Neuroendocrine genomics for tumor variant discovery

• Main Author: Tessmann, Jonathon
• Other Authors: Braun, Terry A.
• Format: Others
• Language: English
• Published: University of Iowa 2018
• Subjects: Bioinformatics; Neuroendocrine; Tumor; Variant; Biomedical Engineering and Bioengineering
• Online Access: https://ir.uiowa.edu/etd/6305; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=7637&context=etd

An exome sequencing analysis pipeline was constructed to analyze NET germline and somatic samples. SNPs and INDELs were called and annotated from germline and somatic tissue. CNVs were also called for the tumor samples. This was accomplished using open source bioinformatics software that has been developed by the research community. Broad Institute "best practices" were followed. Some of the tools that were used include BWA, SAMtools, GATK, Varscan, VT, VEP, and GEMINI. Computational resources were provided by The University of Iowa NEON computer cluster. 57 germline samples and 15 tumor samples across 23 families with a history of NETs produced 4,452 germline variants, 1,695 somatic variants, 5,853 LOH events, and 627 CNV calls. False positive and driver candidacy filtering was applied. One family with Currarino syndrome has an inherited germline missense variant in MNX1. This variant has a phred-scaled Combined Annotation Dependant Depletion score of 35, putting it in the top 0.031% of deleterious variants. CNV analysis demonstrates that 8 of the 15 tumor samples have large-scale deletions of chromosome 18, three of which have nearly the entire chromosome deleted. An affected tumor suppressor gene in this region includes DCC, which is present in all three variant discovery techniques. Variant prioritization techniques are effective, but need further development to increase candidate variant/gene discovery rate.