Lung cancer stands as the most common reason for mortality from malignant tumors worldwide. It is typically classified into two main categories. SCLC constitutes approximately 15% of all lung cancers (1, 2). Compared with NSCLC, SCLC has the characteristics of higher malignancy, shorter tumor cell doubling time, and an earlier susceptibility to distant metastasis (3). A great deal of SCLC patients are already in the extensive stage when diagnosed, with extremely poor clinical prognosis (4). Over the past three decades, platinum combined with etoposide chemotherapy remains the typical treatment for SCLC (5). In the last several years, the use of PD-L1 inhibitors to treat SCLC patients has made tremendous progress. In the phase III clinical trial known as IMpower133, the combination of Atezolizumab with first-line chemotherapy resulted in an increased OS for patients compared to chemotherapy alone. In another clinical study CASPIAN, found that the median OS of patients receiving the combination of Durvalumab and chemotherapy was 2.7 months longer than the control group (6, 7). Durvalumab and Atezolizumab were approved as one of the first-line treatments for SCLC in February and March 2020, respectively.

However, immune checkpoint inhibitors (ICIs) can also lead to a range of immune-related adverse events (irAEs), including rash, pruritus, pneumonitis, diarrhea, and endocrine system problems (8). Only a portion of SCLC patients can benefit from immunotherapy, so it is crucial to search for biomarkers that can predict the prognosis and accurately identify subgroups of patients suitable for immunotherapy. At present, numerous studies have explored biomarkers for the efficacy of immunotherapy, proposing the predictive value of PD-L1 expression level, tumor mutation burden (TMB), tumor microenvironment, etc. (9). However, these biomarkers face difficulties in clinical application due to limitations in tissue sampling, inconsistent detection standards, and tumor expression heterogeneity. Their predictive value for the efficacy of SCLC immunotherapy is still unclear. Numerous investigations have revealed that the occurrence, development, invasion, metastasis, and anti-tumor response of tumors are strongly influenced by inflammation, immunity, and overall nutritional status (10). Research have found that some composite inflammatory indicators, such as the systemic immune-inflammation index (SII) and the prognostic nutritional index (PNI), can predict the prognosis of many malignant tumors, especially lung cancer (11–15). These hematological inflammatory biomarkers are inexpensive and readily accessible, but their predictive effectiveness in the efficacy of immunotherapy for ES-SCLC remains largely uncertain.

The SII-PNI is highly useful in predicting the curative effect and prognosis of non-small cell lung cancer (16) and gastric cancer (17) patients in earlier studies. The predictive ability of SII-PNI score for the efficacy and prognosis of ES-SCLC patients undergoing immunotherapy is the main focus of this study, providing guidance for screening the most suitable patients for immunotherapy.

For patients with extensive-stage SCLC, the median survival time typically ranges from 9 to 10 months, and the first-line standard chemotherapy is the main treatment option. The emergence of ICIs has changed the treatment approach of SCLC. ICIs prevent tumor cells from completing immune evasion through immune checkpoints, promoting the immune system to fully exert its anti-tumor activity (18, 19). Chemotherapy can induce immunogenic cell death of tumor cells, improve the ability of immune system to recognize the tumor cells (20), upregulate the expression of PD-L1, and enhance the efficacy of ICIs (21). Therefore, it is believed that chemotherapy and immunotherapy have a synergistic effect. The current research results show that the combination of PD-L1 inhibitors and chemotherapy can benefit patients, but the factors affecting its efficacy and prognosis are still unclear. Immunotherapy can also bring serious adverse reactions, and only a portion of patients can benefit from it. Therefore, before PD-L1 inhibitor combined with first-line chemotherapy is utilized to those suffering from ES-SCLC, it would be advantageous to have a simple indicator that can reliably predict the efficacy and prognosis. This would significantly assist in formulating and selecting individualized treatment regimens.

High expression of PD-L1 is associated with greater immune benefits in NSCLC and some malignant tumors (22–24). However, unlike NSCLC, biopsy samples of SCLC often contain necrotic tissue, so it is usually not possible to provide sufficient samples for PD-L1 detection. In addition, among detectable specimens, compared with NSCLC, the expression frequency of PD-L1 in SCLC is significantly lower (25, 26), with only about 25% of SCLC patients showing positive PD-L1 expression in tumor cells (27). Moreover, due to the strong heterogeneity of SCLC, there are significant differences in PD-L1 detection results, which limits the application of PD-L1 in predicting the efficacy of immunotherapy in SCLC populations (28). TMB has been approved by the FDA as a predictive indicator for the efficacy of pan-tumor immunotherapy (29). However, issues such as insufficient sample size for testing and inconsistent cut-off values also prevent TMB from accurately predicting the efficacy of SCLC immunotherapy. Increasing numbers of investigations have discovered the predictive role of hematological indicators in the efficacy of cancer immunotherapy. Finding effective biomarkers from economically available test items such as blood routine and liver and kidney function is currently one of the most active areas of cancer treatment research.

The occurrence and development of malignancies are heavily influenced by systemic nutrition and inflammation status. An increasing number of studies have demonstrated the correlation between chronic inflammation and an elevated risk of getting cancer (10). A high SII level has shown a strong correlation with poorer prognosis in malignant solid tumors (30–32). Many studies have also identified that the SII correlates with the prognosis of SCLC patients. In addition, the nutritional status also matters greatly in disease progression and prognosis of cancers (8). PNI is a widely used nutritional index that was originally introduced to evaluate patients’ nutritional status before surgery and to predict the risk of complications after surgery (33, 34). More and more studies have found that PNI has prognostic value for many types of cancers, including lung (35), gastric (36), colorectal (37), and hepatocellular carcinoma (38). Previous studies have indicated that the SII-PNI score is a significant prognostic tool in NSCLC (16), gastric cancer (17, 39), and urothelial carcinoma (40). As far as we know, we are pioneering in integrating the SII and PNI of ES-SCLC patients to define the SII-PNI score as a novel indicator for predicting efficacy and prognosis.

Tumor response plays a crucial role in predicting the survival outcome of ES-SCLC patients treated with PD-L1 inhibitors. It is challenging to anticipate how tumors will respond based solely on clinical pathological data before immunotherapy. We pay attention to SII and PNI to address the difficulties in predicting tumor response. The results of this study indicated a significant correlation between the baseline SII-PNI score and the tumor response to immunotherapy. Patients with lower SII-PNI scores were found to have a higher possibility of responding well to PD-L1 inhibitors. These findings indicated that the SII-PNI score may function as a potential biomarker for predicting short-term efficacy in ES-SCLC patients taking immunotherapy with chemotherapy.

The prognostic analysis revealed that a higher SII-PNI score has a strong association with a poorer prognosis. The following elements could potentially contribute to the SII-PNI score’s predictive capability: (1) An elevated SII-PNI score suggests that the count of neutrophils or platelets has increased proportionally compared to the lymphocyte count present within the blood sample. Elevated neutrophil count has been seen having as an unwelcome role in tumor development, invasion, metastasis, and resistance to treatment (41). Previous studies have shown that neutrophils can suppress anti-tumor cytotoxic T lymphocytes in vitro and produce cytokines that promote angiogenesis (42). Platelets can activate a large number of bioactive factors, increase tumor vascular permeability, and serve as a shield for tumor cells against the infiltration of cytotoxic lymphocytes and natural killer cells, as well as promote the development of an immunosuppressive tumor microenvironment (43). (2) An elevated SII-PNI score also reflects a relative reduction in lymphocyte levels. As immune cells in the body, lymphocytes can inhibit cancer cell division and metastasis by inducing cell apoptosis, playing a role in immune monitoring and tumor defense (44, 45). The decrease in lymphocyte count can also reduce the efficacy of ICIs, which primarily release inhibitory signaling properties of T lymphocytes. Research has shown that increased infiltration of tumor-infiltrating lymphocytes (TILs) is associated with better efficacy and prognosis of immunotherapy in patients with solid tumors (46). (3) Malnutrition and inflammatory status of the body can both affect the synthesis of serum albumin. Low levels of albumin often suggest malnutrition and weakened immune function in patients. It can also reflect the body’s inflammatory status and anti-tumor immune response. Cachexia can also lead to disease progression (47).

We found liver metastasis, tumor response, and SII-PNI score were independently associated with OS. Following this, we developed a nomogram using the above-mentioned factors. To ensure our results were reliable and accurate, we conducted additional analyses using ROC curves and calibration curves. These statistical tools confirmed that our model had excellent predictive accuracy, with its prediction closely aligning with actual observations. In addition, we implemented a system stratifying mortality risk that categorizes patients into high- and low-risk subgroups. This system facilitates the effective management of risk stratification and the provision of individual treatment. A significant difference in OS was identified between the two different levels of risk.

Nevertheless, this study had certain limitations. Because PD-L1 inhibitors are prohibitively expensive, only a small population of patients have access to them. This study was constrained by a limited sample size. Additionally, there was a lack of an external validation cohort to further validate the effectiveness and predictive power of the nomogram for patients with ES-SCLC. Due to the small sample size of the study and the overfitting issues in the model, the SII-PNI score system has a certain risk of bias. Thirdly, systemic inflammation and nutritional status can be affected by various factors unrelated to cancer. However, this study did not address the potential confounding factors that may affect SII-PNI scores, such as concurrent infections or drug use (such as antibiotics or proton pump receptor inhibitors). Lastly, due to the fact that most patients have not undergone PD-L1 expression level and TMB testing, the correlation between these indicators and efficacy has not been explored. In the future, we may consider the possibility of combining the SII-PNI score with biomarkers such as PD-L1 expression level and TMB to improve the prognostic accuracy. In order to explore predictive biomarkers for ES-SCLC immunotherapy and identify the most suitable population for immunotherapy, more large-scale, prospective, and multi-center studies are still needed.

In summary, our study showed that the SII-PNI score can effectively predict the efficacy and prognosis of extensive-stage SCLC patients treated with PD-L1 inhibitors combined with first-line chemotherapy. Baseline hematological inflammation indicators can be obtained through blood routine, which has the advantages of simplicity, economy, and minimal trauma. Clinicians should take into account this innovative biomarker when making clinical decisions, managing risk stratification, and selecting the best patients for PD-L1 inhibitors.