Isabella Imirowicz
Pronouns: She/Her/Hers
Research Mentor(s): Marcin Cieslik,
Research Mentor School/College/Department: Computational Medicine and Bioinformatics / Pathology, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 2 (11am – 11:50am)
Breakout Room: Room 10
Presenter: 1
Abstract
Pediatric cancers often appear pathologically similar to their adult counterparts, yet they have different genetic makeups. Germline variants are common in pediatric cancers with approximately 7-8% of malignancies carrying a germline mutation.While it is understood that germline variants are important pathogenic drivers of pediatric cancers, there have been few studies to systematically identify pathogenic variants in pediatric tumors. Identifying pathogenic germline variants will play a central role in advancing understandings of pediatric cancer. This project seeks to systematically identify statically significant rare, pathogenic germline variants from a cohort of pediatric nuroblastoma tumors. Both patient tumor and genetic information was sequenced using with a matched normal. Variants were then annotated to produce a list of calls that were then matched with relevant database information when applicable. Following annotation of variants, functions were developed in R in order to sort and filter which variants are germline, pathogenic by both known association and protein impact, and rare using a GNOMAD allele frequency of less than 0.005. A separate filter was applied for variant quality. The resulting high confidence calls were then queried against databases to identify their relevance in nuroblastoma. Finally, the rare, pathogenic, germline variants were visualized using various plots. The project has yet to finish, however we hypothesize a moderate number of rare pathogenic germline variants to be present in each tumor sample based upon preliminary data. Identification of these rare variants will aid in oncologist’s understanding of the genetics of neuroblastomas and inform the development of personalized medicine
Authors: Marcin Cieslik, Isabella Imirowicz
Research Method: Computer Programming
