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Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Author

Listed:
  • Igor Dolgalev

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Hua Zhou

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Nina Murrell

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Hortense Le

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Theodore Sakellaropoulos

    (NYU Grossman School of Medicine)

  • Nicolas Coudray

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Kelsey Zhu

    (NYU Grossman School of Medicine)

  • Varshini Vasudevaraja

    (NYU Grossman School of Medicine)

  • Anna Yeaton

    (The Optical Profiling Platform at The Broad Institute of MIT And Harvard)

  • Chandra Goparaju

    (NYU Grossman School of Medicine)

  • Yonghua Li

    (NYU Grossman School of Medicine)

  • Imran Sulaiman

    (NYU Grossman School of Medicine)

  • Jun-Chieh J. Tsay

    (NYU Grossman School of Medicine)

  • Peter Meyn

    (NYU Grossman School of Medicine)

  • Hussein Mohamed

    (NYU Grossman School of Medicine)

  • Iris Sydney

    (NYU Grossman School of Medicine)

  • Tomoe Shiomi

    (NYU Grossman School of Medicine)

  • Sitharam Ramaswami

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Navneet Narula

    (NYU Grossman School of Medicine)

  • Ruth Kulicke

    (Celsius Therapeutics, Cambridge)

  • Fred P. Davis

    (Celsius Therapeutics, Cambridge)

  • Nicolas Stransky

    (Celsius Therapeutics, Cambridge)

  • Gromoslaw A. Smolen

    (Celsius Therapeutics, Cambridge)

  • Wei-Yi Cheng

    (Roche Innovation Center New York)

  • James Cai

    (Roche Innovation Center New York)

  • Salman Punekar

    (New York University Langone Health)

  • Vamsidhar Velcheti

    (New York University Langone Health)

  • Daniel H. Sterman

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • J. T. Poirier

    (New York University Langone Health)

  • Ben Neel

    (New York University Langone Health)

  • Kwok-Kin Wong

    (New York University Langone Health)

  • Luis Chiriboga

    (NYU Grossman School of Medicine)

  • Adriana Heguy

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    New York University Langone Health)

  • Thales Papagiannakopoulos

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • Bettina Nadorp

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    NYU Grossman School of Medicine)

  • Matija Snuderl

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • Leopoldo N. Segal

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • Andre L. Moreira

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • Harvey I. Pass

    (NYU Grossman School of Medicine
    New York University Langone Health)

  • Aristotelis Tsirigos

    (NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    NYU Grossman School of Medicine
    New York University Langone Health)

Abstract

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Suggested Citation

  • Igor Dolgalev & Hua Zhou & Nina Murrell & Hortense Le & Theodore Sakellaropoulos & Nicolas Coudray & Kelsey Zhu & Varshini Vasudevaraja & Anna Yeaton & Chandra Goparaju & Yonghua Li & Imran Sulaiman &, 2023. "Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42327-x
    DOI: 10.1038/s41467-023-42327-x
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    References listed on IDEAS

    as
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