Smoking Status at Time of Diagnosis Affects the Efficacy of Anti-PD-1/L1 Therapy in Patients With Advanced NSCLC

Abstract

Background/Aim: Programmed death ligand-1 (PD-L1) expression is known to be a predictive biomarker for response to immunotherapy in non-small cell lung cancer (NSCLC). However, PD-L1 is not always a reliable predictive biomarker. In the present study, we aimed to compare responses to immunotherapy according to smoking status in NSCLC patients receiving immunotherapy in second line or further line treatment. Patients and Methods: The lung cancer registry database of the Catholic Medical Center, Seoul, Republic of Korea was used. Patients were eligible for this study if they were diagnosed with histologically confirmed NSCLC and received immune checkpoint inhibitors (ICIs) as second-line or further line therapy from January 2017 to December 2021. Results: Overall, 220 patients with NSCLC treated with ICIs were enrolled. There were 40 never smokers, 73 former smokers, and 107 current smokers. In multivariate analysis, smoking status, pathologic type, and PD-L1 expression were significant factors affecting PFS. Sex, ECOG performance status, pathologic type, and PD-L1 expression were significant factors affecting OS. Conclusion: Smoking status at diagnosis of lung cancer could be a predictive biomarker for response to ICIs in patients with advanced NSCLC.

Identification of C-PLAN index as a novel prognostic predictor for advanced lung cancer patients receiving immune checkpoint inhibitors

“…Our team has previously proved prognostic nutritional index (PNI) as a reliable prognostic indicator for both ICI-treated NSCLC and small cell lung cancer (SCLC) patients ( 14 ). However, these prognostic indexes are mainly based on laboratory tests, ignoring individual status such as performance status (PS) and smoking status, which may be also crucial for ICI efficacy ( 15 , 16 ). Therefore, one or two parameters are far from sufficient to precisely stratify the outcome of ICI-treated NSCLC patients and novel predictive methods are urgently needed.…”