Functional Analysis Provides Insight into Missing Heritability
Research Mentor Name
Michael Tainsky
Research Mentor Email Address
tainskym@med.wayne.edy
Institution / Department
Center for Molecular Medicine and Genetics
Document Type
Research Abstract
Research Type
basicbio
Level of Research
yes
Type of Post-Bachelor Degree
PhD
Abstract
Accurate ascertainment of genetic risk can be potentially lifesaving for patients who inherit cancer promoting mutations. However, even with the most extensive panel testing clinically available, a large number of patients will test negative despite family history of cancer or test positive for a variant of unknown significance (VUS). For these patients, clinical management is complicated; patients want to know their risk, and may fear disease they are not at great risk for (benign VUS) or they may not be given access to potentially lifesaving early screening procedures (pathogenic VUS). ATM has proven a challenge to clinicians due to its well defined nature as a moderate penetrance gene. The variable penetrance of damaging ATM alleles makes it more difficult to assess risk both by population studies and by computational means. To address this problem, we have developed and validated an approach that can be applied to rapidly investigate likely pathogenic mutations in at risk populations. In this study, six ATM VUS were analyzed for their biochemical significance. Analysis showed consistent results, with two of the likely pathogenic variants performing similarly to the known pathogenic control variants. With further study, the data generated by this project can be applied clinically to improve diagnostic accuracy for hundreds of patients a year. In addition, this search and validation strategy can be applied to different cohorts, small or large, of at risk patients with hereditary cancer patterns to improve the accuracy and reliability of cancer genetic testing.
Disciplines
Cancer Biology | Genetics | Medicine and Health Sciences | Molecular Genetics
Recommended Citation
Baughan, Scott L.; Tainsky, Michael A.; and Darwiche, Fatima, "Functional Analysis Provides Insight into Missing Heritability" (2023). Medical Student Research Symposium. 253.
https://digitalcommons.wayne.edu/som_srs/253