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). Display omitted •Computational pipeline to integrate cancer metabolomics data across studies•Free, open-source pipeline, data, and online visualization portal available•Recurrent patterns of differentially abundant metabolites across cancer types•Discovery of metabolites associated with aggressive disease across cancer types Reznik et al. develop a computational approach for integrative analysis of metabolomics data and apply it to data from 900 tissue samples spanning seven different cancer types. They identify recurrent metabolic changes associated with tumor initiation and progression to aggressive disease.