Oncotarget

Research Papers:

CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery

Min-Seok Kwon, Seungyoon Nam, Sungyoung Lee, Young Zoo Ahn, Hae Ryung Chang, Yon Hui Kim and Taesung Park _

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Oncotarget. 2017; 8:69808-69822. https://doi.org/10.18632/oncotarget.19270

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Abstract

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

Correspondence to:

Taesung Park, email: tspark@stats.snu.ac.kr

Yon Hui Kim, email: yhkim@corestem.com

Keywords: biomarker performance, claudin, gastric cancer, transcriptome, data mining

Received: December 10, 2016    Accepted: May 22, 2017    Published: July 15, 2017

ABSTRACT

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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