Abstract 2472: LOCUS: Queryable database of cancer genomics and pharmacologic response enables rapid selection of in vitro and in vivo preclinical tumor models

• Published in: Cancer research (Chicago, Ill.) Ill.), 2019-07, Vol.79 (13_Supplement), p.2472-2472
• Main Authors: Amin, Armand, Sanga, Sandeep, Gan, Shanshan, Hu, Yong, Broudy, Thomas
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-2472

Preclincal drug testing in oncology research has benefited from the development of thousands of tumors models—both in vitro and in vivo. Within BioDuro laboratories alone, there is a collection of >500 models, including traditional 2D human cell lines grown in vitro, primary 3D lines, cell-derived xenograft (CDX), patient derived xenograft (PDX) and syngeneic murine models (SYN). Identifying the most relevant models to support cancer drug discovery is a challenge. In an era of precision medicine, the need to understand tumor model characteristics, such as functional drug response profiles or genomic status, has become a critical path. In this report, we present LOCUS, a SQL backed database with graphical user interface, populated with functional pharmacologic response and genomic data for hundreds of tumor models including in vitro 2D, 3D, CDX, PDX and SYN. Models are readily searched by any number of conditions, including cancer type, model type, specific mutation, or target expression level. LOCUS combines relational databases for pharmacology and genomics data with scientific statistical models through a spring integrator. A scientific data centered domain architecture allows for pharmacogenomic queries and analysis. Pharmacogenomic data is pushed to dynamic scientific statistical models running on a python environment, the results of which are visualized to a Java EE UI Framework. All tumor models are genomically characterized, with searchable fields for single nucleotide variations, insertion deletions, and transcript expression levels. Extracted RNA was profiled using next generation sequencing performed on Illumina HiSeq using a paired end protocol. For xenograft samples, reads were first aligned and filtered by the mouse reference genome using the Bowtie2 algorithm. The remaining reads were aligned to the GRCh38 human reference genome using the MapSplice2 algorithm. Transcript- and Gene-level expression were determined by counting the number of sequence alignments per exon according to the RefSeq gene model, and computing Fragments Per Killobase Million (FPKM) and Transcripts Per Million (TPM) values using best practices. Opossum software was used to further prepare alignments produced by MapSplice2 algorithm for small variant calling. The Platypus variant caller was used to identify single nucleotide variant, small insertion, and small deletion events. The variation events were annotated using SnpEff and custom perl scripts to overlay known somati