Bulk Tumor RNA Sequencing Data.
| Τίτλος: | CDState Resolves Malignant Cell Heterogeneity from Bulk Tumor RNA Sequencing Data. |
|---|---|
| Συγγραφείς: | Kraft A; Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland.; Department of Thoracic Surgery, University Hospital Zurich, Zürich, Switzerland.; SIB, Swiss Institute of Bioinformatics, Lausanne, Switzerland., Yates J; Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland.; SIB, Swiss Institute of Bioinformatics, Lausanne, Switzerland.; ETH AI Center, ETH Zurich, Zürich, Switzerland., Barkmann F; Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland.; SIB, Swiss Institute of Bioinformatics, Lausanne, Switzerland.; ETH AI Center, ETH Zurich, Zürich, Switzerland., Boeva V; Department of Computer Science, Institute for Machine Learning, ETH Zurich, Zürich, Switzerland.; SIB, Swiss Institute of Bioinformatics, Lausanne, Switzerland.; ETH AI Center, ETH Zurich, Zürich, Switzerland. |
| Πηγή: | Cancer research [Cancer Res] 2026 Oct 15; Vol. 86 (20), pp. 5023-5042. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Association for Cancer Research Country of Publication: United States NLM ID: 2984705R Publication Model: Print Cited Medium: Internet ISSN: 1538-7445 (Electronic) Linking ISSN: 00085472 NLM ISO Abbreviation: Cancer Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: Baltimore, Md. : American Association for Cancer Research Original Publication: Chicago [etc.] |
| Ιατρικοί όροι (MeSH): | Neoplasms*/genetics , Neoplasms*/pathology , Sequence Analysis, RNA*/methods , Computational Biology*/methods , Genetic Heterogeneity*, Gene Expression Profiling/methods ; Humans ; Single-Cell Gene Expression Analysis ; Gene Expression Regulation, Neoplastic ; Transcriptome |
| Περίληψη: | Intratumor transcriptional heterogeneity (ITTH), defined as the coexistence of diverse cell states within a single tumor, complicates cancer treatment and contributes to variable therapeutic responses. Although single-cell RNA sequencing (scRNA-seq) can resolve this complexity, its cost and technical demands limit large-scale use. Bulk RNA-seq provides a scalable alternative but requires computational methods to deconvolve bulk transcriptomes into distinct cell states. Existing supervised approaches rely on accurate reference data, which are lacking for many cancer types, whereas unsupervised methods are not tailored to capture heterogeneity within the malignant compartment. To address these limitations, we developed CDState, an unsupervised deconvolution method based on nonnegative matrix factorization with a sum-to-one constraint and a cosine similarity-based optimization, which infers malignant cell states using bulk RNA-seq data. CDState demonstrated robustness using pseudobulk scRNA-seq datasets from five cancer types, outperforming existing unsupervised methods in estimating both state-specific gene expression and cell proportions. Applied to 33 cancer types from The Cancer Genome Atlas, CDState revealed recurrent gene programs, including epithelial-mesenchymal transition, MYC targets, and oxidative phosphorylation, as major contributors to malignant ITTH. The malignant state proportions were linked to clinical features, including patient survival and therapeutic response. Finally, mutations and copy-number alterations in genes such as TP53, KRAS, PIK3CA, SOX2, and SATB1 were identified as potential genetic drivers of malignant cell ITTH across cancer types. This study demonstrates the utility of CDState for characterization of malignant cell states from bulk RNA-seq data, establishing a framework for investigating malignant cell ITTH in large-scale cancer atlases. Significance: CDState leverages bulk RNA sequencing to resolve malignant states, revealing recurrent patterns of tumor heterogeneity that may help interpret outcomes and guide future biomarker development. (©2026 The Authors; Published by the American Association for Cancer Research.) |
| References: | Nat Commun. 2023 Sep 16;14(1):5758. (PMID: 37717006) Nat Med. 2016 Jan;22(1):105-13. (PMID: 26618723) Science. 2014 Jun 20;344(6190):1396-401. (PMID: 24925914) Cell Stem Cell. 2019 Jan 3;24(1):65-78. (PMID: 30554963) Nat Genet. 2021 Sep;53(9):1334-1347. (PMID: 34493872) Proc Natl Acad Sci U S A. 2014 Nov 4;111(44):E4726-35. (PMID: 25339441) Brief Bioinform. 2021 Jan 18;22(1):416-427. (PMID: 31925417) Nat Commun. 2013;4:2612. (PMID: 24113773) Nat Biotechnol. 2019 Jul;37(7):773-782. (PMID: 31061481) Nat Rev Genet. 2023 Aug;24(8):573-584. (PMID: 37258725) IEEE Trans Pattern Anal Mach Intell. 2014 Apr;36(4):698-714. (PMID: 26353194) Nat Commun. 2019 May 17;10(1):2209. (PMID: 31101809) Nat Commun. 2019 Apr 16;10(1):1787. (PMID: 30992437) Science. 2018 Feb 16;359(6377):801-806. (PMID: 29301960) Genome Res. 2026 Mar 02;36(3):630-644. (PMID: 41708334) Sci Rep. 2016 Jan 07;6:18909. (PMID: 26739359) Comput Intell Neurosci. 2008;:764206. (PMID: 18497868) Nat Rev Cancer. 2019 Jul;19(7):377-391. (PMID: 31209265) Cell. 2017 Feb 9;168(4):613-628. (PMID: 28187284) Nat Genet. 2022 Aug;54(8):1192-1201. (PMID: 35931863) Nat Biotechnol. 2012 May;30(5):413-21. (PMID: 22544022) Oncotarget. 2016 Mar 1;7(9):10051-63. (PMID: 26840267) Cancer Cell. 2018 Apr 9;33(4):676-689.e3. (PMID: 29622463) Brief Bioinform. 2022 Jul 18;23(4):. (PMID: 35788277) PLoS Med. 2015 Feb 10;12(2):e1001786. (PMID: 25668320) Cell Syst. 2022 Sep 21;13(9):690-710.e17. (PMID: 35981544) Science. 1983 May 13;220(4598):671-80. (PMID: 17813860) Nature. 2014 Jul 31;511(7511):543-50. (PMID: 25079552) Cell Rep Med. 2025 Jun 17;6(6):102188. (PMID: 40499545) Nature. 2011 Jun 29;474(7353):609-15. (PMID: 21720365) Nature. 2012 Oct 4;490(7418):61-70. (PMID: 23000897) Sci Rep. 2016 Nov 24;6:37767. (PMID: 27883053) Bioinformatics. 2007 Jun 15;23(12):1495-502. (PMID: 17483501) Nat Commun. 2020 May 8;11(1):2285. (PMID: 32385277) Cell. 2020 Jul 23;182(2):497-514.e22. (PMID: 32579974) Bioinformatics. 2020 Jun 1;36(12):3927-3929. (PMID: 32219387) Biostatistics. 2018 Apr 1;19(2):185-198. (PMID: 29036413) Mol Cell. 2014 Jun 5;54(5):716-27. (PMID: 24905005) Nature. 2023 Jun;618(7965):598-606. (PMID: 37258682) Nat Cancer. 2023 Aug;4(8):1063-1082. (PMID: 37537300) Nat Commun. 2020 Apr 24;11(1):1971. (PMID: 32332754) Cell. 2013 Oct 10;155(2):462-77. (PMID: 24120142) Commun Biol. 2023 Apr 11;6(1):394. (PMID: 37041233) Nat Commun. 2019 Jan 22;10(1):380. (PMID: 30670690) Bioinformatics. 2024 Jul 1;40(7):. (PMID: 38970377) Nature. 2013 Jul 4;499(7456):43-9. (PMID: 23792563) PLoS One. 2018 Mar 1;13(3):e0193067. (PMID: 29494600) JCO Precis Oncol. 2020 Sep 4;4:. (PMID: 33015524) Cancer Cell. 2009 Jan 6;15(1):21-34. (PMID: 19111878) Cell. 2019 Aug 8;178(4):835-849.e21. (PMID: 31327527) Cell. 2018 Aug 9;174(4):843-855.e19. (PMID: 30017245) Cell. 2021 Oct 14;184(21):5482-5496.e28. (PMID: 34597583) Commun Biol. 2020 Sep 11;3(1):505. (PMID: 32917965) Nucleic Acids Res. 2024 May 22;52(9):4761-4783. (PMID: 38619038) Nat Med. 2015 Aug;21(8):846-53. (PMID: 26248267) Nature. 2022 Dec;612(7941):778-786. (PMID: 36517593) Cell. 2017 Dec 14;171(7):1611-1624.e24. (PMID: 29198524) Methods Mol Biol. 2020;2120:263-276. (PMID: 32124326) Nat Cancer. 2022 Apr;3(4):505-517. (PMID: 35469013) N Engl J Med. 2015 Jun 25;372(26):2481-98. (PMID: 26061751) Cell Rep. 2014 Feb 13;6(3):514-27. (PMID: 24462293) |
| Grant Information: | 205321_207931 Swiss national science foundation (SNSF) |
| Entry Date(s): | Date Created: 20260730 Date Completed: 20261015 Latest Revision: 20261017 |
| Update Code: | 20261017 |
| PubMed Central ID: | PMC13669743 |
| DOI: | 10.1158/0008-5472.CAN-25-4102 |
| PMID: | 42531606 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!