Abstract

Background/Objectives: Immune checkpoint inhibitors (ICIs) have broadened treatment options for non-small cell lung cancer (NSCLC), but many patients show limited response. Baseline biomarkers and indices, including C-reactive protein (CRP), albumin, Neutrophil-to-Lymphocyte Ratio (NLR), Glasgow Prognostic Score (GPS), Prognostic Nutrition Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI), may predict treatment outcomes. This study evaluated whether these biomarkers associate with three-month mortality and disease progression assessed by disease control rate (DCR) using RECIST. Methods: We conducted a retrospective cohort study of consecutive patients with NSCLC from the OncoLifeS biobank (2015–2020). Three-month mortality was defined by status alive or deceased at three months after treatment start. DCR was based on CT-assessments using RECIST. Univariate and multivariable logistic regression models with backward selection and bootstrapping were developed to test biomarker associations with three-month mortality and DCR. Sensitivity analysis was performed to compare results with the standard definition of objective response rate (ORR). Results: Among 505 patients, 421 were alive and 84 deceased; 297 were responders and 208 non-responders. In the final progression model, higher GPS was associated with increased odds of 3-month progression, whereas higher ALI (OR 0.99, 95% CI 0.97–1.00) and higher PNI (OR 0.93, 95% CI 0.87–0.99) were associated with decreased odds of 3-month progression. Higher ALI (OR 0.97, 95% CI 0.94–0.99), and higher PNI (OR 0.83, 95% CI 0.78–0.89) were associated with lower odds of 3-month mortality. The mortality-model showed an AUC of 0.82 and 0.73 for the disease progression model. Sensitivity analysis with the standard RECIST definition revealed similar results. Conclusions: Higher GPS was associated with increased risk of progression, whereas higher PNI and ALI were associated with lower risk of progression and mortality. These findings warrant external validation before clinical implementation.

IPC Classification

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