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Driver somatic mutations identify distinct disease
Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia by Luca Malcovati, Elli Papaemmanuil, Ilaria Ambaglio, Chiara Elena, Anna Gallì, Matteo G. Della Porta, Erica Travaglino, Daniela Pietra, Cristiana Pascutto, Marta Ubezio, Elisa Bono, Matteo C. Da Vià, Angela Brisci, Francesca Bruno, Laura Cremonesi, Maurizio Ferrari, Emanuela Boveri, Rosangela Invernizzi, Peter J. Campbell, and Mario Cazzola Blood Volume 124(9):1513-1521 August 28, 2014 ©2014 by American Society of Hematology Representation of unsupervised hierarchical clustering analyses including somatic mutations and current classification features according to WHO criteria within MDS without excess blasts. Luca Malcovati et al. Blood 2014;124:1513-1521 ©2014 by American Society of Hematology Survival and risk of leukemic evolution of patients with MDS classified according to the clusters resulting from the unsupervised analysis including WHO classification criteria and mutation patterns. Luca Malcovati et al. Blood 2014;124:1513-1521 ©2014 by American Society of Hematology Relationship between mutation pattern and disease phenotype in TET2, SRSF2, and ZRSR2mutated myeloid neoplasms with myelodysplasia. Luca Malcovati et al. Blood 2014;124:1513-1521 ©2014 by American Society of Hematology Mutation pattern in MDS and MDS/MPN with thrombocytosis. Luca Malcovati et al. Blood 2014;124:1513-1521 ©2014 by American Society of Hematology Algorithm illustrating the classification process based on morphologic and genetic criteria identified by the unsupervised clustering analyses. Luca Malcovati et al. Blood 2014;124:1513-1521 ©2014 by American Society of Hematology