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Background <p>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.</p> Methods <p>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).</p> Results <p>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 (<i>P</i> &lt; 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 (<i>p</i> &lt; 0.0001).</p> Conclusion <p>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.</p>

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A novel nomogram based on DLL3 and PD-L1 for predicting the prognosis of patients with small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Xiaoling Zhang,
  • Weiling Li,
  • Yunyi Du,
  • Yuexiang Zhang,
  • Ying Zhang,
  • Rui Shi,
  • Fei Su,
  • Yan Zhang,
  • Wenxuan Fan,
  • Yu Wang,
  • Yong’ai Li,
  • Wenqing Hu,
  • Jun Zhao

摘要

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.