One-carbon metabolism in cancer

Cells require one-carbon units for nucleotide synthesis, methylation and reductive metabolism, and these pathways support the high proliferative rate of cancer cells. As such, anti-folates, drugs that target one-carbon metabolism, have long been used in the treatment of cancer. Amino acids, such as serine are a major one-carbon source, and cancer cells are particularly susceptible to deprivation of one-carbon units by serine restriction or inhibition of de novo serine synthesis. Recent work has also begun to decipher the specific pathways and sub-cellular compartments that are important for one-carbon metabolism in cancer cells. In this review we summarise the historical understanding of one-carbon metabolism in cancer, describe the recent findings regarding the generation and usage of one-carbon units and explore possible future therapeutics that could exploit the dependency of cancer cells on one-carbon metabolism

Computational approaches for understanding one-carbon metabolism in cancer

Cancer metabolism is an emerging research area in cancer biology and therapeutics. One of the major metabolic pathways known to play important roles in the pathogenesis of cancer is one-carbon (1-C) metabolism. 1-C metabolism integrates the status of many dietary nutrients as inputs, and in turn regulates a variety of cellular processes including de novo nucleotide synthesis, lipid metabolism, protein biosynthesis, redox metabolism, transsulfuration, and epigenetics. As the regulation of these cellular processes is critical to cells, the tuning of the activity of 1-C metabolism plays important roles in cancer. Previous studies have established implications of genetic and dietary perturbations of multiple components of 1-C metabolism in human cancers. However, the heterogeneity among cancer types and subtypes with respect to the usage and flux distribution of 1-C metabolism has not been systematically quantified. There remain great potentials in deciphering how 1-C metabolism plays different roles in different human cancers, especially since this metabolic pathway is targeted by a number of the existing antimetabolite chemotherapeutic agents. In this dissertation, I quantitatively characterize various aspects of 1-C metabolism across human cancers. I first investigate the between-cancer-type variation in the usage of serine by 1-C metabolism using flux distribution analyses and find substantial heterogeneity. I also show that a common feature across cancers is correlated activation of nucleotide and redox metabolism. Next I assess the link between 1-C metabolism and DNA methylation using computational modeling and machine-learning. I find significant contribution from particular enzymes within 1-C metabolism— such as methionine adenosyltransferases— in explaining the within- cancer-type (inter-individual) variation in DNA methylation. My results provide evidence that misregulation of 1-C metabolism is at least in part responsible for disrupted DNA methylation profiles in tumors leading to epigenetic instability and higher malignancy. Given evidence for the role of 1-C metabolism and the methionine cycle in methylation dynamics, I next evaluate the potential for dietary intervention using the amino acid methionine. To this end, I model human serum methionine levels and quantify the contribution of various factors in determining the concentration of methionine. I discover that dietary factors could together explain nearly 30% of overall variation in methionine concentrations, and also provide evidence that the relationship between 1-C metabolism and methylation exists at physiological concentrations of methionine. Finally, I use a novel approach to identify gene expression markers of tumor response to 5-FU and Gemcitabine —two of the commonly used antimetabolite chemotherapies that target enzymes in 1-C metabolism. I discover that response to these agents is to a large degree determined by the metabolic state of tumors and the expression levels of specific target pathways of each of these agents. Together, my findings provide quantitative information about the heterogeneity among tumors with respect to the usage of 1-C metabolism, and delineate some of the ways this information can be translated into clinical decision- making

Molecular features that predict the response to antimetabolite chemotherapies

Abstract Background Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of heterogeneity remain largely unknown. Methods Here, we introduce a computational method for identifying gene expression signatures of response to chemotherapies and apply it to human tumors and cancer cell lines. Furthermore, we characterize a set of 17 antimetabolite agents in various contexts to investigate determinants of sensitivity to these agents. Results We identify distinct favorable and unfavorable metabolic expression signatures for 5-FU and Gemcitabine. Importantly, we find that metabolic pathways targeted by each of these antimetabolites are specifically enriched in its expression signatures. We provide evidence against the common notion about non-specific cytotoxic functions of antimetabolite drugs. Conclusions This study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response

Characterization of the Usage of the Serine Metabolic Network in Human Cancer

The serine, glycine, one-carbon (SGOC) metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

Abstract DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts

Additional file 1: of Molecular features that predict the response to antimetabolite chemotherapies

Supplementary Figures. Figure S1. Relationship between target enzyme expression and response to Gemcitabine in TCGA pancreatic cancer. A) Kaplan-Meier plot compares progression free survival in high-RRM1 expression vs. low-RRM1 expression subgroups of TCGA PAAD patients. B) Kaplan-Meier plot compares progression free survival in high-RRM2 expression vs. low-RRM2 expression subgroups TCGA PAAD patients. Figure S2. Relationship between target enzyme expression and survival in an independent pancreatic cancer cohort. A) Kaplan-Meier plot compares overall survival in high-RRM1 expression vs. low-RRM1 expression subgroups of patients. B) Kaplan-Meier plot compares overall survival in high-RRM2 expression vs. low-RRM2 expression subgroups of patients. C) Kaplan-Meier plot compares overall survival in subgroups of patients divided based on our gene signature (see Methods). Figure S3. Identifying gene expression signatures of sensitivity to Gemcitabine in pancreatic cancer cell lines. A) Schematic of the step-wise filtering used for gene selection in pancreatic cancer (COSMIC PAAD). B) Hierarchical clustering heatmap of the discretized gene favorability scores. Columns represent genes and rows represent individuals. Favorable scores are shown by the color red (F=1), unfavorable by blue (F= -1), and neutral by yellow (F=0) (see Methods). C) Box-plots comparing the resistance to Gemcitabine (log IC-50 values) between the two cell line subgroups identified in part B (error bars show the range of the data points in each group). (DOCX 225 kb

Targeting One Carbon Metabolism with an Antimetabolite Disrupts Pyrimidine Homeostasis and Induces Nucleotide Overflow

Antimetabolites that affect nucleotide metabolism are frontline chemotherapy agents in several cancers and often successfully target one carbon metabolism. However, the precise mechanisms and resulting determinants of their therapeutic value are unknown. We show that 5-fluorouracil (5-FU), a commonly used antimetabolite therapeutic with varying efficacy, induces specific alterations to nucleotide metabolism by disrupting pyrimidine homeostasis. An integrative metabolomics analysis of the cellular response to 5-FU reveals intracellular uracil accumulation, whereas deoxyuridine levels exhibited increased flux into the extracellular space, resulting in an induction of overflow metabolism. Subsequent analysis from mice bearing colorectal tumors treated with 5-FU show specific secretion of metabolites in tumor-bearing mice into serum that results from alterations in nucleotide flux and reduction in overflow metabolism. Together, these findings identify a determinant of an antimetabolite response that may be exploited to more precisely define the tumors that could respond to targeting cancer metabolism

RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards

<div><p>With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm—RRmix—within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.</p></div

Comparison of performance of 6 methods in differential abundance analysis.

<p></p><p></p><p></p><p>A) Schematic depicting the enzymatic steps in glycolysis pathway and the step where 2DG inhibits upon treatment. The metabolites shown in the diagram were analyzed by LC-MS.</p><p></p><p></p><p>B) Plot depicting the fraction of positive controls discovered by each method as significantly differentially abundant between the control and 2DG treated samples in the presence of batch effects.</p><p></p><p></p><p>C) Venn diagram comparing total number of discoveries made by each of the methods in the combined dataset (at 0.9 posterior probability for RRmix and 10% FDR correction for other methods).</p><p></p><p></p><p></p> <p>A) Schematic depicting the enzymatic steps in glycolysis pathway and the step where 2DG inhibits upon treatment. The metabolites shown in the diagram were analyzed by LC-MS.</p> <p>B) Plot depicting the fraction of positive controls discovered by each method as significantly differentially abundant between the control and 2DG treated samples in the presence of batch effects.</p> <p>C) Venn diagram comparing total number of discoveries made by each of the methods in the combined dataset (at 0.9 posterior probability for RRmix and 10% FDR correction for other methods).</p

Evaluation of DET using simulations.

<p></p><p></p><p></p><p>A) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 6 observations and 265 metabolites.</p><p></p><p></p><p>B) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 12 observations and 265 metabolites.</p><p></p><p></p><p>C) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 50 observations and 265 metabolites.</p><p></p><p></p><p>D) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 100 observations and 265 metabolites.</p><p></p><p></p><p>E) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 100 observations and 265 metabolites.</p><p></p><p></p><p>F) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 200 observations and 265 metabolites.</p><p></p><p></p><p>G) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 100 observations and 500 metabolites.</p><p></p><p></p><p>H) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 200 observations and 500 metabolites.</p><p></p><p></p><p></p> <p>A) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 6 observations and 265 metabolites.</p> <p>B) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 12 observations and 265 metabolites.</p> <p>C) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 50 observations and 265 metabolites.</p> <p>D) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 100 observations and 265 metabolites.</p> <p>E) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 100 observations and 265 metabolites.</p> <p>F) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 200 observations and 265 metabolites.</p> <p>G) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the absence of batch effects with 100 observations and 500 metabolites.</p> <p>H) Detection-error tradeoff (DET) plots comparing the performance of the 9 methods in the presence of batch effects with 200 observations and 500 metabolites.</p