Abstract
The TNM classification is well established as a state-of-the-art prognostic and treatment-decision-making tool for non-small cell lung cancer (NSCLC) patients. However, incorporation of biological data may hone the TNM system. This article focuses on choosing and incorporating subsets of tissue-infiltrating lymphocyte (TIL), detected by specific immunohistochemistry and automatically quantified by open source software, into a TNM-Immune cell score (TNM-I) for NSCLC. We use common markers (CD3, CD4, CD8, CD20 and CD45RO) of TILs to identify TIL subsets in tissue micro-arrays comprising tumor tissue from 553 patients resected for primary NSCLC. The number of TILs is automatically quantified using open source software (QuPath). Their prognostic efficacy, alone and within a TNM-I model, is evaluated in all patients and histological subgroups. Compared with previous manual semi-quantitative scoring of TILs in the same cohort, the present digital quantification proved superior. As a proof-of-concept, we construct a TNM-I, using TNM categories and the CD8+ TIL density. The TNM-I is an independent prognosticator of favorable diagnosis in both the overall cohort and in the main histological subgroups. In conclusion, CD8+ TIL density is the most promising candidate marker for a TNM-I in NSCLC. The prognostic efficacy of the CD8+ TIL density is strongest in lung squamous cell carcinomas, whereas both CD8+ TILs and CD20+ TILs, or a combination of these, may be candidates for a TNM-I in lung adenocarcinoma. Furthermore, based on the presented results, digital quantification is the preferred method for scoring TILs in the future.