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Revised classification of histiocytoses and neoplasms of the macrophage-dendritic cell lineages.

ABSTRACT: The histiocytoses are rare disorders characterized by the accumulation of macrophage, dendritic cell, or monocyte-derived cells in various tissues and organs of children and adults. More than 100 different subtypes have been described, with a wide range of clinical manifestations, presentations, and histologies. Since the first classification in 1987, a number of new findings regarding the cellular origins, molecular pathology, and clinical features of histiocytic disorders have been identified. We propose herein a revision of the classification of histiocytoses based on histology, phenotype, molecular alterations, and clinical and imaging characteristics. This revised classification system consists of 5 groups of diseases: (1) Langerhans-related, (2) cutaneous and mucocutaneous, and (3) malignant histiocytoses as well as (4) Rosai-Dorfman disease and (5) hemophagocytic lymphohistiocytosis and macrophage activation syndrome. Herein, we provide guidelines and recommendations for diagnoses of these disorders.

SUBMITTER: Emile JF

PROVIDER: S-EPMC5161007 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Revised classification of histiocytoses and neoplasms of the macrophage-dendritic cell lineages.

Emile Jean-François JF Abla Oussama O Fraitag Sylvie S Horne Annacarin A Haroche Julien J Donadieu Jean J Requena-Caballero Luis L Jordan Michael B MB Abdel-Wahab Omar O Allen Carl E CE Charlotte Frédéric F Diamond Eli L EL Egeler R Maarten RM Fischer Alain A Herrera Juana Gil JG Henter Jan-Inge JI Janku Filip F Merad Miriam M Picarsic Jennifer J Rodriguez-Galindo Carlos C Rollins Barret J BJ Tazi Abdellatif A Vassallo Robert R Weiss Lawrence M LM

Blood 20160310 22

The histiocytoses are rare disorders characterized by the accumulation of macrophage, dendritic cell, or monocyte-derived cells in various tissues and organs of children and adults. More than 100 different subtypes have been described, with a wide range of clinical manifestations, presentations, and histologies. Since the first classification in 1987, a number of new findings regarding the cellular origins, molecular pathology, and clinical features of histiocytic disorders have been identified. ...[more]

PMID: 26966089

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Project description:The selection of relevant genes for sample classification is a common task in many gene expression studies. Although a number of tools have been developed to identify optimal gene expression signatures, they often generate gene lists that are too long to be exploited clinically. Consequently, researchers in the field try to identify the smallest set of genes that provide good sample classification. We investigated the genome-wide expression of the inflammatory phenotype in dendritic cells. Dendritic cells are a complex group of cells that play a critical role in vertebrate immunity. Therefore, the prediction of the inflammatory phenotype in these cells may help with the selection of immune-modulating compounds.A data mining protocol was applied to microarray data for murine cell lines treated with various inflammatory stimuli. The learning and validation data sets consisted of 155 and 49 samples, respectively. The data mining protocol reduced the number of probe sets from 5,802 to 10, then from 10 to 6 and finally from 6 to 3. The performances of a set of supervised classification models were compared. The best accuracy, when using the six following genes --Il12b, Cd40, Socs3, Irgm1, Plin2 and Lgals3bp-- was obtained by Tree Augmented Naïve Bayes and Nearest Neighbour (91.8%). Using the smallest set of three genes --Il12b, Cd40 and Socs3-- the performance remained satisfactory and the best accuracy was with Support Vector Machine (95.9%). These data mining models, using data for the genes Il12b, Cd40 and Socs3, were validated with a human data set consisting of 27 samples. Support Vector Machines (71.4%) and Nearest Neighbour (92.6%) gave the worst performances, but the remaining models correctly classified all the 27 samples.The genes selected by the data mining protocol proposed were shown to be informative for discriminating between inflammatory and steady-state phenotypes in dendritic cells. The robustness of the data mining protocol was confirmed by the accuracy for a human data set, when using only the following three genes: Il12b, Cd40 and Socs3. In summary, we analysed the longitudinal pattern of expression in dendritic cells stimulated with activating agents with the aim of identifying signatures that would predict or explain the dentritic cell response to an inflammatory agent.

Project description:BackgroundMyeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.MethodsWe sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.ResultsA total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.ConclusionsComprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

Dataset Information

Revised classification of histiocytoses and neoplasms of the macrophage-dendritic cell lineages.

Publications

Revised classification of histiocytoses and neoplasms of the macrophage-dendritic cell lineages.

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