Cost-effectiveness of serplulimab as first-line therapy for extensive-stage small cell lung cancer in China.

Objective: The ASTRUM-005 trial demonstrated that adding serplulimab to chemotherapy significantly prolonged the survival of patients with extensive-stage small cell lung cancer (SCLC), but also increased the risk of adverse events. Given the high cost of serplulimab compared to chemotherapy, this study aimed to evaluate the cost-effectiveness of serplulimab plus chemotherapy as a first-line treatment for extensive-stage SCLC from the perspective of China's healthcare system. Methods: A Markov model was developed to simulate the disease process of extensive-stage SCLC and estimate the health outcomes and direct medical costs of patients. Scenario analyses, univariate sensitivity analyses, and probabilistic sensitivity analyses were conducted to explore the impact of different parameters on model uncertainty. The primary model outcomes included costs, life-years (LYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). Results: Compared to placebo plus chemotherapy, serplulimab plus chemotherapy resulted in an additional 0.25 life-years and 0.15 QALYs, but also increased costs by $26,402, resulting in an ICER of 179,161 USD/QALY. Sensitivity analysis showed that the ICER was most sensitive to the cost of serplulimab, and the probability that serplulimab was cost-effective when added to chemotherapy was only 0 at the willingness-to-pay threshold of 37,423 USD/QALY. Scenario analysis revealed that price discounts on serplulimab could increase its probability of being cost-effective. Conclusion: Serplulimab plus chemotherapy is not a cost-effective strategy for first-line treatment of extensive-stage SCLC in China. Price discounts on serplulimab can enhance its cost-effectiveness.

Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin ß family-related genes.

BACKGROUND: Integrin ß (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear. METHODS: In this study, the samples were assigned to different subgroups utilizing non-negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan-Meier (K-M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9-feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD. RESULTS: In accordance with the median risk score, LUAD samples were divided into high- and low-risk groups. The receiver operating characteristic (ROC) curve of LUAD patients' survival was predicted via K-M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high- and low-risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1-, 3-, and 5-year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram. CONCLUSION: We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment.