Introduction: Hematopoietic stem cell transplantation (HSCT) is an effective treatment option for patients with hematological and neoplastic diseases. Despite recent improvements, HSCT is still associated with a significant risk of mortality. Determining risk factors for death within the first 100 days after HSCT could help to identify patients who would benefit from interventions in order to decrease that risk. We have previously reported that increased fluid accumulation during conditioning chemotherapy and in the early period after HSCT was associated with decreased survival (Costa EMM et al, ASH 2013Abstract #4512). Herein, we expand that series of patients and we further analyze the impact of weight gain on early (100-days) mortality in patients who underwent HSCT at our institution. Objective: To determine the impact of weight gain during the first 10 days post-HSCT on 100 days mortality. Methods:We retrospectively reviewed the medical charts of 331 patients who underwent HSCT at our institution from January, 2007 until December, 2013. Information on patients' body weight (BW) was measured daily, starting at admission. The highest BW recorded until until the first 10 days post-SCT (D+10) was used to calculate the BW increase in relation to the baseline BW. Based on our previous study, we used a cutoff of 6% gain in BW to identify a group of patients with increased risk of complications. The primary endpoint was mortality within 100 days post HSCT. Overall survival (OS) was estimated from the time of HSCT until death, and surviving patients were censored at last follow-up. A logistic regression model and a Cox model were fit to determine variables that predicted death within 100 days and OS, respectively. Statistical analysis was performed with STATA (v11.0) and alfa error was defined as 5%. Results: Median age was 43 years old (range <1 year-76 years) and 60% were male. HSC sources included autologous (46%), matched related donors (16%), matched unrelated donors (12%), cord blood units (16%) and mismatched related/unrelated donors (10%). Diagnosis included acute leukemia or chronic myeloid disorders (34%), lymphoma/multiple myeloma (42%) and non-malignant hematological disorders (24%). Twenty-one percent of patients had a ≥6% gain in BW until the first 10 days post-HSCT. These patients had developed an increased inflammatory state after the start of the conditioning regimen: there was no difference in baseline levels of C-reactive protein (4.8 mg/L vs. 7.4, p=0.37), but by D+10 patients who gained more BW had higher CRP (116.7 mg/L vs. 178.6 mg/L; p=0.01). Patients with increased gain in BW until D+10 had a decreased OS (HR 2.33, p<0.0001, 95% CI 1.45-3.73). The mortality within 100 days was 47% in the increased BW vs. 17% in the control group (p<0.0001). Among 18 patients who died within 100 days and had a ≥6% BW gain by D+10, causes of death included pneumonia (N=4), septic shock (N=9), fungal endocarditis (N=1), ischemic stroke (N=1) and disease progression (N=3). In a logistic regression analysis, after adjusting for age, sex, diagnosis and type of SCT, a ≥6% BW gain by D10 was associated with an increased risk of being dead within 100 days (coefficient 1.75; p<0.0001; 95% CI 0.90-2.61). Similarly, in a multivariate Cox analysis after adjusting for age, sex, diagnosis and type of SCT, a ≥6% BW gain by D10 was an independent risk factor for survival (HR 2.72, p<0.0001, 95% CI 1.65-4.48). A landmark analysis at D+100 revealed that the negative impact of weight gain by D+10 on survival was restricted to the first 100 days, as after this time point there was no survival difference between the two groups (HR 1.35; p=0.41; 95% CI 0.65-2.83). Conclusion: A ≥6% BW gain by D+10 is a risk factor for early mortality in both autologous and allogeneic HSCT. The most common cause of death in these patients is infectious-related complications. An increase in BW is related to the development of an inflammatory state, probably induced by the conditioning regimen. BW gain is a simple variable that can be easily used to determine prognosis of patients post-allogeneic HSCT, and further studies are needed to determine its etiology. Disclosures No relevant conflicts of interest to declare.
Introduction Several studies have suggested that an increased peripheral blood absolute lymphocyte count (ALC) at day 15 after ASCT is associated with improved survival and decreased relapse rate. Recently, the ratio of ALC to the absolute monocyte count (AMC) (Lymphocyte:Monocyte Ratio; LMR) has been described as a strong prognostic factor at time of diagnosis in patients with various lymphoid malignancies. In most reports, a LMR cut-off value of less than 1.1 indicates patients who have a worse outcome. Objective To evaluate the prognostic impact of the LMR at start of conditioning regimen and at day 15 post ASCT in patients with a diagnosis of lymphoma and myeloma Methods We retrospectively reviewed the medical records of 121 adult patients with a diagnosis of lymphoma or myeloma who underwent ASCT at Hospital Israelita Albert Einstein from January, 2005 to July, 2012. Lymphocyte count was registered at the 15th day after SCT and lymphopenia was defined as an ALC< 500 at this time point. The LMR was calculated considering the ALC and AMC at baseline (start of conditioning regimen) and at day 15 post-ASCT. Overall survival (OS) was estimated from the time of transplant until death, with surviving patients censored at last follow-up. Variables entered into the multivariate Cox analysis were those with a p-value <0.10 in the univariate analysis. Statistical analysis was performed with STATA (v11.0) and alfa error was defined as 5%. Results The majority of patients were male (69%) and the median age was 58 years old (range: 3–76). Peripheral stem cell harvest was the main source of cells (61%). Diagnosis included multiple myeloma (49%), non-Hodgkin’s lymphoma (45%) and Hodgkin’s lymphoma (6%). The median LMR at start of conditioning regimen was 0.60, while at day 15 it was 1.75. Seventy-three percent of patients at start of conditioning had a LMR <1.1, while the same percentage at day 15 was 25%. Considering LMR cut-off at 1.1, an increased LMR value at baseline was associated with improved survival (HR 0.44; p=0.03), while it was not predictive at day 15 (HR=0.99; p=0.99). At 2 years, the OS was 48% for patients with a LMR<1.1 at start of conditioning regimen versus 76% for those patients with a LMR ≥1.1 (p=0.03 by logrank). In a multivariate Cox analysis considering age, sex, diagnosis, day 15 lymphopenia and baseline LMR, baseline LMR remained an independent variable associated with survival (HR=0.40, p=0.044), while day15 lymphopenia had no prognostic value (HR=0.80. p=0.56). Conclusion In our cohort of patients, the presence of an increased LMR at baseline before start of conditioning regimen identified a subgroup of patients who had a very good outcome. These results should be validated in other cohorts. Strategies to improve outcome for patients who present with decreased LMR and a better understanding of the role of LMR should be the focus of future studies. Disclosures: No relevant conflicts of interest to declare.
Introduction: The development of next-generation sequencing has made it feasible to interrogate the entire genome or exome (coding genome) in a single experiment. Accordingly, our knowledge of the somatic mutations that cause cancer has increased exponentially in the last years. MPNs and MDS/MPD are chronic myeloid neoplasms characterized by an increased proliferation of one or more hematopoietic cell lineages, and an increased risk of transformation to acute myeloid leukemia (AML). MPNs and MDS/MPDs are heterogenous disorders, both in clinical presentation and in prognosis. We sought to determine the genetic landscape of Ph-negative MPNs and MDS/MPD through next-generation sequencing. Methods: Paired DNA (sorted CD66b-granulocytes/skin biopsy) from 102 patients with MPNs or MDS/MPD was subjected to whole exome sequencing on a Illumina HiSeq 2000 platform using Agilent SureSelect kit. Diagnosis included primary myelofibrosis (MF; N=42), essential thrombocythemia (ET; N=28), polycythemia vera (PV; N=12), chronic myelomonocytic leukemia (CMML; N=10), systemic mastocytosis (MS; N=6), MDS/MPD-Unclassified (N=2) and post-MPN AML (N=2). Tumor coverage was 150x and germline coverage was 60x. Somatic variants calls were generated by combining the output of Somatic Sniper (Washington University), Mutect (Broad Institute) and Pindel (Washington University). The combined output of these 3 tools was further filtered by in-house criteria in order to reduce false-positive calls (minimum coverage at both tumor/germline ≥8 reads; fraction of reads supporting alternate allele ≥10% in tumor and ≤10% in germline; ratio of allele fraction tumor:germline >2; excluding mutations seen in SNP databases). All JAK2 and CALR mutations were validated through Sanger sequencing. Validation of other somatic mutations is currently underway. Analysis of driver mutations was made with the Intogen web-based software, using the Oncodrive-FM and Oncodrive-cluster algorithms (www.intogen.org). Significantly mutated genes were considered as those with a q-value of <0.10. Results: We identified a total of 309 somatic mutations in all patients, with each patient having an average of 3 somatic abnormalities, fewer than most solid tumors that have been sequenced so far. Mutations occurred in 166 genes, and 40 of these were recurrently somatically mutated in Ph-negative MPNs. By the Oncodrive-FM algorithm, the following genes were identified as the most significantly mutated driver genes in Ph-negative MPNs and MDS/MPDs (in order of significance): CALR, ASXL1, JAK2, CBL, DNMT3A, U2AF1, TET2, TP53, RUNX1, EZH2, SH2B3 and KIT. By the Oncodrive-cluster algorithm, which considers clustering of mutations at a hotspot, the following genes were significantly mutated: KIT, JAK2, SRSF2 and U2AF1. Somatic mutations were seen in genes that are mutated at a low frequency in Ph-negative MPNs, including ATRX, BCL11A, BCORL1, BIRC5, BRCC3, CSF2RB, CUX1, IRF1, KDM2B, ROS1 and SUZ12. Consistent with the clinical phenotype, 96 patients (94%) had mutations that lead to increased cellular proliferation, either through activation of the JAK-STAT pathway (e.g. JAK2, CALR) or mutations that activated directly or indirectly signaling by receptor tyrosine kinases (e.g. FLT3, KIT, CBL). Besides biological pathways regulating cell proliferation, the most commonly implicated pathways included regulation of DNA methylation (e.g. DNMT3A, TET2), mRNA splicing (e.g. U2AF1, SRSF2) and histone modifications (e.g. ASXL1, EZH2), seen in 27%, 25% and 22% of patients, respectively. Abnormalities in these 3 pathways were more often seen in MF, MDS/MPD and CMML, as compared to PV and ET (65% vs. 20%; p<0.0001). Conclusions: Our study represents one of the largest series of patients with these neoplasms evaluated by whole exome sequencing, and together with the published data helps to delineate the genomic landscape of Ph-negative MPNs and MDS/MPDs. The majority of the most frequent mutations seen in Ph-negative MPNs have already been reported. Nevertheless, there are several low frequency mutations that need to be further studied and functionally validated in vitro and in vivo for a deeper knowledge of the pathophysiology of MPNs. Besides activation of cellular proliferation, abnormalities of DNA methylation, histone modification and mRNA splicing emerge as the most important biological pathways in these disorders. Disclosures No relevant conflicts of interest to declare.
Introduction It has been previously reported that increased fluid accumulation during peripheral blood hematopoietic stem cell (HSC) mobilization is associated with poor outcome in patients with amyloidosis who undergo autologous HSCT. It is unknown whether increased fluid accumulation during the early phases of HSCT is associated with poor survival in patients undergoing HSCT for other diseases. Objective To determine the impact of fluid accumulation during conditioning and in the first 10 days post HSC infusion on survival and risk of complications of patients who underwent both autologous and allogeneic HSCT. Methods We retrospectively reviewed the medical charts of 257 consecutive patients who underwent HSCT at our institution from January, 2007 until December, 2012. Information on patients' body weight (BW) was measured daily, starting at admission. The highest BW recorded until HSC infusion (D0) and until the first 10 days post-SCT (D+10) was used to calculate the BW increase in relation to the baseline BW. A ROC curve was built to determine the best cut-off point in BW increase that predicted for mortality. Information on the incidence of post-transplant complications was extracted from the time period that patients were admitted for transplant until discharge from the hospital. Endpoints analyzed included the presence or absence of respiratory failure, acute renal failure, sinusoidal obstruction syndrome (SOS), septic shock and requirement of diuretic use, hemodialysis, mechanical ventilation and ICU admission. Overall survival (OS) was estimated from the time of HSCT until death, and surviving patients were censored at last follow-up. Variables entered into the multivariate Cox analysis were those with a p-value<0.10 in the univariate analysis. Statistical analysis was performed with STATA (v11.0) and alfa error was defined as 5%. Results Mean age was 39.4 years old (range <1 year-76 years) and 61% were male. HSC sources included autologous (47%), matched related donors (15%), matched unrelated donors (13%), cord blood units (19%) and mismatched related/unrelated donors (6%). Diagnosis included acute leukemia or chronic myeloid disorders (37%), lymphoma/multiple myeloma (42%) and non-malignant hematological disorders (21%).The results of the ROC curve defined the cut-point of 6% BW gain by D+10 as the best predictor for OS. A total of 69 patients (27%) had a BW increase ≥6% by D+10. This was associated with an increased risk of mortality, with a 100-days OS of 67% vs. 92% (HR 3.25, p<0.0001, 95% CI 2.04-5.18; Figure). A greater than 6% gain in BW by D+10 was also associated with an increased risk of developing SOS (31% vs. 6%; p<0.0001), septic shock (29% vs. 7%; p<0.0001), respiratory failure (35% vs. 9%; p<0.0001) and requiring diuretic use (91% vs. 71%; p<0.001), hemodialysis (13% vs. 4%, p=0.007), mechanical ventilation (33% vs. 9%; p<0.0001) and ICU admission (42% vs. 24%; p<0.0001). In a multivariate analysis considering age, diagnosis, type of SCT and sex, a ≥6% BW gain by D+10 was an independent variable associated with an increased risk of mortality (HR 3.28; p<0.0001; 95% CI 2.02-5.32). We next evaluated the prognostic impact of a ≥6% BW increase in the time period from admission until D0. Our results showed that it was similarly associated with an increased risk of mortality (HR 2.26; p=0.003; 95% CI 1.32-3.86), of developing SOS (32% vs. 9%; p<0.0001), respiratory failure (27%vs. 14%; p=0.04) and requiring hemodialysis (15% vs. 5%; p=0.01) and ICU admission (37% vs. 18%; p=0.008). After adjusting for age, sex, diagnosis and type of SCT, ≥6% BW gain by D0 was associated with an increased mortality (HR 1.94; p=0.026; 95% CI 1.08-3.48). Conclusion In our cohort of patients, fluid accumulation during the early stages of conditioning regimen and HSCT, reflected by a ≥6% increase in BW, was associated with an increased mortality and risk of developing severe complications. This may reflect the presence of increased endothelial damage, and further studies are needed to better clarify the mechanism behind weight gain during HSCT. Our results demonstrate that patients who have a ≥ 6% gain in BW by D0 and D+10 have an increased risk of complications, and more intensive monitoring of these patients is needed. Disclosures: No relevant conflicts of interest to declare.
Cell dose is a major criterion for cord blood unit (CBU) selection for allogeneic stem cell transplantation (allo-SCT). The aim of this study was the characterization of CBU cellular composition after thaw, and comparison with corresponding values at cryopreservation as reported by cord blood (CB) banks. The study included 87 CBUs, that were thawed for infusion in the context of single (n¼3) or dual-unit (n¼42) allo-SCT in adults with hematologic malignancies, from 8/2006 to 6/2013. Upon thawing, the cryoprotective solution (DMSO 10%) was either removed by centrifugation/washing (38 CBUs) or diluted in a less hypertonic solution of Dextran 40/Human Albumin 2.5% (49 CBUs). Total nucleated cells (TNC) were measured with a hematology analyzer, while enumeration of CD34+ stem cells was performed by singleplatform flow cytometry, according to ISHAGE guidelines. In 49 units, TNC and CD34+ cell viability was evaluated by addition of 7-AAD dye and sequential Boolean gating strategy. TNC counts after thawing were lower compared to their values at freezing (Wilcoxon test, p <10-4), and the difference was more pronounced in the units that were washed prior to infusion (Tables 1 and 2). Total cell viability was low (mean value, 42.6%), but this was mainly due to neutrophils. Regarding CD34+ cells, there was a significant difference between absolute counts at cryopreservation and at thaw (p<10-4). Despite reduction postthaw, the counts of both TNC and CD34+ cells did correlate with the corresponding values at cryopreservation by Spearman's analysis. Of note, washing seemed slightly advantageous in terms of CD34+ recovery (Tables 1 and 2). CD34+ cells retained high viability after thaw, with 90% of CBUs (44 out of 49 tested) demonstrating CD34+ viability !80%. Viability of <50% was noticed in only one CBU that failed to engraft. In conclusion, CB cellular content and especially the CD34+ cell count is frequently shown to be inferior at thaw compared to cryopreservation. This probably reflects both the lack of standardization of CD34+ cell measurement and the effect of thawing procedure. Therefore, CD34+ cell viability may be a more meaningful marker for determining CBU quality.
Introduction: Mutations that activate the RAS-RAF-MEK-ERK pathway have long been known to occur in patients with solid tumors and hematological malignancies. The most common mutations occur in the Ras family of GTPases (HRAS, NRAS, KRAS) and the Raf family of serine-threonine kinases (ARAF, BRAF, CRAF). In myeloid malignancies, RAS mutations have mainly been described in patients with acute myeloid leukemia, chronic myelomonocytic leukemia (CMML) and myelodysplastic syndrome. There are few studies describing the incidence of mutations of the RAS-RAF-MEK-ERK pathway in patients with MPNs other than CMML. Objective: To describe the incidence, clinical features and prognostic impact of Ras and Raf mutations in patients with Ph-negative MPNs and MPN/MDS-U Methods: Paired DNA (sorted CD66b-granulocytes/skin biopsy) from patients with MPNs or MPN/MDS was subjected to whole exome sequencing on a Illumina HiSeq 2000 platform using Agilent SureSelect kit (see our abstract “Whole Exome Sequencing of Myeloproliferative Neoplasms and Myelodysplastic/Myeloproliferative Disorders”). Tumor coverage was 150x and germline coverage was 60x. Somatic variants calls were generated by combining the output of Somatic Sniper (Washington University), Mutect (Broad Institute) and Pindel (Washington University), followed by in-house filters to reduce false positive calls. Statistical calculations were done in Stata, v11.0. Results: We found clonal activating mutations of the RAS-RAF-MEK-ERK pathway in 8 patients (6.7% of cases). Diagnosis included primary myelofibrosis (PMF; N=5), MDS/MPD-U (N=2) and essential thrombocythemia (ET; N=1). Their clinical features are summarized in Table 1 (three of these patients [UPIs #11, #13, #99] are also described in the abstract “Genomic Profile of Patients with Triple Negative (JAK2, CALR and MPL) Essential Thrombocythemia and Primary Myelofibrosis”). There were 7 NRAS mutations and 1 BRAF mutation. In 5 cases the variant allele fraction (VAF) of reads in the tumor sample indicated that the mutation was present in a subclone at the time of sequencing. We next compared the clinical features of these 8 patients with 79 patients (MF=43, ET=35, MDS/MPD=1) who did not harbor these mutations. Patients with NRAS/BRAF mutations had lower hemoglobin (8.3 vs. 11.8 g/dL, p=0.001), higher white blood cell counts (28.37 vs. 7.7 x109/L, p=0.008) and had higher lactate dehydrogenase (1041 vs. 685 IU/L, p=0.02). They also had worse overall survival compared to unmutated cases (Hazard ratio [HR]=11.57; p=0.001). Most patients with NRAS/BRAF mutations had a high number of concomitant driver mutatons (median 5 vs. 1; p<0.0001). When the number of driver mutations was analyzed together with NRAS/BRAF mutations in a Cox model, NRAS/BRAF mutations were no longer independent predictors of survival (HR=1.48; p=0.61). Conclusions: Activating mutations of the RAS-RAF-MEK-ERK pathway occur in 6-7% of patients with Ph-negative MPNs, and they tend to co-occur with a high number of concomitant driver mutations. In most cases the mutation was present in a subclone, suggesting that they are late occurring. Patients with NRAS/BRAF mutations had a trend for worse outcome, but that was mainly dependent on the total number of driver mutations. The activity of MEK and BRAF inhibitors needs to be explored in patients with Ph-negative MPNs who harbor activating mutations of the RAS-RAF-MEK-ERK pathway. Table 1. Clinical features of patients with NRAS/BRAF mutations UPI Diagnosis Mutation VAF Concomitant driver genes and Chromosomal abnormalities Outcomes 7 MF NRAS p.G12S 47% ASXL1, CALR, STAG2, U2AF1 Died from disease progression 11 MF NRAS p.G12R 5% ASXL1, CBL, CUX1 (double mutant), EZH2 Died from disease progression 13 MF NRAS p.G12D 48% ASXL1, DNMT3A, ETV6 (double mutant) JARID2, U2AF1 Died from disease progression 18 MF NRAS p.G13D 25% JAK2, Del(5q) Underwent allogeneic transplantation; disease relapsed day+80; alive 29 MDS/MPD-U BRAF p.D594G 25% JAK2, Del(5q) Transformed to AML; entered CR with induction chemotherapy; underwent allogeneic transplantation; disease relapsed day+35; alive 99 ET NRAS p.G12D 43% ASXL1, CSF3R, STAG2 Alive 109 MF NRAS p.Q61R 19% CALR, DNMT3A, ZRSR2 Alive 122 MDS/MPD-U NRAS p.G12S 7% ASXL1, EZH2 (double mutant), PTPN11, TET2 (double mutant) Transformed to AML; underwent allogeneic transplantation; died on day+58 Disclosures No relevant conflicts of interest to declare.
Introduction: Primary Myelofibrosis (PMF) and Essential Thrombocythemia (ET) are myeloproliferative neoplasms with similar genetic backgrounds. Both diseases are characterized, at the molecular level, by mutations in the genes JAK2, MPL and CALR. In addition recurring mutations is several other genes have been described in myeloid malignancies in general. Although the differential diagnosis between PMF and ET may be straight forward in most cases, there is a significant clinical and pathologic overlap between these two conditions, making the differential diagnosis difficult sometimes, mostly between early PMF and ET. With the goal of utilizing genomic information to better differentiate ET from PMF we decided to identify and compare all genomic alterations present in patients with ET and PMF, through whole exome / genome sequencing of paired granulocytes and skin. Methods: A total of 84 patients with either PMF (N=48) or ET (N=36) were analyzed. DNA was extracted from CD66b+ magnetic bead selected granulocytes (EasySep, Stem Cell Technologies) and matched skin biopsies with QiaAmp DNA Mini kit (Qiagen). Whole-exome targeted capture was carried out on 3 μg of genomic DNA, using the SureSelect Human Exome Kit 51Mb version 4 (Agilent Technologies, Inc., Santa Clara, CA, USA). The exome library was sequenced with 100 bp paired-end reads on an Illumina HiSeq2000. Somatic variants calls were generated by combining the output of Somatic Sniper (Washington University), Mutect (Broad Institute) and Pindel (Washington University). Tumor coverage was 150x and germline was 60x. The combined output of these 3 softwares was further filtered by in-house criteria in order to reduce false-positive calls (minimum coverage at both tumor/germline ≥8 reads; fraction of reads supporting alternate allele ≥5% in tumor and ≤10% in germline; ratio of allele fraction tumor:germline >2). All JAK2 and CALR mutations were validated through Sanger sequencing. Validations of other somatic mutations are under way at this point. For this work, other myeloid driver mutations were defined as mutations occurring recurrently in myeloid malignancies in the medical literature, and in this cohort of patients these mutations were present in the following genes: ASXL1, ATM, CALR, CBL, CUX1, DNMT3A, EZH2, GATA2, GNAS, IDH1, IDH2, JAK2, MPL, NRAS, SH2B3, SF3B1, STAG2, TET2, NFE2, SMC3, SUZ12, PRPF8, SRSF2, U2AF1, TP53. Fisherxs exact test was used for statistical comparisons. Results: The most common mutated genes after JAK2 and CALR were ASXL1 (n=16), TET2 (n=9) and DNMT3A (n=9). After data analysis, the patients could be divided in 7 groups based on the genomic profile: A – JAK2 mutation as the single genetic abnormality (JAK2_Single) (N=24), B – JAK2 plus other myeloid driver mutations (JAK2_Plus) (N=25), C - CALR mutation as the single genetic abnormality (CALR_Single) (N=11), D – CALR plus other myeloid driver mutations (CALR_Plus) (N=9), E – MPL mutation (N=1), F – Triple negative without other myeloid driver mutations (TN_Single) (N=8), G – No JAK2, CALR or MPL (triple negative) but with other myeloid driver mutations (TN_plus) (N=6) 1 – The presence of 3 or more total myeloid driver mutations was strongly associated with a diagnosis of PMF Table 1mut<3mut>2TE282PMF2521 P= 0.0002 2 – The presence of ASXL1 mutations was strongly associated with a diagnosis of PMF Table 2ASXL1+ASXL1-TE135PMF1533 P=0.0007 In order to validate our findings in an independent cohort of patients, we performed the same analysis using data from 2 published studies that evaluated myeloid multi-gene panels in ET and PMF (Nangalia J, NEJM 2013) (Lundberg P, Blood, 2014). We pooled together all patients with ET (N=117) and PMF (N=56) from both studies and repeated the two previous analyses, that confirmed the previous results: Table 3mut<3mut>2TE1106PMF4214P=0.0005ASXL1+ASXL1-TE4113PMF1442P=3.9E-05 Conclusions: We have demonstrated that ASXL1 mutations as well as a number of myeloid driver mutations higher than two is strongly associated with PMF. This information may be useful in the near future to improve the differential diagnosis between ET and PMF. Disclosures No relevant conflicts of interest to declare.
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