Inhibition of de novo pyrimidine synthesis augments Gemcitabine induced growth inhibition in an immunocompetent model of pancreatic cancer

Recreating metabolic interactions of the tumour microenvironment

Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.

Integrative Analyses of Pyrimidine Salvage Pathway-Related Genes Revealing the Associations Between UPP1 and Tumor Microenvironment

Background

The pyrimidine salvage pathway plays a critical role in tumor progression and patient outcomes. The roles of pyrimidine salvage pathway-related genes (PSPGs) in cancer, however, are not fully understood. This study aims to depict the characteristics of PSPGs across various cancers.

Methods

An integrative pan-cancer analysis of six PSPGs (CDA, UCK1, UCK2, UCKL1, UPP1, and UPP2) was conducted using TCGA data, single-cell RNA sequencing datasets, and patient samples. Single-cell transcriptome analysis and RT-qPCR were used to validate the relation between UPP1 and cytokines. Flow cytometry was performed to validate the role of UPP1 in immune checkpoint regulation. The correlation between UPP1 and tumor associated neutrophils (TAN) were investigated and validated by single-cell transcriptome analysis and tissue microarrays (TMAs).

Results

PSPGs showed low mutation rates but significant copy number variations, particularly amplifications in UCKL1, UPP1, and UCK2 across various cancers. DNA methylation patterns varied, with notable negative correlations between methylation and gene expression in UPP1. PSPGs were broadly up-regulated in multiple cancers, with correlations to clinical staging and prognosis. Proteomic data further confirmed these findings. Functional analysis revealed PSPGs' associations with tumor proliferation, metastasis, and various signaling pathways. UPP1 showed strong correlations with the tumor microenvironment (TME), particularly with cytokines, immune checkpoints, and various immune cells. Single-cell transcriptome analysis confirmed these associations, highlighting UPP1's influence on cytokine expression and immune checkpoint regulation. In esophageal squamous cell carcinoma (ESCC), UPP1-high tumor cells were significantly associated with immunosuppressive cells in the TME. Spatial analysis using TMAs revealed that UPP1+ tumor cells were predominantly located at the invasive margin and closely associated with neutrophils, correlating with poorer patient prognosis.

Conclusion

Our study depicted the multi-dimensional view of PSPGs in cancer, with a particular focus on UPP1's role in the TME. Targeting UPP1 holds promise as a potential strategy for cancer therapy.

Performance of a blood-based RNA signature for gemcitabine-based treatment in metastatic pancreatic adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, and chemotherapy is a key treatment for advanced PDAC. Gemcitabine chemotherapy is still an important component of treatment; however, there is no routine biomarker to predict its efficacy. Predictive tests may help clinicians to decide on the best first-line chemotherapy.

Methods

This study is a confirmatory study of a blood-based RNA signature, called the GemciTest. This test measures the expression levels of nine genes using real-time polymerase chain reaction (PCR) processes. Clinical validation was carried out, through a discovery and a validation phases, on 336 patients (mean 68.7 years; range, 37-88 years) for whom blood was collected from two prospective cohorts and two tumor biobanks. These cohorts included previously untreated advanced PDAC patients who received either a gemcitabine- or fluoropyrimidine-based regimen.

Results

Gemcitabine-based treated patients with a positive GemciTest (22.9%) had a significantly longer progression-free survival (PFS) {5.3 vs. 2.8 months; hazard ratio (HR) =0.53 [95% confidence interval (CI): 0.31-0.92]; P=0.023} and overall survival (OS) [10.4 vs. 4.8 months; HR =0.49 (95% CI: 0.29-0.85); P=0.0091]. On the contrary, fluoropyrimidine-based treated patients showed no significant difference in PFS and OS using this blood signature.

Conclusions

The GemciTest demonstrated that a blood-based RNA signature has the potential to aid in personalized therapy for PDAC, leading to better survival rates for patients receiving a gemcitabine-based first-line treatment.

Prognosis stratification in breast cancer and characterization of immunosuppressive microenvironment through a pyrimidine metabolism-related signature

Pyrimidine metabolism is a hallmark of cancer and will soon become an essential part of cancer therapy. In the tumor microenvironment, cells reprogram pyrimidine metabolism intrinsically and extracellularly, thereby promoting tumorigenesis. Metabolites in pyrimidine metabolism have a significant impact on promoting cancer advancement and modulating immune system responses. In preclinical studies and practical clinical applications, critical targets in pyrimidine metabolism are acted upon by drugs to exert promising therapeutic effects on tumors. However, the pyrimidine metabolism in breast cancer (BC) is still largely underexplored. In this study, 163 credible pyrimidine metabolism-related genes (PMGs) were retrieved, and their somatic mutations and expression levels were determined. In addition, by using The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, 12 PMGs related to the overall survival (OS) were determined using the univariate Cox regression analysis. Subsequently, by performing the LASSO Cox hazards regression analysis in the 12 PMGs in TCGA-BRCA dataset, we developed a prognosis nomogram using eight OS-related PMGs and then verified the same in the METABRIC, GSE96058, GSE20685, GSE42568 and GSE86166 data. Moreover, we validated relationships between the pyrimidine metabolism index (PMI) and the survival probability of patients, essential clinical parameters, including the TNM stage and the PAM50 subtypes. Next, we verified the predictive capability of the optimum model, including the signature, the PAM50 subtype, and age, using ROC analysis and calibration curve, and compared it with other single clinical factors for the predictive power of benefit using decision curve analysis. Finally, we investigated the potential effects of pyrimidine metabolism on immune checkpoints, tumor-infiltrating immune cells, and cytokine levels and determined the potential implications of pyrimidine metabolism in BC immunotherapy. In conclusion, our findings suggest that pyrimidine metabolism has underlying prognostic significance in BC and can facilitate a new management approach for patients with different prognoses and more precise immunotherapy.

Mitochondria-Shaping Proteins and Chemotherapy

The emergence, in recent decades, of an entirely new area of “Mitochondrial dynamics”, which consists principally of fission and fusion, reflects the recognition that mitochondria play a significant role in human tumorigenesis and response to therapeutics. Proteins that determine mitochondrial dynamics are referred to as “shaping proteins”. Marked heterogeneity has been observed in the response of tumor cells to chemotherapy, which is associated with imbalances in mitochondrial dynamics and function leading to adaptive and acquired resistance to chemotherapeutic agents. Therefore, targeting mitochondria-shaping proteins may prove to be a promising approach to treat chemotherapy resistant cancers. In this review, we summarize the alterations of mitochondrial dynamics in chemotherapeutic processing and the antitumor mechanisms by which chemotherapy drugs synergize with mitochondria-shaping proteins. These might shed light on new biomarkers for better prediction of cancer chemosensitivity and contribute to the exploitation of potent therapeutic strategies for the clinical treatment of cancers.

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.

Comparative Untargeted Metabolomic Profiling of Induced Mitochondrial Fusion in Pancreatic Cancer

Mitochondria are dynamic organelles that constantly alter their shape through the recruitment of specialized proteins, like mitofusin-2 (Mfn2) and dynamin-related protein 1 (Drp1). Mfn2 induces the fusion of nearby mitochondria, while Drp1 mediates mitochondrial fission. We previously found that the genetic or pharmacological activation of mitochondrial fusion was tumor suppressive against pancreatic ductal adenocarcinoma (PDAC) in several model systems. The mechanisms of how these different inducers of mitochondrial fusion reduce pancreatic cancer growth are still unknown. Here, we characterized and compared the metabolic reprogramming of these three independent methods of inducing mitochondrial fusion in KPC cells: overexpression of Mfn2, genetic editing of Drp1, or treatment with leflunomide. We identified significantly altered metabolites via robust, orthogonal statistical analyses and found that mitochondrial fusion consistently produces alterations in the metabolism of amino acids. Our unbiased methodology revealed that metabolic perturbations were similar across all these methods of inducing mitochondrial fusion, proposing a common pathway for metabolic targeting with other drugs.