Germline DDX41 mutations cause ineffective hematopoiesis and myelodysplasia

DDX41 mutations are the most common germline alterations in adult myelodysplastic syndromes (MDSs). The majority of affected individuals harbor germline monoallelic frameshift DDX41 mutations and subsequently acquire somatic mutations in their other DDX41 allele, typically missense R525H. Hematopoietic progenitor cells (HPCs) with biallelic frameshift and R525H mutations undergo cell cycle arrest and apoptosis, causing bone marrow failure in mice. Mechanistically, DDX41 is essential for small nucleolar RNA (snoRNA) processing, ribosome assembly, and protein synthesis. Although monoallelic DDX41 mutations do not affect hematopoiesis in young mice, a subset of aged mice develops features of MDS. Biallelic mutations in DDX41 are observed at a low frequency in non-dominant hematopoietic stem cell clones in bone marrow (BM) from individuals with MDS. Mice chimeric for monoallelic DDX41 mutant BM cells and a minor population of biallelic mutant BM cells develop hematopoietic defects at a younger age, suggesting that biallelic DDX41 mutant cells are disease modifying in the context of monoallelic DDX41 mutant BM

Malignant Transformation Involving CXXC4 Mutations Identified in a Leukemic Progression Model of Severe Congenital Neutropenia

Olofsen et al. show that acquisition of a mutation in Cxxc4 results in increased CXXC4 protein levels, reduced TET2 protein, increased inflammatory signaling, and leukemic progression of a CSF3R/RUNX1 mutant mouse model of severe congenital neutropenia (SCN).Severe congenital neutropenia (SCN) patients treated with CSF3/G-CSF to alleviate neutropenia frequently develop acute myeloid leukemia (AML). A common pattern of leukemic transformation involves the appearance of hematopoietic clones with CSF3 receptor (CSF3R) mutations in the neutropenic phase, followed by mutations in RUNX1 before AML becomes overt. To investigate how the combination of CSF3 therapy and CSF3R and RUNX1 mutations contributes to AML development, we make use of mouse models, SCN-derived induced pluripotent stem cells (iPSCs), and SCN and SCN-AML patient samples. CSF3 provokes a hyper-proliferative state in CSF3R/RUNX1 mutant hematopoietic progenitors but does not cause overt AML. Intriguingly, an additional acquired driver mutation in Cxxc4 causes elevated CXXC4 and reduced TET2 protein levels in murine AML samples. Expression of multiple pro-inflammatory pathways is elevated in mouse AML and human SCN-AML, suggesting that inflammation driven by downregulation of TET2 activity is a critical step in the malignant transformation of SCN

PU.1 is linking the glycolytic enzyme HK3 in neutrophil differentiation and survival of APL cells

The transcription factor PU.1 is a master regulator of myeloid differentiation and function. On the other hand, only scarce information is available on PU.1-regulated genes involved in cell survival. We now identified the glycolytic enzyme hexokinase 3 (HK3), a gene with cytoprotective functions, as transcriptional target of PU.1. Interestingly, HK3 expression is highly associated with the myeloid lineage and was significantly decreased in acute myeloid leukemia patients compared with normal granulocytes. Moreover, HK3 expression was significantly lower in acute promyelocytic leukemia (APL) compared with non-APL patient samples. In line with the observations in primary APL patient samples, we observed significantly higher HK3 expression during neutrophil differentiation of APL cell lines. Moreover, knocking down PU.1 impaired HK3 induction during neutrophil differentiation. In vivo binding of PU.1 and PML-RARA to the HK3 promoter was found, and PML-RARA attenuated PU.1 activation of the HK3 promoter. Next, inhibiting HK3 in APL cell lines resulted in significantly reduced neutrophil differentiation and viability compared with control cells. Our findings strongly suggest that HK3 is: (1) directly activated by PU.1, (2) repressed by PML-RARA, and (3) functionally involved in neutrophil differentiation and cell viability of APL cells

Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials

10.1111/ijlh.13691INTERNATIONAL JOURNAL OF LABORATORY HEMATOLOGY441127-13

Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials

Objectives: Typically, prognostic capability of gene expression profiling (GEP) is studied in the context of clinical trials, for which 50%-80% of patients are not eligible, possibly limiting the generalizability of findings to routine practice. Here, we evaluate GEP analysis outside clinical trials, aiming to improve clinical risk assessment of multiple myeloma (MM) patients. Methods: A total of 155 bone marrow samples from MM patients were collected from which RNA was analyzed by microarray. Sixteen previously developed GEP-based markers were evaluated, combined with survival data, and studied using Cox proportional hazard regression. Results: Gene expression profiling-based markers SKY92 and the PR-cluster were shown to be independent prognostic factors for survival, with hazard ratios and 95% confidence interval of 3.6 [2.0-6.8] (P <.001) and 5.8 [2.7-12.7] (P <.01) for overall survival (OS). A multivariate model proved only SKY92 and the PR-cluster to be independent prognostic factors compared to cytogenetic high-risk patients, the International Staging System (ISS), and revised ISS. A substantial number of high-risk individuals could be further identified when SKY92 was added to the cytogenetic, ISS, or R-ISS. In the cytogenetic standard-risk group, ISS I/II, and R-ISS I/II, 13%, 23%, and 23% of patients with adverse survivals were identified. Conclusions: For the first time, this study confirmed the prognostic value of GEP markers outside clinical trials. Conventional prognostic models to define high-risk MM are improved by the incorporation of GEP markers

A Personalized Clinical-Decision Tool to Improve the Diagnostic Accuracy of Myelodysplastic Syndromes

Background While histo- and cytomorphological examinations are central to the diagnosis of myelodysplastic syndromes (MDS), significant inter-observer variability exists. The diagnosis can be challenging in pancytopenic patients (pts) without evidence of dysplasia and is contingent on observer expertise. We developed and externally validated a geno-clinical model that uses mutational data and peripheral blood counts/clinical variables to distinguish MDS from other myeloid malignancies. Methods Clinical and genomic data, including commercially available next-generation sequencing panels, were obtained for patients (pts) treated at the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP, 536 pts). All patients had carried a diagnosis of MDS, chronic myelomonocytic leukemia (CMML), MDS/myeloproliferative neoplasm overlap (MDS/MPN), myeloproliferative neoplasm (MPN; either polycythemia vera, essential thrombocythemia, or myelofibrosis), clonal cytopenia of undetermined significance (CCUS), or idiopathic cytopenia of undetermined significance (ICUS). All diagnoses were established with bone marrow aspiration and according to World Health Organization 2017 criteria. The training cohort included data from CC and UP and randomly divided into learner (80%) and test (20%) cohorts. The final model was independently validated in the MLL cohort. A machine learning algorithm was used to build the model; multiple extraction algorithms were used to extract genomic/clinical variables on both the cohort and individual levels. Performance was evaluated according to the area under the curve of the receiver operating characteristic (ROC-AUC) and accuracy matrices. Results Among the 2697 pts included from all sites, the median age was 70 years [36 - 86]. Median hemoglobin (Hb) was 10.4g/dl [6.9 - 15.7], median platelet count (PLT) was 132 k/dL [14 - 722], median WBC count was 5.3 k/dL [1.4 - 49.9], median ANC was 2.8 k/dL [0.3 - 27.7], median monocyte count was 0.3 k/dL [0 - 9.9], and median lymphocyte count (ALC) was 1.1 k/dL [0.1 - 5.4], and median peripheral blast percentage 0% [0 - 8]. The most commonly mutated genes in all patients were (list top 5 genes) and among pts with MDS were SF3B1 (27%), TET2 (25%), ASXL1 (19%), SRSF2 (16%), and DNMT3A (11%); among patients with MDS-MPN/CMML, the most commonly mutated genes were MDS-MPN/CMML (TET2 46%, ASXL1 34%, SRSF2 29%, RUNX1 13%, CBL 12%) ; among patients with MPNs, the most commonly mutated genes were (JAK2 64%, ASXL1 27%, TET2 14%, DNMT3A 8%, U2AF1 7%); among patients with CCUS the most commonly mutated genes were (TET2 41%, DNMT3A 27%, ASXL1 19%, SRSF2 17%, ZRSR2 10%). The most important features for model predictions (ranked from the most to the least important) included: number of mutations detected/sample, peripheral blast percentage, AMC, JAK2 status, Hb, basophil count, age, eosinophil count, ALC, WBC, EZH2 mutation status, ANC, mutation status of KRAS and SF3B1, platelets, and gender. The final model achieved an average AUROC of 0.95 (95% CI 0.93-0.96) when applied to the test cohort and 0.93 (95% CI 0.91 - 0.94) when it was applied to the MLL cohort. The model also provides individual-level explanations for predictions, providing top differential diagnoses and individual-level explanations of how features influence a putative diagnosis (Figure 1b). Conclusions We developed and externally validated a highly accurate and interpretable model that can distinguish MDS from other myeloid malignancies using clinical and mutational data from a large international cohort. The model can provide personalized interpretations of its outcome and can aid physicians and hematopathologists in recognizing MDS with high accuracy when encountering pts with pancytopenia and with a suspected diagnosis of MDS. Disclosures Sekeres: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Bristol Myers Squib: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Roche/Genentech: Research Funding; CTI Biopharma: Consultancy, Research Funding; AstraZeneca/MedImmune: Consultancy; Gilead Sciences: Research Funding; Pfizer: Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee