title: Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas.
abstract: Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations. Protein expression profiling on the basis of immunohistochemistry (IHC) staining images is a technique commonly used during such filtering procedures. To systematically investigate the protein expression in different cancer versus normal tissues and cell types, the Human Protein Atlas is a most comprehensive resource because it includes millions of high-resolution IHC images with expert-curated annotations. To facilitate the filtering of potential biomarker candidates from large-scale omics datasets, in this study we have proposed a scoring approach for quantifying IHC annotation of paired cancerous/normal tissues and cancerous/normal cell types. We have comprehensively calculated the scores of all the 17219 tested antibodies deposited in the Human Protein Atlas based on their accumulated IHC images and obtained 457110 scores covering 20 different types of cancers. Statistical tests demonstrate the ability of the proposed scoring approach to prioritize cancer-specific proteins. Top 100 potential marker candidates were prioritized for the 20 cancer types with statistical significance. In addition, a model study was carried out of 1482 membrane proteins identified from a quantitative comparison of paired cancerous and adjacent normal tissues from patients with colorectal cancer (CRC). The proposed scoring approach demonstrated successful prioritization and identified four CRC markers, including two of the most widely used, namely CEACAM5 and CEACAM6. These results demonstrate the potential of this scoring approach in terms of cancer marker discovery and development. All the calculated scores are available at http://bal.ym.edu.tw/hpa/.
authors.affiliation: Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.
authors.forename: Su-Chien
authors.initials: SC
authors.lastname: Chiang
authors.forename: Chia-Li
authors.initials: CL
authors.lastname: Han
authors.forename: Kun-Hsing
authors.initials: KH
authors.lastname: Yu
authors.forename: Yu-Ju
authors.initials: YJ
authors.lastname: Chen
authors.forename: Kun-Pin
authors.initials: KP
authors.lastname: Wu
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D007150:Immunohistochemistry; D009363:Neoplasm Proteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D064878:Web Browser
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins