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BMC Cancer. 2018 Jan 4;18(1):26. doi: 10.1186/s12885-017-3923-z.

A quantitative multimodal metabolomic assay for colorectal cancer.

Author information

1
Department of Surgery, University of Calgary, Calgary, AB, Canada.
2
Department of Oncology, University of Calgary, Calgary, AB, Canada.
3
Department Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.
4
Population Health Research, Alberta Health Services, Calgary, AB, Canada.
5
Department of Medicine, University of Calgary, Calgary, AB, Canada.
6
Forzani & MacPhail Colon Cancer Screening Centre, Calgary, AB, Canada.
7
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada.
8
Department of Biological Sciences, University of Calgary, Calgary, AB, Canada.
9
Department of Surgery, University of Calgary, Calgary, AB, Canada. bathe@ucalgary.ca.
10
Department of Oncology, University of Calgary, Calgary, AB, Canada. bathe@ucalgary.ca.
11
Division of Surgical Oncology, Tom Baker Cancer Centre, 1331 - 29th St NW, Calgary, AB, T2N 4N2, Canada. bathe@ucalgary.ca.

Abstract

BACKGROUND:

Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation.

METHODS:

The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed.

RESULTS:

The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R2Y = 0.83, Q2Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93-1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R2Y = 0.30, Q2Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72-0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly.

CONCLUSION:

The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.

KEYWORDS:

Cancer biomarker; Colorectal adenocarcinoma; Colorectal adenoma; Colorectal cancer; Mass spectrometry; Metabolomics; Metabolomics profiling

PMID:
29301511
PMCID:
PMC5755335
DOI:
10.1186/s12885-017-3923-z
[Indexed for MEDLINE]
Free PMC Article

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