Abstract 187: PCaT: An integrated platform for the analysis of pediatric cancer genomic data
Authors: Yang Juechen , Li Qilin , Wang Xiaojing , Houghton Peter , Zheng Siyuan 
Published: September 2020

Pediatric cancer is the leading cause of disease related mortality in children, affecting approximately 15,000 new cases each year in the US. Compared with their adult counterparts, pediatric cancers harbor ten times less mutations. Their relatively quiet genomes dictate that most patients do not have alterations in clinically actionable targets. Transcriptomic changes reflect genetic and epigenetic perturbations, thus represent an important tool to study molecular mechanisms and therapeutic responses of pediatric cancers. However, the complexity of current multidimensional datasets poses a great challenge for researchers, especially bench scientists, to navigate these data.

Here, we present a new, intuitive, and integrated tool, Pediatric Cancer Transcriptome Database (PCaT, http://www.pedtranscriptome.org), to help address this issue. PCAT focuses on transcriptome data, but integrates clinical, histopathological, genetic, and drug response data. Built under the Rshiny framework, it currently offers three function modules (single gene analysis, multi-gene analysis, preclinical testing), and is under active expansion. Each module enables pan-cancer analysis and cancer type specific analysis when appropriate, thus providing both a panoramic and focused view for user input queries. The single gene analysis module allows users to correlate gene expression with clinical, histological parameters and patient prognosis. Users can examine how copy number and mutation impact a gene's expression. Importantly, users can also identify genes and functional modules related to the query through co-expression analysis. Multiple gene analysis allows for visualization, clustering, functional enrichment analysis, pairwise correlation, and calculation of a signature score using single sample gene set enrichment analysis. A distinctive feature of PCAT is the integration of preclinical testing data for more than 60 patient derived xenograft models (PDXs) to nearly 80 FDA approved or experimental agents (preclinical module). This feature provides users with the opportunity to explore correlations between gene expression and drug responses across a diverse range of pediatric cancer types. Currently, PCAT includes multimodal datasets from Therapeutically Applicable Research to Generate Effective Treatments consortium (TARGET) and Pediatric Preclinical Testing Consortium (PPTC).

In summary, PCAT is a new user-friendly online portal that offers an intuitive interface for users to explore multidimensional data, especially the transcriptome, of pediatric cancers. We are currently expanding the portal with more datasets and more functional modules, particularly gene fusions. PCAT is complementary to existing tools such as St Jude Cloud, R2 portal, and pedcbio, and offers an alternative route to navigating an increasing accumulation of pediatric cancer genomic data.

Citation Format: Juechen Yang, Qilin Li, Xiaojing Wang, Peter Houghton, Siyuan Zheng. PCaT: An integrated platform for the analysis of pediatric cancer genomic data [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 187.