Mining human prostate cancer datasets: The "camcAPP" shiny app.

Journal article

Obtaining access to robust,well-annotated human genomic datasets is an important step in demonstrating the relevance of experimental findings and, often, in generating the hypotheses that led to those experiments being conducted in the first place. We recently published data from the CamCaP Study Group which comprised two cohorts of men with prostate cancer who had undergone prostatectomy in Cambridge, UK and Stockholm, Sweden (Ross-Adams et al., 2015).We considered how we might best share our output with those who wish to interrogate the data with their own ideas, gene lists and clinical questions. Werecognised that finding, down-loading, pre-processing and assimilating any such dataset into a usable format is daunting andmay put off many researchers.We also felt that interrogation tools generated to date (e.g. cBioPortal) lack functionality as they either cover too many organ types, or are limited in the extent, precision and tumour-site specificity of their clinical annotation. We therefore determined to produce an accessible web-based platform that would permit straightforward interrogation of these datasets with individual gene identifiers or gene sets. Furthermore, we decided to include additional ‘publicly-accessible’ human prostate cancer sets in order to increase the number of samples available and provide a degree of validation of any observations made across independent cohorts. We included a number of prominent publicly available sets with both gene expression and copy number data leading to a cohort of almost 500 men (Ross-Adams et al., 2015; Taylor et al., 2010; Grasso et al., 2012). We also included a small landmark series of expression data (Varambally et al., 2005). These studies are summarised in Table 1. We plan to include additional studies in the app as well-annotated datasets become publicly available.

Authors: Dunning, M; Vowler, S; Lalonde, E; Ross-Adams, H; Boutros, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: EBioMedicine
• Volume: 17
• Pages: 5-6
• Publication date: 2017-03-01
• Acceptance date: 2017-02-22
• DOI: 10.1016/j.ebiom.2017.02.022
• EISSN: 2352-3964
• Pmid: 28286059
• Language: English
• Keywords: Big data; Gene signature; Prognostication; Prostate cancer; Online interface