The Cancer Genome Atlas Clinical Explorer: a web and mobile interface for identifying clinical–genomic driver associations

Abstract

BackgroundThe Cancer Genome Atlas (TCGA) project has generated genomic data sets covering over 20 malignancies. These data provide valuable insights into the underlying genetic and genomic basis of cancer. However, exploring the relationship among TCGA genomic results and clinical phenotype remains a challenge, particularly for individuals lacking formal bioinformatics training. Overcoming this hurdle is an important step toward the wider clinical translation of cancer genomic/proteomic data and implementation of precision cancer medicine. Several websites such as the cBio portal or University of California Santa Cruz genome browser make TCGA data accessible but lack interactive features for querying clinically relevant phenotypic associations with cancer drivers. To enable exploration of the clinical–genomic driver associations from TCGA data, we developed the Cancer Genome Atlas Clinical Explorer.DescriptionThe Cancer Genome Atlas Clinical Explorer interface provides a straightforward platform to query TCGA data using one of the following methods: (1) searching for clinically relevant genes, micro RNAs, and proteins by name, cancer types, or clinical parameters; (2) searching for genomic/proteomic profile changes by clinical parameters in a cancer type; or (3) testing two-hit hypotheses. SQL queries run in the background and results are displayed on our portal in an easy-to-navigate interface according to user’s input. To derive these associations, we relied on elastic-net estimates of optimal multiple linear regularized regression and clinical parameters in the space of multiple genomic/proteomic features provided by TCGA data. Moreover, we identified and ranked gene/micro RNA/protein predictors of each clinical parameter for each cancer. The robustness of the results was estimated by bootstrapping. Overall, we identify associations of potential clinical relevance among genes/micro RNAs/proteins using our statistical analysis from 25 cancer types and 18 clinical parameters that include clinical stage or smoking history.ConclusionThe Cancer Genome Atlas Clinical Explorer enables the cancer research community and others to explore clinically relevant associations inferred from TCGA data. With its accessible web and mobile interface, users can examine queries and test hypothesis regarding genomic/proteomic alterations across a broad spectrum of malignancies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0226-3) contains supplementary material, which is available to authorized users.

Mutational signatures of DNA mismatch repair deficiency inC. elegansand human cancers

“…was obtained using R-package "tsne" (Donaldson 2016) using the cosine similarity as distance measure between mutational profiles. In order to confirm the link between signatures MMR-1-3 and MMR deficiency, we defined MMR-deficient samples as those annotated as MSI-H (microsatellite instable high) in TCGA Clinical Explorer (Lee et al 2015). Relative contributions of every signature to the samples from the combined dataset were tested for association with MSI/MSS status using one-tailed Wilcoxon rank sum test.…”

Significance of histone methyltransferase SETDB1 expression in colon adenocarcinoma

“…We used the online biomarker validation tool SurvExpress to further examine whether SETDB1 mRNA expression is associated with cumulative survival and risk in colon adenocarcinoma patients, . Specifically, we used SurvExpress to investigate SETDB1 expression in COAD‐TCGA‐colon adenocarcinoma . This software examines differences in mRNA expression levels to calculate Kaplan–Meier curves and risk groups.…”

“…SurvExpress can be used to facilitate Kaplan–Meier log‐rank analysis and risk assessment using a biomarker gene list as input to a Cox proportional‐hazards regression . In this study, we analyzed data from the SurvExpress database that pertained to the expression of SETDB1 in COAD‐TCGA‐colon adenocarcinoma . Specifically, we used normalized datasets that included overall survival times.…”

Systems Medicine Approaches to Improving Understanding, Treatment, and Clinical Management of Neuroendocrine Prostate Cancer

“…Publicly available resources such as mRNA transcriptional profiles in databases like The Cancer Genome Atlas (TCGA) [176][177][178][179], cBioPortal [180], ArrayExpress (https: //www.ebi.ac.uk/ arrayexpress/) and Gene Expression Omnibus (GEO, http: //www.ncbi.nlm.nih.gov/geo/) can be integrated to design a global expression map of NEPC. This map can be matched in-silico using bioinformatics resource like Connectivity Map (CMap, http: //www.broadinstitute.org/cmap/) and LINCS (http: //www.lincsproject.org/) to discover drugs that can potentially reverse the direction of gene expression phenotypes.…”