Utility of Targeted Next Generation Sequencing (NGS) to Predict Hematologic Neoplasms with Germline Predisposition

Abstract Introduction/Objective Hematologic neoplasms with germline predisposition are increasingly gaining clinical relevance as associated gene variants are emerging. These variants confer an increased risk for developing hematologic neoplasms as additional somatic aberrations accumulate. Recognition of germline predisposition is crucial to patient management as it would prompt genetic counseling and regular surveillance in affected patients and biological family members, as well as guide therapy, as many patients undergo allogeneic stem cell transplantation for which relatives are typically the preferred donors. Furthermore, these patients may respond differently to conventional therapies. Clinical recognition of germline predisposition is challenging due to phenotypic heterogeneity and a lack of germline testing standards and resources. Next generation sequencing is routinely utilized to detect somatic variants associated with hematopoietic neoplasms. Molecular profiling may also potentially serve as a screening tool to suggest pathogenic germline variants. Methods/Case Report Comprehensive genomic profiling data was reviewed for 301 patients with known hematologic neoplasms. A somatic-germline-zygosity (SGZ) computational method classified variants by origin. Variants of unknown clinical significance were excluded. The final study showed 94 patients predicted to have germline variants. The variables analyzed include germline variants, concurrent mutations, diagnosis, sex, age, and ancestry. Results (if a Case Study enter NA) Acute myeloid leukemia was the most frequent diagnosis, followed by B-cell acute lymphoblastic leukemia, myeloproliferative neoplasms, myelodysplastic syndrome, and chronic myelomonocytic leukemia. The median patient age was 60 years, ranging from 6 months to 91 years. Male gender and European ancestry were proportionally greater. In this study, we identified 70 distinct genes with 145 germline pathogenic variants. The most common pathogenic germline variants were TET2, SRSF2, RUNX1, DNMT3A, and CHEK2. Conclusion In conclusion, SGZ computational methods can be used as a screening tool to predict possible germline mutations in hematologic malignancies, which will help guide treatment and improve patient and family outcomes