Manfredini, Rossella

Background: The JAK2 46/1, also referred to as the GGCC, haplotype, is represented by 4 main SNPs spanning a region of 250kb in chr9 including the JAK2 gene, that are in complete linkage disequilibrium. The GGCC haplo was found to confer susceptibility to JAK2 mutated myeloproliferative neoplasms (MPN) (Jones AV, 2009; Olcaydu D, 2009; Kilpivaara O, 2009) while association with JAK2-wildtype MPN remains controversial (Pardanani A, 2010; Jones AV, 2010; Guglielmelli P, 2010; Trifa A, 2016). The GGCC haplo was also found enriched in MPN pts with splancnic vein thrombosis (SVT) (Smalberg JH, 2011), and to correlate with higher JAK2V617F variant allele frequency (VAF) (Alvarez-Larran, A, 2012). Nullizigosity for GGCC haplo was associated with reduced survival (OS) in pts with myelofibrosis (MF) (Tefferi A, 2019), but no information on the impact of GGCC haplo status with phenotype and outcome in PV is available. Aim: To assess the clinical correlates and the prognostic significance of GGCC haplo status in a large cohort of pts with PV. Pts and Methods: A total of 399 PV pts, all JAK2V617F mutated, diagnosis revised according to 2022 ICC criteria, were genotyped for 46/1 haplotype by PCR allele discrimination with rs12343867 (with the “C” allele standing for 46/1 haplotype). Results: A total of 301 pts (75.4%) had the C allele, of which 143 (47.5%) were CC homo and 158 (52.5%) CT hetero; 98 (24.6%) were nullizygous for GGCC (TT). Since preliminary analysis disclosed no significant difference between CT and TT haplotype for all variables considered, homozygous CC pts were compared to TT+CT. The CC haplotype was associated with higher leukocytes (median 11.3x10e9/L vs 9.6, p<.001), higher hemoglobin (18.4 g/dL vs 17.5, p<.001), lower platelets (441x10e9/L vs 514, p=.002) more aquagenic pruritus (51.8% vs 33.2%, p=.001), and higher JAK2VAF (60.8+-24.8% vs 36.8+-19.7%, p<.001) at diagnosis. Pts with a VAF >50% were 69% in CC versus 24.1% in TT+CT pts (p<.001). ASXL1 mutations were enriched among CC pts (20.3% vs 9.4%, p=0.04), otherwise no difference regarding additional myeloid mutations was noted. All above differences remained significant when considering CC versus TT and CT separately. More pts with CC haplotype suffered from venous thrombosis (VT) during the FU (16.1% vs 4.3%, p<.001), no difference was noted for arterial thrombosis nor for splancnic vein thrombosis. VT-FS by Kaplan Meyer was significantly shorter (p<.001) in CC pts; the rate of VT at 5, 10, 15y after diagnosis, was 5.5%, 12%, 20.2% for CC versus 2.6%, 3.4%, 6.4% for TT+CT, respectively (all p<.001). In multivariable analysis, including JAK2VAF >50%, age >60y, history of VT, WBC >15x10e9/L, only CC haplo remained significant (HR 3.6, 95%CI 1.4-9.5, p=.009). Progression to post-PV MF occurred in 81 pts (20.3%), significantly more among CC (n=45, 31.5%) than TT+CT (n=36; 14.1%; p<.001). Myelofibrosis-FS was significantly shorter in CC vs other genotypes (p=.02). However, in multivariable analysis, only BM fibrosis G1 remained significant (HR 3.2, 1.7-5.9; p<.001). Evolution to blast-phase occurred in 13 pts (3.2%), 9 (6.3%) with CC haplo vs 4 TT+CT (1.6%; p=.01). During the follow-up, 102 pts (25.5%) died, 50 (35%) CC and 52 TT+CT (20.3%; p=.001). In multivariable analysis, including MIPSS-PV variables (Tefferi A 2020), the CC haplo remained significant (HR 1.8, 1.04-3.2, p=.03) together with age (HR 3.0, 1.6-5.7; p<.001), WBC (HR 2.6, 1.3-4.8; p=.004), and thrombosis history (HR 1.9, 1.0-3.7; p=.03). Conclusions. Homozygosity for the 46/1 or CC haplotype contributes to the phenotype diversity of PV, in part possibly mediated by associated higher JAK2V67F VAF, and predicts for incident VT and shorter OS. Supported by AIRC, 21267; MIUR,2022F4WMR3; IHM, PNRR-MAD-2022-12376695

Abstract BACKGROUND. In primary myelofibrosis (PMF), survival from time of diagnosis is predicted by the international prognostic scoring system (IPSS), and from time of referral, by the dynamic IPSS (DIPSS) or DIPSS-plus. JAK2/CALR/MPL mutational status and presence and number of other prognostically-relevant mutations (ASXL1, SRSF2, EZH2, IDH1/2) provide IPSS/DIPSS-plus independent prognostic information. The objective in the current study was to revise IPSS, by including mutation-relevant prognostic information. METHODS. The study included 986 PMF patients divided into learning (n=588) and validation (n=398) cohorts. Previously published methods were used to screen for mutations involving JAK2,MPL, CALR, EZH2, ASXL1, IDH1/2 and SRSF2. The clinical variables assessed were those previously identified as being prognostically-relevant by IPSS or DIPSS-plus. The prognostic model (MIPSS) was developed through a stepwise selection process, based on a z-test of the regression coefficients, and its relative quality was measured by means of the Akaike information criterion. RESULTS. In the Italian learning cohort (n=588),median follow-up was 3.6years (95% CI, 0.03-30.8) for alive patients and 196 (33.3%) deaths and 67 leukemic transformations were documented. IPSS risk distribution was low in 26%, intermediate-1 in 30%, intermediate-2 in 24% and high in 20%. Mutational frequencies were approximately 63% for JAK2, 20% CALR, 6% MPL, 7% EZH2, 22% ASXL1, 2.5% IDH1/2 and 9% SRSF2. Constitutional symptoms were recorded in 28.6% of patients, splenomegaly 75% (18% >10cm from LCM) and grade 3 fibrosis 21%. Overall 252 patients were evaluable for karyotype and abnormalities were detected in 35%, including 9.5% unfavorable. Univariate analysis identified the following risk factors for inferior survival: age >60 years, constitutional symptoms, hemoglobin <100g/L, leukocytes >25x109/L, platelets <200x109/L, circulating blasts 1% or greater, splenomegaly >10cm, grade 3 fibrosis, unfavorable karyotype, triple-negativity for JAK2, MPL and CALR (TN), JAK2 or MPL mutation, and mutations in ASXL1, SRSF2, EZH2 or IDH1/2. In multivariable analysis, age >60 years, constitutional symptoms, hemoglobin <100g/L, platelets <200x109/L, TN, JAK2 or MPL mutation, ASXL1 and SRSF2 mutation remained significant; these variables were subsequently assigned the following HR-weighted adverse points: 1.5, 0.5, 0.5, 1.0, 1.5, 0.5, 0.5 and 0.5, respectively. Subsequently, four risk groups with no overlap in the 95% CI of their survival curves were delineated (Figure 1A): low (score 0-0.5); Intermediate-1 (score 1-1.5); Intermediate-2 (score 2-3.5); and high (score 4 or greater). The number of patients in each risk category, median survivals and HR and 95% CI values are included in figure 1A. Akaike information criterion indicated that MIPSS performed better than IPSS in predicting survival (1611.6 vs 1649.0). Leukemia-free survival was also predicted by high (p<0.01) and intermediate 1/2 (p<0.01) risk MIPSS. In the Mayo Clinic validation cohort (n=398), median age 63 years; males 64%,median follow-up for alive patients was 7.5 years and 271 (68%) deaths and 51 (13%) leukemic transformations were documented. DIPSS-plus risk distribution was low in 13%, intermediate-1 18%, intermediate-2 37% and high 32%. Mutational frequencies were approximately 51% for JAK2, 30% CALR, 7.5% MPL, 5% EZH2, 36% ASXL1, 5.6% IDH1/2 and 13% SRSF2. MIPSS performed equally well in this cohort, as illustrated in figure 1B, which includes number of patients in each risk category with their median survival and HR and 95% CI values. Leukemia-free survival in the Mayo cohort was also predicted by high (p<0.01) and intermediate-2 (p=0.04) risk MIPSS. Patients in the Mayo cohort were fully annotated for karyotype, which was MIPSS-independent in its prognostic value for both overall (p<0.01) and leukemia-free (p<0.01) survival. CONCLUSIONS. MIPSS addresses the mutation-relevant prognostic information gap in IPSS and performs equally well in the setting of patients seen at time of diagnosis or referral. Karyotype carries MIPSS-independent prognostic value that is recognized in an alternative GIPSS prognostic model (see accompanying ASH 2014 abstract), which complements MIPSS. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.