Dataset Information

A joint analysis of metabolomics and genetics of breast cancer.

ABSTRACT:

Introduction

Remodeling of cellular metabolism appears to be a consequence and possibly a cause of oncogenic transformation in human cancers. Specific aspects of altered tumor metabolism may be amenable to therapeutic intervention and could be coordinated with other targeted therapies. In breast cancer, the genetic landscape has been defined most comprehensively in efforts such as The Cancer Genome Atlas (TCGA). However, little is known about how alterations of tumor metabolism correlate with this landscape.

Methods

In total 25 cancers (23 fully analyzed by TCGA) and 5 normal breast specimens were analyzed by gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry, quantitating 399 identifiable metabolites.

Results

We found strong differences correlated with hormone receptor status with 18% of the metabolites elevated in estrogen receptor negative (ER-) cancers compared to estrogen receptor positive (ER+) including many glycolytic and glycogenolytic intermediates consistent with increased Warburg effects. Glutathione (GSH) pathway components were also elevated in ER- tumors consistent with an increased requirement for handling higher levels of oxidative stress. Additionally, ER- tumors had high levels of the oncometabolite 2-hydroxyglutarate (2-HG) and the immunomodulatory tryptophan metabolite kynurenine. Kynurenine levels were correlated with the expression of tryptophan-degrading enzyme (IDO1). However, high levels of 2-HG were not associated with somatic mutations or expression levels of IDH1 or IDH2. BRCA1 mRNA levels were positively associated with coenzyme A, acetyl coenzyme A, and GSH and negatively associated with multiple lipid species, supporting the regulation of ACC1 and NRF2 by BRCA1. Different driver mutations were associated with distinct patterns of specific metabolites, such as lower levels of several lipid-glycerophosphocholines in tumors with mutated TP53. A strong metabolomic signature associated with proliferation rate was also observed; the metabolites in this signature overlap broadly with metabolites that define ER status as receptor status and proliferation rate were correlated.

Conclusions

The addition of metabolomic profiles to the public domain TCGA dataset provides an important new tool for discovery and hypothesis testing of the genetic regulation of tumor metabolism. Particular sets of metabolites may reveal insights into the metabolic dysregulation that underlie the heterogeneity of breast cancer.

SUBMITTER: Tang X

PROVIDER: S-EPMC4187326 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Publications

A joint analysis of metabolomics and genetics of breast cancer.

Tang Xiaohu X Lin Chao-Chieh CC Spasojevic Ivan I Iversen Edwin S ES Chi Jen-Tsan JT Marks Jeffrey R JR

Breast cancer research : BCR 20140805 4

<h4>Introduction</h4>Remodeling of cellular metabolism appears to be a consequence and possibly a cause of oncogenic transformation in human cancers. Specific aspects of altered tumor metabolism may be amenable to therapeutic intervention and could be coordinated with other targeted therapies. In breast cancer, the genetic landscape has been defined most comprehensively in efforts such as The Cancer Genome Atlas (TCGA). However, little is known about how alterations of tumor metabolism correlate ...[more]

PMID: 25091696

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Project description:Background:Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive. Methods:In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected ? level for 617 statistical tests = P < 8.10?×?10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk. Results:Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR?=?1.67, 95% CI?=?1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.84, 95% CI?=?1.27 to 2.67), 3-methylglutarylcarnitine (OR?=?1.91, 95% CI?=?1.23 to 2.96), allo-isoleucine (OR?=?1.76, 95% CI?=?1.23 to 2.51), and 2-methylbutyrylcarnitine (OR?=?1.89, 95% CI?=?1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR?=?1.14, 95% CI?=?1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR?=?1.06, 95% CI?=?0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI?=?21.8% to 100.0+%). Conclusion:These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.

Dataset Information

A joint analysis of metabolomics and genetics of breast cancer.

Introduction

Methods

Results

Conclusions

Publications

A joint analysis of metabolomics and genetics of breast cancer.

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