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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Cancer 1/2018

A quantitative multimodal metabolomic assay for colorectal cancer

BMC Cancer > Ausgabe 1/2018
Farshad Farshidfar, Karen A. Kopciuk, Robert Hilsden, S. Elizabeth McGregor, Vera C. Mazurak, W. Donald Buie, Anthony MacLean, Hans J. Vogel, Oliver F. Bathe
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Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12885-017-3923-z) contains supplementary material, which is available to authorized users.



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.


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.


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.


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.
Additional file 1: Fig. S1. A. Principal Component Analysis (PCA) of CRC and control samples in the training set. B. PCA of CRC and control samples in the training set, colored by pre-sampling chemotherapy status. C. PCA of CRC samples from 4 stages, colored by their TNM stages. D. PCA of CRC samples from 4 stages, colored by pre-sampling chemotherapy status. E. PCA of CRC stage IVa patients, to study the potential confounding effect of chemotherapy on the described CRC metabolomic profile. (DOCX 103 kb)
Additional file 2: Fig. S2. Metabolomic profile of CRC, Stages I to IVa by OPLS-DA supervised analysis. A. Scores scatter plot of discriminant analysis. Model characteristics are indicated. The first component clearly distinguishes between CRC and control groups, while the second component identifies locoregional CRC from liver-metastatic CRC (stage IVa). (PPTX 8816 kb)
Additional file 3: Tables S1. S2 and S3. Lists of metabolites incorporated into each metabolomic signature for colorectal cancer and colorectal adenoma (PPTX 2288 kb)
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