JAK2V617F/TET2 mutations: does the order matter?

Pronier, Elodie; Quivoron, Cyril; Bernard, Olivier A.; Villeval, Jean‐Luc

doi:10.3324/haematol.2011.042846

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…JAK2 and TET2 concomitant mutations are frequent in MPNs [ 16 , 37 ]. Whether they are both necessary for the various phases of the disease and their order of appearance are a matter of debate [ 106 ]. An NGS study indeed showed that the ten mutations identified in an MDS patient can be detected together in most studied single cells, suggesting a linear evolution of the disease and the existence of a dominant clone [ 103 ].…”

Section: Reviewmentioning

confidence: 99%

Myeloid malignancies: mutations, models and management

et al. 2012

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Myeloid malignant diseases comprise chronic (including myelodysplastic syndromes, myeloproliferative neoplasms and chronic myelomonocytic leukemia) and acute (acute myeloid leukemia) stages. They are clonal diseases arising in hematopoietic stem or progenitor cells. Mutations responsible for these diseases occur in several genes whose encoded proteins belong principally to five classes: signaling pathways proteins (e.g. CBL, FLT3, JAK2, RAS), transcription factors (e.g. CEBPA, ETV6, RUNX1), epigenetic regulators (e.g. ASXL1, DNMT3A, EZH2, IDH1, IDH2, SUZ12, TET2, UTX), tumor suppressors (e.g. TP53), and components of the spliceosome (e.g. SF3B1, SRSF2). Large-scale sequencing efforts will soon lead to the establishment of a comprehensive repertoire of these mutations, allowing for a better definition and classification of myeloid malignancies, the identification of new prognostic markers and therapeutic targets, and the development of novel therapies. Given the importance of epigenetic deregulation in myeloid diseases, the use of drugs targeting epigenetic regulators appears as a most promising therapeutic approach.

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Section: Reviewmentioning

confidence: 99%

Myeloid malignancies: mutations, models and management

et al. 2012

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“…The evolutionary history of a tumor can be represented by a mutation tree. It captures the evolutionary relationships among different tumor subclonal populations at the time of the biopsy, i.e., the (partial) temporal order in which the genomic aberrations were acquired [4], which can be used to predict tumor progression and clinical outcome [32, 41, 42]. At the same time, the different shapes and branching patterns of the mutation trees reflect different modes of tumor evolution governed by selection and by the spatial architecture of the tumor [39].…”

Section: Introductionmentioning

confidence: 99%

oncotree2vec – A method for embedding and clustering of tumor phylogenetic trees

Baciu-Drăgan,

Beerenwinkel

2023

Preprint

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Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient’s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor sub-types, for more accurate clinical diagnosis and prognosis. Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies, and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

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confidence: 99%

The First Case Report of JAK2–BCR–PPP1R32 Fusion Genes Because of a Translocation (9;22;11)(p24;q11.2;q13) in a Patient With Myeloproliferative Neoplasm

2022

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Dear Editor, The most common genomic abnormality in Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) is a functional V617F mutation in the Janus kinase 2 (JAK2) kinase-like structural domain [1,2]. Although chromosomal translocation involving JAK2 is rare, it has been reported in hematological malignancies [3]. Because of similar clinical features, experts advocate that all rare JAK2 rearrangement cases should be classified into the same category [4]. We report a case of MPN with BCR-JAK2 and BCR-PPP1R32 rearrangements, showing t(9;22; 11)(p24;q11.2;q13). To our knowledge, this is the first report of PPP1R32 as a novel fusion partner of BCR. Written informed consent was obtained from the patient for publication of this case report and accompanying images. This case study was approved by the Institutional Review Board of Army Medical University, Chongqing, China (No.2021-219).A 56-year-old man was admitted to our outpatient clinic in March 2020 for fatigue and left upper abdominal pain. Peripheral blood examination indicated a white blood cell (WBC) count of 178.1 × 10 9 /L (25.0% neutrophils, 4.0% lymphocytes, 1.0% monocytes, 4.0% eosinophils, 0% basophils, 18.0% late granu-

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JAK2V617F/TET2 mutations: does the order matter?

Cited by 9 publications

References 23 publications

Myeloid malignancies: mutations, models and management

Myeloid malignancies: mutations, models and management

oncotree2vec – A method for embedding and clustering of tumor phylogenetic trees

The First Case Report of JAK2–BCR–PPP1R32 Fusion Genes Because of a Translocation (9;22;11)(p24;q11.2;q13) in a Patient With Myeloproliferative Neoplasm

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