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<p>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 <i>CASP8</i> and <i>RAS</i> pathway genes).</p>

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Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • 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,
  • Adam Margolin,
  • Alana Weinstein,
  • Andrea Sboner,
  • Andrew Blair,
  • Angela Brooks,
  • Benjamin Berman,
  • Benjamin Raphael,
  • Brian Craft,
  • Dante Bortone,
  • David Heimain,
  • David Gibbs,
  • David Haan,
  • Doron Betel,
  • Duncan McColl,
  • Emek Demir,
  • Faeze Brahman,
  • Farshad Farshidfar,
  • Gaddy Getz,
  • Galen Gao,
  • Gordon Robertson,
  • Huy Dinh,
  • Hyo Young Choi,
  • Ilya Shmulevich,
  • Ioannis Anastopoulous,
  • Jasmine Yang,
  • Jeff Damrauer,
  • Ken Chen,
  • Lauren Sanders,
  • Lee Cooper,
  • Liang-Bo Wang,
  • Matt Reyna,
  • Mike Noble,
  • Mohammed El-Kebir,
  • Molly Zhang,
  • Nicole Yeager,
  • Paul Little,
  • Richard Moffitt,
  • Sam Meier,
  • Teresa Swatloski,
  • Theo Knijnenburg,
  • Thomas Matthew,
  • Vicky Chen,
  • Vlado Uzunangelov,
  • Xinghua Lu,
  • Yige Wu,
  • Yulia Newton,
  • Zeya Wang,
  • Paul Spellman,
  • Olivier Elemento

摘要

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).