Abstract A1-32: The Stanford-TCGA portal: An interactive web/mobile interface for exploring the clinical phenotypic relevance of specific cancer drivers
Authors: Lee HoJoon , Palm Jennifer , Ji Hanlee 
Published: November 2015

Abstract
The Stanford-TCGA Portal (http://genomeportal.stanford.edu/pan-tcga) enables users to easily navigate cancer genomic/proteomic data of cancer patients with their clinical information from the Cancer Genome Atlas (TCGA) project. As of 2014 January, the TCGA processed thousands of samples from over 20 cancer types and the data is publically available. This huge data set can offer many valuable insights about cancer research. However, exploring data from TCGA remains a challenge, particularly for researchers and others who lack formal bioinformatics training. With the Stanford-TCGA Portal, anyone can use a personal device such as laptops or smart phones to explore the TCGA data in regards to clinically relevant questions. For example, “What genes are associated with advanced breast cancer?”, “What is the difference in the frequency of TP53 mutations between diffuse and intestinal gastric cancers?”, or “What is the frequency of copy number deletion of APC for samples with/without PIK3CA mutations?”.
While several websites such as cBio portal or UCSC cancer genomics browser was developed in order to make TCGA data more accessible to a community, there are few interactive features that enable querying of clinically-relevant phenotypic associations with drivers and modifier genes. The Stanford-TCGA portal allows users to navigate TCGA data in three different ways; i) search for clinically relevant genes/micro RNAs (miRs)/proteins by names, cancer types or clinical parameters, ii) profile genomic/proteomic changes by clinical parameters in a cancer type, or iii) test two-hit hypothesis. Thus, one can address specific questions regarding the association of cancers drivers with clinical parameters and outcome.
We identify clinically relevant genes/miRs/proteins by our rigorous statistical analysis, which has been previously published, from 17 cancer types with 19 clinical parameters such as clinical stage or smoking history. Basically, elastic-net method estimates an optimal multiple linear regularized regression of the clinical parameters on the space of genomic/proteomic features. This analysis identified the set of top gene predictors of each clinical parameter in each cancer type respectively. Users can easily access the lists of these identified genes through the Stanford-TCGA Portal.
In summary, the Stanford-TCGA Portal enables the cancer research community and others to fully utilize TCGA data. It provides simple yet clinically relevant information for web and mobile interface, thus one can examine queries and test hypothesis regarding genomic/proteomic alterations in cancers from any time and place. This will be an important step towards translation of genomic/proteomic data into clinics.
Citation Format: HoJoon Lee, Jennifer Palm, Hanlee Ji. The Stanford-TCGA portal: An interactive web/mobile interface for exploring the clinical phenotypic relevance of specific cancer drivers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-32.