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

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 and David L. Rimm ()
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Balazs Acs: Yale School of Medicine
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

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13043-2

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