The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to in...

Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various c...

BACKGROUD The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to catalogue genetic mutations responsible for cancer using genome analysis techniques. One of the aims of this project is to create a comprehensive and open repository of cancer related molecular analysis, to be exploited by bioinformaticians towards advancing cancer knowledge. However, devising bioinfo...

BACKGROUND & OBJECTIVE Managing data from large-scale projects (such as The Cancer Genome Atlas (TCGA)) for further analysis is an important and time consuming step for research projects. Several efforts, such as the Firehose project, make TCGA pre-processed data publicly available via web services and data portals, but this information must be managed, downloaded and prepared for subsequent st...

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies down...

The Cancer Genome Atlas (TCGA) promises to transform the treatment of patients with cancer by identifying new drug targets. However, extracting mechanistically insightful, therapeutically actionable information from complex multidimensional datasets remains a significant challenge. In this issue of Cancer Discovery, Genovese and colleagues apply a context-dependent modeling algorithm to the gli...

BACKGROUND Clear-cell renal-cell carcinomas display divergent clinical behaviours. However, the molecular genetic events driving these behaviours are unknown. We discovered that BAP1 is mutated in about 15% of clear-cell renal-cell carcinoma, and that BAP1 and PBRM1 mutations are largely mutually exclusive. The aim of this study was to investigate the clinicopathological significance of these m...

The Cancer Genome Atlas (TCGA) has compiled genomic, epigenomic, and proteomic data from more than 10,000 samples derived from 33 types of cancer, aiming to improve our understanding of the molecular basis of cancer development. Availability of these genome-wide information provides an unprecedented opportunity for uncovering new key regulators of signaling pathways or new roles of pre-existing...