Kwon Min-Seok, Nam Seungyoon, Lee Sungyoung, Ahn Young Zoo, Chang Hae Ryung, Kim Yon Hui, Park Taesung
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon 21565, Korea.
Oncotarget. 2017 Jul 15;8(41):69808-69822. doi: 10.18632/oncotarget.19270. eCollection 2017 Sep 19.
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.
近期创建的与癌症相关的大型“大数据”公共数据库是理解肿瘤发生的有力手段。然而，临床上始终缺乏一种准确的单生物标志物/多生物标志物评估系统，并且有人断言，生物标志物在临床应用中常常失败，而这种失败在生物标志物评估的早期阶段就已出现。为应对这些挑战，我们开发了一种基于网络的、可临床测试的工具——癌症特异性单生物标志物/多生物标志物评估系统（CANES），用于评估来自94147个生物样本（涵盖18种肿瘤类型）的2134个全转录组数据集的生物标志物有效性。对于用户提供的单生物标志物/多生物标志物，CANES基于支持向量机、随机森林、神经网络和分类与回归树这四种分类方法，评估单生物标志物/多生物标志物候选物的性能。此外，与早期的分析工具相比，CANES具有多项优势，包括：1）生存分析；2）将成熟的微小RNA评估为用于用户定义的诊断或预后目的的标志物；3）基于每个单一标志物提供“泛癌”汇总视图。我们认为，这种针对诊断、治疗/预后决策的单生物标志物/多生物标志物“全景”评估，对于现有生物标志物（及其特定靶向疗法）的发现和“重新利用”将具有极高价值，从而改善患者的治疗分层，这是避免治疗耐药性的靶向治疗成功的关键组成部分。
