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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Main Authors:	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
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-07-01
Series:	Scientific Reports
Subjects:	Tumor microenvironment iScores Integrated scores Cell type estimation Deconvolution Pan-cancer analysis
Online Access:	https://doi.org/10.1038/s41598-025-09075-y
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Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles

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