Transforming Big Data into Cancer-Relevant Insight: An Initial, Multi-Tier Approach to Assess Reproducibility and Relevance

Abstract

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.

Figure 1

As Tiers successively increase, the strength of evidence associated with data under each Tier also increases. The data in the Portal are accompanied by a statement summarizing the purpose for which they were collected and detailed experimental protocols so that others can readily reproduce them. Here are a few examples of how molecular targets, prognostic biomarkers, and modulators of targets may progress through Tier rankings toward phase 0/I clinical trials.

Figure 2

The three vignettes that exemplify how Tier I observations were validated in vitro and in vivo as Tier II and Tier III respectively before the results were translated into the clinical setting are summarized.