A quantitative multimodal metabolomic assay for colorectal cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-01-04

AUTHORS

Farshad Farshidfar, Karen A. Kopciuk, Robert Hilsden, S. Elizabeth McGregor, Vera C. Mazurak, W. Donald Buie, Anthony MacLean, Hans J. Vogel, Oliver F. Bathe

ABSTRACT

BackgroundEarly 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.MethodsThe 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.ResultsThe 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.ConclusionThe 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. More... »

PAGES

26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12885-017-3923-z

DOI

http://dx.doi.org/10.1186/s12885-017-3923-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100194585

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29301511


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31 schema:description BackgroundEarly 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.MethodsThe 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.ResultsThe 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.ConclusionThe 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.
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38 BackgroundEarly diagnosis
39 CRC
40 MethodsThe
41 acid
42 acylcarnitines
43 addition
44 adenomas
45 advantages
46 age
47 amino acids
48 analysis
49 approach
50 assays
51 biomarker performance
52 biomarkers
53 cancer
54 chemical classes
55 chromatography-mass spectrometry
56 class
57 classifier
58 coefficient
59 coefficient of variance
60 colorectal adenomas
61 colorectal cancer
62 colorectal neoplasia
63 control
64 diagnosis
65 diagnostic biomarkers
66 diagnostic power
67 diagnostic signature
68 disease-free controls
69 diverse chemical classes
70 efforts
71 features
72 future efforts
73 gas chromatography-mass spectrometry
74 group
75 healthy controls
76 independent set
77 independent validation
78 kit
79 least squares classifier
80 lipidome
81 metabolites
82 metabolomic biomarkers
83 metabolomic features
84 metabolomic profiles
85 metabolomic tests
86 metabolomics assays
87 multimodal approach
88 neoplasia
89 objective
90 optimal diagnostic biomarker
91 outcomes
92 part
93 partial least squares classifier
94 patients
95 performance
96 phosphatidylcholine
97 power
98 profile
99 quantification
100 quantification of metabolites
101 quantitative assay
102 representation
103 sensitivity
104 separation
105 set
106 signatures
107 simplifies treatment
108 specificity
109 spectrometry
110 sphingomyelin
111 test
112 training set
113 treatment
114 treatment outcomes
115 validation
116 variance
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