Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative

Abstract

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Keywords: breast cancer; colorectal cancer; dietary biomarkers; metabolite predictors; metabolomics.

Figure 1

Cross-validated area under the receiver operating characteristic curve (CV-AUC) averaged over 100 Monte Carlo replications of each variable selection + regression procedure for predicting breast cancer and colorectal cancer, with 95% confidence intervals (CIs). (panel A) an analysis using only the covariates. (panel B) analyses using covariates only (circles), LC-MS metabolites + base set of covariates (triangles), and LC-MS metabolites + all covariates (squares). Point estimates of CV-AUC are provided at the bottom of each panel (on the right-hand panel, the point estimates correspond to the LC-MS metabolites + all covariates analysis).