Algorithmic Diagnostic Approach And Spectrum Of Hematological Malignancies In Patients With Germline Predisposition Syndromes

Context: Germline predisposition syndromes (GPS) are genetic disorders that increase the risk of all types of malignancies, with a growing list of genes associated specifically with hematological malignancies. Although many of these conditions have syndromic features, some GPS patients present without identifiable traits. We have established a hematology GPS clinic that uses an algorithmic approach to diagnose GPS patients. Objective: To illustrate the heterogeneity of GPS and the benefits of an algorithmic approach to identify non-syndromic GPS patients. Design: Medical records were reviewed retrospectively from patients diagnosed with GPS at our institution from 1994 to 2018. Prospectively, we investigated new pediatric and adult patients with GPS from 2017 to 2020 using an algorithmic approach summarized as follows: 1) Evaluation of relevant family history, 2) phenotype-specific functional assays (telomere measurement, breakage assay, erythrocyte adenosine deaminase testing), 3) germline genetic testing through a custom-designed gene panel, and 4) research whole exome sequencing for negative cases. Both a detailed explanation of the algorithm and the methodology for genetic variant interrogation are included in Mangaonkar et al. MC Proc 2019. Setting: This study was carried out at the Division of Hematology at Mayo Clinic. Patients or Other Participants: GPS was investigated in pediatric and adult patients with one or more first-degree relatives with hematological/visceral malignancies, in those with antecedent thrombocytopenia, or in those with specific features indicative of inherited bone marrow failure (IBMF) (short telomere syndromes, GATA2 haploinsufficiency, Fanconi anemia, Schwachman-Diamond syndrome). Interventions: N/A Main Outcomes Measures: The number of patients diagnosed with GPS both retrospectively and prospectively were tallied and compared. Results: A total of 146 individuals were included in the study divided as follows: 28 (19.4%) presented with hematological malignancy with precedent thrombocytopenia, 27 (18.8%) without precedent thrombocytopenia, 74 (51.3%) showed IBMF, and 15 (10.4%) showed general cancer predisposition syndrome. Eighty-five patients were retrospectively included, while 61 were prospectively evaluated through our algorithmic approach. Among retrospective patients, 7.1% (6 patients) did not present syndromic features, while this value increased to 14.7% (9 patients) when using the algorithmic approach. Conclusions: By using an algorithmic approach, we can identify a higher percentage of GPS patients without syndromic features.