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The study uses context-specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from TCGA and MMRF-COMMPASS. Results show metabolism clustering into modes reflecting differential glycolysis, oxidative phosphorylation, and growth rate. Simulated pathway activities are prognostic across cancer types, and patient-specific modeling enables survival-informed metabolic biomarkers with insights into potential therapy repurposing.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/pan-cancer-genome-scale-metabolic-network-analysis-of-over-10000-patients-elucidates-relationship-between-metabolism-and-survival/332224/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/pan-cancer-genome-scale-metabolic-network-analysis-of-over-10000-patients-elucidates-relationship-between-metabolism-and-survival/332224.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data sources and patient count were used for the metabolic analysis?","Question",{"text":112,"@type":113},"The analysis integrates 10,915 patients across 34 cancer types from The Cancer Genome Atlas (TCGA) and MMRF-COMMPASS.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the study characterize metabolic modes across cancer types?",{"text":117,"@type":113},"It used context-specific genome-scale metabolic modeling and found cancer metabolism clusters into modes driven by differential glycolysis, oxidative phosphorylation, and growth rate.",{"name":119,"@type":110,"acceptedAnswer":120},"Which metabolic features showed prognostic value across cancers?",{"text":121,"@type":113},"Simulated activities of metabolic pathways were prognostic, particularly those related to tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332224,1790196635,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","cancers   \nArticle  \nPan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival  \nJesse Bucksot†, Katherine Ritchie, Matthew Biancalana, John A. Cole and Daniel Cook *,†  \nCitation: Bucksot, J.; Ritchie, K.; Biancalana, M.; Cole, J.A.; Cook, D. Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival. Cancers 2024, 16, 2302. [https://](https://)[ ](https://)[doi.org/10.3390/cancers16132302](doi.org/10.3390/cancers16132302)  \nAcademic Editor: Michael  \nI. Koukourakis  \nReceived: 23 May 2024  \nRevised: 17 June 2024  \nAccepted: 20 June 2024  \nPublished: 22 June 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSimBioSys, Inc., 180 N La Salle St., Chicago, IL 60601, USA  \n* Correspondence: [daniel@simbiosys.com](daniel@simbiosys.com)  \n† These authors contributed equally to this work.  \nSimple Summary: This study describes a large-scale analysis of cancer metabolism across The Cancer Genome Atlas and Multiple Myeloma Research Foundation MMRF-COMMPASS study encompassing 10,915 patients from 34 different cancer types. To the best of our knowledge, this is the first such large-scale metabolic analysis performed using these data. Our approach identified biologically interpretable metabolic biomarkers of overall survival in 31 out of 34 cancer types evaluated. Additionally, in a subset of 7 cancer types, we predict chemosensitivity to antifolate-based therapies and a biomarker of response based on our patient-specific metabolic models. Finally, this work introduces the concept of “interpretation-driven biomarker identification”. In this framework, biological interpretability of biomarker signatures is ensured first by using network-based simulations to characterize tumor biology, then using survival analysis to identify any biological features associated with overall survival—essentially decoupling biomarker discovery and biological interpretation of biomarker scores.  \nAbstract: Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.  \nKeywords: systems biology; pan-cancer; metabolic modeling; prognosis; therapy repurposing  \n1. Introduction  \nCancer is a highly heterogeneous disea","cbCaistXioySfDGx","https://ap.wps.com/l/cbCaistXioySfDGx","pdf",9960967,17,"English","# 1. Introduction\n## Cancer heterogeneity and integrative methodology\n## Warburg effect and hallmarks of cancer\n## Linking cross-cancer commonalities to prognosis","[{\"question\":\"What data sources and patient count were used for the metabolic analysis?\",\"answer\":\"The analysis integrates 10,915 patients across 34 cancer types from The Cancer Genome Atlas (TCGA) and MMRF-COMMPASS.\"},{\"question\":\"How did the study characterize metabolic modes across cancer types?\",\"answer\":\"It used context-specific genome-scale metabolic modeling and found cancer metabolism clusters into modes driven by differential glycolysis, oxidative phosphorylation, and growth rate.\"},{\"question\":\"Which metabolic features showed prognostic value across cancers?\",\"answer\":\"Simulated activities of metabolic pathways were prognostic, particularly those related to tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism.\"}]","Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival | PDF",1789997418,43]