Survival benefit of combined immunotherapy and chemoradiotherapy in locally advanced unresectable esophageal cancer: an analysis based on the SEER database

Radiotherapy modulates autophagy to reshape the tumor immune microenvironment to enhance anti-tumor immunity in esophageal cancer

The combination of radiotherapy and immunotherapy exerts synergistic antitumor in a range of human cancers, and also in esophageal cancer. Radiotherapy-induced tumor immune microenvironment (TIME) reprogramming is an essential basis for the synergistic antitumor between radiotherapy and immunotherapy. Radiotherapy can induce autophagy in tumor cells and immune cells of TIME, and autophagy activation is involved in the modification of immunological characteristics of TIME. The TIME landscape of esophageal cancer, especially ESCC, can be affected by radiotherapy or autophagy regulation. In this review, we depicted that local radiotherapy-induced autophagy could promote the maturation, migration, infiltration, and function of immune cells by complicated mechanisms to make TIME from immune "cold" to "hot", resulting in the synergistic antitumor of RT and IO. We argue that unraveling the relevance of radiotherapy-initiated autophagy to driving radiotherapy reprogramming TIME will open new ideas to explore new targets or more efficiently multimodal therapeutic interventions in ESCC.

The roles of enhancer, especially super-enhancer-driven genes in tumor metabolism and immunity

Abnormal metabolism is a characteristic of malignant tumors. Numerous factors play roles in the regulation of tumor metabolism. As epigenetic regulators, enhancers, especially the super-enhancers (SEs), serve as platforms for transcription factors that regulate the expression of metabolism-related enzymes or transporters at the gene level. In this study, we review the effects of enhancer/ SE-driven genes on tumor metabolism and immunity. Enhancers/SEs play regulatory roles in glucose metabolism (glycolysis, gluconeogenesis, tricarboxylic acid (TCA) cycle, pyruvate, and pentose phosphate pathway, lipid metabolism (cholesterol, fatty acid, phosphatide, and sphingolipid), and amino acid metabolism (glutamine, tryptophan, arginine, and cystine). By regulating tumor metabolism, enhancers and SEs can reprogram tumor microenvironment, especially the status of various immune cells. Therefore, interfering enhancers/SEs that regulate the tumor metabolism is likely to enhance the effectiveness of immunotherapy.

Combination of radiotherapy and immunochemotherapy improves survival outcomes in non-small cell lung cancer patients with liver metastasis

Background

Non-small cell lung cancer (NSCLC) with liver metastasis carries a poor prognosis, and evidence for optimal treatment strategies remains limited. The combination of radiotherapy (RT) and immunochemotherapy has shown promise in improving survival outcomes for patients with advanced NSCLC, however, large cohort studies targeting NSCLC with liver metastasis are lacking. The purpose of this study was to analyze the impact of RT combined with immunochemotherapy on the long-term survival of NSCLC patients with liver metastasis leveraging data from the Surveillance, Epidemiology, and End Results Program (SEER) database and Xinqiao Hospital in China.

Methods

Patients diagnosed with NSCLC and liver metastasis between 2010 and 2020 were screened from the SEER 17 registry. Patients were categorized into three cohorts: immunochemotherapy alone (IOC), RT + immunochemotherapy (RT + IOC) and chemotherapy + RT (CRT). Survival analysis, propensity score matching (PSM), subgroup analysis, and Cox regression were performed. The primary endpoints were overall survival (OS) and cancer-specific survival (CSS). Additionally, data from Xinqiao Hospital were used for validation.

Results

A total of 6,309 patients were enrolled, including 1,691 in the IOC cohort, 1,605 in the RT + IOC cohort, and 3,013 in the CRT cohort. The median overall survival (mOS) was significantly higher in the RT + IOC cohort compared to the IOC cohort (9 vs. 7 months, P<0.001). Similar results were observed for median cancer-specific survival (mCSS). After PSM, the survival benefits of the RT + IOC cohort persisted. Subgroup analysis revealed that most subgroups favored RT + IOC treatment. Xinqiao Hospital data further validated these findings with better median progression-free survival (mPFS) in RT + IOC cohort compared to the IOC cohort (9.3 vs. 4.1 months, P=0.03) and mOS (13.2 vs. 8.7 months, P=0.02). Furthermore, the discrepancies in survival between RT + IOC cohort and CRT cohort were compared. The SEER data revealed that the mOS and mCSS were better in RT + IOC cohort both before and after PSM. Our single-center data further validated the survival benefits of RT + IOC treatment when compared to CRT treatment.

Conclusions

The combination of radiotherapy and immunochemotherapy provides better survival benefits for NSCLC patients with liver metastasis than immunochemotherapy alone or chemotherapy + radiotherapy. Further research is necessary to explore the optimal radiotherapy methods for this patient population.

Impact of Collaborative Empowerment Education on Psychological Distress, Quality of Life, and Nutritional Status in Esophageal Cancer Patients Undergoing Concurrent Chemoradiotherapy

Esophageal cancer (EC) patients undergoing concurrent chemoradiotherapy (CCRT) often face significant psychological distress, impaired quality of life (QoL), and poor nutritional status. This study evaluates the impact of multidisciplinary collaborative empowerment education (MCEE) in addressing these challenges. According to the inclusion criteria, 160 patients were recruited and randomly assigned to either the MCEE group (n = 80) or the control group (n = 80). The MCEE group received a tailored program consisting of psychological support, nutritional counseling, and educational interventions. Outcome measures, including psychological distress (using the Kessler Psychological Distress Scale), quality of life (using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire), and nutritional status (using hemoglobin, serum protein, and albumin levels), were evaluated at baseline and after four cycles of concurrent chemoradiotherapy. Post-intervention, the MCEE group showed significant improvements in psychological distress. QoL improvements were noted across all functional domains, including physical, emotional, cognitive, and social functions (all Ps ≤ 0.001), with significant reductions in fatigue, insomnia, and pain. Nutritional status also improved, with higher levels of hemoglobin, serum protein, and albumin, as well as less weight loss in the intervention group (all Ps ≤ 0.001). MCEE effectively reduces psychological distress, improves QoL, and enhances nutritional status in EC patients undergoing CCRT. This patient-centered, multidisciplinary approach offers a promising strategy for improving treatment outcomes and overall well-being in cancer care.

A machine learning model utilizing CT radiomics features and peripheral blood inflammatory markers predicts the prognosis of patients with unresectable esophageal squamous cell carcinoma undergoing PD-1 inhibitor combined with concurrent chemoradiotherapy

Objective: To investigate the value of a machine learning model that integrates radiomics features and peripheral blood inflammatory markers in predicting the prognosis of patients with unresectable esophageal squamous cell carcinoma (ESCC) receiving PD-1 inhibitor combined with concurrent chemoradiotherapy. Methods: A retrospective collection was conducted involving 105 patients with unresectable ESSC who received PD-1 inhibitors combined with concurrent chemoradiotherapy at the First Affiliated Hospital of the University of Science and Technology of China from January 2020 to August 2023. These patients were randomly divided into a training set (n=74) and a validation set (n=31). Radiomics features were extracted from arterial phase CT images obtained before initial treatment, with feature selection performed using Pearson Correlation and LASSO-COX methods. Baseline clinical characteristics were analyzed, and hematological parameters were collected before the start of immunotherapy and within 4-6 weeks post-treatment to calculate inflammatory markers. Subsequently, independent radiomics features influencing patient prognosis were identified using a multivariate Cox proportional hazards model, and these features were incorporated into a clinical feature-based multivariate Cox model to derive independent prognostic factors combining radiomics and clinical characteristics. Nomograms were constructed to predict the 2-year progression-free survival (PFS) of patients based on the results of COX analysis involving clinical characteristics, radiomic features, and combined indicators. The models were evaluated and assessed using ROC curves and calibration curves. Results: In the training cohort, the AUC was 0.705 for the clinical model, 0.573 for the radiomics model, and 0.834 for the combined model. In the validation cohort, the AUC was 0.784 for the clinical model, 0.775 for the radiomics model, and 0.872 for the combined model. Conclusion: The combined model integrating the radiomic feature NGTDM-busyness, the inflammatory marker ΔNLR, and the clinical characteristic M stage offers the optimal predictive value for the 2-year PFS in patients.

A nomogram for predicting cancer-specific survival in patients with locally advanced unresectable esophageal cancer: development and validation study

Background

Immunotherapy research for esophageal cancer is progressing rapidly, particularly for locally advanced unresectable cases. Despite these advances, the prognosis remains poor, and traditional staging systems like AJCC inadequately predict outcomes. This study aims to develop and validate a nomogram to predict cancer-specific survival (CSS) in these patients.

Methods

Clinicopathological and survival data for patients diagnosed between 2010 and 2021 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into a training cohort (70%) and a validation cohort (30%). Prognostic factors were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. A nomogram was constructed based on the training cohort and evaluated using the concordance index (C-index), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plots, and area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to validate the prognostic factors.

Results

The study included 4,258 patients, and LASSO-Cox regression identified 10 prognostic factors: age, marital status, tumor location, tumor size, pathological grade, T stage, American Joint Committee on Cancer (AJCC) stage, SEER stage, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.660 (training set) and 0.653 (validation set), and 1-, 3-, and 5-year AUC values exceeded 0.65. Calibration curves showed a good fit, and decision curve analysis (DCA), IDI, and NRI indicated that the nomogram outperformed traditional AJCC staging in predicting prognosis.

Conclusions

We developed and validated an effective nomogram model for predicting CSS in patients with locally advanced unresectable esophageal cancer. This model demonstrated significantly superior predictive performance compared to the traditional AJCC staging system. Future research should focus on integrating emerging biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), into prognostic models to enhance their predictive accuracy and adapt to the evolving landscape of immunotherapy in esophageal cancer management.