Abstract
Purpose:
This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an artificial intelligence (AI)-powered spatial TME analyzer. We then assessed the predictive efficacy of immune-checkpoint inhibitors (ICIs)-based treatment.
Experimental design:
An AI-powered whole-slide image analyzer was used to segment cancer areas (CAs) and cancer stroma and to identify tumor-infiltrating lymphocytes (TILs), tertiary lymphoid structures, fibroblasts, and endothelial cells (ECs) in the tumor tissue. We analyzed 143 NSCLC samples after resistance to EGFR-TKIs from two cohorts: (1) 89 patients treated with ICI monotherapy and (2) 54 patients from the ATTLAS phase III trial comparing atezolizumab plus bevacizumab, paclitaxel, and carboplatin (ABCP) versus pemetrexed plus carboplatin.
Results:
Post-TKI samples showed reduced TILs in the CA (p=0.045) and increased ECs in the CA (p=0.005) compared with pre-TKI samples. These changes differed according to EGFR mutation subtype. Higher TILs in CA were associated with a better overall response rate (ORR) and progression-free survival (PFS). Similarly, higher EC levels in CA correlated with improved ORR and PFS. In the ATTLAS cohort, these factors were associated with clinical benefits from ABCP, with a significant association with TILs and a marginal association with ECs.
Conclusion:
Our findings suggest that EGFR-TKIs affect the immune landscape of patients with EGFR-mutated NSCLC. Higher TILs or ECs in the CA were significantly associated with a favorable response to subsequent ICI-based treatment.
Trial registration number:
NCT03991403.
Keywords:
biomarker; lung cancer; tumor microenvironment - TME.