A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics

Abstract

Background: Timely diagnosis and classification of colorectal cancer (CRC) are hindered by unsatisfactory clinical assays. Our aim was to construct a blood-based biomarker series using a single assay, suitable for CRC detection, prognostication and staging.

Methods: Serum metabolomic profiles of adenoma (N=31), various stages of CRC (N=320) and healthy matched controls (N=254) were analysed by gas chromatography-mass spectrometry (GC-MS). A diagnostic model for CRC was derived by orthogonal partial least squares-discriminant analysis (OPLS-DA) on a training set, and then validated on an independent data set. Metabolomic models suitable for identifying adenoma, poor prognosis stage II CRC and discriminating various stages were generated.

Results: A diagnostic signature for CRC with remarkable multivariate performance (R(2)Y=0.46, Q(2)Y=0.39) was constructed, and then validated (sensitivity 85%; specificity 86%). Area under the receiver-operating characteristic curve was 0.91 (95% CI, 0.87-0.96). Adenomas were also detectable (R(2)Y=0.35, Q(2)Y=0.26, internal AUROC=0.81, 95% CI, 0.70-0.92). Also of particular interest, we identified models that stratified stage II by prognosis, and classified cases by stage.

Conclusions: Using a single assay system, a suite of CRC biomarkers based on circulating metabolites enables early detection, prognostication and preliminary staging information. External population-based studies are required to evaluate the repeatability of our findings and to assess the clinical benefits of these biomarkers.

Figure 1

The metabolomic profile of CRC patients as determined by GC-MS is distinct from disease-free controls. (A) Principal component analysis scores scatter plot of CRC and matched controls. (B) Supervised (O2PLS-DA) analysis scores scatter plot of CRC and matched controls. (C) Coefficient column plot for OPLS-DA of CRC vs matched control, illustrating changes in individual compounds. (D) Receiver-operating characteristic curve curve for validation of metabolomic classification of CRC and control, in an independent sample set (NM, not matched (unidentified)).

Figure 2

Gender-specific variations in metabolomic profile. The column plot consists of individual metabolites comprising the diagnostic model for CRC. Column length is related to the degree of statistical significance (expressed as negative log of P-value) based on univariate analysis.

Figure 3

Metabolomic profile of colorectal adenoma, as determined by GC-MS (A–C). (A) Principal component analysis comparison of the GC-MS spectra of colorectal adenomas and disease-free controls. (B) Supervised (OPLS-DA) analysis scores scatter plot of adenomas and controls, from GC-MS spectra. (C) Receiver-operating characteristic curve curve of the GC-MS-derived biomarker for adenoma, from internal cross-validation.

Figure 4

Metabolomic changes related to disease stage. (A) Scores scatter plot of supervised (OPLS-DA) analysis illustrating that the metabolomic profile of locoregional CRC is dependent on its T-staging status. (B) Box and whisker plot of OPLS-DA scores for each of four different T statuses. Points shown are out of the range of 2.5–97.5%. (C) Heatmap representing relative concentrations for each of the 45 compounds composing the OPLS-DA model for differentiation of T status. (D) Supervised OPLS-DA scores scatter plot representing the alterations in the metabolomic profile of lymph node-positive vs lymph node-negative CRC.

Figure 5

Evaluation of the capability of the metabolomic profile to separate stage II patients by prognosis. (A) Orthogonal partial least squares-discriminant analysis (OPLS-DA) scores scatter plot demonstrating differences in the metabolomic signature in good prognosis and bad prognosis stage II patients. (B) Analysis of stage II patients to determine whether their profile is more stage I-like or stage III-like, using the OPLS-DA predictive scores derived from the model distinguishing these two stages. Red triangles represent individuals who had a recurrence.