Pre-diagnostic serum metabolome and breast cancer risk: a nested case-control study.

Abstract

Background: Metabolomics offers a promising approach to identify biomarkers for timely intervention and enhanced screening of individuals at increased risk of developing breast cancer.

Methods: We conducted a study of 593 female breast cancer cases and 593 matched controls nested in two prospective cohort studies. Mass spectrometry, without liquid chromatography, was used to conduct untargeted metabolomics profiling of serum samples collected, on average, 5.3 years before cancer diagnosis. Logistic regression was used to estimate odds ratios (OR) for a one standard deviation increase of metabolite intensities. Partial least squares discriminant analyses were applied to those metabolites significantly associated with breast cancer to develop risk prediction models.

Results: Associations were evaluated with a total of 837 metabolites. Twenty-four metabolites were significantly associated with overall breast cancer risk, including 13 associated with decreased risk and 11 associated with increased risk. Putative identities of the metabolites included various amino acids (n = 3), dietary factors (n = 10), fatty acids (n = 2), phosplipids (n = 4), sex hormone derivatives (n = 2), and xenobiotics (n = 3). For example, a metabolite identified as acetyl tributyl citrate, a plasticizer in food wrappings, was associated with an increased risk of breast cancer (OR = 1.21; 95% CI: 1.07-1.37). Risk prediction models for overall breast cancer and the various subtypes were found to have modest levels of prediction accuracy (area under the curve ranged from 0.60 to 0.63).

Conclusions: Additional studies are needed to confirm the identities of the metabolites and validate their associations with breast cancer risk. Metabolomics should be evaluated in conjunction with other 'omics' technologies for creation of clinically useful risk prediction models.