Premium
Evaluation of a 4‐protein serum biomarker panel—biglycan, annexin‐ A 6, myeloperoxidase, and protein S 100‐ A 9 ( B‐AMP )—for the detection of esophageal adenocarcinoma
Author(s) -
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.
Publication year - 2014
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/cncr.28963
Subject(s) - biomarker , biomarker discovery , medicine , proteomics , biology , biochemistry , gene
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 risk‐assessment 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. Cancer 2014;120:3902–3913. © 2014 American Cancer Society .