Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.
Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.
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DOI：
10.1016/j.celrep.2018.03.077
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发表时间：
2018-04-03
期刊：
影响因子：
8.8
通讯作者：
Liang H
中科院分区：
文献类型：
--
作者：
Peng X;Chen Z;Farshidfar F;Xu X;Lorenzi PL;Wang Y;Cheng F;Tan L;Mojumdar K;Du D;Ge Z;Li J;Thomas GV;Birsoy K;Liu L;Zhang H;Zhao Z;Marchand C;Weinstein JN;Cancer Genome Atlas Research Network;Bathe OF;Liang H
代谢重编程为临床肿瘤学提供了重要信息。使用来自癌症基因组图谱的9，125例患者样本的分子数据，我们根据7种主要代谢过程的mRNA表达模式确定了33种癌症类型的肿瘤亚型，并评估了其临床相关性。我们的代谢表达亚型与临床结果广泛相关：碳水化合物、核苷酸和维生素/辅因子代谢上调的亚型与预后不良最相关，而脂质代谢上调的亚型则相反。代谢亚型与不同的体细胞驱动因子相关，但在癌症标志性途径上表现出会聚效应，并且在癌症类型中受到高度复发的主调节因子的调节。作为一个概念验证的例子，我们证明了敲低SNAI 1或RUNX 1-碳水化合物代谢亚型的主要调节因子-调节代谢活性和药物敏感性。我们的研究提供了癌症类型内和癌症类型间代谢异质性的系统水平视图，并确定了通路串扰，表明了相关的预后，治疗和预测效用。Peng等人分析了33种癌症类型的9，125个TCGA样本的队列，以基于七种代谢途径的表达来表征肿瘤亚型。他们发现代谢表达亚型与患者生存率相关，并提示亚型相关主调节因子的治疗和预测相关性。
Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators.
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影响因子：
29
作者：
Pavlova NN;Thompson CB
通讯作者：
Thompson CB
影响因子：
64.8
作者：
Cancer Genome Atlas Research Network;Analysis Working Group: Asan University;BC Cancer Agency;Brigham and Women’s Hospital;Broad Institute;Brown University;Case Western Reserve University;Dana-Farber Cancer Institute;Duke University;Greater Poland Cancer Centre;Harvard Medical School;Institute for Systems Biology;KU Leuven;Mayo Clinic;Memorial Sloan Kettering Cancer Center;National Cancer Institute;Nationwide Children’s Hospital;Stanford University;University of Alabama;University of Michigan;University of North Carolina;University of Pittsburgh;University of Rochester;University of Southern California;University of Texas MD Anderson Cancer Center;University of Washington;Van Andel Research Institute;Vanderbilt University;Washington University;Genome Sequencing Center: Broad Institute;Washington University in St. Louis;Genome Characterization Centers: BC Cancer Agency;Broad Institute;Harvard Medical School;Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University;University of North Carolina;University of Southern California Epigenome Center;University of Texas MD Anderson Cancer Center;Van Andel Research Institute;Genome Data Analysis Centers: Broad Institute;Brown University:;Harvard Medical School;Institute for Systems Biology;Memorial Sloan Kettering Cancer Center;University of California Santa Cruz;University of Texas MD Anderson Cancer Center;Biospecimen Core Resource: International Genomics Consortium;Research Institute at Nationwide Children’s Hospital;Tissue Source Sites: Analytic Biologic Services;Asan Medical Center;Asterand Bioscience;Barretos Cancer Hospital;BioreclamationIVT;Botkin Municipal Clinic;Chonnam National University Medical School;Christiana Care Health System;Cureline;Duke University;Emory University;Erasmus University;Indiana University School of Medicine;Institute of Oncology of Moldova;International Genomics Consortium;Invidumed;Israelitisches Krankenhaus Hamburg;Keimyung University School of Medicine;Memorial Sloan Kettering Cancer Center;National Cancer Center Goyang;Ontario Tumour Bank;Peter MacCallum Cancer Centre;Pusan National University Medical School;Ribeirão Preto Medical School;St. Joseph’s Hospital &Medical Center;St. Petersburg Academic University;Tayside Tissue Bank;University of Dundee;University of Kansas Medical Center;University of Michigan;University of North Carolina at Chapel Hill;University of Pittsburgh School of Medicine;University of Texas MD Anderson Cancer Center;Disease Working Group: Duke University;Memorial Sloan Kettering Cancer Center;National Cancer Institute;University of Texas MD Anderson Cancer Center;Yonsei University College of Medicine;Data Coordination Center: CSRA Inc.;Project Team: National Institutes of Health
通讯作者：
Project Team: National Institutes of Health
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
12.3
作者：
Haider S;McIntyre A;van Stiphout RG;Winchester LM;Wigfield S;Harris AL;Buffa FM
通讯作者：
Buffa FM
DOI：
10.1093/bioinformatics/btw216
发表时间：
2016-07-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lachmann A;Giorgi FM;Lopez G;Califano A
通讯作者：
Califano A