Accessing TCGA cancer data has been approached in a variety of ways. This week’s tip of the week highlights a web-based portal for improved access to the data in different ways. The Stanford Cancer Genome Atlas Clinical Explorer is aimed at helping identify clinically relevant genes in the cancer data sets.
They note that the data is available in other places and tools, from tools we’ve talked about before such as cBioPortal, UCSC Cancer Genomics Browser, and interacting with the StratomeX features. But this portal helps peoplt to quickly focus on clinical parameters in ways that aren’t as straightforward in the other tools.
You can learn more about the project on their site from their Overview at the site, and you can see their publication about it (below). The paper also covers some issues they had with the downloaded data that might be worth noting. And they also supplemented their analysis and information with COSMIC and TARGET (tumor alterations relevant for genomics-driven therapy) data as well.
The interface offers several quick ways to dive into the data.
There are 3 main query types: genes associated with certain clinical parameters; query directly by gene/protein/miR; and a two-hit hypothesis test. The first query is the image I’ve shown here. When you get to the the results, you can explore them in more detail with sortable tabular outputs, and on gene pages tabs for copy number changes, mutations, and RNA-seq values.
They give you some “example queries” that you can use as a way to get started and see what’s available underneath. And although we usually like to highlight a video, the tutorial that they provide is a slide embed.
So have a look at this interface if you’d like to explore TCGA data with a handy and quick query strategy. It might offer some hunting license on genes you are interested in, or some ideas for other investigations in tumor types you study.
Lee, H., Palm, J., Grimes, S., & Ji, H. (2015). The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical–genomic driver associations Genome Medicine, 7 (1) DOI: 10.1186/s13073-015-0226-3