UnitedMet harnesses RNA–metabolite covariation to impute metabolite levels in clinical samples Journal Article

   

Authors:	Xie, A. X.; Tansey, W.; Reznik, E.
Article Title:	UnitedMet harnesses RNA–metabolite covariation to impute metabolite levels in clinical samples
Abstract:	Comprehensively studying metabolism requires metabolite measurements. Such measurements, however, are often unavailable in large cohorts of tissue samples. To address this basic barrier, we propose a Bayesian framework (‘UnitedMet’) that leverages RNA–metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. UnitedMet is equally capable of imputing whole pool sizes and outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying an association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to anti-angiogenic therapy and that kidney cancer metastases demonstrate elevated oxidative phosphorylation. UnitedMet provides a scalable tool for assessing metabolic phenotypes when direct measurements are infeasible, facilitating unexplored avenues for metabolite-focused hypothesis generation. © The Author(s) 2025.
Keywords:	adult; clinical article; survival analysis; genetics; mutation; angiogenesis inhibitor; bevacizumab; sunitinib; mass spectrometry; phenotype; metabolism; breast cancer; bayes theorem; gene expression; cohort analysis; gene function; in vivo study; in vitro study; pathology; transcriptomics; prediction; kidney neoplasms; rna; immunotherapy; isotope labeling; kidney tumor; training; tumor suppressor proteins; benchmarking; kidney cancer; tumor suppressor protein; oxidative phosphorylation; metabolite; ubiquitin thiolesterase; non small cell lung cancer; pyruvic acid; metabolomics; clinical outcome; aerobic metabolism; procedures; amino acid metabolism; humans; human; male; female; article; rna sequencing; atezolizumab; metabolome; bayesian network; transfer of learning; cross validation; bap1 protein, human; unitedmet harnesses
Journal Title:	Nature Cancer
Volume:	6
Issue:	5
ISSN:	2662-1347
Publisher:	Nature Research
Date Published:	2025-05-01
Start Page:	892
End Page:	906
Language:	English
DOI:	10.1038/s43018-025-00943-0
PUBMED:	40251399
PROVIDER:	scopus
PMCID:	PMC12122372
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005165313&doi=10.1038%2fs43018-025-00943-0&partnerID=40&md5=9f35d5f28b4643ca21075677b4eea7da
Notes:	The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK authors are Wesley Tansey and Ed Reznik -- Source: Scopus

https://synapse.mskcc.org/synapse/works/has_related_data_chk/226539