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

• Published in: Nature communications 2019-11, Vol.10 (1), p.5440-7, Article 5440
• Main Authors: Acs, Balazs, Ahmed, Fahad Shabbir, Gupta, Swati, Wong, Pok Fai, Gartrell, Robyn D., Sarin Pradhan, Jaya, Rizk, Emanuelle M., Gould Rothberg, Bonnie, Saenger, Yvonne M., Rimm, David L.
• Format: Article
• Language: English
• Subjects: 14/105; 14/63; 692/308/53/2422; 692/53/2422; 692/699/67/1813/1634; Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Automation; Female; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted; Immunotherapy; Lymphocytes; Lymphocytes, Tumor-Infiltrating - pathology; Male; Medical prognosis; Medicin och hälsovetenskap; Melanoma; Melanoma - mortality; Melanoma - pathology; Middle Aged; multidisciplinary; Neoplasm Staging; Open source software; Prognosis; Retrospective Studies; Science; Science (multidisciplinary); Skin Neoplasms - mortality; Skin Neoplasms - pathology; Standardization; Survival Rate; Tumors; Young Adult
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-13043-2

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. Histology data exists for many cancer samples and the ability to automatically image this data may provide prognostic information. Here, the authors generated an algorithm to measure tumour infiltrating lymphocytes in melanoma histology specimens and show that the ratio of these immune cells to tumour cells has prognostic value.