Evaluation of a 4‐protein serum biomarker panel—biglycan, annexin‐A6, myeloperoxidase, and protein S100‐A9 (B‐AMP)—for the detection of esophageal adenocarcinoma. Issue 24 (5th August 2014)
- Record Type:
- Journal Article
- 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. Issue 24 (5th August 2014)
- Main 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
- Authors:
- Zaidi, Ali H.
Gopalakrishnan, Vanathi
Kasi, Pashtoon M.
Zeng, Xuemei
Malhotra, Usha
Balasubramanian, Jeya
Visweswaran, Shyam
Sun, Mai
Flint, Melanie S.
Davison, Jon M.
Hood, Brian L.
Conrads, Thomas P.
Bergman, Jacques J.
Bigbee, William L.
Jobe, Blair A. - Abstract:
- <abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="cncr28963-sec-0001" sec-type="section"> <title>BACKGROUND</title> <p>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.</p> </sec> <sec id="cncr28963-sec-0002" sec-type="section"> <title>METHODS</title> <p>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 risk‐assessment model to predict the presence of disease.</p> </sec> <sec id="cncr28963-sec-0003" sec-type="section"> <title>RESULTS</title> <p>With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="cncr28963-sec-0001" sec-type="section"> <title>BACKGROUND</title> <p>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.</p> </sec> <sec id="cncr28963-sec-0002" sec-type="section"> <title>METHODS</title> <p>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 risk‐assessment model to predict the presence of disease.</p> </sec> <sec id="cncr28963-sec-0003" sec-type="section"> <title>RESULTS</title> <p>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 (<italic>P</italic>&lt;.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%.</p> </sec> <sec id="cncr28963-sec-0004" sec-type="section"> <title>CONCLUSIONS</title> <p>Serum biomarkers hold significant promise for the early, noninvasive detection of EAC. <bold><italic>Cancer</italic> 2014;120:3902–3913.</bold> © <italic>2014 American Cancer Society</italic>.</p> </sec> </abstract> … (more)
- Is Part Of:
- Cancer. Volume 120:Issue 24(2014)
- Journal:
- Cancer
- Issue:
- Volume 120:Issue 24(2014)
- Issue Display:
- Volume 120, Issue 24 (2014)
- Year:
- 2014
- Volume:
- 120
- Issue:
- 24
- Issue Sort Value:
- 2014-0120-0024-0000
- Page Start:
- 3902
- Page End:
- 3913
- Publication Date:
- 2014-08-05
- Subjects:
- Cancer -- Periodicals
Cancer -- Cytopathology -- Periodicals
616.99405 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0142 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/cncr.28963 ↗
- Languages:
- English
- ISSNs:
- 0008-543X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3046.450000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3104.xml