Reichard, Kaaren K.

Abstract Background A previous multicenter study of patients with TP53-mutated myeloproliferative neoplasms showed a significantly shorter survival in chronic phase myelofibrosis (MF-CP) that included primary (PMF) and secondary MF and was mostly based on research-based mutation screening and was not systematically controlled for patients without TP53 mutations (AJH 2025;100:552). The objective of the current study was to examine the prognostic impact of TP53 mutations (VAF ≥2%) that were discovered during clinical NGS testing among consecutive patients with PMF-CP. Impact on overall (OS) and leukemia-free (LFS) survival was analyzed in the context of other genetic risk factors and calculated from the time of mutation detection. Methods The current retrospective study includes consecutive patients with PMF-CP who underwent clinical NGS testing at the Mayo Clinic between 2015 and 2025. Diagnoses of PMF-CP and leukemic transformation (LT) were retrospectively fitted to be in accordance with the International Consensus Classification criteria (Blood 2022;140:1200). Mutation and cytogenetic analyses were performed according to institutional protocols for clinical use and reported in conformity with international guidelines. OS and LFS analyses were censored for treatment with allogeneic stem cell transplantation (ASCT) and calculated from the date of NGS. Conventional statistical methods were utilized (JMP Pro 17.1.0, SAS Institute, Cary, NC, USA). Results A total of 633 patients with PMF-CP underwent clinical NGS testing prior to LT and prior to ASCT (median age 65 years; males 59%). Median interval from initial diagnosis/referral to NGS testing was 2 months; 357 (56%), 392 (62%), and 429 (68%) patients had NGS performed within 3, 6 and 12 months of diagnosis, respectively. Risk distribution according to the dynamic International Prognostic Scoring System (DIPSS; Blood 2010;115:1703) were low (19%), intermediate-1 (44%), intermediate-2 (31%), or high (6%). TP53 mutations (TP53MUT) with variant allele frequency (VAF) of ≥2% were detected in 28 (4.4%) patients with a median VAF of 25.3% (range 2.4-85; ICC-defined multihit 43%). Patients with TP53MUT were more likely to harbor IDH2MUT (14% vs. 3%; p<0.01), EZH2MUT (14% vs. 4%; p=0.03), and unfavorable karyotype (36% vs. 19%; p=0.047) and less likely to harbor CALR type 1/like (4% vs. 18%; p=0.02) and SF3B1MUT (0% vs. 6%; p=0.07). The association with unfavorable karyotype was mainly driven by multihit configuration. No significant associations with clinical features were apparent. At a median follow-up of 40 months for surviving patients, 157 (25%) deaths, 48 (8%) LTs and 135 (21%) ASCTs were documented. A near-two-fold lower, albeit not statistically significant OS was observed in the presence of TP53MUT (median 123 vs. 228 months; HR 1.9, 95% CI 0.94-3.7; p=0.074); the difference reached significance for multihit TP53MUT (median 81 months; HR 3.6, 1.7-7.7; p<0.01) but not for non-multihit TP53MUT (median not reached; HR 0.7, 0.2-2.8; p=0.6), both compared to TP53WT (median 228 months). The significant impact of multihit TP53MUT on OS was confirmed in multivariable analysis (MVA) that included other high molecular risk (HMR) mutations and unfavorable karyotype: multihit TP53MUT (HR 2.5, 1.1-5.5; p=0.02), SRSF2MUT (2.4, 1.5-3.8; p<0.01), ASXL1MUT (1.8, 1.2-2.6; p<0.01), U2AF1MUT-Q157 (4.8, 2.6-8.6; p<0.01), and absence of CALR type 1/like (4.0, 2.0-7.8; p<0.01); additional prognostic contribution was not evident for unfavorable karyotype (p=0.17), EZH2MUT (p=0.3), IDH1MUT (p=0.6), IDH2MUT (p=0.7), or any other HMR mutation. Multihit TP53MUT was also associated with inferior LFS (10-year risk 62% vs. 32% for non-multihit TP53MUT vs. 15% for TP53WT; p=0.03) but significance was lost during MVA that included SRSF2MUT, which was the only HMR mutation showing a significant effect on LFS (HR 5.0, 2.5-9.8; p<0.01). Post-ASCT survival among 135 transplanted patients was significantly shorter for patients with multihit TP53MUT (N=4; median 5 months) vs. those with non-multihit TP53MUT (N=7; median not reached) vs. patients with TP53WT (N=124; median not reached) [p=0.02]. Conclusion The current study implicates multihit TP53MUT as an HMR mutation-independent prognostic factor for OS in PMF-CP. By contrast, prognosis in PMF-CP patients with non-multihit TP53MUT was more likely to be influenced by other HMR mutations, as was the case for those with TP53WT.

Abstract Background In our first exploration of mutations in essential thrombocythemia (ET; N=183), we described somatic variants other than JAK2/CALR/MPL in 53% (N=96) of patients with the most frequent being TET2 (16%), ASXL1 (11%), DNMT3A (6%), and SF3B1 (5%) [Blood Adv 2016;1:21]. In a subsequent report (N=270; BJH 2020;189:291), we highlighted the prognostic relevance of SF3B1, SRSF2, and EZH2 mutations for overall survival (OS), SRSF2, EZH2, and TP53 for leukemia-free survival (LFS), and SF3B1 and U2AF1 for myelofibrosis-free survival (MFFS). We have now expanded our NGS-informative patient cohort to 500 and examined the distribution and frequencies of specific mutations at different stages of the disease and corresponding prognostic correlates. Methods Molecularly-annotated ET patients diagnosed at the Mayo Clinic between January 1980 and May 2025 were considered. Diagnoses of chronic phase ET (ET-CP), post-ET MF, and post-ET acute myeloid leukemia (AML) were retrospectively fitted to be in accordance with the International Consensus Classification criteria (Blood 2022;140:1200). Mutation and cytogenetic analyses were performed according to institutional protocols for clinical use that included multi-gene next-generation sequencing (NGS); in a subset of patients, historically archived samples were accessed for analysis in a research setting. Patients were monitored until death or time of last follow-up, determined through electronic medical records or direct communication with patients or healthcare providers. Conventional statistical methods were utilized (JMP Pro 17.1.0, SAS Institute, Cary, NC, USA). Results A total of 500 ET patients (median 57 years; Females 61%) were informative for NGS-derived mutations that were obtained in the chronic (N=401; 80.2%; group A), post-ET MF (N=88; 17.6%; group B), or post-ET AML (N=11; 2.2%; group C) phase of the disease; group C included 9 (1.8%) patients who transformed directly to AML without transitioning through the MF phase. The most frequent mutations with at least 1% (N=5) mutational frequency were (approximately) TET2 (10%), ASXL1 (10%), DNMT3A (7%), SF3B1 (4%), TP53 (5%), SRSF2 (3%), EZH2 (2%), U2AF1 (2%), CBL (1%), IDH1 (1%), IDH2 (1%), RUNX1 (1%), ZRSR2 (1%), and PPM1D (1%). Comparison of distribution of mutations and their frequencies based on timing of NGS (AML phase vs. MF phase vs. ET-CP) revealed significant differences for TP53MUT (72.7% vs. 11.4% vs. 1%; p<0.01), ASXL1MUT (18.2% vs. 25% vs. 7.2%; p<0.01), and EZH2MUT (18.2% vs. 6.8% vs. 0.3%; p<0.01), respectively; borderline significance was also apparent for RUNX1MUT (9.1% vs. 0% vs. 1.3%; p=0.10) and ZRSR2MUT (9.1% vs. 2.3% vs. 0.5%; p=0.08). Notably, among 9 patients in whom NGS was performed at the time of direct transition from ET to AML, without an intermediary MF phase, 8 (88.9%) displayed TP53MUT and only one (11.1%) EZH2MUT and none (0%) ASXL1MUT. At a median follow-up of 66 months, 93 (18.6%) deaths, 26 (5.2%) leukemic transformations (LT), and 108 (21.6%) MF transformations were reported. In group A patients, age-adjusted multivariable analysis (MVA) of mutations identified ASXL1MUT (N=29; HR 3.8, 1.9-7.7; p<0.01) and SF3B1MUT (N=12; HR 3.9, 1.4-11.0; p=0.01) to be associated with inferior OS, ASXL1MUT (HR 4.9, 1.01-24.2; p=0.048) and SRSF2MUT (N=11; HR 11.9, 1.3-104.8; p=0.03) with inferior LFS, and ASXL1MUT (HR 9.0, 2.8-29.5; p<0.01) and SF3B1MUT (HR 15.5, 3.1-77.5; p<0.01) with inferior MFFS, all calculated from time of NGS. A similar analysis in group B patients identified ASXL1MUT (N=22; HR 3.7, 1.2-11.7; p=0.03) to be associated with inferior OS and TP53MUT (N=10; HR 13.4, 1.7-108.6; p=0.02) with inferior LFS. Age-adjusted MVA combining groups A and B (N=489) confirmed the association between inferior OS and ASXL1MUT (N=51; p<0.01) and SF3B1MUT (N=18; p<0.01) while inferior LFS was associated with TP53MUT (N=14; p<0.01) and ASXL1MUT (p=0.03). Conclusions The majority (⁓73%) of patients with post-ET AML harbor TP53MUT, at the time of LT. By contrast, TP53MUT are infrequent in ET-CP (⁓1%) and did not appear to predict LT, as was the case with post-ET MF. ASXL1MUT was shown to be an independent risk factor for OS, MFFS, and LFS in ET-CP and for OS in post-ET MF. SF3B1MUT was associated with inferior OS and MFFS in ET-CP and SRSF2 with inferior LFS in ET-CP. Identifying ET patients with adverse mutations allows for closer surveillance and potentially more tailored therapeutic interventions.

Abstract Background: The recently published BLAST clinical and BLAST molecular (BLAST-Mol) risk models for chronic myelomonocytic leukemia (CMML) incorporate clinical and molecular variables, respectively, for prediction of overall survival (OS), while BLAST-BT (blast transformation) risk model was predictive of BT free survival (BTFS) [Blood 2025;doi:]. In the current study, the Mayo Clinic (USA) CMML cohort used in the aformentioned study was expanded from 457 to 600 patients and merged with an external cohort of 501 patients from the Humanitas Research Hospital (HRH; Italy) in order to validate the BLAST, BLAST-Mol and BLAST-BT risk models. Methods: CMML diagnosis and sub-categorization were according to the International Consensus Classification, which included CMML-1 (BM/PB blasts <10%/<5%) vs. CMML-2 (BM/PB blasts ≥10%/≥5%) and CMML-MP (leukocytes ≥13 x 10⁹/L) vs. CMML-MD (leukocytes <13 x 10⁹/L) [Blood 2022;140:1200]. The BLAST score is based on clinical risk factors including PB blasts ≥2%, leukocytes ≥13 x 109/L, and severe or moderate anemia resulting in BLAST low, intermediate, and high risk groups. BLAST-Mol also accounts for mutations and karyotype and identifies PHF6 and TET2 as “favorable” and DNMT3A, U2AF1, BCOR, SETBP1, ASXL1, NRAS, PTPN11, RUNX1, TP53, as “unfavorable” mutations with adverse karyotype also considered unfavorable. BLAST-BT variables include absence of PHF6MUT, BM blasts ≥10%, presence of DNMT3AMUT, PB blasts ≥2%, leukocyte count ≥13 x 109 /L, and ASXL1MUT. OS analyses were censored for allogeneic stem cell transplant (ASCT). Conventional statistical methods were employed, using JMP Pro 18.0.0 software SAS Institute, Cary, NC, USA. Results: A total of 1,101 patients (median age 73 years; 67% males) with newly-diagnosed CMML were considered: Mayo cohort 600 and HRH cohort 501. Compared to the HRH cohort, the Mayo cohort was more likely to include CMML-MP (44% vs. 28%; p<0.01), CMML-1 (87% vs. 81%; p<0.01), ASXL1MUT (48% vs. 35%; p<0.01), SRSF2MUT (43% vs. 33%; p<0.01), NRASMUT (15% vs 9%; p<0.01), PHF6MUT (7% vs. 4%; p<0.01), and less likely to harbor TET2MUT (50% vs. 58%; p<0.01), and SF3B1MUT (4% vs. 10%; p<0.01) [Table 1]. At a median follow-up of 55 months (range 0-202), 686 (62%) deaths, 218 (20%) BTs, and 142 (13%) ASCTs were documented. CPSS-mol risk categories at diagnosis included low (N=190; median OS 67 months), intermediate-1(N=271; median 38 months), intermediate-2 (N=407; median 26 months), and high (N=231; median 16 months; p<0.01) [AUC at 5 years 0.75]. BLAST clinical risk distribution was low (N=356; median OS 65 months), intermediate (N=393; median 30 months), and high (N= 352; median 16 months; p<0.01) [Figure 1; AUC at 5 years 0.79]. The corresponding AUCs calculated for the Mayo and HRH cohorts, separately, were 0.81 and 0.76, respectively. BLAST-Mol risk assignments included low (N=357; median OS 66 months), intermediate (N=512; median 27 months) and high (N=232; median 15 months; p<0.01) [Figure 2; AUC at 5 years 0.79]. The corresponding AUCs for the Mayo and HRH cohorts were 0.81 and 0.77, respectively. The 3-year cumulative incidence of BT (accounting for death from another cause) stratified by BLAST-BT risk model included 8% for very low risk (N=54), 11% for low risk (N=332), 20% for intermediate-risk (N=281), 27% for high risk (N=250), and 34% for very high risk (N=176) [p<0.01; Figure 3]. Multivariable analysis of the combined Mayo-Humanitas cohort (N=1,101) identified age >65 years (HR 1.7, 95% CI 1.3-2.1), CMML-2 subtype (HR 2.0; 95% CI 1.6-2.5), and a number of other mutations or co-mutations with BLAST-Mol-independent and CPSS-mol-independent prognostic contribution (to be elaborated further at the time of meeting presentation). Conclusions: The current study validates the predictive performance of the BLAST, BLAST-Mol and BLAST-BT risk models in a large two-center international cohort of patients with CMML. The possibility of further enhancement of the BLAST-Mol model is suggested by the demonstration of independent prognostic relevance from additional mutations and co-mutations.

Abstract Background: TP53-mutated myeloid neoplasms (MNs) represent one of the most lethal subgroups of blood cancers. The World Health Organization (WHO-5) recognize TP53-mutated (TP53mut) myelodysplastic syndrome (MDS) and International Consensus Classification (ICC) recognize TP53mut MDS and acute myeloid leukaemia (AML) as distinct disease entities, acknowledging their dismal prognosis and the urgent need for novel therapeutic strategies. However, key discrepancies exist between the classifications, creating uncertainty in diagnosis, influencing therapeutic decisions, and complicating clinical trial enrolment for patients with uniformly poor survival. Importantly, neither classifications included TP53-mutated myelofibrosis (MF), other myeloproliferative neoplasms (MPN), and MDS/MPN overlap syndromes, despite growing evidence that these cases carry similar adverse outcomes. To address these critical gaps, we conducted a comprehensive analysis across the full spectrum of TP53-mutated MN. Methods: We included TP53mut cases with variant allele frequency (VAF ≥2%) of AML, MDS, MDS/MPN overlap, myelofibrosis and other MPN managed at the Mayo clinic (Rochester, USA) and Local Health Network in South Australia (Australia). Cases were classified according to the revised 4th edition WHO classification (WHO-4R). Biallelic TP53 loss was defined as: (i) ≥2 mutations with VAF ≥2%; (ii) single TP53mut with VAF ≥2% plus 17p loss on karyotype, FISH, or SNP array; or (iii) single TP53mut with VAF ≥50% with or without 17p loss. Monoallelic loss was defined as a single TP53mut with VAF <50% without 17p loss or copy neutral loss of heterozygosity. The primary endpoint was overall survival (OS) from diagnosis, censored at allogeneic transplantation. Conditional inference tree analysis was performed to define the effect of individual predictors on OS. Results: We identified 853 TP53mutMN cases including WHO-4R-defined AML (BM/PB blasts ≥20%, n=314; 36.8%), MDS with excess blasts-2 (EB-2; BM 10–19% or PB 5–19%, n=129; 15.1%), MDS-EB-1 (BM 5–9% or PB 2–4%, n=100; 11.7%), and MDS with low blasts (LB; BM <5% and PB <1%, n=228; 26.7%). MF included chronic phase (MF-CP, n=48; 5.6%), accelerated phase (MF-AP, n=7; 0.8%), and blast phase (MF-BP, n=17; 2.0%). Additional cases comprised other MPN-chronic phase (n=3, 0.4%) and MDS/MPN overlap syndromes (n=7, 0.8%). Of 848 evaluable TP53mut MN, 616 (72.6%) harbored biallelic TP53 loss and 232 (27.4%) had monoallelic loss, with 42.4% (n=98) of monoallelic cases harboring a complex karyotype (CK). Biallelic loss was highly prevalent in AML (83.1%), MDS (70.4%), and MDS/MPN overlap (71.4%), but was less frequent in cases with MPN (33.3%) and MF-chronic phase (25.0%). Across the spectrum, the frequency of biallelic loss increased stepwise with disease progression, rising from MDS-LB (62.8%) to MDS-EB1 (76.0%), MDS-EB2 (79.5%), AML (83.1%), and MF-AP/BP (79.2%) (P<0.0001). In multivariable analysis, WHO-4R category, TP53mut VAF, biallelic loss and CK were independent predictors of poor survival in TP53mut MN. Conditional inference tree analysis stratified the cohort into seven prognostic groups with distinct median OS: (1) AML, MF-BP/AP, and MDS/MPN overlap regardless of mono/biallelic and CK status (n=335, 39.7%; median OS 4.0 months); (2) MDS-EB1/EB2 with biallelic loss or monoallelic plus CK (n=212, 25.1%; 7.2 months); (3) MDS-LB, MF-CP and MPN with CK (n=156, 18.5%, OS 10.2 months); (4) biallelic MDS-LB (n=28, 3.3%; OS 15.6 months), (5) monoallelic MDS-EB1/EB2 without CK (n=20, 2.4%; OS 26.2 months), (6) monoallelic MDS-LB without CK (n=57, 6.7%; OS 33.4 months), and (7) MF-CP and other MPN without CK (n=36, 4.3%; OS 37.0 months). This analysis defines a clear stepwise risk gradient, identifying subsets with extremely poor prognosis versus a relatively indolent disease. Conclusions: This is the first study to evaluate the prognostic impact of TP53mut MN across the entire MN spectrum, providing a framework for future refinement of TP53-driven disease classification. Key findings include: (i) MF-AP/BP and MDS/MPN overlap exhibit survival outcomes as dismal as AML, irrespective of the TP53 allele status; and (ii) complex karyotype is a surrogate for “biallelic equivalent”, refining risk interpretation in monoallelic cases. Overall, our study defines seven prognostic subgroups spanning all TP53mut MN, offering a foundation for risk-adapted clinical trial design and future classification updates.

Abstract Background: VEXAS (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic) syndrome is a hemato-inflammatory syndrome due to somatic mutations in UBA1 gene. Recently, DNA methyltransferase inhibitors (specifically, 5-azacitidine) and allogeneic hematopoietic stem cell transplantation (alloHSCT) have shown success in eradication of UBA1 clones and clinical remission in some patients. This study aims to provide a comparative efficacy analysis between DNA methyltransferase inhibitors (hypomethylating agents/HMA) versus alloHSCT in VEXAS syndrome patients. Methods: This is a multi-center, retrospective cohort study. UBA1 mutations were detected either through next generation sequencing [NGS, limit of detection (LOD) 2% variant allele fraction (VAF)] or quantitative droplet digital polymerase chain reaction testing (ddPCR, LOD 0.5% VAF). Outcomes of interest included overall survival (OS), glucocorticoid (GC) discontinuation/reduction in dose (prednisone < 10 mg/day), and complete molecular remission (CMR, defined as an undetectable UBA1 mutation). OS was calculated from the time of diagnosis to death or last follow up. Cumulative incidence of reduction in GC dose was calculated using competing risk analysis after accounting for death. Results: Between years 2020-2024, a total of thirty-nine male patients (median age 67, range 45-82 years) with VEXAS syndrome, necessity for therapy (GC-refractory inflammation and/or progressive marrow failure/myeloid neoplasm) and confirmed UBA1 pathogenic mutation were included. Of these, 18 (46%) received HMA therapy (5-azacitidine 72% and Decitabine 28%) with a median of 4 (range 1-36) cycles. and 21 (54%) underwent alloHSCT (MSD=4, MUD=12, MMUD=2, and haploidentical=3), all received reduced intensity conditioning (Flu/Mel =11, Flu/Bu=5, Flu/Cy/TBI=2, and TBF=3) and 91% received post-transplant cyclophosphamide-based graft-versus-host disease (GVHD) prophylaxis (only 2 patients in alloHSCT group received prior HMA without benefit). Baseline characteristics were well-balanced between HMA and alloHSCT groups; age at treatment (median 70 vs 65 years, p=0.2), and UBA1 M41V subtype (25% vs 40%, p=0.3), cytogenetic and non-UBA1 mutation abnormalities (p=0.4), prior lines of therapy (median 4 in both, p=0.5), time from diagnosis to therapy (median 7 vs 13 months, p=0.15), indication for therapy (p=0.12), Charlson CI scores (median 4 in both, p=0.4) and HCI-CI scores (median 4 in both, p=0.7). At a median follow-up of 45 (range 7-57) months, 11 (28%) deaths (alloHSCT=3 and HMA=8) were documented. Causes of death included infections (n=6), cardiac disease (n=2), poor graft function (n=2) and pulmonary GVHD (n=1). Compared to HMA therapy, alloHSCT group demonstrated higher rates of GC discontinuation (52% vs 6%, p<0.01) and CMR (evaluable= 22, 100% vs 50%, p<0.01). In the HMA group, 50% (n=9) discontinued therapy after a median of 4 months due to infectious complications or lack of benefit. In the alloHSCT group, 3 (14%) patients developed grade 2 acute GVHD (1-skin, 2-gut, no grade 3-4 acute GVHD) and 4 (19%) experienced moderate to severe chronic GvHD (2-lung, 2-mucocutaneous). In multivariable analysis (MVA) adjusted for age and HCT-CI, alloHSCT was identified as the strongest favorable predictor for OS (HR 0.18 95% CI 0.04-0.8) with similar results when analysis was restricted to transplant-eligible patients < 75 years of age (HR 0.1, 95% CI 0.03-0.6). In a 6-month post-treatment landmark analysis (n=31 evaluable), patients who underwent alloHSCT (n=17) exhibited numerically superior GC discontiuation rate (29% vs 14%, p=0.049) and subsequent OS compared to HMA (2-years OS: 84% vs. 61%, median not reached vs 25 months, 95% CI: 0.06–1.7). In a post-treatment competing risk [GC dose reduction (death)] analysis alloHSCT was associated with superior 2-, and 3-year rates of 57% (10%), and 76% (10%), compared to HMA 22% (24%), and 31% (42%) [p<0.01 (0.04)]. AlloHSCT was also associated with higher rate of CMR (evaluable= 21, alloHSCT N=16 and HMA N=5, 100% vs 60%, p=0.01). At the last follow up, none of the patients who achieved CMR had re-emergence of the UBA1 clone (despite early mixed chimerism). Conclusions: This study represents the largest comparative analysis of alloHSCT versus HMA therapy in VEXAS syndrome, and confirms superiority of alloHSCT in achieving molecular remission, GC discontinuation/dose reduction, and overall survival. Prospective clinical trial confirmation is warranted.

Abstract Introduction: VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) is an adult-onset autoinflammatory disorder defined by somatic UBA1 mutations in hematopoietic stem cells (HSCs). Given the higher median age at onset and prevalent inflammation, approximately 60% of patients have concurrent clonal hematopoiesis (CH) involving epigenetic regulators, DNMT3A and TET2. We have previously demonstrated (Gutierrez-Rodrigues, Blood 2023) that DNMT3A and TET2 mutations in VEXAS are independently associated with increased mortality, without an increased risk of myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). To define the role of CH in VEXAS, we investigated the impact of CH on mortality and related epigenetic dysregulation in primary VEXAS patient samples and validated these findings using an adenine base-editing (ABE) strategy to model cooccurring UBA1 and DNMT3A mutations in CD34+ HSCs. Methods: After IRB approval, 114 VEXAS patients from the Mayo Clinic and the NIH were retrospectively assessed, including 104 screened for CH by error-corrected DNA sequencing (ECS). DNA methylation changes were identified using the Illumina Infinium MethylationEPIC BeadChip and genome-wide using Biomodal 6-base sequencing on peripheral blood samples (PB). Cellular indexing of transcriptomes and epitopes (CITE)-seq was performed using the 10x Chromium 3' GEX platform; 21,114 pooled cells were subjected to UMAP dimensionality reduction, followed by Azimuth based cell identification. HSCs from different donors were base edited for UBA1 M41T mutation with and without cooccurring DNMT3A splice-site mutations. Levels of cytokines in cell culture supernatants were measured using Olink technology, transcriptomic changes were assessed using bulk RNA-seq. Statistical analyses were carried out using R. Results: Patients' median age was 68 (62-74) years; 95% were white and 99% were males. 39% had a pathogenic CH variant, with DNMT3A (18%; median VAF: 42%; range 6-46%) and TET2 (11.5%; median VAF: 11%; range: 2-37%) being most common (2% had both). While the presence of any CH mutation did not impact survival (7.7 vs 11.5yrs, p =0.69), DNMT3Amt CH had an age-independent negative impact on mortality (9.2 vs 5.6yrs, p=0.02), without an increased risk of MDS/AML (p=0.31). Changes in DNA methylation were profiled using the methylation array in 19 VEXAS patients without CH (Median Age: 68 years) and in 9 with DNMT3Amt CH (Median Age: 74 years), respectively. VEXAS samples with DNMT3Amt CH displayed a hypomethylation phenotype compared to VEXAS samples with no CH, with 4,096 unique hypomethylated CpGs, occurring mainly at gene enhancers (Enh) and transcriptional states (Tx, TxWk). This loss of DNA methylation was enriched in inflammatory pathways with extensive hypomethylation observed in the promoters and gene bodies of IL-1 family genes. CITE-seq performed on 7 VEXAS samples (4 DNMT3Amt CH vs 3 no CH) demonstrated an upregulation of gene expression predominantly in T-cell subsets. This increased expression was enriched in inflammatory genes, largely centered around IL-1 and IL-18 family signaling. Pathway analysis revealed an enrichment in respiratory electron transport and oxidative phosphorylation (oxphos), with the VEXAS samples with DNMT3Amt CH demonstrating significantly increased oxphos gene signatures. Base editing of CD34+ cells was done using an adenine base-editing (ABE) strategy to model dual UBA1 and DNMT3A splice-site mutations. We observed that the HSCs base edited for the UBA1 M41T recapitulated the cellular hallmarks of VEXAS demonstrating reduced ubiquitination, unfolded protein response (UPR) activation, and increased inflammation. The addition of cooccurring DNMT3A mutations resulted in decreased DNMT3A expression, further increasing inflammatory markers, confirming altered inflammatory and metabolic pathways. Conclusion Our study validates the age-independent, negative prognostic impact of DNMT3Amt CH on survival in VEXAS syndrome. DNMT3A mutations mediate epigenetic and transcriptional priming associated with upregulation of inflammatory and mitochondrial function pathways. These findings have been validated in a base edited cell model with UBA1 and DNMT3A co-mutations.

Abstract Introduction Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm with overlapping features of myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPN). Blast transformation (BT) is a frequent complication of CMML with a 3-year risk of approximately 31%. MPN driver mutations (i.e., JAK2, CALR, MPL) are known to occur in CMML but their impact on overall survival (OS) or BT remains uncertain. Methods The current study was conducted under institutional review board approved minimum risk protocols that allowed retrospective collection and analysis of patient data. Diagnostic criteria were according to the International Consensus Classification (Blood 2022; 140:1200). For survival analysis, patients were censored at the time of allogeneic stem cell transplant (ASCT). All statistical analyses were conducted using JMP Pro 17.0.0 software (SAS Institute, Cary, NC, USA) and Bluesky Statistics Software (Worldwide Headquarters, Chicago, IL, USA). Results A total of 612 CMML patients were considered (median age 71 years; 68% males). MPN driver mutations (MPNMUT) were detected in 56 (9%) patients, including 43 (77%) with JAK2, 10 (18%) MPL, and 3 (5%) CALR. The median variant allele frequencies were 44% for JAK2 (range, 0.3–100%), 49% for MPL (range, 0.3–97%), and 50% for CALR (range, 20–80%). Age and sex distributions and CMML-1 vs. CMML-2 representation were similar between CMML patients with and without MPNMUT. On the other hand, CMML patients with MPNMUT were more likely to display higher hemoglobin level (p <0.01), platelet count (p <0.01), absolute neutrophil count (p = 0.02) and leukocytosis (≥13 ×109/L; p <0.01); they were also less likely to be red blood transfusion dependent (p <0.01), display platelet counts of <100 ×109/L (p <0.01) or abnormal karyotype (p= 0.03), present with severe anemia (p= 0.03) or harbor NRAS mutation (p = 0.02). Higher Mutational frequencies of SRSF2 (median 53% vs. 41%; p = 0.01), TP53 (median 8% vs. 2%; p = 0.02) and IDH2 (median 13% vs. 4%; p = 0.01) were noted in CMML patients with MPNMUT. During a median follow-up of 23 months (inter-quantile range 11-51 months) for all 612 CMML patients, 402 (67%) deaths, 118 (19%) BT and 108 (18%) ASCT were documented. Of these, 36 (9%) deaths, 18 (15%) BT and 13 (12%) ASCT occurred in patients with MPNMUT. Median OS for the entire group (N=612) was 32 months with 1-, 2- and 3-year OS rates of 78%, 58% and 47%, respectively. There was no significant difference in OS between CMML patients with or without MPNMUT (median 41 vs 31 months; p = 0.27). Thrombotic events were reported in 53 (9%) patients after diagnosis of CMML, including 48 venous and 5 arterial events. There was no significant difference in arterial (p = 0.10) or venous (p = 0.76) thrombosis-free survival between CMML patients with or without MPNMUT. Among all 612 patients with CMML, followed for a median of 18 months for living patients, 118 (19%) experienced BT, with 1-, 3-, and 5-year BT rates of 10.8%, 23.1%, and 31%, respectively. Cumulative incidence analysis function (CIF) was calculated in order to assess the BT-free survival while accounting for death as a competing risk; 3-year CIF was 30% for CMML patients with vs. 18% for those without MPNMUT (Gray's test; p = 0.01). This observation was confirmed in univariate analysis of time to BT (HR 1.7; p=0.03) as well as the corresponding multivariable analysis involving other mutations and clinical risk factors: MPNMUT (HR 1.8; 0.036), ASXL1MUT (HR 2.3; p<0.01), DNMT3AMUT (HR 2.6; p<0.01), CMML-2 vs. CMML-1 (HR 3.4; p<0.01), and leukocyte count ≥13 ×109/L (HR 2.0; p<0.01). SRSF2, IDH2, and TP53 mutations did not impact BT. Notably, the aforementioned risk factors for BT, including ASXL1MUT and DNMT3AMUT, were no longer significant when analysis was restricted to patients with MPNMUT. Conclusions The current study suggests a higher risk of BT in CMML patients with MPNMUT, independent of other molecular or clinical risk factors for BT. On the other hand, overall and thrombosis-free survival did not appear to be similarly influenced. The differential impact of previously recognized risk factors for BT in CMML (e.g., ASXL1MUT, DNMT3AMUT, CMML-2, CMML-MP), in the presence (lost significance) vs. absence (remained significant) of MPNMUT was intriguingly noted.

Abstract The importance of bone marrow (BM) microenvironment and its immune interactions within and their role in chronic myelomonocytic leukemia (CMML) disease progression have not been well studied. Our previous work in CMML identified type 1 helper T cell (Th1) and CD4 terminal effector cell suppression in peripheral blood from untreated patients, in comparison to age-matched normal controls (Mangaonkar A et al. Blood Adv 2020). Furthermore, clonal dendritic cell (DC) aggregates in CMML are present in approximately 20-30% patients at diagnosis, display significant cell heterogeneity (plasmacytoid DCs + myeloid DCs and other myeloid/immune cells), express indoleamine 2,3-dioxygenase-1 (IDO), associate with T regulatory cell (Treg) expansion, and transformation to AML [blast transformation or BT, Lucas N et al. Leukemia 2019, Mangaonkar A et al. Blood Adv 2020, and Mangaonkar A et al. Blood 2023 (abstract)]. The purpose of this study was to define the myeloid-immune cell interactions within the BM microenvironment critical for CMML progression to AML. To phenotypically identify cell types and their interactions within the CMML BM, we used single-cell type spatial omics through two distinct modalities on archival formalin-fixed paraffin-embedded (FFPE) BM biopsy specimens. Ten serial CMML samples at chronic phase [CP, n=5, median BM blast% 5 (2-15)] and at BT [n=5, median BM blast% 60 (25-74)] were used for both techniques. Spatial transcriptomics was conducted through GeoMx Digital Spatial Profiler (DSP) platform that uses UV-photocleavable, spatial-barcoded oligo probes to profile the whole transcriptome in defined regions of interest (ROI). CD123 (marker for DC-myeloid aggregates) immunofluorescence antibody was used to identify and segment ROI. 16-color flow cytometry was done to validate cell-specific expression. Spatial cellular phenotyping of immune cells on the same samples (serial cuts) was done through multiplex imaging by co-detection by indexing (CODEX) using 27 markers: CD31, Ki67, CD11b, CD20, CD44, Ecad, CD123, CD107a, CD8, CD11c, CD45RO, FoxP3, CD14, PDL1, CD56, CD68, CD163, PD-1, CD38, HLA-DR, CD4, IDO, CD45, PanCK, Na/K/ATPase, CD3e, and DAPI. CD8T+ cell anergy was demonstrated through CFSE cell proliferation assay. Benjamini-Hochberg correction was used to adjust for multiple hypotheses. After filtering poor quality ROIs [ROIs with total read count < 1000 were removed, ROIs with trim, stitch, alignment < 80% or saturation < 50% were removed], 3rd-quartile normalization [BiocGenerics (v. 0.48.1)] and batch correction [limma (v. 3.58.1)], we compared transcriptomics signatures between CD123+ and CD123- ROIs in all samples. Results showed that LIPA, SLC4A1, HBA2, TIMD4 and HMOX1 werethe top five genes upregulated in CD123+ areas. Among all gene transcripts, TIMD4 expression increased the most (Log2FC=0.6, P-value=8.79E-10, FC=1.52, FDR=1.68E-07) when CD123+ ROIs were compared between CMML-CP and CMML-BT serial samples, suggesting a biological relevance in disease progression. Previous work in metastatic peritoneal cancer has established that TIM-4 expressing macrophages suppress Th1/Th2 ratios and impair CD8+ T cell function (Chow A et al. Cancer Cell 2021). To confirm expression at the protein level, we used a multicolor flow cytometry panel and confirmed that TIM4 is expressed on macrophages (Lineage-negative, HLA-DR+, CD68+ cells) in independent CMML BM aspirate samples (n=3). Next, we conducted spatial phenotyping using CODEX on serial cuts from the same serial samples used for spatial transcriptomics. Anatomical co-localization of macrophages with leukemic cells, monocytes and T cells was visually inspected and confirmed. Cell-interaction analysis (after Benjamini-Hochberg correction) indicated that macrophages-monocyte, macrophage-leukemic cells, and macrophage-memory CD4+T cells were significantly (P<0.05) increased in CMML-BT versus CMML-CP. CD8T+ cell anergy was confirmed through a CFSE cell proliferation assay in untreated CMML PBMC samples. In conclusion, our data suggests that TIM4-expressing macrophages and their interactions with monocytes/monocyte precursors and immune cells within a DC aggregate create a protective leukemic stem cell niche and T cell tolerance, promoting blast transformation in CMML.

Abstract Background Blast transformation (BT) occurs in approximately 15-30% of patients with chronic myelomonocytic leukemia (CMML) and remains a leading cause of death. Allogeneic stem cell transplantation (ASCT) is currently the only treatment modality with the potential to cure the disease or prolong survival. Optimal timing of ASCT is critical for maximizing benefit while minimizing risks. To that end, contemporary risk models have focused on the prognostic relevance of individual, as opposed to concurrent mutations. In the current study, we looked into the possibility of prognostic prominence from concurrent mutations in predicting BT in CMML. Methods CMML diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200). A machine-learning hierarchical clustering algorithm was developed and tailored for patient stratification using survival outcomes and co-expression of genomic alterations. To reduce complexity and improve interpretability, we generalized each cluster using existing rule induction algorithms, including the in Trees framework and JRip. The final output was a set of mutation-based cluster definitions, each representing a distinct patient subgroup with similar survival trajectories. Competing risk analysis and cumulative incidence functions were used in downstream evaluation to validate the clinical distinctiveness of the clusters. For all survival analysis, patients were censored at the time of ASCT. Time-to-BT was calculated from the date of diagnosis to the date of BT or ASCT or last contact. BT-free-survival was calculated from the date of diagnosis to the date of BT, ASCT, death, or last contact. Results The core study cohort included 605 patients from the Mayo Clinic, USA, and the external validation cohort 501 patients from Humanitas Cancer Center, Milan, Italy. Using the patient cohort from the Mayo Clinic, machine-learning algorithms identified five molecular clusters with 3-year blast transformation (BT) rates ranging from 0% to 100% (AUC at 3 years 0.78): the order of molecular signature assignment (probability of BT/death from another cause) was i) PHF6MUT/ASXL1WT (0%/17% at 3 years; N=32), ii) NPM1MUT OR BCORMUT/ASXL1MUT OR SETBP1MUT/NRASMUT (48%/30% at 1 year; N=24), iii) RUNX1MUT/ASXL1MUT OR SRSF2MUT/NRASMUT OR EZH2MUT/ASXL1MUT OR SETBP1MUT OR BCORMUT (31%/55% at 3 years; N=132), iv) ASXL1MUT/TET2MUT OR DNMT3AMUT OR JAK2MUT (24%/28% at 3 years; N=123), and v) all other permutations (10%/40% at 3 years; N=294) [Figure 1]. Additional analysis confirmed significant differences in survival between RUNX1MUT/ASXL1MUT vs. RUNX1MUT/ASXL1WT (p<0.01) OR RUNX1WT/ASXL1WT (p<0.01) OR RUNX1WT/ASXL1MUT (p=0.038) [Figure 2]. Similar patterns of differences in survival were also documented for NRAS/SETBP1, ASXL1/EZH2, and NRAS/SRSF2 mutation combinations (Figure 2). A subsequent Cox regression analysis confirmed independent prognostic contributions from “PHF6MUT/ASXL1WT” (HR 5.43e-10; p<0.01), NPM1MUT (HR 26.7; p<0.01), “SETBP1MUT/NRASMUT” (HR 12.7; p<0.01), BCORMUT(HR 5.8; p<0.01), “RUNX1MUT/ASXL1MUT” (HR 2.3, p<0.01), JAK2MUT (HR 2.1; p<0.01), and “ASXL1MUT/TET2MUT” (HR 1.7; p=0.02). The prognostic relevance of “SETBP1MUT/NRASMUT”, “RUNX1MUT/ASXL1MUT”, NPM1MUT, and BCORMUT was validated in the external cohort from Italy (N=501). In the Mayo Clinic cohort, presence of any of the latter mutations was associated with 1-, 3-, and 5-year BT (death from other cause) rates of 27% (21%), 44% (49%), and 44% (51%), respectively (Figure 3). The corresponding values in the absence of high risk mutations were 7% (15%), 15% (37%), and 18% (52%) [Gray's p value <0.01 for both BT and death from another cause; Figure 3]. Similarly, the 1-, 3-, and 5-year BT (death from another cause) rates in the Italian cohort were 21% (19%), 37% (47%), and 42% (54%) in the presence and 8% (14%), 20% (35%), and 24% (45%) in the absence of high-risk mutations (Gray's p value <0.01 for BT and 0.18 for death from another cause; Figure 4). Conclusions In the current study, machine-learning algorithms enabled the discovery of concurrent mutations in CMML that were shown to be prognostically more significant than their individual constituents. Such prognostic interaction might have contributed to some of the discrepancies noted in current literature regarding the prognostic relevance of certain mutations in CMML and should be accounted for in the development of future risk models.

Abstract Background Red blood cell distribution width (RDW) is an easily accessible diagnostic test that is underutilized as a prognostic marker, despite substantial supporting evidence in cancer, cardiovascular diseases, and helathy individuals (Pilling et al. PLoS One 2028;13:e0203504). The association between increased RDW and incident acute myeloid leukemia (AML) has long been suggested in healthy volunteers (Abelson et al. Nature 2019;559:400). We looked into a similar phenomenon in patients with polycythemia vera (PV) or essential thrombocythemia (ET) in large discovery and validation cohorts. Methods A machine learning (ML) approach was used to identify baseline clinical and laboratory parameters that were predictive of leukemic transformation (LT) and develop a predictive model in a large discovery cohort of PV and ET, obtained from electronic medical records of the Maccabi Healthcare Services (MHS; Tel-Aviv, Israel). Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal cutoff points for RDW and other continuous variables. Mayo Clinic databases (Rochester, MN, USA) for patients with PV and ET were utilized as validation cohorts. Identification of study patients in the latter was according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200) while in the discovery cohort was based on presence of ICD-9 codes associated with documentation of disease-specific driver mutations or treatment records that are consistent. Statistical analyses were performed using the R statistical software 4.4.2 (November, 2024) (Foundation for Statistical Computing, Vienna, Austria) or JMP Pro 18.0.0 software (SAS Institute, Cary, NC, USA). Results The discovery cohorts from Israel included 4,592 patients with PV (median age 63 yesrs; 53% males) and 5,968 with ET (median age 61 yesrs; males 40%). The validation cohorts from the Mayo Clinic included 633 PV (median 65 years; 51% males) and 634 ET (median 60 years; females 64%) patients that were informative for RDW. In the discovery cohorts, median follow-up (incident AML) was 6.1 years (N=186; 4.1%) in PV and 6.1 years (N=85; 1.4%) in ET. The corresponding values for the validation cohorts were 6.6 years (N=24; 3.8%) in PV and 7.0 years (N=22; 3.5%) in ET. Discovery cohort analyses Covariate-adjusted Cox regression analysis was utilized to estimate associations between LT and several clinical variables followed by machine learning pipeline to predict LT in a given timeframe. Conversion from continuous to binary variables was accompliched by ROC analysis. Shapley Additive exPlanations (SHAP) application enabled selection of top-ranked features that included RDW >15.5 (HR 3.2, 1.1-9.5; p=0.03), age >60 years (HR 2.9, 1.4-5.9; p<0.01), and LDH >1.5 x UNL (HR 4.0 (1.6-10.0; p<0.01). A similar analysis in ET also identified RDW >15.5 (p<0.01), age >60 years (p=0.01) as well as male sex (p=0.03) as independent risk factors for LT. Subseqent risk models based on these risk factors were externally validated and enabled robust risk stratification with 10-20 year AUCs rangening from 0.75 to 0.81. Validation cohorts analyses The indpendent predictive value of increased RDW observed in the discovery cohort was confirmed in the validation cohorts for both PV and ET and further gender-specified: female/male RDW ≥16.4/15.8 in PV (HR 7.9, 1.9-33.8) and ≥16.1/14.4 in ET (HR 5.1, 2.2-11.9). Also confirmed were the additional prognostic contributions of age ≥60 years (HR 3.8, 1.3-10.9) and LDH >1.5 x UNL (HR 3.9, 1.6-9.4), in PV, while no other clinical risk factor showed RDW-independent prognostic relevance in ET. The prognostic impact of RDW in ET was most apparent in females (p<0.01), as opposed to males (p=0.32) while the effect was equally significant in PV. In multivariable analysis that included previously recognized high risk mutations in PV (SRSF2) and ET (SF3B1), both RDW and mutations sustained significance (p<0.05). Conclusion The current study identifies higher RDW as a key risk factor for LT in both PV and ET, independent of other clinical or genetic risk factors. Possible underlying mechanisms include increased RDW as a marker of long-term pre-leukemic clones with sub-clinical ineffective erythropoiesis or an associated pro-leukemic inflammatory state.