Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms

Background The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment. Methods We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H & E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes. Findings Five previously-described TIL variables were each significantly associated with overall survival (p < 0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models sug-gests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells. Interpretation eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial. Copyright (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

We evaluated the prognostic value of automated electronic TIL quantification in melanoma patients and found that electronic total TILs outperformed eTILs. We also identified the molecular subtype of TILs and their associations with survival outcomes.