Mahya Mehrmohamadi

Integrative modelling of tumour DNA methylation quantifies the contribution of metabolism.

Altered DNA methylation is common in cancer and often considered an early event in tumorigenesis. However, the sources of heterogeneity of DNA methylation among tumours remain poorly defined. Here we capitalize on the availability of multi-platform data on thousands of human tumours to build integrative models of DNA methylation. We quantify the contribution of clinical and molecular factors in explaining intertumoral variability in DNA methylation. We show that the levels of a set of metabolic genes involved in the methionine cycle is predictive of several features of DNA methylation in tumours, including the methylation of cancer genes. Finally, we demonstrate that patients whose DNA methylation can be predicted from the methionine cycle exhibited improved survival over cases where this regulation is disrupted. This study represents a comprehensive analysis of the determinants of methylation and demonstrates the surprisingly large interaction between metabolism and DNA methylation variation. Together, our results quantify links between tumour metabolism and epigenetics and outline clinical implications.

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.

Context dependent utilization of serine in cancer.

Serine and glycine have diverse biological functions but the general and context-dependent utilization of these nutrients in cancer is poorly understood. Our recent work integrates genomics data and isotope tracing using computational tools to study serine utilization across multiple cancer and normal human samples.

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

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.

Molecular features that predict the response to antimetabolite chemotherapies

BackgroundAntimetabolite 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.MethodsHere, 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.ResultsWe 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.ConclusionsThis study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response.

Integrative modeling of tumor DNA methylation identifies a role for metabolism

DNA methylation varies across genomic regions, tissues and individuals in a population. Altered DNA methylation is common in cancer and often considered an early event in tumorigenesis. However, the sources of heterogeneity of DNA methylation among tumors remain poorly defined. Here, we capitalize on the availability of multi-platform data on thousands of molecularly-and clinically-annotated human tumors to build integrative models that identify the determinants of DNA methylation. We quantify the relative contribution of clinical and molecular factors in explaining within-cancer (inter-individual) variability in DNA methylation. We show that the levels of a set of metabolic genes involved in the methionine cycle that are constituents of one-carbon metabolism are predictive of several features of DNA methylation status in tumors including the methylation of genes that are known to drive oncogenesis. Finally, we demonstrate that patients whose DNA methylation status can be predicted from the genes in one-carbon metabolism exhibited improved survival over cases where this regulation is disrupted. To our knowledge, this study is the first comprehensive analysis of the determinants of methylation and demonstrates the surprisingly large contribution of metabolism in explaining epigenetic variation among individual tumors of the same cancer type. Together, our results illustrate links between tumor metabolism and epigenetics and outline future clinical implications.