Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma

Ali H. Zaidi; Vanathi Gopalakrishnan; Pashtoon M. Kasi; Xuemei Zeng; Usha Malhotra; Jeya Balasubramanian; Shyam Visweswaran; Mai Sun; Melanie S. Flint; Jon M. Davison; Brian L. Hood; Thomas P. Conrads; Jacques J. Bergman; William L. Bigbee; Blair A. Jobe

doi:10.1002/cncr.28963

Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma

Ali H. Zaidi, Vanathi Gopalakrishnan, Pashtoon M. Kasi, Xuemei Zeng, Usha Malhotra, Jeya Balasubramanian, Shyam Visweswaran, Mai Sun, Melanie S. Flint, Jon M. Davison, Brian L. Hood, Thomas P. Conrads, Jacques J. Bergman, William L. Bigbee, Blair A. Jobe

Hematology / Oncology / Cancer Center / Breast Clinic

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.

METHODS: A vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric riskassessment model to predict the presence of disease.

RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%.

CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive detection of EAC.

Original language	English (US)
Pages (from-to)	3902-3913
Number of pages	12
Journal	Cancer
Volume	120
Issue number	24
DOIs	https://doi.org/10.1002/cncr.28963
State	Published - Dec 1 2014

Keywords

Biomarker
Early detection
Esophageal adenocarcinoma
Monitoring
Protein
Serum

ASJC Scopus subject areas

Oncology
Cancer Research

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10.1002/cncr.28963

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Zaidi, A. H., Gopalakrishnan, V., Kasi, P. M., Zeng, X., Malhotra, U., Balasubramanian, J., Visweswaran, S., Sun, M., Flint, M. S., Davison, J. M., Hood, B. L., Conrads, T. P., Bergman, J. J., Bigbee, W. L., & Jobe, B. A. (2014). Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma. Cancer, 120(24), 3902-3913. https://doi.org/10.1002/cncr.28963

Zaidi, AH, Gopalakrishnan, V, Kasi, PM, Zeng, X, Malhotra, U, Balasubramanian, J, Visweswaran, S, Sun, M, Flint, MS, Davison, JM, Hood, BL, Conrads, TP, Bergman, JJ, Bigbee, WL & Jobe, BA 2014, 'Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma', Cancer, vol. 120, no. 24, pp. 3902-3913. https://doi.org/10.1002/cncr.28963

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title = "Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma",

abstract = "BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.METHODS: A vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric riskassessment model to predict the presence of disease.RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%.CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive detection of EAC.",

keywords = "Biomarker, Early detection, Esophageal adenocarcinoma, Monitoring, Protein, Serum",

author = "Zaidi, {Ali H.} and Vanathi Gopalakrishnan and Kasi, {Pashtoon M.} and Xuemei Zeng and Usha Malhotra and Jeya Balasubramanian and Shyam Visweswaran and Mai Sun and Flint, {Melanie S.} and Davison, {Jon M.} and Hood, {Brian L.} and Conrads, {Thomas P.} and Bergman, {Jacques J.} and Bigbee, {William L.} and Jobe, {Blair A.}",

note = "Publisher Copyright: {\textcopyright} 2014 American Cancer Society.",

year = "2014",

month = dec,

day = "1",

doi = "10.1002/cncr.28963",

language = "English (US)",

volume = "120",

pages = "3902--3913",

journal = "Cancer",

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T1 - Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma

AU - Zaidi, Ali H.

AU - Gopalakrishnan, Vanathi

AU - Kasi, Pashtoon M.

AU - Zeng, Xuemei

AU - Malhotra, Usha

AU - Balasubramanian, Jeya

AU - Visweswaran, Shyam

AU - Sun, Mai

AU - Flint, Melanie S.

AU - Davison, Jon M.

AU - Hood, Brian L.

AU - Conrads, Thomas P.

AU - Bergman, Jacques J.

AU - Bigbee, William L.

AU - Jobe, Blair A.

PY - 2014/12/1

Y1 - 2014/12/1

N2 - BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.METHODS: A vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric riskassessment model to predict the presence of disease.RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%.CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive detection of EAC.

AB - BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC.METHODS: A vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric riskassessment model to predict the presence of disease.RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%.CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive detection of EAC.

KW - Biomarker

KW - Early detection

KW - Esophageal adenocarcinoma

KW - Monitoring

KW - Protein

KW - Serum

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Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma

Abstract

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