VPAC2 Receptor Signaling Promotes Growth and Immunosuppression in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) harbors a complex tumor microenvironment, and cross-talk among cells in the tumor microenvironment can contribute to drug resistance and relapse. Vasoactive intestinal peptide (VIP) is overexpressed in PDAC, and VIP receptors expressed on T cells are a targetable pathway that sensitizes PDAC to immunotherapy. In this study, we showed that pancreatic cancer cells engage in autocrine VIP signaling through VIP receptor 2 (VPAC2). High coexpression of VIP with VPAC2 correlated with reduced relapse-free survival in patients with PDAC. VPAC2 activation in PDAC cells upregulated Piwi-like RNA-mediated gene silencing 2, which stimulated cancer cell clonogenic growth. In addition, VPAC2 signaling increased expression of TGFß1 to inhibit T-cell function. Loss of VPAC2 on PDAC cells led to reduced tumor growth and increased sensitivity to anti-PD-1 immunotherapy in mouse models of PDAC. Overall, these findings expand our understanding of the role of VIP/VPAC2 signaling in PDAC and provide the rationale for developing potent VPAC2-specific antagonists for treating patients with PDAC. Significance: Autocrine VIP signaling via VPAC2 promotes cancer cell growth and inhibits T-cell function in pancreatic ductal adenocarcinoma, highlighting its potential as a therapeutic target to improve pancreatic cancer treatment.

CT-based peritumoral radiomics nomogram on prediction of response and survival to induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.

PURPOSE: The study aims to harness the value of radiomics models combining intratumoral and peritumoral features obtained from pretreatment CT to predict treatment response as well as the survival of LA-NPC(locoregionally advanced nasopharyngeal carcinoma) patients receiving multiple types of induction chemotherapies, including immunotherapy and targeted therapy. METHODS: 276 LA-NPC patients (221 in the training and 55 in the testing cohort) were retrospectively enrolled. Various statistical analyses and feature selection techniques were applied to identify the most relevant radiomics features. Multiple machine learning models were trained and compared to build signatures for the intratumoral and each peritumoral region, along with a clinical signature. The performance of each model was evaluated using different metrics. Subsequently, a nomogram model was constructed by combining the best-performing radiomics and clinical models. RESULTS: In the testing cohort, the nomogram model exhibited an AUC of 0.816, outperforming the other models. The nomogram model's calibration curve showed good agreement between predicted and observed outcomes in both the training and testing sets. When predicting survival, the model's concordance index (C-index) was 0.888 in the training cohort and 0.899 in the testing cohort, indicating its robust predictive ability. CONCLUSION: In conclusion, the combined nomogram model, incorporating radiomics and clinical features, outperformed other models in predicting treatment response and survival outcomes for LA-NPC patients receiving induction chemotherapies. These findings highlight the potential clinical utility of the model, suggesting its value in individualized treatment planning and decision-making.

Cost-Effectiveness Analysis of Sintilimab Combined with Chemotherapy Versus Chemotherapy Alone as the First-Line Treatment for Advanced Esophageal Cancer.

Background: Esophageal cancer has a poor prognosis and currently ranks sixth in global cancer mortality rates. The ORIENT-15 trial showed sintilimab plus chemotherapy significantly improved survival when compared to chemotherapy alone. This study aimed to evaluate the cost-effectiveness of sintilimab, a programmed death-ligand 1 (PD-L1) inhibitor, plus chemotherapy in treating patients with esophageal cancer compared with chemotherapy alone. Methods: A Markov model with a 10-year horizon was developed based on the perspective of the Chinese healthcare payers. We conducted a cost-effectiveness analysis for sintilimab combined with chemotherapy based on a questionnaire. Patients were grouped into the sintilimab group based on a positive score of 10 or more (combined positive score (CPS) ≥ 10 groups), and those with any other PD-L1 expression were randomized into patient groups. We estimated the cost and the effectiveness of sintilimab on the quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER) was computed. One-way and probabilistic sensitivity analyses were conducted to explore the impact of uncertainties on the cost-effectiveness results. Results: In the base-case analysis, compared with chemotherapy alone, the ICER of sintilimab plus chemotherapy for all patients was $21024.05 per QALY, and in the CPS≥10 group, it was $20974.23 per QALY. This was lower than $37653 per QALY. One-way sensitivity analysis demonstrated that ICERs were most sensitive to the price of sintilimab. Conclusion: The study demonstrated that sintilimab plus chemotherapy for advanced esophageal cancer as its first-line treatment would be more cost-effective than chemotherapy alone in Chinese patients.

Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma.

Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival. Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved. Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data.

The interplay of circulating tumor DNA and chromatin modification, therapeutic resistance, and metastasis.

Peripheral circulating free DNA (cfDNA) is DNA that is detected in plasma or serum fluid with a cell-free status. For cancer patients, cfDNA not only originates from apoptotic cells but also from necrotic tumor cells and disseminated tumor cells that have escaped into the blood during epithelial-mesenchymal transition. Additionally, cfDNA derived from tumors, also known as circulating tumor DNA (ctDNA), carries tumor-associated genetic and epigenetic changes in cancer patients, which makes ctDNA a potential biomarker for the early diagnosis of tumors, monitory and therapeutic evaluations, and prognostic assessments, among others, for various kinds of cancer. Moreover, analyses of cfDNA chromatin modifications can reflect the heterogeneity of tumors and have potential for predicting tumor drug resistance.