Forecasting individual breast cancer risk using plasma metabolomics and biocontours.
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ABSTRACT:
SUBMITTER: Bro R
PROVIDER: S-EPMC4559100 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Forecasting individual breast cancer risk using plasma metabolomics and biocontours.
Bro Rasmus R Kamstrup-Nielsen Maja H MH Engelsen Søren Balling SB Savorani Francesco F Rasmussen Morten A MA Hansen Louise L Olsen Anja A Tjønneland Anne A Dragsted Lars Ove LO
Metabolomics : Official journal of the Metabolomic Society 20150310 5
Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a <i>biocontour</i>, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk fac ...[more]
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Project description:BackgroundBreast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors.MethodsWe conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features.ResultsWe found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%.ConclusionsIf replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.
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