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Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects
BACKGROUND: The aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format—electrochemiluminescence immunoassays—to quantify...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526294/ https://www.ncbi.nlm.nih.gov/pubmed/28769740 http://dx.doi.org/10.1186/s12014-017-9163-z |
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author | Croner, Lisa J. Dillon, Roslyn Kao, Athit Kairs, Stefanie N. Benz, Ryan Christensen, Ib J. Nielsen, Hans J. Blume, John E. Wilcox, Bruce |
author_facet | Croner, Lisa J. Dillon, Roslyn Kao, Athit Kairs, Stefanie N. Benz, Ryan Christensen, Ib J. Nielsen, Hans J. Blume, John E. Wilcox, Bruce |
author_sort | Croner, Lisa J. |
collection | PubMed |
description | BACKGROUND: The aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format—electrochemiluminescence immunoassays—to quantify protein concentrations. The aim was achieved by building and validating a CRC classifier using concentration measures from a large sample set representing a true intent-to-test (ITT) symptomatic population. METHODS: 4435 patient samples were drawn from the Endoscopy II sample set. Samples were collected at seven hospitals across Denmark between 2010 and 2012 from subjects with symptoms of colorectal neoplasia. Colonoscopies revealed the presence or absence of CRC. 27 blood plasma proteins were selected as candidate biomarkers based on previous studies. Multiplexed electrochemiluminescence assays were used to measure the concentrations of these 27 proteins in all 4435 samples. 3066 patients were randomly assigned to the Discovery set, in which machine learning was used to build candidate classifiers. Some classifiers were refined by allowing up to a 25% indeterminate score range. The classifier with the best Discovery set performance was successfully validated in the separate Validation set, consisting of 1336 samples. RESULTS: The final classifier was a logistic regression using ten predictors: eight proteins (A1AG, CEA, CO9, DPPIV, MIF, PKM2, SAA, TFRC), age, and gender. In validation, the indeterminate rate of the new panel was 23.2%, sensitivity/specificity was 0.80/0.83, PPV was 36.5%, and NPV was 97.1%. CONCLUSIONS: The validated classifier serves as the basis of a new blood-based CRC test for symptomatic patients. The improved performance, resulting from robust concentration measures across a large sample set mirroring the ITT population, renders the new test the best available for this population. Results from a test using this classifier can help assess symptomatic patients’ CRC risk, increase their colonoscopy compliance, and manage next steps in their care. |
format | Online Article Text |
id | pubmed-5526294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55262942017-08-02 Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects Croner, Lisa J. Dillon, Roslyn Kao, Athit Kairs, Stefanie N. Benz, Ryan Christensen, Ib J. Nielsen, Hans J. Blume, John E. Wilcox, Bruce Clin Proteomics Research BACKGROUND: The aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format—electrochemiluminescence immunoassays—to quantify protein concentrations. The aim was achieved by building and validating a CRC classifier using concentration measures from a large sample set representing a true intent-to-test (ITT) symptomatic population. METHODS: 4435 patient samples were drawn from the Endoscopy II sample set. Samples were collected at seven hospitals across Denmark between 2010 and 2012 from subjects with symptoms of colorectal neoplasia. Colonoscopies revealed the presence or absence of CRC. 27 blood plasma proteins were selected as candidate biomarkers based on previous studies. Multiplexed electrochemiluminescence assays were used to measure the concentrations of these 27 proteins in all 4435 samples. 3066 patients were randomly assigned to the Discovery set, in which machine learning was used to build candidate classifiers. Some classifiers were refined by allowing up to a 25% indeterminate score range. The classifier with the best Discovery set performance was successfully validated in the separate Validation set, consisting of 1336 samples. RESULTS: The final classifier was a logistic regression using ten predictors: eight proteins (A1AG, CEA, CO9, DPPIV, MIF, PKM2, SAA, TFRC), age, and gender. In validation, the indeterminate rate of the new panel was 23.2%, sensitivity/specificity was 0.80/0.83, PPV was 36.5%, and NPV was 97.1%. CONCLUSIONS: The validated classifier serves as the basis of a new blood-based CRC test for symptomatic patients. The improved performance, resulting from robust concentration measures across a large sample set mirroring the ITT population, renders the new test the best available for this population. Results from a test using this classifier can help assess symptomatic patients’ CRC risk, increase their colonoscopy compliance, and manage next steps in their care. BioMed Central 2017-07-25 /pmc/articles/PMC5526294/ /pubmed/28769740 http://dx.doi.org/10.1186/s12014-017-9163-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Croner, Lisa J. Dillon, Roslyn Kao, Athit Kairs, Stefanie N. Benz, Ryan Christensen, Ib J. Nielsen, Hans J. Blume, John E. Wilcox, Bruce Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title | Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title_full | Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title_fullStr | Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title_full_unstemmed | Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title_short | Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
title_sort | discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526294/ https://www.ncbi.nlm.nih.gov/pubmed/28769740 http://dx.doi.org/10.1186/s12014-017-9163-z |
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