Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.

A clinicopathologic study of malignancy in VCP-associated multisystem proteinopathy.

BACKGROUND: Valosin containing protein (VCP) is an important protein with many vital functions mostly related to the ubiquitin-proteasome system that provides protein quality control. VCP-associated inclusion body myopathy with Paget disease of bone and frontotemporal dementia, also termed VCP disease and multisystem proteinopathy (MSP 1), is an autosomal dominant disorder caused by monoallelic variants in the VCP gene on human chromosome 9. VCP has also been strongly involved in cancer, with over-activity of VCP found in several cancers such as prostate, pancreatic, endometrial, esophageal cancers and osteosarcoma. Since MSP1 is caused by gain of function variants in the VCP gene, we hypothesized our patients would show increased risk for developing malignancies. We describe cases of 3 rare malignancies and 4 common cancers from a retrospective dataset. RESULTS: Upon surveying 106 families with confirmed VCP variants, we found a higher rate of rare tumors including malignant peripheral nerve sheath tumor, anaplastic pleomorphic xanthoastrocytoma and thymoma. Some of these subjects developed cancer before displaying other classic VCP disease manifestations. We also present cases of common cancers; however, we did not find an increased rate compared to the general population. This could be related to the early mortality associated with this disease, since most patients die in their 50-60 s due to respiratory failure or cardiomyopathy which is earlier than the age at which most cancers appear. CONCLUSION: This is the first study that expands the phenotype of VCP disease to potentially include rare cancers and highlights the importance of further investigation of the role of VCP in cancer development. The results of this study in VCP disease patients suggest that patients may be at an increased risk for rare tumors. A larger study will determine if patients with VCP disease develop cancer at a higher rate than the general population. If that is the case, they should be followed up more frequently and screened for recurrence and metastasis of their cancer.