Mol Cancer Res. 2016 Aug;14(8):675-82. doi: 10.1158/1541-7786.MCR-16-0090. Epub 2016 Jul 11.
The Cancer Target Discovery and Development (CTD(2)) Network was established to accelerate the transformation of "Big Data" into novel pharmacologic targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This article represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. Mol Cancer Res; 14(8); 675-82. ©2016 AACR.
癌症靶点发现与开发（CTD(2)）网络的建立是为了加速将“大数据”转化为新型药理学靶点、先导化合物和生物标志物，以便迅速转化为改善患者预后的成果。在这个合作网络中很快就清楚地认识到，一个关键的核心问题是确定什么构成了足够的计算或实验证据来支持生物学或临床相关的发现。本文首次尝试描述在合作工作环境中支持和确认从大规模数据资源的系统分析中产生的发现所面临的挑战，并提供一个框架，以开启一场社区讨论来解决这些挑战。该网络实施了一个多层框架，旨在证实其合作活动所产生的数据和见解的生物学和生物医学相关性以及可重复性。广大科学界可以采用同样的方法来推动癌症新型治疗和生物标志物策略的发展。《分子癌症研究》；14(8)；675 - 82。©2016美国癌症研究协会。
