Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study

Abstract

Background: Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening.

Methods: Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls.

Results: A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%.

Conclusions: We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial.

Impact: A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application.

Figure 1 (a, b, c, and d)

Normalized concentrations of metabolites for controls, CRC-CAD, and CRC-MSK study groups for: a) diacetylspermine; b) HPHPA; c) aspartic acid; and, d) butyric acid.

Figure 2 (a, b, and c)

Results showing: a) separation plot from sPLS-DA with component 1 and component 2; b) variables selected by the sPLS-DA model for a component 1; and c) variables selected by the sPLS-DA model for a component 2.

Figure 3 (Ia, Ib, IIa, IIb, IIIa, IIIb)

The ROC curve of: Ia) Model I on training set using 17 metabolites, Ib) Model I on testing set using 17 metabolites, IIa) Model II on training set using 4 metabolites, IIb) Model II on testing set using 4 metabolites, IIIa) Model III on training set using diacetylspermine and kynurenine, and IIIb) Model III on testing set using diacetylspermine and kynurenine.

Figure 4 (a,b)

The normalized concentration trend for a) diacetylspermine and b) kynurenine from controls, to Stage 0, to Stage I, to Stage II, to stage III and to Stage IV.