Abstract 3385: Comparison of genomic biomarkers identified by the whole exome, RNASeq and whole genome sequencing pipelines developed for the PDMR

Background: The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR; www.pdmr.cancer.gov) of patient-derived xenografts (PDXs) with clinical annotation and comprehensive genomic characterization using whole exome sequencing (WES) and RNASeq. An in-house data analysis pipeline has been developed and validated to call germline and somatic variants and to perform transcriptional profiling in these models. There is a need to incorporate additional biomarkers into standard data analysis pipeline, including loss of heterozygosity (LOH), microsatellite instability (MSI), structure variants (SVs)/fusions and copy number variation (CNV) for identifying appropriate PDX models for preclinical drug studies. Validation of the methods used for the assessment of these and other genomic biomarkers is a crucial aspect in the development of the PDMR data analysis pipeline.

Methods: WGS, WES and RNASeq were conducted on 58 PDX samples and genomic biomarkers were derived from different assays. For LOH calling, a set of ~800,000 heterozygous SNPs was first constructed from a population level genomic database (gnomAD) and a specific list of ~3000 highly heterozygous SNPs from a previous study. LOH regions were detected using Runs of Homozygosity (BCFtools/RoH) based on the genotypes of ~800,000 SNPs. Finally, percent of genomic LOH was calculated as the percent of eligible LOH regions in the whole genome. For MSI calling. mSINGS was used to assign a microsatellite instability score based on the fraction of unstable microsatellite loci. Gene fusions were detected using Tophat-fusion and Fusion-catcher from RNASeq data and Manta from WGS. CNVs were derived from WGS and WES using CNVkit.

Results: Genomic biomarkers derived from WES and RNASeq were highly concordant with the ones derived from WGS. Specifically, we found 1) the percent of genomic LOH was highly correlated between WGS and WES across 52 samples with R2=0.99, where LOH% ranged from <1% to ~50% and specimens within the same models had consistent data; 2) a strong concordance rate (91%) of MSI score was observed between WGS and WES across 49 samples; 3) clinically and diagnostically relevant structural variants/fusions (e.g. FGFR3-TACC3 and EWSR1-FLI1) detected from RNASeq data can be detected and validated from WGS data; and 4) CNV genomic profiles were highly correlated between WGS and WES and amplifications/deletions in clinically relevant genes were consistently detected by the two assays.

Conclusions: We observed excellent consistency between WGS, WES and RNASeq data in the assessment of percent of LOH, MSI score, SVs/fusions and CNVs. Our data analysis pipeline can accurately call genomic biomarkers from WES and RNASeq data, which facilitates the molecular characterization and prioritization of PDMR models for preclinical drug treatment.

Citation Format: Li Chen, Rajesh Patidar, Biswajit Das, Chris Karlovich, Tomas Vilimas, Corinne Camalier, Vivekananda Datta, Shahanawaz Jiwani, William Walsh, Palmer Fliss, Sean McDermott, Justine N. McCutcheon, Amanda Peach, Michelle Ahalt-Gottholm, Carrie Bonomi, Kelly Dougherty, John Carter, Sergio Y. Alcoser, Tiffanie Chase, Raymond Divelbiss1, Marion Gibson, Kelly Hedger, Candace Mallow, Chelsea McGlynn, Malorie Morris, Marianne Radzyminski, Howard Stotler, Jesse Stottlemyer, Debbie Trail, Yvonne Evrard, Melinda G. Hollingshead, Mickey Williams, James H. Doroshow. Comparison of genomic biomarkers identified by the whole exome, RNASeq and whole genome sequencing pipelines developed for the PDMR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3385.

Abstract 4524: Comparison of PDX, PDC, and PDOrg models from the National Cancer Institute’s Patient-Derived Models Repository (PDMR)

The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR) comprised of quality-controlled, early-passage, clinically-annotated patient-derived tumor xenografts (PDXs), in vitro tumor cell cultures (PDCs), cancer associated fibroblasts (CAFs), and patient-derived organoids (PDOrg). NCI has focused on generating models to complement existing PDX collections and address unmet needs in the preclinical model space. These models are offered to the extramural community for research use (https://pdmr.cancer.gov), along with clinical annotation and molecular information (whole exome sequence, gene expression using RNASeq), via a publicly accessible database. Currently, over 200 PDX models, 50 PDC models, and 100 CAF models are available for distribution to the US research community. Approximately 50 PDOrg models will be released in early 2019. As part of its rare cancer initiative, the NCI is also targeting the collection of infrequently-observed tumor histologies to advance both biological investigations and drug development efforts for under-studied malignancies. Comparison of matched models, models where more than one model type are available (e.g., PDX and PDC), demonstrate a high degree of concordance across the model types. Genetic stability across the models is assessed using multiple criteria including genetic assessment of CNVs and presence of driver mutations. Optimal CNV assessment uses whole exome sequence data corrected for cellularity in the patient specimen using germline reads and corrected for cellularity in the PDX specimens by subtraction of the mouse reads. Histomorphologic comparison of PDXs and cell line xenografts (CLX) generated from in vitro PDCs and PDOrgs also overall show a high degree of concordance, though loss of features and dedifferentiation can be observed in some models. Overall these models demonstrate a high degree of conservation at the genetic and pathologic level when compared to the patient tumor. These models can provide researchers the ability to perform high- or mid-throughput screening in 2D or 3D culture followed by targeted selection of PDX models for in vivo studies. Funded by NCI Contract No. HHSN261200800001E

Citation Format: Yvonne A. Evrard, Dianne Newton, Biswajit Das, Sergio Y. Alcoser, Kaitlyn Arthur, Mariah Baldwin, Carrie Bonomi, Suzanne Borgel, John Carter, Tiffany Chase, Alice Chen, Lily Chen, Nikki E. Craig, Vivekananda Datta, Emily Delaney, Raymond Divelbiss, Kelly Dougherty, Thomas Forbes, Kyle Georgius, Joe Geraghty, Marion Gibson, Michelle M. Gottholm-Ahalt, Tara Grinnage-Pulley, Kelly Hedger, Sierra Hoffman, Chris Karlovich, Wiem Lassoued, Shahanawaz Jiwani, Candace Mallow, Chelsea McGlynn, Mallorie Morris, Jenna Moyer, Mike Mullendore, Matt Murphy, Rajesh Patidar, Kevin Plater, Marianne Radzyminski, Nicki Scott, Luke H. Stockwin, Howard Stotler, Jesse Stottlemyer, Savanna Styers, Debbie Trail, Tomas Vilimas, Anna Wade, Abigail Walke, Thomas Walsh, P. Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. Comparison of PDX, PDC, and PDOrg models from the National Cancer Institute’s Patient-Derived Models Repository (PDMR) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4524.