A Landscape of Metabolic Variation across Tumor Types

Abstract

Tumor metabolism is reorganized to support proliferation in the face of growth-related stress. Unlike the widespread profiling of changes to metabolic enzyme levels in cancer, comparatively less attention has been paid to the substrates/products of enzyme-catalyzed reactions, small-molecule metabolites. We developed an informatic pipeline to concurrently analyze metabolomics data from over 900 tissue samples spanning seven cancer types, revealing extensive heterogeneity in metabolic changes relative to normal tissue across cancers of different tissues of origin. Despite this heterogeneity, a number of metabolites were recurrently differentially abundant across many cancers, such as lactate and acyl-carnitine species. Through joint analysis of metabolomic data alongside clinical features of patient samples, we also identified a small number of metabolites, including several polyamines and kynurenine, which were associated with aggressive tumors across several tumor types. Our findings offer a glimpse onto common patterns of metabolic reprogramming across cancers, and the work serves as a large-scale resource accessible via a web application (http://www.sanderlab.org/pancanmet).

An integrated landscape of protein expression in human cancer

“…For example, Lukk et al 28 integrated thousands of microarray files to compile a map of human gene expression. In metabolomics, Reznik et al 29 used MS data from eleven studies to measure the extent of metabolic variation across tumours. A similar trend is starting to be observed in proteomics, where reuse of public datasets is becoming increasingly popular, with multiple applications 30,31 .…”

Meta-analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

“…This might suggest that they tend to play more significant roles than others in the associated metabolic pathways under many different conditions. For example, lactate, taurine, methionine, and acetylcarnitine were recently found to be more frequently differentially abundant in tumors compared to normal tissues across different cancer types (Reznik et al, 2018). Our study offers a systematic guideline and a reference point in targeted metabolomics analysis by either RPLC-Pos-dMRM or HILIC-Neg-dMRM method for more reliable biological interpretations without sample-dependent optimization of the LC-MS system.…”

Challenges and Opportunities in Cancer Metabolomics

“…A metanalysis of curated data from clinical metabolic profiling studies in cancer patients confirmed well‐known increases in glycolytic metabolites and also unveiled unprecedented changes in other metabolites such as ketone bodies and amino acids (such as histidine and tryptophan) . Another extensive computational analysis of metabolomics data from 900 tissue samples covering >900 metabolites revealed several polyamines and kynurenines being associated with several aggressive tumor types …”