Multiple immune features based signature for predicting recurrence and survival of inoperable la-NSCLC patients.

Abstract

e21066 Background: The immune status of tumor microenvironment is extremely complex. One single immune feature cannot reflect the integral immune status and its prognostic value was limited. We postulated that the immune signature based on multiple immuno-features could markedly improve the prediction of post-chemoradiotherapeutic survival in inoperable locally advanced non-small-cell lung cancer (LA-NSCLC) patients. Methods: In this study, 100 patients who were diagnosed as inoperable LA-NSCLC between January 2005 and January 2016 were analyzed. A 5-immune feature-based signature was then constructed using the nested repeat 10-fold cross validation with LASSO Cox regression model. Nomograms were then established for predicting prognosis. Results: Immune signature combining 5 immuno-features were significantly associated with OS and PFS (P = 0.002 and P = 0.014, respectively) in patients with inoperable LA-NSCLC, and at a cutoff of -0.198 stratified patients into two groups with 5-year OS rates of 39.8% and 8.8%, and 2-year PFS rates of 22.2% and 5.5% for the high- and low-immune signature groups, respectively. Using immune signature, we proposed immune signature nomograms, which were better than the traditional TNM staging system in terms of discriminating ability (OS: 0.692 vs. 0.588; PFS: 0.672 vs. 0.586, respectively) or net weight classification (OS: 32.96%; PFS: 9.22%), suggesting that immune signature plays a complementary role in the prognosis prediction of patients with inoperable LA-NSCLC. Conclusions: Multiple immune features based immune signature could effectively predict recurrence and survival of inoperable LA-NSCLC patients, and complemented the prognostic value of the TNM staging system.