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• W4385655612 abstract "Topic: 16. Myeloproliferative neoplasms - Clinical Background: Erdheim-Chester Disease (ECD) is a rare non-Langerhans cell histiocytosis characterized by tissue infiltration by foamy histiocytes. The disease is driven by mutations in proto-oncogenes such as BRAF, which activate mitogen-activated protein kinase (MAPK) pathways, while immune-mediated mechanisms contribute to disease development and progression. Clinical manifestations are extremely heterogeneous and virtually every organ system can be affected. Aims: In the present cohort study, we aimed at classifying patients with ECD based on clinical presentation and mutational status using an unsupervised data-driven clustering approach. Methods: This study was based on a dataset of 356 patients with ECD that contained data referred to demographics, clinical presentation, mutational status (BRAF and other relevant genes), and follow-up including treatment and outcome. Clinical features at presentation along with mutational status were included as twenty input variables in the clustering model. The dimensionality of the dataset was reduced through a principal component method, and then a hierarchical clustering approach was used. Each generated cluster was characterized by analyzing its summary features. Results: The clustering algorithm produced two models, one with four clusters and one with five. The generated clusters were labeled with their prominent features or given a representative name. The clusters named “Digestive/Hematologic/Myelodysplastic Syndrome” (around 3% of subjects), “Testis/Lymph Nodes/MAP2K1 mutation” (8%), and “Limited Disease” (30%) were identical across the two models. The remaining patients showed a high rate of BRAF mutation (around 90%) and were aggregated in a single large cluster named “Widespread Disease” (59%) in the four-cluster model, whereas they were split into two different clusters - the “Hypothalamic/Pituitary/Associated Langerhans Cell Histiocytosis” (25%) and the “Cardiovascular/Perirenal”(34%) - in the five-cluster model. Summary/Conclusion: Hierarchical clustering on principal components subdivided the ECD patient sample into clusters which reflect what has often already been observed in the clinical practice, with regard to the widespread disease phenotype as opposed to the limited one, and with regard to the phenotypes of ECD associated with other types of histiocytosis or myelodysplastic syndrome. Keywords: Patient, Real world data, Phenotype, Clinical data" @default.
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• W4385655612 date "2023-08-01" @default.
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• W4385655612 title "PB2208: CLUSTER ANALYSIS UNVEILS THE CLINICAL SPECTRUM OF ERDHEIM-CHESTER DISEASE" @default.
• W4385655612 doi "https://doi.org/10.1097/01.hs9.0000975584.47405.25" @default.
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