Project description:Metabolic reprogramming, due in part to the overexpression of metabolic enzymes, is a key hallmark of cancer cells. Lactate dehydrogenase (LDHA), a metabolic enzyme that catalyzes the interconversion of lactate and pyruvate, is overexpressed in a wide variety of cancer types, including pancreatic ductal adenocarcinoma (PDAC). Furthermore, the genetic or pharmacological inhibition of LDHA suppresses cancer growth, demonstrating a cancer-promoting role for this enzyme. Therefore, several pharmacological LDHA inhibitors are being developed and tested as potential anti-cancer therapeutic agents. Because cancer cells are known to rapidly adapt and become resistant to anti-cancer therapies, in this study, we modeled the adaptation of cancer cells to LDHA inhibition. Using PDAC as a model system, we studied the molecular aspects of cells resistant to the competitive LDHA inhibitor sodium oxamate. We performed unbiased RNA-sequencing (RNA-seq), assay for transposase-accessible chromatin with sequencing (ATAC-seq), and metabolomics analyses of parental and oxamate-resistant PDAC cells treated with and without oxamate to identify the transcriptional, chromatin, and metabolic landscapes of these cells. We found that oxamate-resistant PDAC cells were significantly different from parental cells at the levels of mRNA expression, chromatin accessibility, and metabolites. Additionally, an integrative analysis combining the RNA-seq and ATAC-seq datasets identified a subset of differentially expressed mRNAs that directly correlated with changes in chromatin accessibility. Finally, functional analysis of differentially expressed metabolic genes in parental and oxamate-resistant PDAC cells treated with and without oxamate, together with an integrative analysis of RNA-seq and metabolomics data, revealed changes in metabolic enzymes that might explain the changes in metabolite levels observed in these cells. Collectively, these studies identify the transcriptional, chromatin, and metabolic landscapes of LDHA inhibitor resistance in PDAC cells. Future functional studies related to these changes remain necessary to reveal the direct roles played by these changes in the development of LDHA inhibitor resistance and uncover approaches for more effective use of LDHA inhibitors in cancer therapy.

Project description:The paper describes a model on the size of pancreatic tumour. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling Pancreatic Cancer Dynamics with Immunotherapy Xiaochuan Hu, Guoyi Ke and Sophia R.-J. Jang Bulletin of Mathematical Biology (2019) 81:1885–1915 Abstract: We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunother- apies between adoptive ex vivo expanded immune cells and TGF-β inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-β and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-β inhibition first followed by adoptive immune cell transfers can yield better survival outcomes. This model is hosted on BioModels Database and identified by: MODEL1907050003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Metabolomics in pancreatic cancer cells influenced by MTHFD2

Publications

The folate cycle enzyme MTHFD2 induces cancer immune evasion through PD-L1 up-regulation.

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