Abstract 2018P
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
Variable | Point | Overall point | 12-m survival probability | |
NSE per 50mg/ml | 6.16 | 0 | 0.78 | |
Stage | Limited | 0 | 10 | 0.72 |
Extensive | 20.47 | 20 | 0.65 | |
Treatment | Yes | 0 | 30 | 0.57 |
No | 100 | 40 | 0.47 | |
DLL3 | Negative | 0 | 50 | 0.37 |
Positive | 24.69 | 60 | 0.27 | |
PD-L1 | Negative | 0 | 70 | 0.17 |
Positive | 44.50 | 80 | <0.10 |
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.
Resources from the same session
1866P - Unmasking the extent of hidden sexual distress in young breast cancer survivors
Presenter: Zeineb Naimi
Session: Poster session 05
1867P - A cross-sectional examination of information disclosure and health literacy amongst patients with lymphoma
Presenter: Steve Kalloger
Session: Poster session 05
1868P - Challenges for shared decision making in incurable cancer, with a focus on health literacy
Presenter: Chloe Holden
Session: Poster session 05
1871P - The PainRELife ecosystem: A new aid for improving clinical care and shared decision-making in breast cancer patients with chronic pain
Presenter: Marianna Masiero
Session: Poster session 05
1872P - Financial distress of a cancer disease in Germany: A new patient reported outcome measure (PROM) and first results from a bi-centered cross-sectional analysis
Presenter: Sophie Pauge
Session: Poster session 05
1873P - Patients with myeloproliferative neoplasms and self-care behaviours: Preliminary data of a cross-sectional study
Presenter: Valentina Biagioli
Session: Poster session 05
1874P - Beliefs about chemotherapy in Tunisian patients newly diagnosed with cancer
Presenter: Hadhemi Ayadi
Session: Poster session 05
1875P - Behind the use of ChatGPT for oncological purposes: Fears and challenges
Presenter: Ilaria Durosini
Session: Poster session 05
1876P - Cancer stigma: How Tunisian patients perceive their cancer
Presenter: sofiene Fendri
Session: Poster session 05