Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles
Abstract Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer type...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-09075-y |
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| author | Bhavneet Bhinder Verena Friedl Sunantha Sethuraman Davide Risso Kami E. Chiotti R. Jay Mashl Kyle P. Ellrott Jordan A. Lee Christopher K. Wong Kofi Gyan Aditya Deshpande Marcin Imielinski Rohan Bareja Josh Stuart Myron Peto Katherine A. Hoadley Alexander J. Lazar Andrew D. Cherniack Jingchun Zhu Shaolong Cao Mark Rubin Wenyi Wang Oliver F. Bathe Nicolas Robine Li Ding Peter W. Laird Wanding Zhou Hui Shen Vésteinn Thorsson Jen Jen Yeh Matthew H. Bailey Daniel Cui Zhou Xianlu L. Peng Mary Goldman Yongsheng Li Anil Korkut Nidhi Sahni D. Neil Hayes Michael K. A. Mensah Ina Felau Anab Kemal Samantha Caesar-Johnson John A. Demchok Liming Yang Martin L. Ferguson Roy Tarnuzzer Zhining Wang Jean C. Zenklusen The Cancer Genome Atlas Analysis Network Paul Spellman Olivier Elemento |
| author_facet | Bhavneet Bhinder Verena Friedl Sunantha Sethuraman Davide Risso Kami E. Chiotti R. Jay Mashl Kyle P. Ellrott Jordan A. Lee Christopher K. Wong Kofi Gyan Aditya Deshpande Marcin Imielinski Rohan Bareja Josh Stuart Myron Peto Katherine A. Hoadley Alexander J. Lazar Andrew D. Cherniack Jingchun Zhu Shaolong Cao Mark Rubin Wenyi Wang Oliver F. Bathe Nicolas Robine Li Ding Peter W. Laird Wanding Zhou Hui Shen Vésteinn Thorsson Jen Jen Yeh Matthew H. Bailey Daniel Cui Zhou Xianlu L. Peng Mary Goldman Yongsheng Li Anil Korkut Nidhi Sahni D. Neil Hayes Michael K. A. Mensah Ina Felau Anab Kemal Samantha Caesar-Johnson John A. Demchok Liming Yang Martin L. Ferguson Roy Tarnuzzer Zhining Wang Jean C. Zenklusen The Cancer Genome Atlas Analysis Network Paul Spellman Olivier Elemento |
| author_sort | Bhavneet Bhinder |
| collection | DOAJ |
| description | Abstract Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes). |
| format | Article |
| id | doaj-art-28f97bb0594a491c992bb80c6f0e11dc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-28f97bb0594a491c992bb80c6f0e11dc2025-08-20T03:45:35ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-09075-yPan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profilesBhavneet Bhinder0Verena Friedl1Sunantha Sethuraman2Davide Risso3Kami E. Chiotti4R. Jay Mashl5Kyle P. Ellrott6Jordan A. Lee7Christopher K. Wong8Kofi Gyan9Aditya Deshpande10Marcin Imielinski11Rohan Bareja12Josh Stuart13Myron Peto14Katherine A. Hoadley15Alexander J. Lazar16Andrew D. Cherniack17Jingchun Zhu18Shaolong Cao19Mark Rubin20Wenyi Wang21Oliver F. Bathe22Nicolas Robine23Li Ding24Peter W. Laird25Wanding Zhou26Hui Shen27Vésteinn Thorsson28Jen Jen Yeh29Matthew H. Bailey30Daniel Cui Zhou31Xianlu L. Peng32Mary Goldman33Yongsheng Li34Anil Korkut35Nidhi Sahni36D. Neil Hayes37Michael K. A. Mensah38Ina Felau39Anab Kemal40Samantha Caesar-Johnson41John A. Demchok42Liming Yang43Martin L. Ferguson44Roy Tarnuzzer45Zhining Wang46Jean C. Zenklusen47The Cancer Genome Atlas Analysis NetworkPaul Spellman48Olivier Elemento49Englander Institute for Precision Medicine, Weill Cornell MedicineBiomolecular Engineering Department, School of Engineering, University of California, Santa CruzDivision of Oncology, Department of Medicine, Washington University in St. LouisDepartment of Statistical Sciences, University of PadovaDepartment of Molecular and Medical Genetics, Oregon Health & Science UniversityDivision of Oncology, Department of Medicine, Washington University in St. LouisDepartment of BioMedical Engineering, Oregon Health & Science UniversityDepartment of BioMedical Engineering, Oregon Health & Science UniversityBiomolecular Engineering Department, School of Engineering, University of California, Santa CruzEnglander Institute for Precision Medicine, Weill Cornell MedicineEnglander Institute for Precision Medicine, Weill Cornell MedicineEnglander Institute for Precision Medicine, Weill Cornell MedicineEnglander Institute for Precision Medicine, Weill Cornell MedicineUC Santa Cruz Genomics InstituteDepartment of Molecular and Medical Genetics, Oregon Health & Science UniversityLineberger Comprehensive Cancer Center, Department of Genetics, University of North Carolina at Chapel HillDepartments of Pathology, Genomics Medicine, Dermatology, & Translational Molecular Pathology, The University of Texas MD Anderson Cancer CenterBroad Institute of Harvard and MITUC Santa Cruz Genomics InstituteDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment for BioMedical Research, University of BernDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDivision of Surgical Oncology, Tom Baker Cancer CentreNew York Genome Center, Computational BiologyDivision of Oncology, Department of Medicine, Washington University in St. LouisVan Andel InstituteVan Andel InstituteVan Andel InstituteInstitute for Systems BiologyDepartment of Pharmacology, Lineberger Comprehensive Cancer Center, University of North CarolinaDivision of Oncology, Department of Medicine, Washington University in St. LouisDivision of Oncology, Department of Medicine, Washington University in St. LouisDepartment of Pharmacology, Lineberger Comprehensive Cancer Center, University of North CarolinaUC Santa Cruz Genomics InstituteDepartment of Systems Biology, The University of Texas MD Anderson Cancer CenterDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer CenterDepartment of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer CenterUniversity of Tennessee Health Sciences CenterNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteNational Cancer InstituteDepartment of Molecular and Medical Genetics, Oregon Health & Science UniversityEnglander Institute for Precision Medicine, Weill Cornell MedicineAbstract Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes).https://doi.org/10.1038/s41598-025-09075-yTumor microenvironmentiScoresIntegrated scoresCell type estimationDeconvolutionPan-cancer analysis |
| spellingShingle | Bhavneet Bhinder Verena Friedl Sunantha Sethuraman Davide Risso Kami E. Chiotti R. Jay Mashl Kyle P. Ellrott Jordan A. Lee Christopher K. Wong Kofi Gyan Aditya Deshpande Marcin Imielinski Rohan Bareja Josh Stuart Myron Peto Katherine A. Hoadley Alexander J. Lazar Andrew D. Cherniack Jingchun Zhu Shaolong Cao Mark Rubin Wenyi Wang Oliver F. Bathe Nicolas Robine Li Ding Peter W. Laird Wanding Zhou Hui Shen Vésteinn Thorsson Jen Jen Yeh Matthew H. Bailey Daniel Cui Zhou Xianlu L. Peng Mary Goldman Yongsheng Li Anil Korkut Nidhi Sahni D. Neil Hayes Michael K. A. Mensah Ina Felau Anab Kemal Samantha Caesar-Johnson John A. Demchok Liming Yang Martin L. Ferguson Roy Tarnuzzer Zhining Wang Jean C. Zenklusen The Cancer Genome Atlas Analysis Network Paul Spellman Olivier Elemento Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles Scientific Reports Tumor microenvironment iScores Integrated scores Cell type estimation Deconvolution Pan-cancer analysis |
| title | Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| title_full | Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| title_fullStr | Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| title_full_unstemmed | Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| title_short | Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| title_sort | pan cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles |
| topic | Tumor microenvironment iScores Integrated scores Cell type estimation Deconvolution Pan-cancer analysis |
| url | https://doi.org/10.1038/s41598-025-09075-y |
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