Treatment of small cell lung cancer: recent advances

Tislelizumab plus chemotherapy versus chemotherapy as first-line treatment for extensive-stage small cell lung cancer: A cost-effectiveness analysis

OBJECTIVE

This study aims to conduct a cost-effectiveness analysis of tislelizumab in combination with platinum and etoposide compared to the standard treatment of etoposide and platinum as first-line therapy for extensive-stage small cell lung cancer(ES-SCLC) from the Chinese medical system perspective.

METHODS

A partitioned survival model was developed utilizing data from the RATIONALE-312 trial to accurately simulate the clinical and economic outcomes of both treatment arms. This model incorporates three distinct health states, namely progression-free survival, disease progression, and death. These states are exclusive of each other, and patients can transition between them as their disease progresses.The model accounted for various cost components such as drug therapy, management of adverse events, disease progression, and overall survival. To evaluate the cost-effectiveness of the interventions, quality-adjusted life-year (QALY) and incremental cost-effectiveness ratio (ICER) were chosen as the metrics. The analysis employed a willingness to pay (WTP) threshold of $39,855.79 per QALY. Additionally, sensitivity analyses were conducted to assess the robustness and reliability of the model.

RESULTS

The tislelizumab group had a total cost of $52,749.69, whereas the chemotherapy group's total expenses amounted to $8,811.62. Additionally, the tislelizumab group experienced a gain of 2.21 QALY compared to the chemotherapy group, albeit incurring an additional cost of $43,938.07. Consequently, this led to an ICER of $19,881.48, which falls below the Chinese WTP threshold of $39,855.79. Sensitivity analyses confirmed the robustness of the findings across a range of scenarios.

CONCLUSION

This cost-effectiveness analysis based on the RATIONALE-312 trial demonstrates that tislelizumab plus platinum and etoposide is a cost-effective treatment option for ES-SCLC compared to the standard chemotherapy from the Chinese medical system perspective.

Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospective Cohort Study

Objective

This study aimed to develop a clinical early warning prediction model to evaluate the prognosis and response to chemoimmunotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC), thereby guiding clinical decision-making.

Methods

A retrospective analysis was conducted on the clinical data and radiomics parameters of 309 patients with ES-SCLC hospitalized at Baotou Cancer Hospital from February 2020 to September 2024. Patients were divided into reactive and non-reactive groups based on their response to chemoimmunotherapy.Machine learning algorithms (including random forests, decision trees, artificial neural networks, and generalized linear regression) were used to predict the combined treatment response. The model's predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). The prognostic evaluation of patients receiving combination therapy was based on the COX regression model, with predictive performance assessed through nomogram visualization and calibration curves.

Results

Out of 309 patients with ES-SCLC, 248 (80.26%) responded to combination therapy. Logistic regression and Least absolute shrinkage and selection operator (LASSO) regression analyses identified Energy, sum of squares(SOS), mean sum(MES), sum variance(SUV), sum entropy(SUE), difference variance(DIV), and pathomics score as independent risk factors for treatment response. The area under the ROC curve for predicting treatment response using machine learning were 0.764 (95% confidence interval [CI]: 0.707~0.821) and 0.901 (95% CI: 0.846~0.956) in the training and validation sets. The C-index of the radiomics and pathomics prognostic nomogram model based on the COX prognostic model was 0.766 and 0.812 in those sets, respectively.

Conclusion

We developed prediction model based on multi-omics demonstrated satisfactory performance in predicting chemoimmunotherapy response in patients with ES-SCLC. The random forest prediction model, in particular, provides accurate response and prognostic risk assessments, thereby assisting clinical decision-making.

Citrulline facilitates the glycolysis, proliferation, and metastasis of lung cancer cells by regulating RAB3C

Lung cancer (LC) is one of the major malignant diseases threatening human health. The study aimed to identify the effect of citrulline on the malignant phenotype of LC cells and to further disclose the potential molecular mechanism of citrulline in regulating the development of LC, providing a novel molecular biological basis for the clinical treatment of LC. The effects of citrulline on the viability, proliferation, migration, and invasion of LC cells (A549, H1299) were validated by CCK-8, colony formation, EdU, and transwell assays. The cell glycolysis was assessed via determining the glucose uptake, lactate production, ATP levels, extracellular acidification rate (ECAR), and oxygen consumption rate (OCR). RNA-seq and molecular docking were performed to screen for citrulline-binding target proteins. Western blotting experiments were conducted to examine the expression of related signaling pathway molecules. In addition, the impacts of citrulline on LC growth in vivo were investigated by constructing mouse models. Citrulline augmented the viability of LC cells in a concentration and time-dependent manner. The proliferation, migration, invasion, glycolysis, and EMT processes of LC cells were substantially enhanced after citrulline treatment. Bioinformatics analysis indicated that citrulline could bind to RAB3C protein. Western blotting results indicated that citrulline activated the IL-6/STAT3 pathway by binding to RAB3C. In addition, animal experiments disclosed that citrulline promoted tumor growth in mice. Citrulline accelerated the glycolysis and activated the IL6/STAT3 pathway through the RAB3C protein, consequently facilitating the development of LC.

Comparative characteristics of small cell lung cancer and Ewing's sarcoma: a narrative review

Background and Objective

Small cell lung cancer (SCLC) and Ewing's sarcoma (ES) at the disseminated stage are not amenable to therapy and have a dismal prognosis with low survival rates. Despite representing different tumor entities, treatment for both malignancies relies on cytotoxic chemotherapy that has not considerably changed for the past decades. The genomic background has been extensively studied and found to comprise inactivation of p53 and RB1 in case of SCLC and EWSR1/FLI1 rearrangement in case of ES resulting in aggressive tumors in adults with heavy tobacco consumption and as bone tumor in juveniles, respectively. New therapeutic modalities are urgently needed to improve the outcomes of both tumor entities, especially in patients with metastatic disease or recurrences. This review summarizes the common cell biologic and clinical characteristics of difficult-to-treat SCLC and ES and discusses their refractoriness and options to improve the therapeutic efficacy.

Methods

PubMed and Euro PMC were searched from January 1^st, 2012 to January 16^th, 2022 using the following key words: "SCLC", "Ewing´s sarcoma", "Genomics" and "Chemoresistance" as well as own work.

Key Content and Findings

Therapy of SCLC and ES involves the use of undirected cytotoxic drugs in multimodal chemotherapy and administration of topotecan for 2^nd line SCLC regimens. Despite highly aggressive chemotherapies, outcomes are dismal for patients with disseminated tumors. A host of unrelated drugs and targeted therapeutics have failed to result in progress for the patients and the underlying mechanisms of chemoresistance are still not clear. Identification of chemoresistance-reversing modulators in vitro and patient-derived xenografts of SCLC and ES has not translated into new therapies.

Conclusions

The global chemoresistance of SCLC and ES may be explained by physiological resistance at the tumor level and formation of larger spheroids that contain quiescent and hypoxic tumor cells in regions that occlude therapeutics. This type of chemoresistance is difficult to overcome and prevent the accumulation of effective drug concentration at the tumor cell level to a significant degree leaving therapeutic interventions of any kind ineffective.