Quantification of Serum Metabolites in Early Colorectal Adenomas Using Isobaric Labeling Mass Spectrometry

Abstract

A major challenge in reducing the death rate of colorectal cancer is to screen patients using low-invasive testing. A blood test shows a high compliance rate with reduced invasiveness. In this work, a multiplex isobaric tag labeling strategy coupled with mass spectrometry is adopted to relatively quantify primary and secondary amine-containing metabolites in serum for the discovery of metabolite level changes of colorectal cancer. Serum samples from patients at different risk statuses and colorectal cancer growth statuses are studied. Metabolite identification is based on accurate mass matching and/or retention time of labeled metabolite standards. We quantify 40 metabolites across all the serum samples, including 18 metabolites validated with standards. We find significantly decreased levels of threonine and asparagine in the patients with growing adenomas or high-risk adenomas (p < 0.05). Glutamine levels decrease in patients with adenomas of unknown growth status or high-risk adenomas. In contrast, arginine levels are elevated in patients with low-risk adenoma. Receiver operating characteristic analysis shows high sensitivity and specificity of these metabolites for detecting growing adenomas. Based on these results, we conclude that a few metabolites identified here might contribute to distinguishing colorectal patients with growing adenomas from normal individuals and patients with unknown growth status of adenomas.

Keywords: colorectal cancer; isobaric tag; mass spectrometry; metabolomics; multiplex labeling; serum biomarker.

Figure 1.

A summary of patient cases enrolled into this study. For patients carrying polyps, the polyps were examined and classified by standard histopathologic criteria as low-risk adenomas or high-risk (advanced) adenomas. Blood draw was performed before and after polypectomy for patients carrying tumor in this study. When available, tumors were classified as growing and static independently based on the longitudinal size profiles. The level of a biomarker of interest was compared between prepolypectomy and postpolypectomy sera.

Figure 2.

Box plots of reporter ion ratios of DiLeu labeled metabolite standards at a theoretical 1: 1: 1: 1 ratio (A) and 1 : 2 : 4 : 8 ratios (B), and box plots of reporter ion ratios of DiLeu labeled serum metabolites at a theoretical 1: 1: 1: 1 ratio (C) and 1 : 2 : 4 : 8 ratios (D). Each box contains 21 data points (average of 3 replicates) from 21 metabolite standards for metabolite standards, and 40 data points for serum metabolites, obtained from LC-ESI-MS/MS analysis. Box denotes 25^th and 75^th percentiles; line within box denotes 50^th percentile; whiskers denote standard deviation.

Figure 3.

Workflow for relative quantification of serum metabolites using DiLeu-labeling strategy.

Figure 4.

Relative ratio-to-pooled serum mixture for six comparison groups in four metabolites that showed statistically significant tumor-associated level changes. Blood draw was performed pre-polypectomy. Mann Whitney U test was performed. Asterisks represent the significance level (*P ≤ 0.05, **P ≤ 0.01, and ***P ≤ 0.001) across the different adenoma groups with Benjaminin-Hochberg to control the false discovery rate q at <0.05.

Figure 5.

Correlation between tumor volume and differential marker level. Correlation heatmap for tumor volume, protein biomarkers and metabolite markers (A). Correlation between ITIH4 and F5 (B). Correlation between CRP and VTN (C). Correlation between LRG1 and asparagine (D). A Spearman test was carried out for correlation between the monotonic rank orders of tumor volume, protein biomarker level and metabolite biomarker. The Spearman ρ values and their P values indicated the strength of the correlation between different biomarkers. The corresponding levels of each of the biomarkers were determined, relative to the pooled serum samples from normal control samples of each metabolite.

Figure 6.

ROC analysis showing sensitivity and specificity of panels of potential metabolite biomarkers for detecting growing adenomas compared with normal controls (A) or compared with adenomas of unknown growth status (B).