Cancer risk and gammopathies in 2123 adults with Gaucher disease type 1 in the International Gaucher Group Gaucher Registry.

There are numerous reports of cancers in Gaucher disease (GD) from mostly small single-center studies; however, precise risk estimates and cancer types involved have not been delineated. We conducted a study involving 2123 patients with GD type 1 (GD1) to assess the incidence of hematological malignancies, gammopathies, and solid tumors in an international observational study, the International Cooperative Gaucher Group Gaucher Registry (Clinicaltrials.gov: NCT00358943). Risk for cancer overall and for each type of malignancy was compared to the United States (US) population using the Surveillance, Epidemiology, and End Results database. Natural history of gammopathy was determined through assessing the progression from a diagnosis of monoclonal gammopathy of unknown significance (MGUS) to multiple myeloma (MM). Risk for hematological malignancies was more than four times higher than expected compared to the general population: non-Hodgkin lymphoma was approximately three times higher; MM was approximately nine times higher. Age-specific incidence rates of MGUS were unexpectedly high among younger patients. The 10-year cumulative incidence of MM after diagnosis of MGUS was 7.9%, comparable to the general population. Compared to the general US population, GD1 patients were at higher risk for solid malignancies of liver (2.9 times), kidney (2.8 times), melanoma (2.5 times), and breast (1.4 times). Colorectal, prostate, and lung cancer risks were lower than expected. These findings help advance care of patients with GD1 by supporting recommendations for individualized monitoring for malignancies and antecedents such as MGUS for MM and provoke important questions of the role of glucosylceramide and related sphingolipids in cancer biology.

Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States.

BACKGROUND: Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. METHODS: We utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. RESULTS: Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. CONCLUSIONS: The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.