Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Standard

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.

I: Clinical Proteomics, Bind 14, 28, 2017.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Croner, LJ, Dillon, R, Kao, A, Kairs, SN, Benz, R, Christensen, IJ, Nielsen, HJ, Blume, JE & Wilcox, B 2017, 'Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects', Clinical Proteomics, bind 14, 28. https://doi.org/10.1186/s12014-017-9163-z

APA

Croner, L. J., Dillon, R., Kao, A., Kairs, S. N., Benz, R., Christensen, I. J., Nielsen, H. J., Blume, J. E., & Wilcox, B. (2017). Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects. Clinical Proteomics, 14, [28]. https://doi.org/10.1186/s12014-017-9163-z

Vancouver

Croner LJ, Dillon R, Kao A, Kairs SN, Benz R, Christensen IJ o.a. Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects. Clinical Proteomics. 2017;14. 28. https://doi.org/10.1186/s12014-017-9163-z

Author

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. I: Clinical Proteomics. 2017 ; Bind 14.

Bibtex

@article{a08ad21a4dfa4047b97f7007d7c0e64d,
title = "Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects",
abstract = "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.",
author = "Croner, {Lisa J} and Roslyn Dillon and Athit Kao and Kairs, {Stefanie N} and Ryan Benz and Christensen, {Ib J} and Nielsen, {Hans J} and Blume, {John E} and Bruce Wilcox",
year = "2017",
doi = "10.1186/s12014-017-9163-z",
language = "English",
volume = "14",
journal = "Clinical Proteomics",
issn = "1542-6416",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects

AU - Croner, Lisa J

AU - Dillon, Roslyn

AU - Kao, Athit

AU - Kairs, Stefanie N

AU - Benz, Ryan

AU - Christensen, Ib J

AU - Nielsen, Hans J

AU - Blume, John E

AU - Wilcox, Bruce

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1186/s12014-017-9163-z

DO - 10.1186/s12014-017-9163-z

M3 - Journal article

C2 - 28769740

VL - 14

JO - Clinical Proteomics

JF - Clinical Proteomics

SN - 1542-6416

M1 - 28

ER -

ID: 195153333