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Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma

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  • Kuang Du

    (New Jersey Institute of Technology)

  • Shiyou Wei

    (Sichuan University
    Duke University School of Medicine
    Duke University Medical Center)

  • Zhi Wei

    (New Jersey Institute of Technology)

  • Dennie T. Frederick

    (Massachusetts General Hospital Cancer Center)

  • Benchun Miao

    (Massachusetts General Hospital Cancer Center)

  • Tabea Moll

    (Massachusetts General Hospital)

  • Tian Tian

    (New Jersey Institute of Technology)

  • Eric Sugarman

    (Philadelphia College of Osteopathic Medicine)

  • Dmitry I. Gabrilovich

    (Cancer Immunology, AstraZeneca)

  • Ryan J. Sullivan

    (Massachusetts General Hospital Cancer Center)

  • Lunxu Liu

    (Sichuan University)

  • Keith T. Flaherty

    (Massachusetts General Hospital Cancer Center)

  • Genevieve M. Boland

    (Massachusetts General Hospital)

  • Meenhard Herlyn

    (Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute)

  • Gao Zhang

    (Duke University School of Medicine
    Duke University Medical Center
    Duke University School of Medicine)

Abstract

Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45–0.69 and 0.85–0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients’ therapeutic response to ICB therapies.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26299-4
    DOI: 10.1038/s41467-021-26299-4
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    Cited by:

    1. Yue Wang & Dhamotharan Pattarayan & Haozhe Huang & Yueshan Zhao & Sihan Li & Yifei Wang & Min Zhang & Song Li & Da Yang, 2024. "Systematic investigation of chemo-immunotherapy synergism to shift anti-PD-1 resistance in cancer," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Andrew Patterson & Noam Auslander, 2022. "Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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