Prognostic significance of tumor microenvironment assessed by machine learning algorithm in surgically resected non-small cell lung cancer.

Abstract

A methodology to assess the immune microenvironment (IME) of non-small cell lung cancer (NSCLC) has not been established, and the prognostic impact of IME factors is not yet clear. This study aimed to assess the IME factors and evaluate their prognostic values. We assessed CD8+ tumor-infiltrating lymphocyte (TIL) density, forkhead box protein P3+ (Foxp3+ ) TIL density, and programmed death receptor ligand-1 (PD-L1) tumor proportion score (TPS) using a machine-learning algorithm in whole-slide imaging (WSI). We dichotomized patients according to TIL density or TPS and compared their clinical outcomes. Between September 2014 and September 2015, 165 patients with NSCLC were enrolled in the study. We assessed IME factors in the epithelium, stroma, and their combination. An improvement in disease-free survival (DFS) was observed in the high CD8+ TIL density group in the epithelium, stroma, and the combination of both. Moreover, the group with high PD-L1 TPS in the epithelium showed better DFS than that with low PD-L1 TPS. In the multivariate analysis, the CD8+ TIL density in the combination of epithelium and stroma and PD-L1 TPS in the epithelium were independent prognostic factors (hazard ratio [HR] = 0.43; 95% confidence interval [CI] = 0.26-0.72; p = .001, HR = 0.49; 95% CI = 0.30-0.81; p = .005, respectively). Our approach demonstrated that the IME factors are related to survival in patients with NSCLC. The quantitative assessment of IME factors enables to discriminate patients with high risk of recurrence, who can be the candidates for adjuvant therapy. Assessing the CD8+ TIL density in the combination of epithelium and stroma might be more useful than their individual assessment because it is a simple and time-saving analysis of TILs in WSI.