CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery
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Min-Seok Kwon1, Seungyoon Nam2,3,4, Sungyoung Lee1, Young Zoo Ahn5, Hae Ryung Chang6, Yon Hui Kim7 and Taesung Park1,8
1Interdisciplinary program in Bioinformatics, Seoul National University, Seoul, 08826, Korea
2Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon, 21565, Korea
3Department of Life Sciences, Gachon Universiy, Sungnam, 13120, Korea
4College of Medicine, Gachon University, Incheon, 21565, Korea
5System Cancer Science, Graduate School of Cancer Science and Policy, National Cancer Center of Korea, Ilsan, Goyang-si Gyeonggi-do, 10408, Korea
6Research Institute of Women’s Health, Sookmyung Women’s University, Seoul, 04310, Korea
7Corestem Inc. 24, Pangyo-ro255beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13486, Korea
8Department of Statistics, Seoul National University, Seoul, 08826, Korea
Taesung Park, email: email@example.com
Yon Hui Kim, email: firstname.lastname@example.org
Keywords: biomarker performance, claudin, gastric cancer, transcriptome, data mining
Received: December 10, 2016 Accepted: May 22, 2017 Published: July 15, 2017
The recent creation of enormous, cancer-related “Big Data” public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types). For user-provided single/multi-biomarkers, CANES evaluates the performance of single/multi-biomarker candidates, based on four classification methods, support vector machine, random forest, neural networks, and classification and regression trees. In addition, CANES offers several advantages over earlier analysis tools, including: 1) survival analysis; 2) evaluation of mature miRNAs as markers for user-defined diagnostic or prognostic purposes; and 3) provision of a “pan-cancer” summary view, based on each single marker. We believe that such “landscape” evaluation of single/multi-biomarkers, for diagnostic therapeutic/prognostic decision-making, will be highly valuable for the discovery and “repurposing” of existing biomarkers (and their specific targeted therapies), leading to improved patient therapeutic stratification, a key component of targeted therapy success for the avoidance of therapy resistance.
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