Dataset Information

Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.

ABSTRACT: 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.

SUBMITTER: Peng X

PROVIDER: S-EPMC5916795 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.

Peng Xinxin X Chen Zhongyuan Z Farshidfar Farshad F Xu Xiaoyan X Lorenzi Philip L PL Wang Yumeng Y Cheng Feixiong F Tan Lin L Mojumdar Kamalika K Du Di D Ge Zhongqi Z Li Jun J Thomas George V GV Birsoy Kivanc K Liu Lingxiang L Zhang Huiwen H Zhao Zhongming Z Marchand Calena C Weinstein John N JN Bathe Oliver F OF Liang Han H

Cell reports 20180401 1

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 correlate ...[more]

PMID: 29617665

Similar Datasets

Project description:BackgroundMolecular subtyping of triple-negative breast cancers (TNBCs) via gene expression profiling is essential for understanding the molecular essence of this heterogeneous disease and for guiding individualized treatment. We aim to devise a clinically practical method based on immunohistochemistry (IHC) for the molecular subtyping of TNBCs.Materials and methodsBy analyzing the RNA sequencing data on TNBCs from Fudan University Shanghai Cancer Center (FUSCC) (n = 360) and The Cancer Genome Atlas data set (n = 158), we determined markers that can identify specific molecular subtypes. We performed immunohistochemical staining on tumor sections of 210 TNBCs from FUSCC, established an IHC-based classifier, and applied it to another two cohorts (n = 183 and 214).ResultsWe selected androgen receptor (AR), CD8, FOXC1, and DCLK1 as immunohistochemical markers and classified TNBCs into five subtypes based on the staining results: (a) IHC-based luminal androgen receptor (IHC-LAR; AR-positive [+]), (b) IHC-based immunomodulatory (IHC-IM; AR-negative [-], CD8+), (c) IHC-based basal-like immune-suppressed (IHC-BLIS; AR-, CD8-, FOXC1+), (d) IHC-based mesenchymal (IHC-MES; AR-, CD8-, FOXC1-, DCLK1+), and (e) IHC-based unclassifiable (AR-, CD8-, FOXC1-, DCLK1-). The κ statistic indicated substantial agreement between the IHC-based classification and mRNA-based classification. Multivariate survival analysis suggested that our IHC-based classification was an independent prognostic factor for relapse-free survival. Transcriptomic data and pathological observations implied potential treatment strategies for different subtypes. The IHC-LAR subtype showed relative activation of HER2 pathway. The IHC-IM subtype tended to exhibit an immune-inflamed phenotype characterized by the infiltration of CD8+ T cells into tumor parenchyma. The IHC-BLIS subtype showed high expression of a VEGF signature. The IHC-MES subtype displayed activation of JAK/STAT3 signaling pathway.ConclusionWe developed an IHC-based approach to classify TNBCs into molecular subtypes. This IHC-based classification can provide additional information for prognostic evaluation. It allows for subgrouping of TNBC patients in clinical trials and evaluating the efficacy of targeted therapies within certain subtypes.Implications for practiceAn immunohistochemistry (IHC)-based classification approach was developed for triple-negative breast cancer (TNBC), which exhibited substantial agreement with the mRNA expression-based classification. This IHC-based classification (a) allows for subgrouping of TNBC patients in large clinical trials and evaluating the efficacy of targeted therapies within certain subtypes, (b) will contribute to the practical application of subtype-specific treatment for patients with TNBC, and (c) can provide additional information beyond traditional prognostic factors in relapse prediction.

Dataset Information

Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.

Publications

Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets