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An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma

Author

Listed:
  • Balazs Acs

    (Yale School of Medicine
    Karolinska Institutet)

  • Fahad Shabbir Ahmed

    (Yale School of Medicine)

  • Swati Gupta

    (Yale School of Medicine)

  • Pok Fai Wong

    (Yale School of Medicine)

  • Robyn D. Gartrell

    (Columbia University Medical Center/New York Presbyterian)

  • Jaya Sarin Pradhan

    (Columbia University Irving Medical Center/New York Presbyterian)

  • Emanuelle M. Rizk

    (Columbia University Irving Medical Center/New York Presbyterian)

  • Bonnie Gould Rothberg

    (Yale School of Medicine)

  • Yvonne M. Saenger

    (Columbia University Irving Medical Center/New York Presbyterian)

  • David L. Rimm

    (Yale School of Medicine
    Yale School of Medicine)

Abstract

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, N = 201, N = 76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.

Suggested Citation

  • Balazs Acs & Fahad Shabbir Ahmed & Swati Gupta & Pok Fai Wong & Robyn D. Gartrell & Jaya Sarin Pradhan & Emanuelle M. Rizk & Bonnie Gould Rothberg & Yvonne M. Saenger & David L. Rimm, 2019. "An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13043-2
    DOI: 10.1038/s41467-019-13043-2
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