{"paper_id":"427f2246-8f4c-4635-bcc3-0d97092ba2c0","body_text":"Clonal megakaryocyte dysplasia with normal blood values (CMD-NBV): an unique form of early myeloproliferative neoplasm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clonal megakaryocyte dysplasia with normal blood values (CMD-NBV): an unique form of early myeloproliferative neoplasm Giovanni Barosi, Vittorio Rosti, Rita Campanelli, Margherita Massa, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6622364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We report an in-depth retrospective analysis of an updated series of 30 subjects with clonal megakaryocyte dysplasia with normal blood values (CMD-NBV). Sixteen were men, median age was 47.5 years (IQR, 39–53 years). A thrombosis-driven situational diagnosis (69% of subjects), high incidence of thrombotic events (6.5 events x 100 subject-years), and indolent disease progression (one case only progressed towards an active disease) were the hallmarks of CMD-NBV. Nineteen subjects (63%) had a high body mass index (BMI) at diagnosis (median value, 26.2 m 2 /kg) and 14 (48%) had ≥ 1 Charlson co-morbidities. In 21 individuals (70%) the driver variant was JAK2 V617F with a median variant allele frequency ( VAF) at diagnosis of 8.9% (IQR, 5.4–18.4%). Twenty-four subjects had undergone next generation sequencing (NGS) for myeloid neoplasm-related genes. Six (25%) had ≥ 1 pathogenic somatic variant in ASXL1 , TET2, DNMT3A , and SRSF2 . Twelve putative germline, non-pathogenic, missense variants in ASXL1 , TET2 , DNMT3A, RUNX1 , CUX1 , ABL1 , NF1 , KIT and CSFR or 5’ UTR in NF1 and 3’ UTR in ASXL1 were detected in 10 subjects (42%). Based on these data we hypothesize rare, low penetrance germline variants underly a predisposition to the early CMD-NBV myeloproliferative neoplasm. Health sciences/Diseases Biological sciences/Cancer Introduction The World Health Organization (WHO) and the International Consensus Conference (ICC) classify BCR::ABL-negative classical myeloproliferative neoplasms (MPNs) into three major types, essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF). PMF is further divided into two distinct subtypes, prefibrotic (pre-MF) and overt myelofibrosis (overt-MF). 1 , 2 We recently proposed two cognate variants in the MPN domain, named clonal megakaryocyte dysplasia with normal blood values (CMD-NBV), 3 and clonal megakaryocyte dysplasia with isolated thrombocytosis (CMD‐IT). 4 Pre-MF, overt-MF, CMD-NBV and CMD-IT share bone marrow (BM) morphological feature of megakaryocyte hyperplasia and dysplasia and were clustered in the new category of myelofibrosis‐type megakaryocyte dysplasia (MTMD). 5 Facing the new classificatory complexity, we conceptualized MTMD as a spectrum of disorders with a distinct phenotype and prognosis. 5 – 7 This view highlights the interest on the molecular events that drive specific disease presentations and explain their clinical features and laboratory findings. Among the MTMD variants, CMD-NBV is the rarest and least characterized. CMD-NBV connotes normal hematologic values or minimal abnormalities and is mostly diagnosed in the context of venous or arterial thrombosis. In the cohort of 15 cases we reported in 2022, 3 10 had the canonical somatic JAK2 V617F mutation, while in the remaining cases the driver of clonal expansion was not identified. With the aim to improve our knowledge on the epidemiological, clinical and pathobiological profile of CMD-NBV, we now report an expanded series of 30 consecutive subjects with CMD-NBV. To delineate subjects’ molecular characteristics that could represent disease specific and defining molecular markers, we studied variant topography by next generation sequencing (NGS) technique. Methods Subjects characteristics and clinical procedures In this single-centre retrospective study, consecutive subjects with CMD-NBV were identified from the institutional database of the Centre for the Study of Myelofibrosis of the IRCCS Policlinico S. Matteo Foundation in Pavia, Italy (Pavia-CSM-database). The database contains consecutive individuals registered since 1998 with a diagnosis of MPN and examined at least once. This report consists of 14 cases we published in 2022, 3 and 16 newly referred cases. One previously reported subject was excluded since a missed history of thrombocytosis (platelet count > 450 x10E + 9/L) contrasted our adjudicated CMD-NBV diagnostic criteria. All the subjects gave written informed consent approved by the IRCCS Policlinico S. Matteo Foundation Institutional Ethics Committee to be included in the database and to donate samples for genetic and molecular research on their disease. Diagnosis of CMD-NBV was based on two distinct criteria: 3 1. BM megakaryocyte hyperplasia and dysplasia consistent with the 2009 WHO diagnostic criteria for pre-MF. 8 2. Failure to meet the clinical-hematological WHO criteria for PV or ET, and any of the four minor diagnostic criteria for pre-MF, i.e. palpable splenomegaly, anemia, white blood cell (WBC) count ≥ 11 x 10E + 9/L, and increased serum lactate dehydrogenase level (LDH). As a deviation from these criteria, in this report we classified as CMD-NBV also subjects presenting with a palpable splenomegaly (no more than 5 cm from the costal margin) who had concurrent splanchnic vein thrombosis. For all subjects, the database contained information at diagnosis about sex, age, body mass index (BMI), spleen size (clinical measurement), complete blood count with differential, and serum LDH level. BMI was categorized into underweight (< 18.5 kg/m 2 ), normal weight (18.5 to < 25 kg/m 2 ), overweight (25 to < 30 kg/m 2 ) and obese (≥ 30 kg/m 2 ). 9 Abnormal blood concentrations were defined as hemoglobin > 153 g/L (female) or > 160 g/L (male); WBC count > 8.8 x 10E + 9/L; monocytes > 0.7 x 10E + 9/L, and platelets > 390 x 10E + 9/L. 10,11 Blood eosinophils percentage > 7% and blood basophils > 1% were defined outside the normal range. 12 In subjects analyzed at diagnosis and from whom we had peripheral blood slides, slides were re-examined for platelet morphology. For the purpose of the current study, platelets with a diameter ≥ 5 µm were considered macroplatelets. 13 The reason for initial clinical presentation and diagnosis and all information on concomitant diseases were retrieved from medical records. Charlson Co-morbidity Index (CCI) was calculated as described. 14 For maintaining a person-centric rather than disease-centric perspective, we defined chronic physical multi-morbidity using the chronic physical illnesses (CPI) based on the modified European Health Interview Survey (EHIS) guidelines. 15 To contextualize co-morbidities in the field of MPNs, we also categorized conditions diagnosed before or concurrent with CMD-NBV as autoimmune, cardiovascular/metabolic, infectious, and other inflammatory or malignant as described. 16 In all subjects, key pathological BM features were obtained from the pathology report. Thrombosis was defined as any venous or arterial thrombo-embolism excluding superficial vein thrombosis. Thrombotic events that occurred within 2 years prior to the diagnosis of CMD-NBV were defined as MPN-related. Data on JAK2 V617F , MPL and CALR mutations and variant allele frequencies (VAFs) were available at the time of diagnosis. NGS analyses were done on DNA from granulocyte collected at diagnosis or within 12 months after diagnosis and stored in our institutional biobank. Myeloid mutations were analyzed by NGS at the Lab of Molecular Hematology of the IRCCS Policlinico San Matteo Foundation and University of Pavia, Pavia, Italy. Screening for myeloid cancer associated mutations was performed using oncoReveal Myeloid Panel (Pillar Biosciences, MA, USA) an amplicon-based library preparation chemistry that interrogates genes recurrently mutated in myeloid neoplasms. Briefly, pairs of DNA oligos targeting each region of interest were used in the first round of gene-specific PCR and the products subsequently purified via size selection. After purification, a second round of PCR adds index adaptors and P5 & P7 sequences to each library for sample tracking and sequencing. The resulting libraries were further purified and 2x250 bp paired-end sequenced on an Illumina MiSeq-system platform. Functionally annotated variants were filtered based on the information retrieved from public databases, and the expected germline allele frequency. The remaining variants were finally tagged as oncogenic, based on the information derived from the literature, the Catalogue of Somatic Mutations in Cancer ( http://cancer.sanger.ac.uk/cancergenome/projects/cosmic ), and in silico prediction effect, as previously described. Rare germline variants were defined as those with population allele frequencies below 1% across ExAC database. Classification of germline variants was performed using Franklin ( https://franklin.genoox.com ) a curation tools based on the American College of Medical Genetics (ACMG) guidelines. Details of library preparation, sequencing, and variant analysis are provided in the Supplemental Methods Statistics Subject co-variates are reported as median and interquartile range (IQR) for continuous variables. Categorical variables are reported as frequency rates and percentages and analyzed using Chi-square test. Independent group t-test was used to analyze normally distributed continuous variables. The Kurskal-Wallis test was used for non-normally distributed data. Major study endpoints were progression to active disease, blast transformation, death and thrombotic events. Progression to active disease was defined as: (1) disease-associated hemoglobin concentration < 100 g/L; (2) spleen > 10 cm below the left costal margin; (3) platelets < 150 x 10E + 9/L; and/or (4) WBC count < 4 x 10E + 9/L or > 12 x 10E + 9/L. To avoid confounding, we censored development of any of these criteria at the start of any disease-modifying intervention or at the diagnosis of a new cancer. Frequency of thrombotic events was expressed as incidence, calculated as numbers of events x 100 subject-years of observation with 95% Confidence Interval (CI). Results were considered statistically significant if P-values were < 0∙05. Computations were done with STATISTICA© software ( Dell Technologies Inc. Round Rock, TX, USA). Results The 30 adults that fulfilled our adjudicated criteria for CMD-NBV represent the 2.4% of all subjects registered in the Pavia-CSM-database for the MTMD category. Sixteen are men and median age is 47.5 years (IQR, 39–53). Diagnosis In 21 subjects (70%) the diagnosis of CMD-NBV was synchronous with an unexplained symptomatic venous or arterial thrombotic event (n = 15), incidental discovery of portal cavernoma (n = 5) or a diagnosis of post-embolic pulmonary hypertension (n = 1). In 9 other subjects, the diagnosis was driven by the incidental finding of laboratory abnormalities consistent with an MPN (n = 8), or of vertebral bone MRI abnormality interpreted as bone marrow involvement by a myeloid disorder (n = 1; Supplemental Table 1 ). Co-variates at diagnosis Subject co-variates at diagnosis are displayed in Table 1 . With median hematological co-variates values in the normal range, 4, 5 and 6 subjects had hemoglobin, WBC and platelet concentration above the upper range of normal, while 4 had platelet count (n = 3) or WBC concentrations (n = 1) under the lower range of normal. Nine subjects diagnosed with a synchronous splanchnic vein thrombosis had a palpable spleen (no more than 3 cm below the costal margin). Two subjects had increased eosinophils, 5 increased basophils and 5 increased monocytes, yet 12 subjects (40%) had at least one of the above reported abnormalities. Blood smears at diagnosis was available in 20 subjects: macro-platelets were documented in 16 of them (80%). Macro-platelets were a small proportion of platelets in coexistence with normal platelets. Mean platelet volume was greater than 12 fl in one subject. JAK2 V617F was detected in 21 subjects (70%) with a median VAF of 7.8% (IQR, 5.2–17.9%). No CALR , MPL or JAK2 exon 12 mutations were detected in the 9 remaining individuals. Median BMI at diagnosis was 26.1 m 2 /kg (IQR, 23.1–28.7). No subject had a BMI < 18.5 m 2 /kg, 11 (37%) were normal weighted, 15 (50%) had a BMI between 25 m 2 /kg and 30 m 2 /kg, and 4 (13%) were obese. Table 1 Baseline co-variates of subjects with CMD-NBV (n = 30). Data are shown for the whole population and according to sex All subjects (n = 30) Males (n = 16) Females (n = 14) p-value Demography and anthropometry Age, yrs, median (IQR) 47.5 (39–53) 49 (30–52) 46 (41–54) 0.53 Sex male, n (%) 16 (53) BMI, kg/m 2 , median (IQR) 26.1 (23.1–28.7) 25.9 (23.6–28.2) 26.4 (21.7–28.7) 0.91 BMI, kg/m 2 , ≥ 30, n (%) 4 (13) 1 (6) 3 (21) Clinical-hematological co-variates Hemoglobin, g/dL, median (IQR) 13.7 (12.8–15) 14.9 (14-15.5) 13.3 (12.7–13.5) 0.003 Mean cell volume, fl, median (IQR) 87.3 (81.2–89.4) 86.7 (80.8–88.5) 88 (85-89.9) 0.61 WBC x 10E + 9/L, median (IQR) 6.2 (5.7–7.8) 6.1 (5.3–7.8) 6.7 (5.9–8.7) 0.41 WBC ≥ 8.8 x 10E + 9/L, n (%) 5 (17) 3 (19) 2 (14) WBC < 4 x 10E + 9/L, n(%) 1 (3) 1 (6) 0 (0) Eosinophils percent, median (IQR) 3.2 (2-4.9) 3.1 (2-3.9) 3.4 (2.7–6.3) 0.075 Eosinophils percent > 7, n (%) 2 (7) 0 2 (14) Basophils percent, median (IQR) 0.7 (0.3-1) 0.5 (0.2-1) 0.9 (0.5–1.1) 0.22 Basophils percent > 1, n (%) 5 (17) 1 (6) 4 (28) Monocytes x 10E + 9, median (IQR) 486 (410–556) 496 (409–548) 463 (411–743) 0.98 Monocytes > 700 x 10E + 9, n (%) 5 (17) 2 (12) 3 (21) Platelets x10E + 9/L, median (IQR) 274 (205–371) 209 (192–278) 356 (277–396) 0.003 Platelets > 390 x 10E + 9/L, n (%) 6 (20) 2 (12) 4 (28) Platelets < 150 x 10E + 9/L, n (%) 3 (10) 3 (19) 0 (0) Spleen size, cm E + 2, median (IQR) 90 (90–110) 90 (90–120) 90 (90–90) 0.36 Spleen size > 90 cm 2 , n (%) 9 (30) 6 (37) 3 (21) Plasma LDH, x ULN, median (IQR) 0.86 (0.78-1.00) 0.78 (0.66–0.90) 0.93 (0.83–1.17) 0.007 Serum cholesterol, mg/dL, median (IQR) 190 (146–217) 178 (133–200) 194 (179–218) 0.21 Blood CD34-positive cells x 10E + 6, median (IQR) 2.37 (1.60–4.31) 2.19 (1.36–4.72) 2.56 (1.63–3.39) 0.85 Molecular co-variates JAK2 V617F , n (%) 21 (70) 10 (62.5) 11 (78.6) 0.33 JAK2 V617F allele frequency, median (IQR) 7.8 (5.2–17.9) 5.9 (3.7–10) 14.2 (6.8–19.5) 0.11 CALR mutation, n (%) 0 0 0 MPL mutation, n (%) 0 0 0 Triple negative, n (%) 9 (30) 6 (37.5) 3 (21.4) 0.33 BMI: Body mass index; IQR: Interquartile range. ULN: upper limit of normal. By dividing subjects according to sex, males had significantly higher hemoglobin concentrations than had females. By contrast, males had lower platelet count and LDH plasma concentration than females Co- and multi-morbidities At the time of our Centre referral, 14 subjects (47%) had one or more comorbidities according to the Charlson co-morbidity criteria (CCI ≥ 1;Table 2 ): 7 had a CCI = 1, 6 a CCI = 2, and 1 a CCI = 3, with a median of 0.8 co-morbidities per subject. The most common co-morbidities were TIA/stroke (n = 4), solid neoplasia (n = 4), peripheral vascular disease (n = 3). Multi-morbidity was present in 14 subjects (47%): 8 had one co-occurring morbidity, while 3 had 2, and 3 had 3 co-occurring morbidities. The most frequent CPI was arterial hypertension (n = 10; Supplemental Table 2 ). According to the Horvat-defined co-morbidities, 15 subjects had 1 or more co-morbid condition ( Supplement Table 3 ). Thirteen subjects had a co-morbidity classified as cardiovascular or metabolic, 9 as inflammatory or autoimmune, and 4 as malignant. Four co-occurring inflammatory/autoimmune diseases were rare diseases: one subject was diagnosed with osteopecilia, a rare benign condensing osteopathy, one had familial sclerosing cholangitis, one Horton arteritis and one dural arteriovenous fistula due to sinus thrombosis (currently defined related to an inflammatory micro-environment). 17 Table 2 Co-morbidities of subjects with CMD-NBV according to the Charlson co-morbidity index. Data were obtained at the first referral at our Center Co-moribidities, n (%) Subjects number (%) Total = 30 Acute myocardial infarction 2 (7) Solid neoplasia - Localized - Metastatic 4 (13) 2 (7) 2 (7) Diabetes mellitus - Uncomplicated - Complicated 1 (3) 1 (3) 0 Transient ischemic attack/stroke 4 (13) Chronic obstructive pulmonary disease 1 (3) Peptic ulcer disease 0 Peripheral vascular disease 3 (10) Liver disease - Mild - Moderate-severe 2 (7) 1 (3) 1 (3) Connective tissue disease 1 (3) Congestive heart failure 1 (3) Chronic cognitive deficit 0 Hemiplegia 0 Lymphoma 1 (3) Leukemia 0 Acquired immune deficiency syndrome 0 Table 3 Detailed analysis of the bone marrow features of 30 subjects with CMD-NBV at diagnosis Quantitative variables Reduced n (%) Normal n (%) Increased n (%) Cellularity 3 (10) 9 (30) 18 (60) Erythropoiesis 2 (7) 11 (37) 17 (57) Granulopoiesis 2 (7) 12 (40) 16 (53) Megakaryopoiesis 0 0 30 (100) Qualitative variables Present, n subjects (%) Megakaryocyte clusters 22 (73) - Loose 19 (63) - Dense 3 (10) Megakaryocyte nuclei - Hyper-lobulated 2 (7) - Bulbous 14 (47) Small megakaryocytes 6 (20) Fibrosis - Grade 0 20 (67) - Grade 1 10 (33) - Grade 2 0 - Grade 3 0 Bone marrow histology Results of bone marrow histology are displayed in Table 3 . Being a necessary criterion for the diagnosis of CMD-NBV, megakaryocyte hyperplasia was a common feature. Age-corrected overall bone marrow cellularity was increased in 1, normal in 9 and decreased in 3. All subjects had ≥ 1 signs of megakaryocyte dysplasia, including loose megakaryocyte clusters (n = 19), dense megakaryocyte clusters (n = 3), bulbous megakaryocytes (n = 14), or micromegakaryocytes (n = 6). No subject had granulocyte or erythroid lineages dysplasia. Bone marrow fibrosis was grade 0 (n = 20) or grade 1 (n = 10). Ten subjects had an increased vascular component and one showed megakaryocytes in the blood vessels. Lymphocyte hyperplasia was present in 20 cases. Absence of lymphocytic clonality was established in all the cases. In 7 subjects an increased number of bone marrow eosinophils and in 3 increased mast cells was reported. Somatic and germline variants A panel of 45 genes was sequenced in 24 out of 30 subjects (80%). A total of 10 variants were classified as pathogenic or likely pathogenic somatic variants spread across 4 genes and 6 subjects (25%) (Table 4 ; Supplemental Table 4 ). Subject UPN14 had 2 mutations in ASXL1 , subject UPN23 co-occurring mutations in DNMT3A, TET2, SRSF2 , while subject UPN29 in DNMT3A and TET2 . The range of variant allele frequency (VAF) at diagnosis was 2.3–41%. Table 4 Genetic and molecular profile of the 30 subjects diagnosed with CMD-NBV. Data were obtained at diagnosis Case # Sex/age Driver mutation (VAF%) NGS-somatic variants (VAF%) NGS- putative germline variants (VAF%) Cytogenetics UPN1 M/25 yrs JAK2 V617F (21) Neg Neg UPN2 M/52 yrs JAK2 V617F (10) ND ND XY UPN3 F/49 yrs JAK2 V617F (5) Neg Neg UPN4 M/57 yrs JAK2 V617F (5.2) Neg ASXL1 (c.3306G > T) (p.Glu1102Asp) (52.6) UPN5 F/44 yrs JAK2 V617F (7.7) Neg DNMT3A (c.1502A > G) (p.Asn501Ser) (42) UPN6 M/23 yrs TN ND ND UPN7 M/49 yrs JAK2 V617F (5.9) Neg Neg UPN8 M/49 yrs TN Neg RUNX1 (c.167C > T) (p.Leu56Ser) (51) XY UPN9 F/38 yrs JAK2 V617F (ND) TET2 (c.4585C > T) (p.Gln1529*) (2.7) Neg XX UPN10 M/44 yrs TN Neg ABL1 (c.589G > A) (p.Glu197Lys) (54) CUX1 (c.2371G > A) (p.Ala791Thr) (47) UPN11 F/42 yrs JAK2 V617F (33) Neg Neg XX UPN12 F/32 yrs TN Neg CSF3R (c.2422G > A) (Glu808Lys) (50) XX UPN13 M/52 yrs JAK2 V617F (16) ND ND UPN14 F/46 yrs JAK2 V617F (ND) ASXL1 (c.2077C > T) (p.Arg693*) (3.8) ASXL1 (c.1900_1922 del) (p.Glu635fs) (41) Neg UPN15 M/49 yrs JAK2 V617F (0.65) TET2 (c.4045-1G > A) (null) (3.9) Neg UPN16 37/F yrs TN Neg Neg UPN17 M/71 yrs TN Neg Neg UPN18 F/46 yrs JAK2 V617F (19) Neg NF1 (c.-22G > C) (null) (35) UPN19 F/54 yrs JAK2 V617F (ND) ND ND XX UPN20 M/29 yrs JAK2 V617F (7.8) Neg ASXL1 (c.*87A > G) (null) (49) XY UPN21 M/52 yrs JAK2 V617F (0.19) Neg Neg UPN22 F/53 yrs TN Neg Neg UPN23 F/70 yrs JAK2 V617F (5) DNMT3A (c.2320G > T) (p.Glu774*) (8) TET2 (c.4791del) (p.Tyr1598Ilefs*12) (4) SRSF2 (c.161C > T) (p.Ser54Phe) (3) Neg XX UPN24 F/43 yrs JAK2 V617F (5) Neg Neg UPN25 M/20 yrs TN Neg TET2 (c.1018A > G) (p.Ile340Val) (50.7) UPN26 M/49 yrs TN ND ND UPN27 F/70 JAK2 V617F (17.9) DNMT3A (c.1656 delC) (p.Asn552fs) (2.5) NF1 (c.6790A > T) (Ile2264Leu) (50.6) UPN28 M/39 JAK2 V617F (3.7) Neg TET2 (c.521C > A) (p.Pro174His)(44) UPN29 F/55 JAK2 V617F (12) DNMT3A (c.1490G > A) (p.Cys4977Tyr) (2.8) TET2 (c.4393C > T) (p.Arg1465*) (2.3) KIT (c.101C > T) (p.Pro34Leu) (52) UPN30 M/75 JAK2 V617F (ND) ND ND Trisomy 9/del Y VAF: variation allele frequency; TN: triple negative; ND: not determined; NGS: next generation sequencing Ten subjects, representing 42% of those tested for NGS, harboured 12 heterozygous variants in RUNX1 , CUX1 , ABL1 , ASXL1 , DNMT3A, CSF3R, TET2, NF1 , and KIT we defined germline having VAFs within the 45–55% range. Subject UPN10 had co-occurring variations in CUX1 and ABL1 . The putative germline gene variations were non-synonymous, missense, single nucleotide changes (n = 10) or 3’ UTR (n = 1), 5’ UTR (n = 1) and were classified by ClinVar ( https://www.ncbi.nlm.nih.gov/clinvar ) as benign (n = 2), benign/likely benign (n = 2), likely benign (n = 2), of uncertain significance (n = 4), with conflicting classification of pathogenicity (n = 2), or were unknown to the ClinVar database (n = 2;Table 4 ). No subject with a putative germline mutation had a family history of a highly penetrant cancer-predisposing variation. Thromboses With a median follow-up of 9.1 years (IQR, 4-14.2 years), 27 subjects (90%) had at least one major thrombotic event from 2 years before diagnosis to last follow-up (Table 5 ). Overall thrombotic events were 38 (mean, 1.3 events x subject) with an incidence of 6.5 events x 100 subject-years (95% CI, 3.4–11.7). Twenty-six out of 38 (68%) thromboses were vein thrombosis in atypical sites including splanchnic (n = 20), Budd-Chiari syndrome (n = 3), and sinus vein thrombosis (n = 3). Post-diagnosis thrombosis occurred in 12 subjects with an incidence of 4.4 events x 100 person-years (95% CI, 2.2–8.8). Table 5 Major thrombotic events occurring in 30 subjects diagnosed with CMD-NBV, considering a time frame of two years before diagnosis up to the last follow-up Number of events Overall thrombotic events, n 38 Arterial thrombosis, n (%) 8 (21) - In 2 years before diagnosis, n 5 - At diagnosis, n 2 - After diagnosis, n 1 Deep vein thrombosis in typical sites, n (%) 3 (8) - In 2 years before diagnosis, n 3 - At diagnosis, n 0 - After diagnosis, n 0 Venous thrombosis in atypical sites, n (%) 27 (71) - In 2 years before diagnosis, n 6 - At diagnosis, n 16 - After diagnosis, n 5 Outcomes Subjects with portal vein thrombosis or Budd-Chiari syndrome were permanently anticoagulated, whilst subjects with peripheral arterial thrombosis or myocardial infarction received anti-platelet therapy. During the follow-up, 13 subjects received hydroxyurea as antithrombotic prophylaxis at a median time from diagnosis of 1.8 months (IQR, 1.2–3.7 months). No subject had a splenectomy or a hematopoietic cell transplant. No subject had blast transformation. Subject UPN14 progressed at 14.2 years after diagnosis towards an active disease consisting in splenomegaly > 10 cm from the costal margin, hemoglobin concentration 103 g/L, platelet concentration, 108 x 10E + 9/L, blood immature myeloid cells, blood CD34-positive cells 44 x 10E + 6/L, JAK2 V617F VAF 98% and bone marrow fibrosis grade-3 (previous grade-1). The subject received hydroxyurea and ruxolitinib sequential therapy. Subjects UPN7, UPN15, and UPN21had a platelet concentration < 150 x10E + 9 at diagnosis and subjects UPN15 also had a WBC < 4 x10E + 9 at diagnosis. These abnormalities recovered without intervention. No subject other than UPN14 had a > 10% increase in JAK2 V617F VAF. Twenty eight subjects had a 2nd bone marrow biopsy. Three of 19 subjects with grade-0 bone marrow fibrosis at diagnosis progressed to grade 1, and 2 of 9 with bone marrow fibrosis grade-1 progressed to ≥ grade 2. There were no concurrent changes in blood cell concentrations save in subject UPN14. The 10-year CMD-NBV-specific survival was 100%. Subject UPN3 developed intra-ductal breast cancer 10 months after diagnosis. Subject UPN23 developed breast cancer 2 years after diagnosis. Subject UPN29 developed lung carcinoma 4 years after diagnosis and she died 6 months thereafter. Subject UPN30 developed small lymphocytic lymphoma 8 years after diagnosis followed by lung adenocarcinoma 10 years after diagnosis. Subject UPN14 was diagnosed with monoclonal gammopathy of uncertain significance 10 years after diagnosis. In summary, 5 out of 30 subjects (17%) developed 6 primary second malignant or premalignant diseases, giving a post-diagnosis incidence of 6 events x 100 subject-years (95% CI, 2.4–12.5). Subject UPN19 died 12 years after diagnosis for liver sequelae of Budd-Chiari syndrome. The 20-year survival was 84% (95% CI, 64–94%) from diagnosis. Discussion Our analysis of 30 consecutive subjects with CMD-NBV highlights the uniqueness of the clinical characteristics we delineated in the original description of this form of MPN, 3 and allowed us to derive new insights on its bio-pathology. One distinct clinical hallmark of CMD-NBV is the situational diagnosis: in this cohort, 70% of cases had the diagnosis made whilst investigating a possible MPN triggered by an incidental or symptomatic venous or arterial thrombosis. Another clinical feature is the markedly elevated risk of thrombosis, especially splanchnic vein thrombosis, with an incidence of a major thrombotic event of 6.5 events x 100 subject-years. Third, at a median follow-up of 9.1 years, all but one subjects remained asymptomatic with no change in hematological values, despite coincidental increase of bone marrow fibrosis. The situation-based diagnosis and the indolent disease phenotype challenge the knowledge of a trustworthy epidemiology of CMD-NBV. The median age at diagnosis of the cohort was 45 years old with a range from 20 to 75 years. However, the age at diagnosis mostly reflects the age of incidental thrombosis. Moreover, the 3 percent incidence of the variant in our database arguably does not portrait its prevalence since screening for an occult MPN in subjects with thrombosis is case-specific. In particular, it is common in splanchnic vein thrombosis, 18 but uncertain in unexplained peripheral vein or arterial thrombosis, and uncommon in older subjects. 19 – 21 . If normal blood values we entered into the definition of the CMD-NBV variant resulted coherent with the values of blood parameters of the cohort, the morphological picture of peripheral blood does not. In fact, in more than 40% of cases blood eosinophilia, basophilia or monocytosis at diagnosis, and in 90% of cases a small population of macro-thrombocytes was documented. We interpreted these signs as an expression of the early CMD-NBV malignancy. Aligning with the literature suggesting that individuals with MPNs generally have poorer health compared with the normal population, here we documented that the median value of BMI at diagnosis (26.2 kg/m 2 ) felt in the category of overweight, and was higher than that reported in Italian cases with PV (24.2 kg/m 2 ), 22 or PMF candidate to ruxolitinib (23.9 kg/m 2 ), 23 or allogeneic HSCT (24.9 kg/m 2 ). 24 Moreover, 14% of cases were obese. This result suggests a possible mechanistic relation between obesity and myeloproliferation applies in CMD-NBV, as has been documented in other pre-cancers and cancers. 25 We also documented co-morbidities were common in subjects with CMD-NBV. By considering the Charlson co-morbidity index, 48% of subjects had one or more comorbid condition at diagnosis, mirroring the results of Italian PV and PMF cases in whom 40% and 51% of subjects, respectively, had at least one Charlson’s comorbidity. 23 , 24 European Health Interview Survey (EHIS) multimorbidity analysis showed that 48% of individuals with CMD-NBV have one or more comorbidity, a rate higher than the 26.2% reported in an European control population. 26 Finally, by using the Horvat classification of comorbidity, cardiovascular and autoimmune comorbidities resulted to dominate our population of subjects. These findings highlight the importance of host and environmental risk factors in CMD-NBV. Moreover, the co-occurrence of rare diseases, like Horton arteritis, familial sclerosing cholangitis, osteopecilia and dural artero-venous fistula, suggest etiological heterogeneity of CMD-NBV. A major aim of our report was to investigate the clonal architecture of the CMD-NBV by studying the molecular profile of the subjects. We found most subjects with CMD-NBV had JAK2 V617F . What makes CMD-NBV unique is the low JAK2 V617F VAF at diagnosis (median value, 7.8%) and no CALR and MPL variants. Twenty-five percent of subjects had one or more additional non-driver somatic mutations, like people with a chronic phase MPN. 27–29 A result that could open new perspectives on the pathogenesis of CMD-NBV was that a high proportion (42%) of subjects had variants in genes involved in hematopoiesis and leukemia which we interpreted as germline. These variants overlap somatic variants in ASXL1 , TET2 , DNMT3A. None of these putative germline variants is reported as high-penetrance cancer predisposing. However, the RUNX1 (c.167C > T) variant, classified now as benign, may be up-graded to higher level of pathogenicity considering additional segregation data reporting two families where the germline variant was associated with thrombocytopenia and with evolution to a myelodysplastic syndrome (MDS). 30,31 We hypothesize these putative rare germline variants may predispose to CMD-NBV in the presence of JAK2 V617F or by combining with other genes or environmental factors. Literature reinforces this hypothesis. For example, somatic and germline CSF3R (c.2422G > A) is reported in MDS and MDS/MPN. 32–34 Germline ASXL1 (c.3306G > T) is reported in 4 out of 62 children with chronic myeloid leukemia (CML), 35 germline DNMT3A (c.1502A > G) is reported in a child with acute myeloid leukemia (AML). 36 Finally, 3’UTR ASXL1 (c.*87A > G) variant is associated with a low blood basophils concentration and with lower eosinophils and monocytes concentration. 37 Identifying germline variants predisposing to CMD-NBV can potentially improve our understanding of initiating events and help deepen its biology and pathophysiology. For example, RUNX1 variants associated with a megakaryocyte disorder would suggest RUNX1 as a critical mediator of CMD-NBV subtype-specific differences. In conclusion, this expanded cohort of subjects with CMD-NBV highlights the clinical variant presents as a hidden, thrombosis prone, early MPN. The characterization of somatic mutation profiles fosters the development of strategies for early interception and intervention. The hypothesis of high incidence of predisposing germline variants in myeloid genes drives the future research with the perspective to validate the result and to investigate the heritability of the identified germline variants. Declarations Competing interests: Robert Peter Gale is a consultant to BeiGene Ltd., Fusion Pharma LLC, LaJolla NanoMedical Inc., Mingsight Parmaceuticals Inc., Kite Pharma and CStone Pharmaceuticals; Advisor to Antegene Biotech LLC, Medical Director and FFF Enterprises Inc.; Partner in AZACA Inc.; Board of Directors, RakFond Foundation for Cancer Research Support; Scientific Advisory Board, StemRad Ltd. Competing interests Robert Peter Gale is a consultant to BeiGene Ltd., Fusion Pharma LLC, LaJolla NanoMedical Inc., Mingsight Parmaceuticals Inc., Kite Pharma and CStone Pharmaceuticals; Advisor to Antegene Biotech LLC, Medical Director and FFF Enterprises Inc.; Partner in AZACA Inc.; Board of Directors, RakFond Foundation for Cancer Research Support; Scientific Advisory Board, StemRad Ltd. Ethics The research was conducted in accordance with the World Medical Association Declaration of Helsinki. All subjects gave written informed consent approved by the IRCCS Policlinico S. Matteo Foundation Institutional Ethics Committee. The Ethics Committee of the Hospital also approved a written informed consent for patients to donate samples for molecular research (reference number 20110004143 of the 26.9.2011). Authors Contributions GB designed the study, analyzed the data, and wrote the first version of the manuscript. VR and RPG contributed in writing the manuscirpt. AI, CT, MCF, AC, AR, VDS enrolled patients. MM, RC, AC, CA led the database sample collection and clinical characterization efforts. TB, AR and LM revised the typescript and discussed the results. PC did driver mutations genotyping for the dataset. LP and AG reviewed the bone marrow biopsies of patients diagnosed in the Hospital of Bergamo and Crema, respectively. AG, MG, MB performed DNA NGS analysis. ADS helped with the statistical analysis. All authors have read and agreed to the published version of the manuscript. Acknowledgements Supported by AIRC 5 x 1000 call “Metastatic disease: the key unmet need in oncology” to MYNERVA project, #21267 (MYeloid Research Venture AIRC), and AIRC Individual Grant 2024, project #31013. Data Availability All data generated or analysed during this study are included in this article. Further enquires can be directed to the corresponding author. References Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al . 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Supplementary Files CMDNBVLeukemiaTable1.docx Table 1 CMDNBVLeukemiaTable2.docx Table 2 CMDNBVLeukemiaTable3.docx Table 3 CMDNBVLeukemiaTable4.docx Table 4 CMDNBVLeukemiaTable5.docx Table 5 CMDNBVLeukemiaSupplementalmaterial.docx Supplemental material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:18:58\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":18578,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTable 1\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaTable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/af052d52e0a6b7c2d31da4d0.docx\"},{\"id\":82883061,\"identity\":\"9e6899bb-d0ee-4e92-80f5-b57403783d82\",\"added_by\":\"auto\",\"created_at\":\"2025-05-16 11:18:58\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":15506,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTable 2\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaTable2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/e9d7d0df34319fbbf3b34534.docx\"},{\"id\":82883804,\"identity\":\"3d7b6170-0121-47a8-b4a8-614373e58197\",\"added_by\":\"auto\",\"created_at\":\"2025-05-16 11:26:58\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16289,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTable 3\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaTable3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/df40b1c30de2421a624f0aa4.docx\"},{\"id\":82883063,\"identity\":\"206eb23f-d4a8-4543-83fb-ba9d7711e679\",\"added_by\":\"auto\",\"created_at\":\"2025-05-16 11:18:58\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":20729,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTable 4\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaTable4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/b2b8dc5953a31f2909ec7442.docx\"},{\"id\":82883069,\"identity\":\"8d1b4493-58e3-4c2d-8e53-c5df12cf4bcf\",\"added_by\":\"auto\",\"created_at\":\"2025-05-16 11:18:58\",\"extension\":\"docx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":15606,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTable 5\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaTable5.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/649002feaba3b0989363a389.docx\"},{\"id\":82883071,\"identity\":\"354d23cb-491d-49ef-9df4-79ab838a8172\",\"added_by\":\"auto\",\"created_at\":\"2025-05-16 11:18:58\",\"extension\":\"docx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":34197,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplemental material\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"CMDNBVLeukemiaSupplementalmaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6622364/v1/162174fb1a000924517d6021.docx\"}],\"financialInterests\":\"\\u003cb\\u003eYes\\u003c/b\\u003e there is potential conflict of interest.\",\"formattedTitle\":\"Clonal megakaryocyte dysplasia with normal blood values (CMD-NBV): an unique form of early myeloproliferative neoplasm\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe World Health Organization (WHO) and the International Consensus Conference (ICC) classify BCR::ABL-negative classical myeloproliferative neoplasms (MPNs) into three major types, essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF). PMF is further divided into two distinct subtypes, prefibrotic (pre-MF) and overt myelofibrosis (overt-MF).\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e We recently proposed two cognate variants in the MPN domain, named clonal megakaryocyte dysplasia with normal blood values (CMD-NBV),\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e and clonal megakaryocyte dysplasia with isolated thrombocytosis (CMD‐IT).\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e Pre-MF, overt-MF, CMD-NBV and CMD-IT share bone marrow (BM) morphological feature of megakaryocyte hyperplasia and dysplasia and were clustered in the new category of myelofibrosis‐type megakaryocyte dysplasia (MTMD).\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eFacing the new classificatory complexity, we conceptualized MTMD as a spectrum of disorders with a distinct phenotype and prognosis.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e This view highlights the interest on the molecular events that drive specific disease presentations and explain their clinical features and laboratory findings.\\u003c/p\\u003e \\u003cp\\u003eAmong the MTMD variants, CMD-NBV is the rarest and least characterized. CMD-NBV connotes normal hematologic values or minimal abnormalities and is mostly diagnosed in the context of venous or arterial thrombosis. In the cohort of 15 cases we reported in 2022,\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e 10 had the canonical somatic \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e mutation, while in the remaining cases the driver of clonal expansion was not identified.\\u003c/p\\u003e \\u003cp\\u003eWith the aim to improve our knowledge on the epidemiological, clinical and pathobiological profile of CMD-NBV, we now report an expanded series of 30 consecutive subjects with CMD-NBV. To delineate subjects\\u0026rsquo; molecular characteristics that could represent disease specific and defining molecular markers, we studied variant topography by next generation sequencing (NGS) technique.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSubjects characteristics and clinical procedures\\u003c/h2\\u003e \\u003cp\\u003eIn this single-centre retrospective study, consecutive subjects with CMD-NBV were identified from the institutional database of the Centre for the Study of Myelofibrosis of the IRCCS Policlinico S. Matteo Foundation in Pavia, Italy (Pavia-CSM-database). The database contains consecutive individuals registered since 1998 with a diagnosis of MPN and examined at least once. This report consists of 14 cases we published in 2022,\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e and 16 newly referred cases. One previously reported subject was excluded since a missed history of thrombocytosis (platelet count\\u0026thinsp;\\u0026gt;\\u0026thinsp;450 x10E\\u0026thinsp;+\\u0026thinsp;9/L) contrasted our adjudicated CMD-NBV diagnostic criteria. All the subjects gave written informed consent approved by the IRCCS Policlinico S. Matteo Foundation Institutional Ethics Committee to be included in the database and to donate samples for genetic and molecular research on their disease.\\u003c/p\\u003e \\u003cp\\u003eDiagnosis of CMD-NBV was based on two distinct criteria:\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e 1. BM megakaryocyte hyperplasia and dysplasia consistent with the 2009 WHO diagnostic criteria for pre-MF.\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e 2. Failure to meet the clinical-hematological WHO criteria for PV or ET, and any of the four minor diagnostic criteria for pre-MF, i.e. palpable splenomegaly, anemia, white blood cell (WBC) count\\u0026thinsp;\\u0026ge;\\u0026thinsp;11 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, and increased serum lactate dehydrogenase level (LDH). As a deviation from these criteria, in this report we classified as CMD-NBV also subjects presenting with a palpable splenomegaly (no more than 5 cm from the costal margin) who had concurrent splanchnic vein thrombosis.\\u003c/p\\u003e \\u003cp\\u003eFor all subjects, the database contained information at diagnosis about sex, age, body mass index (BMI), spleen size (clinical measurement), complete blood count with differential, and serum LDH level. BMI was categorized into underweight (\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e), normal weight (18.5 to \\u0026lt;\\u0026thinsp;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e), overweight (25 to \\u0026lt;\\u0026thinsp;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) and obese (\\u0026ge;\\u0026thinsp;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e).\\u003csup\\u003e9\\u003c/sup\\u003e Abnormal blood concentrations were defined as hemoglobin\\u0026thinsp;\\u0026gt;\\u0026thinsp;153 g/L (female) or \\u0026gt;\\u0026thinsp;160 g/L (male); WBC count\\u0026thinsp;\\u0026gt;\\u0026thinsp;8.8 x 10E\\u0026thinsp;+\\u0026thinsp;9/L; monocytes\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.7 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, and platelets\\u0026thinsp;\\u0026gt;\\u0026thinsp;390 x 10E\\u0026thinsp;+\\u0026thinsp;9/L.\\u003csup\\u003e10,11\\u003c/sup\\u003e Blood eosinophils percentage\\u0026thinsp;\\u0026gt;\\u0026thinsp;7% and blood basophils\\u0026thinsp;\\u0026gt;\\u0026thinsp;1% were defined outside the normal range.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e In subjects analyzed at diagnosis and from whom we had peripheral blood slides, slides were re-examined for platelet morphology. For the purpose of the current study, platelets with a diameter\\u0026thinsp;\\u0026ge;\\u0026thinsp;5 \\u0026micro;m were considered macroplatelets.\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe reason for initial clinical presentation and diagnosis and all information on concomitant diseases were retrieved from medical records. Charlson Co-morbidity Index (CCI) was calculated as described.\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e For maintaining a person-centric rather than disease-centric perspective, we defined chronic physical multi-morbidity using the chronic physical illnesses (CPI) based on the modified European Health Interview Survey (EHIS) guidelines.\\u003csup\\u003e\\u003cb\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/b\\u003e\\u003c/sup\\u003e To contextualize co-morbidities in the field of MPNs, we also categorized conditions diagnosed before or concurrent with CMD-NBV as autoimmune, cardiovascular/metabolic, infectious, and other inflammatory or malignant as described.\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn all subjects, key pathological BM features were obtained from the pathology report. Thrombosis was defined as any venous or arterial thrombo-embolism excluding superficial vein thrombosis. Thrombotic events that occurred within 2 years prior to the diagnosis of CMD-NBV were defined as MPN-related.\\u003c/p\\u003e \\u003cp\\u003eData on \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e, \\u003cem\\u003eMPL\\u003c/em\\u003e and \\u003cem\\u003eCALR\\u003c/em\\u003e mutations and variant allele frequencies (VAFs) were available at the time of diagnosis. NGS analyses were done on DNA from granulocyte collected at diagnosis or within 12 months after diagnosis and stored in our institutional biobank. Myeloid mutations were analyzed by NGS at the Lab of Molecular Hematology of the IRCCS Policlinico San Matteo Foundation and University of Pavia, Pavia, Italy. Screening for myeloid cancer associated mutations was performed using oncoReveal Myeloid Panel (Pillar Biosciences, MA, USA) an amplicon-based library preparation chemistry that interrogates genes recurrently mutated in myeloid neoplasms. Briefly, pairs of DNA oligos targeting each region of interest were used in the first round of gene-specific PCR and the products subsequently purified via size selection. After purification, a second round of PCR adds index adaptors and P5 \\u0026amp; P7 sequences to each library for sample tracking and sequencing. The resulting libraries were further purified and 2x250 bp paired-end sequenced on an Illumina MiSeq-system platform. Functionally annotated variants were filtered based on the information retrieved from public databases, and the expected germline allele frequency. The remaining variants were finally tagged as oncogenic, based on the information derived from the literature, the Catalogue of Somatic Mutations in Cancer (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://cancer.sanger.ac.uk/cancergenome/projects/cosmic\\u003c/span\\u003e\\u003cspan address=\\\"http://cancer.sanger.ac.uk/cancergenome/projects/cosmic\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and in silico prediction effect, as previously described. Rare germline variants were defined as those with population allele frequencies below 1% across ExAC database. Classification of germline variants was performed using Franklin (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://franklin.genoox.com\\u003c/span\\u003e\\u003cspan address=\\\"https://franklin.genoox.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) a curation tools based on the American College of Medical Genetics (ACMG) guidelines. Details of library preparation, sequencing, and variant analysis are provided in the \\u003cem\\u003eSupplemental Methods\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStatistics\\u003c/h3\\u003e\\n\\u003cp\\u003eSubject co-variates are reported as median and interquartile range (IQR) for continuous variables. Categorical variables are reported as frequency rates and percentages and analyzed using Chi-square test. Independent group t-test was used to analyze normally distributed continuous variables. The Kurskal-Wallis test was used for non-normally distributed data. Major study endpoints were progression to active disease, blast transformation, death and thrombotic events. Progression to active disease was defined as: (1) disease-associated hemoglobin concentration\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 g/L; (2) spleen\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 cm below the left costal margin; (3) platelets\\u0026thinsp;\\u0026lt;\\u0026thinsp;150 x 10E\\u0026thinsp;+\\u0026thinsp;9/L; and/or (4) WBC count\\u0026thinsp;\\u0026lt;\\u0026thinsp;4 x 10E\\u0026thinsp;+\\u0026thinsp;9/L or \\u0026gt;\\u0026thinsp;12 x 10E\\u0026thinsp;+\\u0026thinsp;9/L. To avoid confounding, we censored development of any of these criteria at the start of any disease-modifying intervention or at the diagnosis of a new cancer. Frequency of thrombotic events was expressed as incidence, calculated as numbers of events x 100 subject-years of observation with 95% Confidence Interval (CI). Results were considered statistically significant if P-values were \\u0026lt;\\u0026thinsp;0∙05. Computations were done with STATISTICA\\u0026copy; software \\u003cb\\u003e(\\u003c/b\\u003eDell Technologies Inc. Round Rock, TX, USA).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe 30 adults that fulfilled our adjudicated criteria for CMD-NBV represent the 2.4% of all subjects registered in the Pavia-CSM-database for the MTMD category. Sixteen are men and median age is 47.5 years (IQR, 39\\u0026ndash;53).\\u003c/p\\u003e\\n\\u003ch3\\u003eDiagnosis\\u003c/h3\\u003e\\n\\u003cp\\u003eIn 21 subjects (70%) the diagnosis of CMD-NBV was synchronous with an unexplained symptomatic venous or arterial thrombotic event (n\\u0026thinsp;=\\u0026thinsp;15), incidental discovery of portal cavernoma (n\\u0026thinsp;=\\u0026thinsp;5) or a diagnosis of post-embolic pulmonary hypertension (n\\u0026thinsp;=\\u0026thinsp;1). In 9 other subjects, the diagnosis was driven by the incidental finding of laboratory abnormalities consistent with an MPN (n\\u0026thinsp;=\\u0026thinsp;8), or of vertebral bone MRI abnormality interpreted as bone marrow involvement by a myeloid disorder (n\\u0026thinsp;=\\u0026thinsp;1; \\u003cem\\u003eSupplemental Table\\u0026nbsp;1\\u003c/em\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eCo-variates at diagnosis\\u003c/h3\\u003e\\n\\u003cp\\u003eSubject co-variates at diagnosis are displayed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. With median hematological co-variates values in the normal range, 4, 5 and 6 subjects had hemoglobin, WBC and platelet concentration above the upper range of normal, while 4 had platelet count (n\\u0026thinsp;=\\u0026thinsp;3) or WBC concentrations (n\\u0026thinsp;=\\u0026thinsp;1) under the lower range of normal. Nine subjects diagnosed with a synchronous splanchnic vein thrombosis had a palpable spleen (no more than 3 cm below the costal margin). Two subjects had increased eosinophils, 5 increased basophils and 5 increased monocytes, yet 12 subjects (40%) had at least one of the above reported abnormalities. Blood smears at diagnosis was available in 20 subjects: macro-platelets were documented in 16 of them (80%). Macro-platelets were a small proportion of platelets in coexistence with normal platelets. Mean platelet volume was greater than 12 fl in one subject. \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e was detected in 21 subjects (70%) with a median VAF of 7.8% (IQR, 5.2\\u0026ndash;17.9%). No \\u003cem\\u003eCALR\\u003c/em\\u003e, \\u003cem\\u003eMPL\\u003c/em\\u003e or \\u003cem\\u003eJAK2\\u003c/em\\u003e exon 12 mutations were detected in the 9 remaining individuals. Median BMI at diagnosis was 26.1 m\\u003csup\\u003e2\\u003c/sup\\u003e/kg (IQR, 23.1\\u0026ndash;28.7). No subject had a BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;18.5 m\\u003csup\\u003e2\\u003c/sup\\u003e/kg, 11 (37%) were normal weighted, 15 (50%) had a BMI between 25 m\\u003csup\\u003e2\\u003c/sup\\u003e/kg and 30 m\\u003csup\\u003e2\\u003c/sup\\u003e/kg, and 4 (13%) were obese.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline co-variates of subjects with CMD-NBV (n\\u0026thinsp;=\\u0026thinsp;30). Data are shown for the whole population and according to sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eAll subjects (n\\u0026thinsp;=\\u0026thinsp;30)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMales\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;16)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFemales\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;14)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDemography and anthropometry\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, yrs, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e47.5\\u003c/p\\u003e \\u003cp\\u003e(39\\u0026ndash;53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003cp\\u003e(30\\u0026ndash;52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46\\u003c/p\\u003e \\u003cp\\u003e(41\\u0026ndash;54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex male, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e16 (53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e26.1\\u003c/p\\u003e \\u003cp\\u003e(23.1\\u0026ndash;28.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25.9\\u003c/p\\u003e \\u003cp\\u003e(23.6\\u0026ndash;28.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.4\\u003c/p\\u003e \\u003cp\\u003e(21.7\\u0026ndash;28.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, \\u0026ge; 30, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical-hematological co-variates\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHemoglobin, g/dL, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.7\\u003c/p\\u003e \\u003cp\\u003e(12.8\\u0026ndash;15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.9\\u003c/p\\u003e \\u003cp\\u003e(14-15.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.3\\u003c/p\\u003e \\u003cp\\u003e(12.7\\u0026ndash;13.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMean cell volume, fl, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87.3\\u003c/p\\u003e \\u003cp\\u003e(81.2\\u0026ndash;89.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86.7\\u003c/p\\u003e \\u003cp\\u003e(80.8\\u0026ndash;88.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88\\u003c/p\\u003e \\u003cp\\u003e(85-89.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC x 10E\\u0026thinsp;+\\u0026thinsp;9/L, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.2\\u003c/p\\u003e \\u003cp\\u003e(5.7\\u0026ndash;7.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.1\\u003c/p\\u003e \\u003cp\\u003e(5.3\\u0026ndash;7.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.7\\u003c/p\\u003e \\u003cp\\u003e(5.9\\u0026ndash;8.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC\\u0026thinsp;\\u0026ge;\\u0026thinsp;8.8 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 (19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 (14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC\\u0026thinsp;\\u0026lt;\\u0026thinsp;4 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, n(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 (0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEosinophils percent, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.2\\u003c/p\\u003e \\u003cp\\u003e(2-4.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(2-3.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.4\\u003c/p\\u003e \\u003cp\\u003e(2.7\\u0026ndash;6.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.075\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEosinophils percent\\u0026thinsp;\\u0026gt;\\u0026thinsp;7, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 (14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBasophils percent, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.7\\u003c/p\\u003e \\u003cp\\u003e(0.3-1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5\\u003c/p\\u003e \\u003cp\\u003e(0.2-1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003cp\\u003e(0.5\\u0026ndash;1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBasophils percent\\u0026thinsp;\\u0026gt;\\u0026thinsp;1, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 (28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocytes x 10E\\u0026thinsp;+\\u0026thinsp;9, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e486\\u003c/p\\u003e \\u003cp\\u003e(410\\u0026ndash;556)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e496\\u003c/p\\u003e \\u003cp\\u003e(409\\u0026ndash;548)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e463\\u003c/p\\u003e \\u003cp\\u003e(411\\u0026ndash;743)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocytes\\u0026thinsp;\\u0026gt;\\u0026thinsp;700 x 10E\\u0026thinsp;+\\u0026thinsp;9, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelets x10E\\u0026thinsp;+\\u0026thinsp;9/L, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e274\\u003c/p\\u003e \\u003cp\\u003e(205\\u0026ndash;371)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e209\\u003c/p\\u003e \\u003cp\\u003e(192\\u0026ndash;278)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e356\\u003c/p\\u003e \\u003cp\\u003e(277\\u0026ndash;396)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelets\\u0026thinsp;\\u0026gt;\\u0026thinsp;390 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 (28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelets\\u0026thinsp;\\u0026lt;\\u0026thinsp;150 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 (19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 (0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSpleen size, cm E\\u0026thinsp;+\\u0026thinsp;2, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90\\u003c/p\\u003e \\u003cp\\u003e(90\\u0026ndash;110)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e90\\u003c/p\\u003e \\u003cp\\u003e(90\\u0026ndash;120)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e90\\u003c/p\\u003e \\u003cp\\u003e(90\\u0026ndash;90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSpleen size\\u0026thinsp;\\u0026gt;\\u0026thinsp;90 cm\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePlasma LDH, x ULN, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.86\\u003c/p\\u003e \\u003cp\\u003e(0.78-1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.78\\u003c/p\\u003e \\u003cp\\u003e(0.66\\u0026ndash;0.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.93\\u003c/p\\u003e \\u003cp\\u003e(0.83\\u0026ndash;1.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum cholesterol, mg/dL, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e190\\u003c/p\\u003e \\u003cp\\u003e(146\\u0026ndash;217)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e178\\u003c/p\\u003e \\u003cp\\u003e(133\\u0026ndash;200)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e194\\u003c/p\\u003e \\u003cp\\u003e(179\\u0026ndash;218)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBlood CD34-positive cells x 10E\\u0026thinsp;+\\u0026thinsp;6, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.37\\u003c/p\\u003e \\u003cp\\u003e(1.60\\u0026ndash;4.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.19\\u003c/p\\u003e \\u003cp\\u003e(1.36\\u0026ndash;4.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.56\\u003c/p\\u003e \\u003cp\\u003e(1.63\\u0026ndash;3.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMolecular co-variates\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (62.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11 (78.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e allele frequency, median (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.8\\u003c/p\\u003e \\u003cp\\u003e(5.2\\u0026ndash;17.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.9\\u003c/p\\u003e \\u003cp\\u003e(3.7\\u0026ndash;10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.2\\u003c/p\\u003e \\u003cp\\u003e(6.8\\u0026ndash;19.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eCALR\\u003c/em\\u003e mutation, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMPL mutation, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTriple negative, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (37.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (21.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBMI: Body mass index; IQR: Interquartile range. ULN: upper limit of normal.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBy dividing subjects according to sex, males had significantly higher hemoglobin concentrations than had females. By contrast, males had lower platelet count and LDH plasma concentration than females\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCo- and multi-morbidities\\u003c/h2\\u003e \\u003cp\\u003eAt the time of our Centre referral, 14 subjects (47%) had one or more comorbidities according to the Charlson co-morbidity criteria (CCI\\u0026thinsp;\\u0026ge;\\u0026thinsp;1;Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e): 7 had a CCI\\u0026thinsp;=\\u0026thinsp;1, 6 a CCI\\u0026thinsp;=\\u0026thinsp;2, and 1 a CCI\\u0026thinsp;=\\u0026thinsp;3, with a median of 0.8 co-morbidities \\u003cem\\u003eper\\u003c/em\\u003e subject. The most common co-morbidities were TIA/stroke (n\\u0026thinsp;=\\u0026thinsp;4), solid neoplasia (n\\u0026thinsp;=\\u0026thinsp;4), peripheral vascular disease (n\\u0026thinsp;=\\u0026thinsp;3). Multi-morbidity was present in 14 subjects (47%): 8 had one co-occurring morbidity, while 3 had 2, and 3 had 3 co-occurring morbidities. The most frequent CPI was arterial hypertension (n\\u0026thinsp;=\\u0026thinsp;10; \\u003cem\\u003eSupplemental Table\\u0026nbsp;2\\u003c/em\\u003e). According to the Horvat-defined co-morbidities, 15 subjects had 1 or more co-morbid condition (\\u003cem\\u003eSupplement\\u003c/em\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Thirteen subjects had a co-morbidity classified as cardiovascular or metabolic, 9 as inflammatory or autoimmune, and 4 as malignant. Four co-occurring inflammatory/autoimmune diseases were rare diseases: one subject was diagnosed with osteopecilia, a rare benign condensing osteopathy, one had familial sclerosing cholangitis, one Horton arteritis and one dural arteriovenous fistula due to sinus thrombosis (currently defined related to an inflammatory micro-environment).\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCo-morbidities of subjects with CMD-NBV according to the Charlson co-morbidity index. Data were obtained at the first referral at our Center\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCo-moribidities, n (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSubjects number (%)\\u003c/p\\u003e \\u003cp\\u003eTotal\\u0026thinsp;=\\u0026thinsp;30\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAcute myocardial infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSolid neoplasia\\u003c/p\\u003e \\u003cp\\u003e- Localized\\u003c/p\\u003e \\u003cp\\u003e- Metastatic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (13)\\u003c/p\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes mellitus\\u003c/p\\u003e \\u003cp\\u003e- Uncomplicated\\u003c/p\\u003e \\u003cp\\u003e- Complicated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTransient ischemic attack/stroke\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChronic obstructive pulmonary disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeptic ulcer disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeripheral vascular disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLiver disease\\u003c/p\\u003e \\u003cp\\u003e- Mild\\u003c/p\\u003e \\u003cp\\u003e- Moderate-severe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eConnective tissue disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCongestive heart failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChronic cognitive deficit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHemiplegia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphoma\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeukemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAcquired immune deficiency syndrome\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDetailed analysis of the bone marrow features of 30 subjects with CMD-NBV at diagnosis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eQuantitative variables\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eReduced\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNormal\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIncreased\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCellularity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eErythropoiesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGranulopoiesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 (40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMegakaryopoiesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 (100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQualitative variables\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003ePresent, n subjects (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMegakaryocyte clusters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e22 (73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Loose\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e19 (63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Dense\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMegakaryocyte nuclei\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Hyper-lobulated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e2 (7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Bulbous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e14 (47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSmall megakaryocytes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e6 (20)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eFibrosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Grade 0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e20 (67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Grade 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e10 (33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Grade 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- Grade 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eBone marrow histology\\u003c/h3\\u003e\\n\\u003cp\\u003eResults of bone marrow histology are displayed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Being a necessary criterion for the diagnosis of CMD-NBV, megakaryocyte hyperplasia was a common feature. Age-corrected overall bone marrow cellularity was increased in 1, normal in 9 and decreased in 3. All subjects had\\u0026thinsp;\\u0026ge;\\u0026thinsp;1 signs of megakaryocyte dysplasia, including loose megakaryocyte clusters (n\\u0026thinsp;=\\u0026thinsp;19), dense megakaryocyte clusters (n\\u0026thinsp;=\\u0026thinsp;3), bulbous megakaryocytes (n\\u0026thinsp;=\\u0026thinsp;14), or micromegakaryocytes (n\\u0026thinsp;=\\u0026thinsp;6). No subject had granulocyte or erythroid lineages dysplasia. Bone marrow fibrosis was grade 0 (n\\u0026thinsp;=\\u0026thinsp;20) or grade 1 (n\\u0026thinsp;=\\u0026thinsp;10). Ten subjects had an increased vascular component and one showed megakaryocytes in the blood vessels. Lymphocyte hyperplasia was present in 20 cases. Absence of lymphocytic clonality was established in all the cases. In 7 subjects an increased number of bone marrow eosinophils and in 3 increased mast cells was reported.\\u003c/p\\u003e\\n\\u003ch3\\u003eSomatic and germline variants\\u003c/h3\\u003e\\n\\u003cp\\u003eA panel of 45 genes was sequenced in 24 out of 30 subjects (80%). A total of 10 variants were classified as pathogenic or likely pathogenic somatic variants spread across 4 genes and 6 subjects (25%) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e; \\u003cem\\u003eSupplemental Table\\u0026nbsp;4\\u003c/em\\u003e\\u003cb\\u003e).\\u003c/b\\u003e Subject UPN14 had 2 mutations in \\u003cem\\u003eASXL1\\u003c/em\\u003e, subject UPN23 co-occurring mutations in \\u003cem\\u003eDNMT3A, TET2, SRSF2\\u003c/em\\u003e, while subject UPN29 in \\u003cem\\u003eDNMT3A\\u003c/em\\u003e and \\u003cem\\u003eTET2\\u003c/em\\u003e. The range of variant allele frequency (VAF) at diagnosis was 2.3\\u0026ndash;41%.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGenetic and molecular profile of the 30 subjects diagnosed with CMD-NBV. Data were obtained at diagnosis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCase #\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSex/age\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDriver mutation (VAF%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNGS-somatic variants (VAF%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNGS- putative germline variants (VAF%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCytogenetics\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/25 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/52 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eXY\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/49 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd 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colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/49 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (0.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e (c.4045-1G\\u0026thinsp;\\u0026gt;\\u0026thinsp;A) (null) (3.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e 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\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/54 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (ND)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eXX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/29 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (7.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eASXL1\\u003c/em\\u003e (c.*87A\\u0026thinsp;\\u0026gt;\\u0026thinsp;G) (null) (49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eXY\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/52 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (0.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/53 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/70 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eDNMT3A\\u003c/em\\u003e (c.2320G\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) (p.Glu774*) (8)\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e (c.4791del) (p.Tyr1598Ilefs*12) (4)\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003eSRSF2\\u003c/em\\u003e (c.161C\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) (p.Ser54Phe) (3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eXX\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/43 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/20 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e (c.1018A\\u0026thinsp;\\u0026gt;\\u0026thinsp;G) (p.Ile340Val) (50.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/49 yrs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (17.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eDNMT3A\\u003c/em\\u003e (c.1656 delC) (p.Asn552fs) (2.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNF1\\u003c/em\\u003e (c.6790A\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) (Ile2264Leu) (50.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (3.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNeg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e (c.521C\\u0026thinsp;\\u0026gt;\\u0026thinsp;A) (p.Pro174His)(44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eF/55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eDNMT3A\\u003c/em\\u003e (c.1490G\\u0026thinsp;\\u0026gt;\\u0026thinsp;A) (p.Cys4977Tyr) (2.8)\\u003c/p\\u003e \\u003cp\\u003e\\u003cem\\u003eTET2\\u003c/em\\u003e (c.4393C\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) (p.Arg1465*) (2.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eKIT\\u003c/em\\u003e (c.101C\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) (p.Pro34Leu) (52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUPN30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM/75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e (ND)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eND\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eTrisomy 9/del Y\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eVAF: variation allele frequency; TN: triple negative; ND: not determined; NGS: next generation sequencing\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTen subjects, representing 42% of those tested for NGS, harboured 12 heterozygous variants in \\u003cem\\u003eRUNX1\\u003c/em\\u003e, \\u003cem\\u003eCUX1\\u003c/em\\u003e, \\u003cem\\u003eABL1\\u003c/em\\u003e, \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eDNMT3A, CSF3R, TET2, NF1\\u003c/em\\u003e, and \\u003cem\\u003eKIT\\u003c/em\\u003e we defined germline having VAFs within the 45\\u0026ndash;55% range. Subject UPN10 had co-occurring variations in \\u003cem\\u003eCUX1\\u003c/em\\u003e and \\u003cem\\u003eABL1\\u003c/em\\u003e. The putative germline gene variations were non-synonymous, missense, single nucleotide changes (n\\u0026thinsp;=\\u0026thinsp;10) or 3\\u0026rsquo; UTR (n\\u0026thinsp;=\\u0026thinsp;1), 5\\u0026rsquo; UTR (n\\u0026thinsp;=\\u0026thinsp;1) and were classified by \\u003cem\\u003eClinVar\\u003c/em\\u003e (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ncbi.nlm.nih.gov/clinvar\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ncbi.nlm.nih.gov/clinvar\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) as benign (n\\u0026thinsp;=\\u0026thinsp;2), benign/likely benign (n\\u0026thinsp;=\\u0026thinsp;2), likely benign (n\\u0026thinsp;=\\u0026thinsp;2), of uncertain significance (n\\u0026thinsp;=\\u0026thinsp;4), with conflicting classification of pathogenicity (n\\u0026thinsp;=\\u0026thinsp;2), or were unknown to the \\u003cem\\u003eClinVar\\u003c/em\\u003e database (n\\u0026thinsp;=\\u0026thinsp;2;Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). No subject with a putative germline mutation had a family history of a highly penetrant cancer-predisposing variation.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThromboses\\u003c/h2\\u003e \\u003cp\\u003eWith a median follow-up of 9.1 years (IQR, 4-14.2 years), 27 subjects (90%) had at least one major thrombotic event from 2 years before diagnosis to last follow-up (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Overall thrombotic events were 38 (mean, 1.3 events x subject) with an incidence of 6.5 events x 100 subject-years (95% CI, 3.4\\u0026ndash;11.7). Twenty-six out of 38 (68%) thromboses were vein thrombosis in atypical sites including splanchnic (n\\u0026thinsp;=\\u0026thinsp;20), Budd-Chiari syndrome (n\\u0026thinsp;=\\u0026thinsp;3), and sinus vein thrombosis (n\\u0026thinsp;=\\u0026thinsp;3). Post-diagnosis thrombosis occurred in 12 subjects with an incidence of 4.4 events x 100 person-years (95% CI, 2.2\\u0026ndash;8.8).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMajor thrombotic events occurring in 30 subjects diagnosed with CMD-NBV, considering a time frame of two years before diagnosis up to the last follow-up\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of events\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverall thrombotic events, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eArterial thrombosis, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8 (21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- In 2 years before diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- At diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- After diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeep vein thrombosis in typical sites, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- In 2 years before diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- At diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- After diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVenous thrombosis in atypical sites, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27 (71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- In 2 years before diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- At diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e- After diagnosis, n\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOutcomes\\u003c/h2\\u003e \\u003cp\\u003eSubjects with portal vein thrombosis or Budd-Chiari syndrome were permanently anticoagulated, whilst subjects with peripheral arterial thrombosis or myocardial infarction received anti-platelet therapy. During the follow-up, 13 subjects received hydroxyurea as antithrombotic prophylaxis at a median time from diagnosis of 1.8 months (IQR, 1.2\\u0026ndash;3.7 months). No subject had a splenectomy or a hematopoietic cell transplant. No subject had blast transformation. Subject UPN14 progressed at 14.2 years after diagnosis towards an active disease consisting in splenomegaly\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 cm from the costal margin, hemoglobin concentration 103 g/L, platelet concentration, 108 x 10E\\u0026thinsp;+\\u0026thinsp;9/L, blood immature myeloid cells, blood CD34-positive cells 44 x 10E\\u0026thinsp;+\\u0026thinsp;6/L, \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e VAF 98% and bone marrow fibrosis grade-3 (previous grade-1). The subject received hydroxyurea and ruxolitinib sequential therapy.\\u003c/p\\u003e \\u003cp\\u003eSubjects UPN7, UPN15, and UPN21had a platelet concentration\\u0026thinsp;\\u0026lt;\\u0026thinsp;150 x10E\\u0026thinsp;+\\u0026thinsp;9 at diagnosis and subjects UPN15 also had a WBC\\u0026thinsp;\\u0026lt;\\u0026thinsp;4 x10E\\u0026thinsp;+\\u0026thinsp;9 at diagnosis. These abnormalities recovered without intervention. No subject other than UPN14 had a\\u0026thinsp;\\u0026gt;\\u0026thinsp;10% increase in \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e VAF.\\u003c/p\\u003e \\u003cp\\u003eTwenty eight subjects had a 2nd bone marrow biopsy. Three of 19 subjects with grade-0 bone marrow fibrosis at diagnosis progressed to grade 1, and 2 of 9 with bone marrow fibrosis grade-1 progressed to \\u0026ge;\\u0026thinsp;grade 2. There were no concurrent changes in blood cell concentrations save in subject UPN14. The 10-year CMD-NBV-specific survival was 100%.\\u003c/p\\u003e \\u003cp\\u003eSubject UPN3 developed intra-ductal breast cancer 10 months after diagnosis. Subject UPN23 developed breast cancer 2 years after diagnosis. Subject UPN29 developed lung carcinoma 4 years after diagnosis and she died 6 months thereafter. Subject UPN30 developed small lymphocytic lymphoma 8 years after diagnosis followed by lung adenocarcinoma 10 years after diagnosis. Subject UPN14 was diagnosed with monoclonal gammopathy of uncertain significance 10 years after diagnosis. In summary, 5 out of 30 subjects (17%) developed 6 primary second malignant or premalignant diseases, giving a post-diagnosis incidence of 6 events x 100 subject-years (95% CI, 2.4\\u0026ndash;12.5). Subject UPN19 died 12 years after diagnosis for liver sequelae of Budd-Chiari syndrome. The 20-year survival was 84% (95% CI, 64\\u0026ndash;94%) from diagnosis.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eOur analysis of 30 consecutive subjects with CMD-NBV highlights the uniqueness of the clinical characteristics we delineated in the original description of this form of MPN,\\u003cem\\u003e3\\u003c/em\\u003e and allowed us to derive new insights on its bio-pathology. One distinct clinical hallmark of CMD-NBV is the situational diagnosis: in this cohort, 70% of cases had the diagnosis made whilst investigating a possible MPN triggered by an incidental or symptomatic venous or arterial thrombosis. Another clinical feature is the markedly elevated risk of thrombosis, especially splanchnic vein thrombosis, with an incidence of a major thrombotic event of 6.5 events x 100 subject-years. Third, at a median follow-up of 9.1 years, all but one subjects remained asymptomatic with no change in hematological values, despite coincidental increase of bone marrow fibrosis.\\u003c/p\\u003e \\u003cp\\u003eThe situation-based diagnosis and the indolent disease phenotype challenge the knowledge of a trustworthy epidemiology of CMD-NBV. The median age at diagnosis of the cohort was 45 years old with a range from 20 to 75 years. However, the age at diagnosis mostly reflects the age of incidental thrombosis. Moreover, the 3 percent incidence of the variant in our database arguably does not portrait its prevalence since screening for an occult MPN in subjects with thrombosis is case-specific. In particular, it is common in splanchnic vein thrombosis,\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e but uncertain in unexplained peripheral vein or arterial thrombosis, and uncommon in older subjects.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR20\\\" citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIf normal blood values we entered into the definition of the CMD-NBV variant resulted coherent with the values of blood parameters of the cohort, the morphological picture of peripheral blood does not. In fact, in more than 40% of cases blood eosinophilia, basophilia or monocytosis at diagnosis, and in 90% of cases a small population of macro-thrombocytes was documented. We interpreted these signs as an expression of the early CMD-NBV malignancy.\\u003c/p\\u003e \\u003cp\\u003eAligning with the literature suggesting that individuals with MPNs generally have poorer health compared with the normal population, here we documented that the median value of BMI at diagnosis (26.2 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) felt in the category of overweight, and was higher than that reported in Italian cases with PV (24.2 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e), \\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e or PMF candidate to ruxolitinib (23.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e),\\u003csup\\u003e23\\u003c/sup\\u003e or allogeneic HSCT (24.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e).\\u003csup\\u003e24\\u003c/sup\\u003e Moreover, 14% of cases were obese. This result suggests a possible mechanistic relation between obesity and myeloproliferation applies in CMD-NBV, as has been documented in other pre-cancers and cancers.\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eWe also documented co-morbidities were common in subjects with CMD-NBV. By considering the Charlson co-morbidity index, 48% of subjects had one or more comorbid condition at diagnosis, mirroring the results of Italian PV and PMF cases in whom 40% and 51% of subjects, respectively, had at least one Charlson\\u0026rsquo;s comorbidity.\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e European Health Interview Survey (EHIS) multimorbidity analysis showed that 48% of individuals with CMD-NBV have one or more comorbidity, a rate higher than the 26.2% reported in an European control population.\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e Finally, by using the Horvat classification of comorbidity, cardiovascular and autoimmune comorbidities resulted to dominate our population of subjects. These findings highlight the importance of host and environmental risk factors in CMD-NBV. Moreover, the co-occurrence of rare diseases, like Horton arteritis, familial sclerosing cholangitis, osteopecilia and dural artero-venous fistula, suggest etiological heterogeneity of CMD-NBV.\\u003c/p\\u003e \\u003cp\\u003eA major aim of our report was to investigate the clonal architecture of the CMD-NBV by studying the molecular profile of the subjects. We found most subjects with CMD-NBV had \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e. What makes CMD-NBV unique is the low \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e VAF at diagnosis (median value, 7.8%) and no \\u003cem\\u003eCALR\\u003c/em\\u003e and \\u003cem\\u003eMPL\\u003c/em\\u003e variants. Twenty-five percent of subjects had one or more additional non-driver somatic mutations, like people with a chronic phase MPN.\\u003csup\\u003e27\\u0026ndash;29\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eA result that could open new perspectives on the pathogenesis of CMD-NBV was that a high proportion (42%) of subjects had variants in genes involved in hematopoiesis and leukemia which we interpreted as germline. These variants overlap somatic variants in \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eTET2\\u003c/em\\u003e, DNMT3A. None of these putative germline variants is reported as high-penetrance cancer predisposing. However, the \\u003cem\\u003eRUNX1\\u003c/em\\u003e (c.167C\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) variant, classified now as benign, may be up-graded to higher level of pathogenicity considering additional segregation data reporting two families where the germline variant was associated with thrombocytopenia and with evolution to a myelodysplastic syndrome (MDS).\\u003csup\\u003e30,31\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eWe hypothesize these putative rare germline variants may predispose to CMD-NBV in the presence of \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e or by combining with other genes or environmental factors. Literature reinforces this hypothesis. For example, somatic and germline \\u003cem\\u003eCSF3R\\u003c/em\\u003e (c.2422G\\u0026thinsp;\\u0026gt;\\u0026thinsp;A) is reported in MDS and MDS/MPN.\\u003csup\\u003e32\\u0026ndash;34\\u003c/sup\\u003e Germline \\u003cem\\u003eASXL1\\u003c/em\\u003e (c.3306G\\u0026thinsp;\\u0026gt;\\u0026thinsp;T) is reported in 4 out of 62 children with chronic myeloid leukemia (CML),\\u003csup\\u003e35\\u003c/sup\\u003e germline \\u003cem\\u003eDNMT3A\\u003c/em\\u003e (c.1502A\\u0026thinsp;\\u0026gt;\\u0026thinsp;G) is reported in a child with acute myeloid leukemia (AML).\\u003csup\\u003e36\\u003c/sup\\u003e Finally, 3\\u0026rsquo;UTR \\u003cem\\u003eASXL1\\u003c/em\\u003e (c.*87A\\u0026thinsp;\\u0026gt;\\u0026thinsp;G) variant is associated with a low blood basophils concentration and with lower eosinophils and monocytes concentration.\\u003csup\\u003e37\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003eIdentifying germline variants predisposing to CMD-NBV can potentially improve our understanding of initiating events and help deepen its biology and pathophysiology. For example, \\u003cem\\u003eRUNX1\\u003c/em\\u003e variants associated with a megakaryocyte disorder would suggest \\u003cem\\u003eRUNX1\\u003c/em\\u003e as a critical mediator of CMD-NBV subtype-specific differences.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, this expanded cohort of subjects with CMD-NBV highlights the clinical variant presents as a hidden, thrombosis prone, early MPN. The characterization of somatic mutation profiles fosters the development of strategies for early interception and intervention. The hypothesis of high incidence of predisposing germline variants in myeloid genes drives the future research with the perspective to validate the result and to investigate the heritability of the identified germline variants.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting interests:\\u003c/h2\\u003e \\u003cp\\u003e Robert Peter Gale is a consultant to BeiGene Ltd., Fusion Pharma LLC, LaJolla NanoMedical Inc., Mingsight Parmaceuticals Inc., Kite Pharma and CStone Pharmaceuticals; Advisor to Antegene Biotech LLC, Medical Director and FFF Enterprises Inc.; Partner in AZACA Inc.; Board of Directors, RakFond Foundation for Cancer Research Support; Scientific Advisory Board, StemRad Ltd.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e \\u003cp\\u003eRobert Peter Gale is a consultant to BeiGene Ltd., Fusion Pharma LLC, LaJolla NanoMedical Inc., Mingsight Parmaceuticals Inc., Kite Pharma and CStone Pharmaceuticals; Advisor to Antegene Biotech LLC, Medical Director and FFF Enterprises Inc.; Partner in AZACA Inc.; Board of Directors, RakFond Foundation for Cancer Research Support; Scientific Advisory Board, StemRad Ltd.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eEthics\\u003c/h2\\u003e \\u003cp\\u003e The research was conducted in accordance with the World Medical Association Declaration of Helsinki. All subjects gave written informed consent approved by the IRCCS Policlinico S. Matteo Foundation Institutional Ethics Committee. The Ethics Committee of the Hospital also approved a written informed consent for patients to donate samples for molecular research (reference number 20110004143 of the 26.9.2011).\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthors Contributions\\u003c/h2\\u003e \\u003cp\\u003eGB designed the study, analyzed the data, and wrote the first version of the manuscript. VR and RPG contributed in writing the manuscirpt. AI, CT, MCF, AC, AR, VDS enrolled patients. MM, RC, AC, CA led the database sample collection and clinical characterization efforts. TB, AR and LM revised the typescript and discussed the results. PC did driver mutations genotyping for the dataset. LP and AG reviewed the bone marrow biopsies of patients diagnosed in the Hospital of Bergamo and Crema, respectively. AG, MG, MB performed DNA NGS analysis. ADS helped with the statistical analysis. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eSupported by AIRC 5 x 1000 call \\u0026ldquo;Metastatic disease: the key unmet need in oncology\\u0026rdquo; to MYNERVA project, #21267 (MYeloid Research Venture AIRC), and AIRC Individual Grant 2024, project #31013.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e \\u003cp\\u003eAll data generated or analysed during this study are included in this article. Further enquires can be directed to the corresponding author.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eArber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, \\u003cem\\u003eet al\\u003c/em\\u003e. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200-1228. .\\u003c/li\\u003e\\n\\u003cli\\u003eKhoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, \\u003cem\\u003eet al\\u003c/em\\u003e.. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia. 2022;36(7):1703-1719. \\u003c/li\\u003e\\n\\u003cli\\u003eBarosi G, Rosti V, Massa M, Campanelli R, Villani L, Catarsi P, \\u003cem\\u003eet al\\u003c/em\\u003e. Clonal Megakaryocyte Dysplasia with Normal Blood Values Is a Distinct Myeloproliferative Neoplasm. Acta Haematol. 2022;145(1):30-37.\\u003c/li\\u003e\\n\\u003cli\\u003eBarosi G, Campanelli R, Massa M, Catarsi P, Carolei A, Abb\\u0026agrave; C, \\u003cem\\u003eet al\\u003c/em\\u003e. Clonal Megakaryocyte Dysplasia with Isolated Thrombocytosis Is a Distinct Myeloproliferative Neoplasm Phenotype. Acta Haematol. 2023;146(1):14-25. \\u003c/li\\u003e\\n\\u003cli\\u003eBarosi G, Rosti V, Gale RP. Myelofibrosis-type megakaryocyte dysplasia (MTMD) as a distinct category of BCR::ABL-negative myeloproliferative neoplasms. Challenges and perspectives. Leukemia. 2023;37(4):725-727.\\u003c/li\\u003e\\n\\u003cli\\u003eBarosi G, Rosti V, Bonetti E, Campanelli R, Carolei A, Catarsi P, \\u003cem\\u003eet al\\u003c/em\\u003e. Evidence that prefibrotic myelofibrosis is aligned along a clinical and biological continuum featuring primary myelofibrosis. PLoS One. 2012;7(4):e35631. .\\u003c/li\\u003e\\n\\u003cli\\u003eBarosi G, Catarsi P, Campanelli R, Massa M, Carolei A, Abb\\u0026agrave; C, \\u003cem\\u003eet al\\u003c/em\\u003e. VEGFA rs3025039 is associated with phenotype severity of myelofibrosis-type megakaryocyte dysplasia. E J Haem. 2023;4(3):756-759. \\u003c/li\\u003e\\n\\u003cli\\u003eVardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, \\u003cem\\u003eet al\\u003c/em\\u003e. 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Am J Hum Genet. 2016;99(1):22-39. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6622364/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6622364/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eWe report an in-depth retrospective analysis of an updated series of 30 subjects with clonal megakaryocyte dysplasia with normal blood values (CMD-NBV). Sixteen were men, median age was 47.5 years (IQR, 39\\u0026ndash;53 years). A thrombosis-driven situational diagnosis (69% of subjects), high incidence of thrombotic events (6.5 events x 100 subject-years), and indolent disease progression (one case only progressed towards an active disease) were the hallmarks of CMD-NBV. Nineteen subjects (63%) had a high body mass index (BMI) at diagnosis (median value, 26.2 m\\u003csup\\u003e2\\u003c/sup\\u003e/kg) and 14 (48%) had\\u0026thinsp;\\u0026ge;\\u0026thinsp;1 Charlson co-morbidities. In 21 individuals (70%) the driver variant was \\u003cem\\u003eJAK2\\u003c/em\\u003e\\u003csup\\u003eV617F\\u003c/sup\\u003e with a median variant allele frequency ( VAF) at diagnosis of 8.9% (IQR, 5.4\\u0026ndash;18.4%). Twenty-four subjects had undergone next generation sequencing (NGS) for myeloid neoplasm-related genes. Six (25%) had\\u0026thinsp;\\u0026ge;\\u0026thinsp;1 pathogenic somatic variant in \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eTET2, DNMT3A\\u003c/em\\u003e, and \\u003cem\\u003eSRSF2\\u003c/em\\u003e. Twelve putative germline, non-pathogenic, missense variants in \\u003cem\\u003eASXL1\\u003c/em\\u003e, \\u003cem\\u003eTET2\\u003c/em\\u003e, \\u003cem\\u003eDNMT3A, RUNX1\\u003c/em\\u003e, \\u003cem\\u003eCUX1\\u003c/em\\u003e, \\u003cem\\u003eABL1\\u003c/em\\u003e, \\u003cem\\u003eNF1\\u003c/em\\u003e, \\u003cem\\u003eKIT\\u003c/em\\u003e and \\u003cem\\u003eCSFR\\u003c/em\\u003e or 5\\u0026rsquo; UTR in NF1 and 3\\u0026rsquo; UTR in \\u003cem\\u003eASXL1\\u003c/em\\u003e were detected in 10 subjects (42%). Based on these data we hypothesize rare, low penetrance germline variants underly a predisposition to the early CMD-NBV myeloproliferative neoplasm.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Clonal megakaryocyte dysplasia with normal blood values (CMD-NBV): an unique form of early myeloproliferative neoplasm\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-16 11:18:53\",\"doi\":\"10.21203/rs.3.rs-6622364/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"dc77ffef-0a41-4b69-9915-6579a96af7ce\",\"owner\":[],\"postedDate\":\"May 16th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":48503849,\"name\":\"Health sciences/Diseases\"},{\"id\":48503850,\"name\":\"Biological sciences/Cancer\"}],\"tags\":[],\"updatedAt\":\"2025-06-11T15:21:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-16 11:18:53\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6622364\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6622364\",\"identity\":\"rs-6622364\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}