A Hypothesis-Directed Approach to the Targeted Development of a Multiplexed Proteomic Biomarker Assay for Cancer
Author(s) -
Emily M. Mackay,
Jennifer Koppel,
Pooja Das,
Joanna Woo,
David C. Schriemer,
Oliver F. Bathe
Publication year - 2015
Publication title -
cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 31
ISSN - 1176-9351
DOI - 10.4137/cin.s24388
Subject(s) - biomarker discovery , biomarker , proteomics , ranking (information retrieval) , computational biology , medicine , bioinformatics , candidate gene , computer science , biology , artificial intelligence , biochemistry , gene
In recent years, hundreds of candidate protein biomarkers have been identified using discovery-based proteomics. Despite the large number of candidate biomarkers, few proteins advance to clinical validation. We propose a hypothesis-driven approach to identify candidate biomarkers, previously characterized in the literature, with the highest probability of clinical applicability. A ranking method, called the "hypothesis-directed biomarker ranking" (HDBR) system, was developed to score candidate biomarkers based on seven criteria deemed important in the selection of clinically useful biomarkers. To demonstrate its application, we applied the HDBR system to identify candidate biomarkers for the development of a diagnostic test for the early detection of colorectal cancer. One-hundred and fifty-one candidate biomarkers were identified from the literature and ranked based on the specified criteria. The top-ranked candidates represent a group of biomarkers whose further study and validation would be justified in order to expedite the development of biomarkers that could be used in a clinical setting.
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