BMC cancer · 2025
A novel nomogram based on DLL3 and PD-L1 for predicting the prognosis of patients with small cell lung cancer.
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Zhang Xiaoling, Li Weiling, Du Yunyi, Zhang Yuexiang, Zhang Ying, Shi Rui, Su Fei, Zhang Yan, Fan Wenxuan, Wang Yu, Li Yong'ai, Hu Wenqing, Zhao Jun
Аннотация
BACKGROUND: Small cell lung cancer (SCLC) is known for its highly invasive nature and low 5-year survival rate. Although immunotherapy has shown enhancements in survival among SCLC patients, there is a call for improved precision in targeting beneficiary populations. The development of the Delta-like ligand 3 (DLL3)-targeted drug Rovalpituzumab Tesirine (Rova-T) was halted due to insufficient efficacy, prompting a closer examination of factors influencing treatment effectiveness. This study aims to create a prognostic model for SCLC patients based on protein expression levels to enhance prognosis determination and guide clinical decision-making.
METHODS: Immunohistochemistry assessed DLL3, Programmed death-ligand 1 (PD-L1), and c-kit expression in SCLC patients. Univariate and multivariate Cox proportional hazards regression identified relevant variables such as clinical characteristics, DLL3, and PD-L1 expression to formulate a prognostic model represented as a nomogram. Model performance was evaluated using the consistency index (C-index), bootstrap resampling, and decision curve analysis (DCA).
RESULTS: A total of 141 patients with SCLC were included in this study. Expression rates for DLL3, PD-L1, and c-kit were 62.4%, 10.6%, and 66.0% respectively. Differences in overall survival (OS) were found between SCLC patients by gender, smoking, stage, treatment, DLL3 and PD-L1. Neuron-Specific Enolase, stage, treatment, DLL3 and PD-L1 were independent risk factors for OS in SCLC patients (P < 0.05). Using these factors, a prognostic model predicting the 12-month survival probability for SCLC patients was developed with a distinguishable C-index of 0.739. The model's calibration curve exhibited accurate alignment between predicted and actual 12-month OS probabilities. The DCA curve showcased valuable clinical applicability. Patients were categorized into low-risk and high-risk groups using a cutoff value of 49.11 for the nomogram, underscoring the model's clinical discernibility (p < 0.0001).
CONCLUSION: The prognostic model developed in this study offers predictive value in estimating 12-month survival probability for SCLC patients, aiding clinicians in making more informed treatment decisions.
METHODS: Immunohistochemistry assessed DLL3, Programmed death-ligand 1 (PD-L1), and c-kit expression in SCLC patients. Univariate and multivariate Cox proportional hazards regression identified relevant variables such as clinical characteristics, DLL3, and PD-L1 expression to formulate a prognostic model represented as a nomogram. Model performance was evaluated using the consistency index (C-index), bootstrap resampling, and decision curve analysis (DCA).
RESULTS: A total of 141 patients with SCLC were included in this study. Expression rates for DLL3, PD-L1, and c-kit were 62.4%, 10.6%, and 66.0% respectively. Differences in overall survival (OS) were found between SCLC patients by gender, smoking, stage, treatment, DLL3 and PD-L1. Neuron-Specific Enolase, stage, treatment, DLL3 and PD-L1 were independent risk factors for OS in SCLC patients (P < 0.05). Using these factors, a prognostic model predicting the 12-month survival probability for SCLC patients was developed with a distinguishable C-index of 0.739. The model's calibration curve exhibited accurate alignment between predicted and actual 12-month OS probabilities. The DCA curve showcased valuable clinical applicability. Patients were categorized into low-risk and high-risk groups using a cutoff value of 49.11 for the nomogram, underscoring the model's clinical discernibility (p < 0.0001).
CONCLUSION: The prognostic model developed in this study offers predictive value in estimating 12-month survival probability for SCLC patients, aiding clinicians in making more informed treatment decisions.