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Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers

Author

Listed:
  • Chan-Young Ock

    (UT MD Anderson Cancer Center
    Seoul National University Hospital)

  • Jun-Eul Hwang

    (UT MD Anderson Cancer Center
    Chonnam National University Medical School)

  • Bhumsuk Keam

    (Seoul National University Hospital)

  • Sang-Bae Kim

    (UT MD Anderson Cancer Center)

  • Jae-Jun Shim

    (UT MD Anderson Cancer Center
    School of Medicine, Kyung Hee University)

  • Hee-Jin Jang

    (UT MD Anderson Cancer Center
    The Graduate School of Medicine, Seoul National University)

  • Sarang Park

    (UT MD Anderson Cancer Center)

  • Bo Hwa Sohn

    (UT MD Anderson Cancer Center)

  • Minse Cha

    (UT MD Anderson Cancer Center)

  • Jaffer A. Ajani

    (UT MD Anderson Cancer Center)

  • Scott Kopetz

    (UT MD Anderson Cancer Center)

  • Keun-Wook Lee

    (Seoul National University Bundang Hospital)

  • Tae Min Kim

    (Seoul National University Hospital)

  • Dae Seog Heo

    (Seoul National University Hospital)

  • Ju-Seog Lee

    (UT MD Anderson Cancer Center)

Abstract

Immunotherapy has emerged as a promising anti-cancer treatment, however, little is known about the genetic characteristics that dictate response to immunotherapy. We develop a transcriptional predictor of immunotherapy response and assess its prediction in genomic data from ~10,000 human tissues across 30 different cancer types to estimate the potential response to immunotherapy. The integrative analysis reveals two distinct tumor types: the mutator type is positively associated with potential response to immunotherapy, whereas the chromosome-instable type is negatively associated with it. We identify somatic mutations and copy number alterations significantly associated with potential response to immunotherapy, in particular treatment with anti-CTLA-4 antibody. Our findings suggest that tumors may evolve through two different paths that would lead to marked differences in immunotherapy response as well as different strategies for evading immune surveillance. Our analysis provides resources to facilitate the discovery of predictive biomarkers for immunotherapy that could be tested in clinical trials.

Suggested Citation

  • Chan-Young Ock & Jun-Eul Hwang & Bhumsuk Keam & Sang-Bae Kim & Jae-Jun Shim & Hee-Jin Jang & Sarang Park & Bo Hwa Sohn & Minse Cha & Jaffer A. Ajani & Scott Kopetz & Keun-Wook Lee & Tae Min Kim & Dae , 2017. "Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01018-0
    DOI: 10.1038/s41467-017-01018-0
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    Cited by:

    1. Rachael M. Zemek & Wee Loong Chin & Vanessa S. Fear & Ben Wylie & Thomas H. Casey & Cath Forbes & Caitlin M. Tilsed & Louis Boon & Belinda B. Guo & Anthony Bosco & Alistair R. R. Forrest & Michael J. , 2022. "Temporally restricted activation of IFNβ signaling underlies response to immune checkpoint therapy in mice," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Kuang Du & Shiyou Wei & Zhi Wei & Dennie T. Frederick & Benchun Miao & Tabea Moll & Tian Tian & Eric Sugarman & Dmitry I. Gabrilovich & Ryan J. Sullivan & Lunxu Liu & Keith T. Flaherty & Genevieve M. , 2021. "Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma," Nature Communications, Nature, vol. 12(1), pages 1-16, December.

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