Oliver, Gavin R.

Abstract Background: Extranodal follicular dendritic cell sarcoma (FDCS) is an exceedingly rare tumor type that is limited to few cases in the literature. Given the rarity of this tumor, there is a lack of genetic understanding of this disease resulting in a non-individualized plan of care for patients with this disease. Herein, we are the first to describe extensively molecularly the DNA and RNA of this tumor. Methods: Under an IRB approved protocol, the patient's tumor and whole blood was freshly frozen for molecular analysis. We performed Next Generation Sequencing (NGS) genomic profiling including exome capture, mate pair and mRNA sequencing on this tumor. Exome and mRNA data were processed through Mayo's Bioinformatics Core standard pipelines, GENOME GPS 1.2.1 and MAPRSeq v1.2.1. Mutations and indels were prioritized depending on whether or not they belonged to known cancer associated genes. Copy number variations (CNVs) in Exome and Mate Pair data was found through PatternCNV v 1.0.1 and fusion transcripts were found using SNOWSHOES-FTD and Tophat-Fusion 2.0.6. Results: At the genomic level large scale genomic rearrangements were detected, including chr19 and chr20 trisomy, chr15q and chr17q segmental duplications result in two intra-chromosomal fusion genes, and translocations leading to four inter-chromosomal fusion genes between chr15 and chr17. Of particular interest is a stop codon gain in the CLTC1 (chr22) gene, which is involved in the mitotic spindle organization which has been associated with meningiomas and breast tumorigenesis. The new stop codon is before the site required for the recruitment of the proteins to the spindle. The large scale genomic rearrangement described above is consistent with this defective spindle assembly hypothesis. Another intriguing mutation detected in the VEGFR1 (chr13) gene leads to a 45% mis-spliced transcript. VEGFR1 is thought to modulate VEGFR2 which plays an essential role in angiogenesis, by acting as a decoy receptor. Therefore the loss of function of VEGFR1 might lead to hyper-activity of VEGFR2 pathway and a potential therapeutic strategy is to directly inhibit the VEGFR2 pathway. Furthermore missense mutations in the ATM (chr11) and TP53 (chr17) genes were found. PARP inhibitors have been used with success for cancer with defects in the ATM gene. Finally a novel fusion transcript of HDGRFP3 (chr15) and SHC4 (chr15) was identified. Such fusion results in an overexpressed SHC4 gene which has been associated with metastatic melanomas. Conclusions: In summary, genomic profiling of a rare thyroid FDCS identified drug targetable genes as well as changes related to tumor etiology. Future work is necessary to determine the functional significance of these molecular findings and whether they will predict benefit to targeted therapies. Citation Format: Jaime I. Davila, Jason Starr, Steven Attia, Chen Wang, Xue Wang, Brian Necela, Casler John, David Menke, Vivek Sarangi, Gavin Oliver, Richard Joseph, John Copland, Alexander Parker, E. Aubrey Thompson, Robert Smallridge, Yan W. Asmann. Extensive genomic profiling of a rare extranodal-follicular dendritic cell sarcoma: Implications for future individualized therapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1894. doi:10.1158/1538-7445.AM2014-1894

Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

Abstract Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample. Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity. Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ . Contact: Asmann.Yan@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.