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


METHODS
The 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.


CONCLUSIONS
The 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.
Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
HoJoon
H
Lee
Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA.
Jennifer
J
Palm
Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA.
Susan M
SM
Grimes
Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA. genomics_ji@stanford.edu.|Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA. genomics_ji@stanford.edu.
Hanlee P
HP
Ji
D015894:Genome, Human; D023281:Genomics; D006801:Humans; D020407:Internet; D009369:Neoplasms / Q000235:genetics*; D012984:Software