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.
尽管癌症生物学具有高度异质性，但不同癌症类型仍展现出某些共同特征，其中代谢失调尤为显著。代谢在癌症生物学中占据核心地位，代谢通路的改变与肿瘤侵袭性及治疗反应概率密切相关。因此，本研究旨在探究跨癌种的代谢特征，解析不同适应症内部及之间的固有代谢模式差异及其与患者预后的关联。我们采用特定背景下的基因组规模代谢模型，对来自癌症基因组图谱及MMRF-COMMPASS研究的34种癌症类型共10,915例患者进行代谢模拟分析。研究发现癌症代谢可聚类为以差异性糖酵解、氧化磷酸化和生长速率为特征的代谢模式。代谢通路模拟活性数据本身即具有跨癌种的预后价值，尤其在肿瘤生长速率、脂肪酸生物合成、叶酸代谢、氧化磷酸化、类固醇代谢和谷胱甘肽代谢等方面表现显著。本研究证实了基于个体患者的代谢模型在跨癌种预后评估中的有效性。同时表明，结合生存信息分析大规模癌症代谢模型，能够为揭示跨癌种的潜在关联提供独特视角，并为特定癌症疗法拓展至其他癌种的适应性应用提供理论依据。
肿瘤(癌症)患者之家