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Research Article

Metabolic Profiling, a Noninvasive Approach for the Detection of Experimental Colorectal Neoplasia

David C. Montrose, Xi Kathy Zhou, Levy Kopelovich, Rhonda K. Yantiss, Edward D. Karoly, Kotha Subbaramaiah and Andrew J. Dannenberg
David C. Montrose
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Xi Kathy Zhou
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Levy Kopelovich
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Rhonda K. Yantiss
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Edward D. Karoly
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Kotha Subbaramaiah
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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Andrew J. Dannenberg
Departments of 1Medicine, 2Public Health, 3Pathology and Laboratory Medicine, Weill Cornell Medical College, New York; 4Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; and 5Metabolon, Inc., Durham, North Carolina
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DOI: 10.1158/1940-6207.CAPR-12-0160 Published December 2012
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    Figure 1.

    Experimental design. Male A/J mice were given 6 weekly intraperitoneal injections of AOM (10 mg/kg; n = 40) or 0.9% saline (n = 35) beginning at 5 weeks of age. Feces were collected 3, 5, and 7 weeks after the last injection; plasma was collected 5 and 7 weeks after the last injection; tumor tissue from AOM-injected mice and colorectal mucosa from saline-injected mice were harvested 7 weeks after the last injection. Separate groups of mice (n = 5 mice/time point) were used to determine tumor burden and histology 3, 5, and 7 weeks following the last AOM injection.

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    Figure 2.

    Tumor burden increases over time following AOM administration. A/J mice were administered 6 weekly injections of AOM and sacrificed 3, 5, and 7 weeks after the last injection (n = 5/time point). Colorectal tissue was formalin fixed and tumor number (A) and volume (B) determined by examination of whole mounts following methylene blue staining. C, H&E staining was used to determine the number of invasive lesions. P values were determined by the nonparametric Kruskal–Wallis test.

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    Figure 3.

    Metabolite levels are altered in feces from colorectal tumor-bearing mice. A/J mice were given 6 weekly injections of either AOM or saline, and feces were collected 3, 5, and 7 weeks following the last injection. Metabolite levels were measured in feces from the saline and AOM-injected mice (n = 8/group). A, a heat map of significantly altered metabolites was generated by comparing feces from AOM versus saline injected mice for each time point. Data are rank transformed and displayed as color intensity with low levels indicated by green color and high levels indicated by red color. B, effect size of differences in metabolite levels between mice in AOM and saline-injected groups at different time points following treatment showing time-dependent changes. The effect size is defined as Embedded Image, where Embedded Image and Embedded Image are the average metabolite levels in the AOM and the saline-injected groups, respectively and s is the pooled standard deviation of metabolite levels measured in the 2 groups. Metabolite names are ordered with respect to the effect size of difference in metabolite levels at 7 weeks. Metabolites in different pathways are color coded in A and B: red font, amino acid; yellow font, carbohydrate; purple font, cofactors and vitamins; orange font, energy; black font, lipids; dark blue font, nucleotides; green font, peptides; and light blue font, xenobiotics.

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    Figure 4.

    Metabolite levels are altered in plasma from colorectal tumor-bearing mice. A/J mice were given 6 weekly injections of either AOM or saline and plasma was collected 5 and 7 weeks following the last injection. Metabolite levels were measured in plasma from the saline and AOM-injected mice (n = 8/group) at each of the 2 time points. A, a heat map of significantly altered metabolites was generated by comparing plasma from AOM versus saline injected mice for each time point. Data are rank transformed and displayed as color intensity with low levels indicated by green color and high levels indicated by red color. B, effect size of differences in metabolite levels between mice in AOM and saline-injected groups at different time points following treatment showing time-dependent change. Metabolite names are ordered with respect to the effect size of difference in metabolite levels at 7 weeks. Metabolites in different pathways are color coded in A and B: red font, amino acid; yellow font, carbohydrate; purple font, cofactors and vitamins; orange font, energy; black font, lipids; dark blue font, nucleotides; green font, peptides; light blue font, xenobiotics.

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    Figure 5.

    Metabolite levels are altered in colorectal tumor tissue. A/J mice were given 6 weekly injections of either AOM or saline. Seven weeks following the last injection, colorectal mucosa was collected from saline-injected mice and tumor tissue was harvested from AOM-injected mice. Metabolite levels were measured in both tissue types (n = 6/group) and significantly altered metabolites were determined by comparing tumor tissue vs. normal mucosa. A, lipids. B, amino acids and peptides. C, additional biochemicals. Data are rank transformed and displayed as color intensity with low levels indicated by green color and high levels indicated by red color. Metabolites in different pathways are color coded: red font, amino acid; yellow font, carbohydrate; purple font, cofactors and vitamins; orange font, energy; black font, lipids; dark blue font, nucleotides; green font, peptides; light blue font, xenobiotics. D, significantly altered metabolites were subjected to IPA and significantly changed metabolites in the “Glycine, Serine, and Threonine Metabolism” canonical pathway are shown. Nodes in red indicate increased levels and nodes in green indicate decreased levels in tumor tissue compared with normal mucosa.

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    Figure 6.

    Several metabolite changes are shared across matrices in tumor-bearing versus control mice. The number of significantly altered metabolites was determined in tumor-bearing versus control mice in feces, plasma, and colorectal tissue 7 weeks after the last AOM or saline injection. The number of metabolites whose levels increased (A) or decreased (B) in individual matrices and those that commonly changed across matrices is shown.

Additional Files

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    Files in this Data Supplement:

    • Supplementary Figure 1 - PDF file - 48K, Representative EI fragmentation mass spectra for key metabolites. GC/MS EI fragmentation patterns for sarcosine (A) and 2-hydroxyglutarate (B).
    • Supplementary Figure 2 - muPDF file - 102K, Supplementary Figure 2. Canonical pathways affected in tumor tissue. Metabolites that were altered in tumor tissue vs. normal mucosa were analyzed by IPA and significantly affected canonical pathways are shown.
    • Supplementary Figure Legends 1-2 - PDF file - 59K
    • Supplementary Table 1 - PDF file - 39K, Gene expression levels were determined in tumor tissue and normal colorectal mucosa by quantitative real-time PCR.
    • Supplementary Table 2 - PDF file - 68K, The identity and effect size of changes (AOM vs. saline-treated mice) based on log transformed data are shown for biochemicals whose changes are significant in feces and colorectum, plasma and colorectum and all 3 matrices, 7 weeks after the last injection.
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Cancer Prevention Research: 5 (12)
December 2012
Volume 5, Issue 12
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Metabolic Profiling, a Noninvasive Approach for the Detection of Experimental Colorectal Neoplasia
David C. Montrose, Xi Kathy Zhou, Levy Kopelovich, Rhonda K. Yantiss, Edward D. Karoly, Kotha Subbaramaiah and Andrew J. Dannenberg
Cancer Prev Res December 1 2012 (5) (12) 1358-1367; DOI: 10.1158/1940-6207.CAPR-12-0160

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Metabolic Profiling, a Noninvasive Approach for the Detection of Experimental Colorectal Neoplasia
David C. Montrose, Xi Kathy Zhou, Levy Kopelovich, Rhonda K. Yantiss, Edward D. Karoly, Kotha Subbaramaiah and Andrew J. Dannenberg
Cancer Prev Res December 1 2012 (5) (12) 1358-1367; DOI: 10.1158/1940-6207.CAPR-12-0160
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