4.5 Article

Assessment of serum proteomics to detect large colon adenomas

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CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
卷 17, 期 8, 页码 2188-2193

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AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1055-9965.EPI-07-2767

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  1. National Cancer Institute [2-R01-CA044684]
  2. Epidemiology of Rectal Mucosal Proliferation
  3. National Institute of Diabetes and Digestive and Kidney Diseases [5P30DK034987]
  4. Center for Gastrointestinal Biology and Disease
  5. UNC Lineberger Comprehensive Cancer Center

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A noninvasive blood test that could reliably detect early colorectal cancer or large adenomas would provide an important advance in colon cancer screening. The purpose of this study was to determine whether a serum proteomics assay could discriminate between persons with and without a large (>= 1 cm) colon adenoma. To avoid problems of bias that have affected many studies about molecular markers for diagnosis, specimens were obtained from a previously conducted study of colorectal cancer etiology in which bloods had been collected before the presence or absence of neoplasm had been determined by colonoscopy, helping to assure that biases related to differences in sample collection and handling would be avoided. Mass spectra of 65 unblinded serum samples were acquired using a nanoelectrospray ionization source on a QSTAR-XL mass spectrometer. Classification patterns were developed using the ProteomeQuest (R) algorithm, performing measurements twice on each specimen, and then applied to a blinded validation set of 70 specimens. After removing 33 specimens that had discordant results, the test group comprised 37 specimens that had never been used in training. Although in the primary analysis, no discrimination was found, a single post hoc analysis, done after hemolyzed specimens had been removed, showed a sensitivity of 78%, a specificity of 53%, and an accuracy of 63% (95% confidence interval, 53-72%). The results of this study, although preliminary, suggest that further study of serum proteomics, in a larger number of appropriate specimens, could be useful. They also highlight the importance of understanding sources of noise and bias in studies of proteomics assays.

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