2018P - Novel nomogram based on the expression of DLL3 and PD-L1 for predicting the prognosis of small cell lung cancer patients

Background

Small cell lung cancer (SCLC) is highly invasive and has a low 5-year survival rate. Programmed death receptor 1/programmed death receptor-ligand 1 (PD-L1) inhibitors have improved survival in SCLC patients partially, but more targets and precise beneficiary populations are needed. Delta-like ligand 3 (DLL3)-targeted drug Rova-T was terminated due to insufficient efficacy, considering other factors that may affect efficacy. This study aims to construct a prognostic model for SCLC patients based on the expression of DLL3 and PD-L1 to help determine prognosis and guide clinical therapy.

Methods

Immunohistochemistry was used to detect the expression of DLL3 and PD-L1 in the tissue specimens of SCLC patients. Independent risk factors were identified by Cox proportional risk regression to construct the model. The model was validated by consistency index (C-index), bootstrap resampling, and decision curve analysis (DCA).

Results

Up to March 15, 2023, 141 patients diagnosed with SCLC by pathology at Changzhi People's Hospital were included, and 129 patients have reached the endpoint. The expression rates of DLL3 and PD-L1 were 62.4% (88/141), 10.6% (15/141). NSE, stage, treatment, DLL3 and PD-L1 were independent risk factors for OS in SCLC patients (p<0.05). Based on these factors, a prediction model for the 12-month survival probability of SCLC patients was constructed and a nomogram was plotted. The model was tested for good discrimination (C-index 0.739), accuracy and clinical applicability. In addition, patients were divided into low-risk and high-risk for survival analysis based on the best cut-off value of 49.11 for nomogram, indicating that the model had great discrimination ability (mOS 13.33 vs. 7.80m, p<0.001).

Table: 2018P

Conclusions

The OS prediction model for SCLC patients in this study has excellent predictive performance in predicting the 12-m survival probability and may assist clinicians in making more accurate judgments and guiding therapy of SCLC patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.