Investigation into Clinical Relevance of Filtered Out Novel Mutations in the Thermo Fisher Oncomine Focus Assay

dc.contributor.advisorSeto, Donald
dc.contributor.authorGrissom, Luke R
dc.creatorGrissom, Luke R
dc.date2021-04-27
dc.date.accessioned2021-10-14T13:21:41Z
dc.date.available2021-10-14T13:21:41Z
dc.description.abstractThe Oncomine Focus Assay (OFA) is an assay developed by Thermo Fisher and is run on their Ion Torrent S5 next generation sequencing (NGS) platform. The purpose of this assay is to detect known clinically relevant “hotspot” driver mutations for various cancer types and report those mutations, along with potential therapies and clinical trials, to the oncologist for the patient being tested. Thermo Fisher’s reporter software automatically filters unknown novel mutations out, leaving only known hotspot driver mutations within the reports sent to the oncologists. The potential clinical relevance of these novel mutations is unknown. RStudio was used to compile and filter the exported sequencing data from 25 months of NGS runs from 1336 patient samples, and the PredictSNP2 bioinformatic tool was used to filter out predicted neutral and synonymous variants. Then NCBI and Varsome were utilized to search for relevant clinical data for the unique novel variants. In total, 374 novel SNPs were found to be potentially pathogenic, but only a few had documented evidence to support pathogenicity. Twelve unique variants in 15 samples were identified that have evidence of pathogenicity with references in NCBI. The remaining novel SNPs predicted to be pathogenic but do not have any documented evidence could be a potential source for future research of clinically relevant novel mutations in oncology.
dc.identifier.urihttps://hdl.handle.net/1920/12127
dc.language.isoen
dc.subjectNext generation sequencing
dc.subjectNovel mutations
dc.subjectOncology
dc.subjectBioinformatics
dc.titleInvestigation into Clinical Relevance of Filtered Out Novel Mutations in the Thermo Fisher Oncomine Focus Assay
dc.typeThesis
thesis.degree.disciplineBioinformatics and Computational Biology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelMaster's
thesis.degree.nameMaster of Science in Bioinformatics and Computational Biology

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