Construction of a Prognostic Model for Extensive-Stage Small Cell Lung Cancer Patients Undergoing Immune Therapy in Real-World Settings and Prediction of Treatment Efficacy Based on Response Status at Different Time Points

Abstract

Abstract Background and purpose In recent years, with the clinical application of programmed cell death protein-1 (PD-1) represented by serplumab and programmed cell death ligand-1 (PD-L1) represented by durvalumab, immune checkpoint inhibitors (ICIs) have been used in patients with extensive-stage small cell lung cancer (ES-SCLC). clinical applications, immune checkpoint inhibitors (ICIs) have shown significant efficacy in patients with extensive-stage small cell lung cancer (ES-SCLC), but not all patients are beneficiaries of immunotherapy. Immunomarkers such as PD-L1 expression and tumor mutational burden (TMB), which are good predictors in a variety of malignancies, have been found not to be predictive in small cell lung cancer (SCLC). With the in-depth study of SCLC subtypes, SCLC-Y/SCLC-I molecular subtypes have been recognized as potential immunotherapeutic markers. However, the predictive efficacy of a single marker is limited, so a comprehensive predictive model is needed to achieve precision immunotherapy. National and international studies have found that certain basic clinical characteristics of patients and peripheral blood markers correlate with the prognosis of ES-SCLC immunotherapy. The aim of this study was to establish a model for predicting the prognosis of immunotherapy in ES-SCLC patients using basic clinical characteristics and peripheral hematological indicators of patients, and to explore the potential characteristics of long-term survival of patients, to provide guidance for individualized treatment of patients, and to provide corresponding strategies for clinical immunotherapy. Methods This study utilized a retrospective research method, investigating patients with ES-SCLC who received PD-1/PD-L1 inhibitor treatment at Harbin Medical University Cancer Hospital from March 1, 2019, to October 31, 2022. The research data were randomly divided into a training set and a validation set in a 7:3 ratio. By conducting univariate and multivariate Cox regression analyses, variables related to the overall survival (OS) of patients were identified and used to develop a model. The model was visualized through Kaplan-Meier curves. The discriminative ability of the model was evaluated using Harrell's C-index, time-dependent receiver operating characteristic curve (tROC), and time-dependent area under curve (tAUC). The calibration of the model was assessed using calibration curves. Furthermore, the clinical utility of the model is assessed using Decision Curve Analysis (DCA). Patients are stratified into risk groups using percentile segmentation, and survival curves for Overall Survival (OS) and Progression-Free Survival (PFS) at different risk levels and milestone time points are plotted using the Kaplan-Meier method. The Chi-square test is used to compare differences between groups. Statistical analysis is performed using R 4.1.2 and SPSS 26. Results This study included a total of 113 patients with ES-SCLC who received immunotherapy. Based on the patients' clinical characteristics and hematological indicators, we conducted a series of studies. Firstly, we established a model to predict the prognosis of ES-SCLC patients undergoing immunotherapy, with 79 patients used for model development and 34 patients for model validation. Through univariate and multivariate Cox regression analyses, six variables were identified as being associated with poorer overall survival (OS) in patients: liver metastasis (P=0.001), bone metastasis (P=0.013), neutrophil-to-lymphocyte ratio (NLR) < 2.14 (P=0.005), poor Lung Immune Prognostic Index (LIPI) assessment (P<0.001), Prognostic Nutritional Index (PNI) < 51.03 (P=0.002), and lactate dehydrogenase (LDH) ≥ 146.5 (P=0.037). The model established based on the aforementioned variables demonstrates good discriminability, with Harrell’s C-index of 0.85 (95% CI: 0.76-0.93) for the training set and 0.88 (95% CI: 0.76-0.99) for the validation set. The AUC values corresponding to 12 months, 18 months, and 24 months in the training set's tROC curve are 0.754, 0.848, and 0.819, respectively, while in the validation set, they are 0.858, 0.904, and 0.828, respectively. The tAUC curves indicate that, in both the training and validation sets, the overall tAUC is >0.7 with little fluctuation over time. Calibration plots show the model's good calibration, and the DCA decision curves indicate the model's practical clinical application value. Based on the predicted risk scores in the scatter plot for patients in the training set, patients are categorized into low-risk (0-69 points), medium-risk (70-162 points), and high-risk (≥163 points) groups. In the training set, 52 patients died, with a median OS of 15.0 months and a median PFS of 7.8 months. Compared to the high-risk group, the median Overall Survival (OS) for the medium-risk group was 24.5 months (HR=0.47, P=0.038), and the median OS for the low-risk group was not reached (HR=0.14, P=0.007). Compared to the high-risk group, the median Progression-Free Survival (PFS) for the medium-risk group was 12.7 months (HR=0.45, P=0.026), and the median PFS for the low-risk group was not reached (HR=0.12, P=0.004). In the validation set, 25 patients died, with a median OS of 13.8 months and a median PFS of 6.9 months. Compared to the high-risk group, the median OS for the medium-risk group was 16.8 months (HR=0.47, P=0.047), and the median OS for the low-risk group was not reached (HR=0.40, P=0.001). Compared to the high-risk group, there was no significant improvement in the median PFS for the medium-risk group (HR=0.56, P=0.189), while the median PFS for the low-risk group was significantly extended (HR=0.12, P=0.002). Secondly, we observed that in the real world, patients with ES-SCLC who have undergone immunotherapy demonstrated a median OS (Overall Survival) of 19.5 months for responders, compared to 11.9 months for non-responders at the 6-week mark (P=0.033). At 12 and 20 weeks, the overall survival duration of responders was 20.7 months and 20.7 months, respectively, while for non-responders, it was 11.9 months and 11.7 months (P=0.044 and P=0.015). Additionally, the median PFS (Progression-Free Survival) of responders was significantly prolonged, being 10.6 months at both 6 and 20 weeks, compared to 6.4 months and 6.3 months for non-responders (P=0.036 and P=0.028). At the 12-week time point, the PFS for responders was 9.2 months, while it was 6.3 months for non-responders (P=0.069). Finally, we found that in the real world, ES-SCLC (Extensive-Stage Small Cell Lung Cancer) patients without liver metastasis (P=0.002), bone metastasis (P=0.001), a total number of metastatic organs <2 (P=0.002), and LDH (Lactate Dehydrogenase) ≤ ULN (Upper Limit of Normal) (P=0.09) are more likely to become long-term survivors (LTS) after receiving immunotherapy. Conclusion First, this study constructed a new prognostic model based on basic patient clinical characteristics and peripheral blood indices, which can be a good predictor of the prognosis of immunotherapy in ES-SCLC patients. Second, in the real world, the response status at milestone time points (6, 12, and 20 weeks) can be a good indicator of long-term survival in ES-SCLC patients receiving immunotherapy. Finally, patients with no liver metastases or bone metastases, total metastatic organ count <2 and LDH ≤ULN were more likely to have long-term survival before ES-SCLC patients received immunotherapy.