One of the most common requests as companies are narrowing down indications of interest is evaluating target and pathway expression across a broad range of disease and healthy tissue.
In an example from the oncology space, we pulled together data from primary tumors and large consortium datasets such as TCGA and GTEx. Working with Qiagen OmicSoft, we mined over 36,000 samples spanning common and rare cancers and 95 normal tissue types to identify indications with the greatest differential gene expression, as well as mined hundreds of metadata fields per indication to identify factors associated with target expression.
We built a dashboard on top of these data in order to enable repeatable profiling and analysis of any target of interest, along with easily exportable figures for publication.
Examples of metadata variables analyzed for association with a given target, across indications. Different numbers of metadata (clinical and demographic) variables are available for analysis for any given indication. In some indications, many variables are significantly associated with target expression.
These analyses and the resulting dashboard enabled the company to prioritize indications for in vitro and in vivo work (e.g., PDX model selection), as well as patient populations for a Phase 1 trial.