He, Rong

Abstract Introduction: Somatic mutations in splicing factors (SF) - SF3B1, SRSF2, U2AF1, and ZRSR2 – represent one of the most common drivers of premalignant clonal hematopoesis. While SF mutations are potent drivers of clonal expansion (Fabre et al., Nature 2022), multiple SF mutations are rarely observed in the same individual (negative epistasis). Prior studies have identified rare cases of concurrent SF mutations (<4%) and described specific hotspot combinations that escape negative epistasis (Taylor et al., Blood 2020). We conducted this study to further characterize the frequency and clonal relationship between concurrent SF mutations using a larger patient cohort. Methods: After IRB approval, we assessed 3,369 myeloid next generation sequencing (NGS) results that identified somatic variants in SF3B1, SRSF2, U2AF1, or ZRSR2, in 2,500 adult patients from 08/2015 to 07/2025. Patient demographics, ICD-9/10 codes, and complete blood counts with differentials were obtained, retaining only those datapoints assigned 30 days before or after NGS testing. To allow comparison of clone size between autosomal and the X-linked SF ZRSR2, VAFs were multiplied by two to estimate the cancer cell fraction (CCF), except for ZRSR2 mutations in males. For patients harboring concurrent SF mutations, we categorized them as co-dominant if the difference in respective CCFs was < 10% of the greater CCF. Specific SF variants were categorized into common hotspots (n=13), except for ZRSR2 variants, which do not occur in defined hotspots. Fisher's exact testing was used to assess the enrichment of SF hotspot variants in patients with concurrent SF mutations, as well as combinations of SF variants in patients with co-dominant SF mutations. Results: We identified 2,331 patients with at least 1 pathogenic SF mutation, of which 6.7% (n=156) had > 1 SF mutation (multi-SF MT). The total cohort comprised cases of AML (n=549, 24%), MDS (n=977, 42%), MDS/MPN (n=233, 10%), MPN (n=228,10%), and uncategorized ICD codes (“Other”, n=326), of which the largest category was clonal cytopenia(s). Notably, the prevalence of multi-SF cases was higher than previously described and our cohort included 5 patients with 3 SF mutations and 1 with 4 SF mutations. Of the multi-SF MT patients, we identified 42 (27%) with co-dominant CCFs among their respective SF mutations. Thirty-three multi-SF MT patients had multiple NGS sampling timepoints (median 2, range 2-4) spanning a median of 0.97 years (range 0.06-5.69 years); we found that 17 of these 33 demonstrated correlative SF CCFs over multiple timepoints, suggesting their SF mutations occurred in the same clone (“co-clonal”). Of these 17 patients, 82% had co-dominant CCFs at the single-timepoint level, whereas none of the 16 non-co-clonal demonstrated co-dominance. Thus, we used SF co-dominance as a surrogate for cases likely to be co-clonal. ZRSR2 (p=8.8x10-29), U2AF1Q157 (p=9.4x10-9), SF3B1K666 (p=1.4x10-4) and infrequent SF3B1 (“SF3B1-Other”, p=.0029) mutations were overrepresented in the multi-SF MT cohort compared to patients with a single SF mutation. Within the multi-SF MT cohort, 3 SF mutation combinations showed overrepresentation in codominant cases – SRSF2P95R/H/L+ ZRSR2 (p=0.014), SRSF2P95R/H/L+ SF3B1K666 (p=0.044), and U2AF1S34 + U2AF1Q157 (p=0.024), although not significant after multiple hypothesis correction. Dual ZRSR2 mutations were the most common combination seen in the multi-SF MT cohort but were underrepresented in codominant cases (p=0.025). Although there was no disease subtype uniquely associated with total or co-dominant multi-SF MT cases, occurrence of multiple SF mutations was more common in males (8% vs. 3% in females), an effect driven by ZRSR2 mutations. Hemoglobin and WBC were lower in multi-SF MT cases, with the lower hemoglobin strongly driven by the codominant multi-SF MT cases (median 8.3 g/dL vs 9.4 g/dL, p=0.00046). Conclusions: These findings suggest that SF mutation co-occurrence in myeloid neoplasms is more prevalent than previously estimated (~7%). Furthermore, we identified 6 individuals harboring > 2 SF mutations, which has not been previously described. We validate specific SF variant pairs that appear to exist in the same clone and are working to confirm the clonal architecture and characterize global splicing aberration in selected cases with single-cell studies to fully uncover the mechanism by which negative epistasis is overcome.

Abstract Background: Isocitrate dehydrogenase (IDH) mutations are frequently implicated in the development of acute myeloid leukemia (AML). In a recent study (Salman MY, Blood Adv 2025), it was reported that IDH2 mutation subtypes, R140 and R172, may respond differently to intensive chemotherapy. In this study, we evaluated mid-cycle and end-of-cycle bone marrow blast responses in AML patients with IDH2 R140, IDH2 R172, and IDH1 R132 mutations, aiming to determine whether these subtypes exhibit different response dynamics. Methods: We reviewed the charts of AML patients diagnosed at Mayo Clinic, Rochester, between the years 2016 - 2023 that had available next generation sequencing (NGS) data. Institutional review board approval was obtained. All data were recorded at the time of AML diagnosis. Patients without molecular data or not treated with intensive chemotherapy (7+3 or CPX-351) were excluded from this study. Bone marrow blast percentages were assessed at mid-induction (days 14-21) and end of cycle (counts recovery). Blast clearance dynamics and complete remission rates were compared across IDH mutation subgroups and correlated with outcomes. GraphPad Prism 10.0.0 was used for statistical analysis. Results: Out of 381 AML patients, 31 had IDH2 mutations (23 with R140 and 8 with R172), and 8 had IDH1 mutations (all R132). Among patients with IDH2 mutations, those with R172 had numerically higher median mid-cycle bone marrow blast percentage compared to R140 (10% vs. 4%, p = 0.37). There was no significant difference in mid-cycle blast percentages between patients with IDH1 and IDH2 mutations (4.5% vs. 4%, p = 0.8). At mid-cycle, 68% of patients with IDH2 R140 had blast counts <5%, compared to 38% of those with IDH2 R172, suggesting a trend for faster mid-cycle clearance in the R140 group, though not statistically significant (OR 3.57, p = 0.2). In a broader comparison, 60% of IDH2-mutated patients and 50% of IDH1-mutated patients achieved blast clearance (blasts <5%) at mid-cycle, though not statistically significant (p = 0.7). At the end of the induction, patients with IDH2 R140 had significantly lower median bone marrow blast percentages compared to those with R172mutations (2% vs. 4%, p = 0.01). Similarly, patients with IDH1 mutations showed low blast counts compared to the IDH2 group (1.5% vs. 3%, p = 0.1). A higher proportion of IDH2 R140 patients achieved blast clearance compared to those with R172 mutations (100% vs. 75%, p = 0.06), while proportion of patients who achieved blast clearance were similar between IDH1 and IDH2 patients (87.5% vs 93.1%, P = 0.5). Analysis of overall complete remission rates (including both complete remission [CR] and CR with incomplete hematologic recovery [CRi]) within the IDH2-mutated group showed a higher rate in patients with the R140 mutation compared to those with R172 (87% vs. 75%), though the difference was not statistically significant (p = 0.5). When comparing IDH1- and IDH2-mutated patients, overall complete remission rates were 75% and 84%, respectively, with no significant difference observed (p = 0.6). Kaplan–Meier analysis of time to end-of-induction blast clearance revealed no statistically significant difference between IDH2 R140 and R172 patients (median time to clearance: 31 vs. 37 days, p = 0.3). Similarly, no difference was observed between IDH1 and IDH2-mutated patients (33 vs. 31 days, p = 0.9). Overall survival analyses showed no significant difference between IDH2 R140 and R172 groups (median 66.8 months vs not reached, P = 0.4). Likewise, no difference was observed between IDH1 and IDH2 patients (44.4 months vs not reached, P = 0.7), indicating similar overall survival across mutation subtypes. Conclusion: IDH2-R140 was associated with more effective blast cytoreduction following intensive induction compared to IDH2-R172. Although not statistically significant, IDH2 R172consistently showed trends toward higher blast percentages, delayed response, and lower overall complete remission rates. These findings align with the recent study reporting significantly slower blast reduction and delayed response in R172-mutated AML. Also, no significant differences were found between IDH1- and IDH2-mutated patients in terms of response kinetics or overall survival. Together, these findings highlight potential biological differences among IDH mutation subtypes that may impact treatment response and require further investigation in larger prospective cohorts.

Abstract Introduction Venetoclax combined with hypomethylating agents (VEN/HMA) is an approved frontline induction therapy for older or unfit patients with acute myeloid leukemia (AML). However, data comparing its efficacy and safety to intensive chemotherapy (IC) in intermediate-risk AML—characterized by molecular and cytogenetic heterogeneity—remain limited. Methods This multicenter, real-world retrospective study analyzed newly diagnosed intermediate-risk AML patients who received either VEN/HMA or IC. We evaluated composite complete remission (CRc) rates, transfusion and infection rates, long-term outcomes including overall survival (OS) and progression-free survival (PFS), and performed subgroup analyses to identify mutation-specific predictors of treatment response. Multivariable Cox regression was used to identify independent prognostic factors. Results A total of 229 intermediate-risk AML patients were included. VEN/HMA achieved a comparable CRc rate to IC (75.3% vs. 63.4%) with lower transfusion needs and fewer infections. Patients harboring FLT3-ITD, DNMT3A, or TET2 mutations showed greater benefit from VEN/HMA, while KIT-mutated patients responded more favorably to IC. Among patients who did not undergo allo-HSCT, VEN/HMA significantly improved 3-year OS (P = 0.001) and PFS (P = 0.008). No significant OS or PFS difference was observed in the allo-HSCT subgroup. In MRD-negative patients without transplant, those treated with VEN/HMA had superior OS compared to IC. Multivariable analysis identified VEN/HMA (HR = 0.613, P = 0.038) and allo-HSCT (HR = 0.11, P = 0.001) as independent predictors of better survival. Conclusions VEN/HMA is a safe and effective induction strategy for intermediate-risk AML, particularly in patients with BCL-2 inhibitor–sensitive mutations (FLT3-ITD, DNMT3A, TET2). These findings support a genotype-guided treatment approach in AML, complementing current risk stratification frameworks and informing individualized induction therapy decisions.

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.

Background/Objectives: DDX41, DEAD-box RNA helicase 41 gene located on chromosome 5q25.3, is one of the most mutated genes in patients with germline predisposition to myeloid neoplasms. Germline and somatic mutations often have different locations and patterns of mutation, with some hotspots displaying diversity based on ethnicity. We aimed to explore clinical outcomes in patients with various DDX41 hot-spot mutations. Methods: This was a retrospective study of patients at Mayo Clinic with DDX41 mutation identified through Next Generation Sequencing (NGS) between 2018 and 2024. We completed unadjusted comparisons using continuous or categorical variables, and survival rates were assessed using the Kaplan–Meier method and cox regression analysis. Results: Overall survival appears to be higher in those with p.M1| when compared to p.Asp140GlyFs*2 and p.Arg525His, with comparable survival between p.Arg525His and p.Asp140GlyFs*2. Among males with p.M1| who underwent bone marrow transplant, those who underwent bone marrow transplant appeared to have lower survival rates, although not statistically significant. Our study was limited by a small sample size, therefore limiting our ability to reach significance. Conclusions: Our findings suggest potential implications for clinical outcomes based on DDX41 mutation hot-spots.

Background: Therapy-related myeloid neoplasms (t-MN) occur after exposure to DNA-damaging agentsincluding cytotoxic chemo and radiation therapy and are associated with dismal outcomes. TP53 and PPM1D (protein phosphatase Mn2+/Mg2+-dependent 1D) mutations (MT) disrupt the DNA damage response and repair (DDR) pathway, are often co-mutated (25-35%), and although enriched in t-MN (20%), can also be seen in age-related clonal hematopoiesis (CH) (5-10%). While progressive genomic instability with biallelic inactivation has been described as a mechanism of MN progression with TP53 MT, similar mechanisms remain to be elucidated for PPM1D MT. We conducted this study to assess the clinical phenotype and outcomes of TP53 and PPM1D co-mutated (PPM1DTP53m) versus PPM1D MT and wild type TP53 (PPM1DTP53wt) CH and MN. Methods: After approval from the Mayo Clinic IRB, the CH and MN databases were queried and 119 pts with PPM1D MT (CH= 26, clonal cytopenia of unknown significance [CCUS]= 64, myelodysplastic syndrome [MDS]=16, myeloproliferative neoplasm [MPN]=8, acute myeloid leukemia [AML]= 5) were identified. We analyzed baseline characteristics, karyotypic changes, somatic mutations, and survival outcomes between the PPM1DTP53m (n= 22) and PPM1DTP53wt (n= 97) groups (gp). Results: Baseline characteristics The median age in years (yrs) was 68.5 (range [R], 43-89) and 71 (R,30-92) in the PPM1DTP53m and PPM1DTP53wt gp (p= 0.65). 22% vs 18% (p=0.78), 58% vs 32% (p=0.03), 9% vs 32% (p=0.01), 7% vs 4.5% (p=>0.99) and 3% vs 14% (p=0.07) pts met criteria for CH, CCUS, MDS, MPN and AML in the PPM1DTP53wt and PPM1DTP53m gp, respectively. Similarly, 19% vs 18% (p=>0.99), 47% vs 32% (p=0.23), 7% vs 36% (p=0.001), 2% vs 0% (p=>0.99) and 2% vs 14% (p=0.04) of pts met criteria for t-CH, t-CCUS, t-MDS, t-MPN and t-AML in the PPM1DTP53wt and PPM1DTP53m gp, respectively. All PPM1D variants were truncating, located in exon 6 of the gene, with median variant allele frequency (VAF) (8% [R,1-64] vs 5.5% [R,2-24], p= 0.10) and number of PPM1D MT (1 [R,1-5] vs 1 [R, 1-3], p=>0.99) being similar between the two gp. While proportion of pts with t-MN were similar between the two gps (77% vs 86%, p= 0.40), cytogenetic (CG) abnormalities (67% vs 31%, p= 0.006), including monosomies (28% vs 2%, p= <0.001) and complex CG (53% vs 4%, p= <0.001) were more frequent in PPM1DTP53m gp compared to PPM1DTP53wt gp, respectively. The median TP53 MT VAF was 14% (R, 1-14); 50% had multi-hit TP53 MT. Somatic mutations in PPM1D MT myeloid neoplasm with and without TP53 MT Somatic co-mutations excluding TP53 were seen in 71% of pts; 73.5% in PPM1DTP53wt and 59% in PPM1DTP53m gp (p= 0.20). Apart from CBL (0% vs 9%,p=0.03), MT involving DNMT3A (20% vs 14%, p= 0.56), TET2 (15% vs. 14%, p= >0.99), IDH1 (6% vs 19%, p= 0.07), splicing factors (8% vs 14%, p=0.08), ASXL1 (11% vs 4.5%, p=0.46), and JAK2 (9% vs 0%,p=0.20) were comparable between PPM1DTP53wt and PPM1DTP53m gps, respectively. The proportion of pts with ≥ 2 somatic co-mutations excluding TP53 MT were numerically higher in PPM1DTP53wt gp (43%) compared to PPM1DTP53m gp (23%) (p= 0.09). In the PPM1DTP53wt gp, other somatic driver mutations included RUNX1 (n= 7 [7%]; CH [n=1], CCUS [n=2], MDS [n=3], MPN [n=1]), NRAS (n=1 [1%]; CCUS), BCOR (n=1 [1%]; CCUS), and PTPN11 (n=1 [1%]; CCUS). Leukemic transformation and survival With a median follow up of 6.43 months (mo) (R, 0.23-36.4), median overall survival (mOS) in mo among high-risk MDS/AML (16.4 vs 15.0, p= 0.66) and CHIP/CCUS pts (35.79 vs 26.28, p= 0.93) was similar between PPM1DTP53wt and PPM1DTP53m gps, respectively. CHIP/CCUS pts harboring multiple vs single PPM1D MT had an inferior mOS (23.9 vs 36.7 mo, p= 0.05). Three (2.5%) MDS pts underwent AML transformation; 1 (1%) and 2 (10%) in PPM1DTP53wt and PPM1DTP53m gp, respectively (p= 0.07). Conclusions In our study, 18% of pts had concurrent TP53 and PPM1D MT (82% t-MN), with the PPM1DTP53wt genotype being more frequent in CCUS, while the PPM1DTP53m genotype was more frequent in MN. The PPM1DTP53m genotype had a higher frequency of CG abnormalities, including complex CG. Survival outcome was inferior and comparable in PPM1D MT MN with or without TP53 MT. While 73.5 % of the PPM1DTP53wt gp had somatic driver MT and 31% had karyotypic abnormalities, 22% had no additional MT or CG changes (all CH/CCUS), underscoring the need to understand how PPM1D MT contribute to hematopoietic dysfunction and MN.

Background: The majority of patients with chronic myelomonocytic leukemia (CMML) harbor somatic mutations with the most frequent being TET2 (56%), SRSF2 (42%), and ASXL1 (41%) (Leukemia 2020;34:1407). The prognostic relevance of mutations in CMML has been studied extensively with consistent observations suggesting favorable survival impact from TET2 and unfavorable from DNMT3A, ASXL1, NRAS, and SETBP1 mutations. Among the latter, DNMT3A mutation stands out as being the strongest predictor of inferior overall (OS) and leukemia-free (LFS) survival (AJH 2017;92:56). The current study was inspired by observations from exploratory analyses of a large Mayo Clinic CMML cohort. Methods: The current retrospective study was conducted under an institutional review board approved minimum risk protocol that allowed retrospective collection and analysis of data from patient records (Mayo Clinic, Rochester, MN; Scottsdale, AZ; and Jacksonville, FL). Mutations were screened by multi-gene next-generation sequencing (NGS). Diagnostic criteria were according to the International Consensus Classification (Blood 2022; 140:1200). Conventional statistical methods were employed for comparisons of groups and survival analysis (JMP Pro 17.0.0 software SAS Institute, Cary, NC, USA). Results: Exploratory analysis of a well-characterized cohort of 398 Mayo Clinic patients with CMML suggested an unusually long median survival of 96 months for 7 patients harboring PHF6 mutation vs. 27 months for those without the mutation. A subsequent institutional database search focusing on PHF6-mutated CMML identified 20 more cases, expanding the total number of study patients to 418, including 27 PHF6-mutated cases. PHF6 mutations clustered with (co-mutation frequency) TET2 (93%) and less likely to be co-mutated with TP53 (0%), PTPTN11 (0%) and SF3B1 (0%) mutations. PHF6-mutated patients often presented with thrombocytopenia (platelet count <100 x 10(9)/L; 85% vs. 51% in PHF6 wild-type cases; p<0.01). Median time from initial presentation with thrombocytopenia to CMML diagnosis was 4 years (0-14 years). In order to isolate the prognostic impact of PHF6 mutation without associated DNMT3A mutation and independent of TET2 mutation, we considered 4 mutational categories: i) DNMT3A-mutated (N=19; 2 co-expressed PHF6 and 7 TET2 mutations), ii) PHF6-mutated with wild-type DNMT3A (N=25; 23 co-expressed TET2 mutation), iii) TET2-mutated without DNMT3A or PHF6 co-expression (N=169), and iv) wild-type for DNMT3A, TET2, and PHF6 (N=205). Compared to the other groups, PHF6-mutated patients with wild-type DNMT3A were less likely to display leukocytosis (p<0.01) or red cell transfusion need at presentation (p<0.01). At a median follow-up of 18 months, 302 deaths, 78 leukemic transformations, and 44 allogenic stem cell transplants were documented. Univariate analysis for OS disclosed superiority in favor of PHF6-mutated patients with wild-type DNMT3A (median OS 90 months) vs. the other three groups with median OS ranging from 10 months for DNMT3A-mutated patients (HR 20.7, 95% CI 8.1-52.7; p<0.01) to 40 months for TET2-mutated cases with wild-type DNMT3A (HR 3.5, 95% CI 1.7-7.6; p<0.01). Results were similar for multivariable analysis of LFS, where, remarkably, none (0%) of the 25 PHF6-mutated patients with wild-type DNMT3A experienced leukemic transformation vs. 15-30% risk of leukemic transformation in the other three groups (p<0.01). Multivariable analysis for both OS and LFS, which included other risk factors such as red cell transfusion need, leukocytosis (leukocyte count ≥13 x 109/L) and karyotype, confirmed the independent and favorable impact of PHF6 mutation/wild-type DNMT3A (p<0.01 in both instances). Finally, all-inclusive multivariable analysis of karyotype and mutations, in DNMT3A wild-type patients, confirmed the highly favorable and independent prognostic impact of PHF6 mutation (HR 0.18, 95% CI 0.09-0.37). Conclusion: The current study proposes a 2-gene genomic signature (PHF6 mutated and DNMT3A wild-type) to identify a unique subset of CMML patients who often present with thrombocytopenia and whose clinical course is characterized by a very low risk of leukemic transformation, resulting in superior overall survival. Additional studies are warranted to validate these findings and include the information in contemporary risk models.

Background: TP53-mutated (TP53mut) myeloid neoplasms (MN) have particularly poor survival. Discrepancies in defining TP53mut MN have impact on diagnostic work up, therapeutic decisions, and clinical trial enrolment. Methods: MN cases harboring TP53mut (variant allele frequency, VAF ≥2%) were classified based on bone marrow (BM) and peripheral blood (PB) blast % using the 4th revision of the WHO classification (WHO-4R). Biallelic loss was defined as (i) ≥2 mutations each with VAF ≥2%; (ii) single TP53mut VAF ≥2% plus 17p loss detected on karyotype, FISH, or SNP; or (iii) single TP53mut VAF ≥50%, whereas monoallelic loss was defined as single TP53mut VAF <50% without 17p loss. Primary endpoint was overall survival (OS) from diagnosis. Conditional Inference Trees (CIT) analysis was used to determine the true effect of a predictor. Results: We identified 580 cases including WHO-4R defined acute myeloid leukemia (AML, BM/PB blasts ≥20%, n=219, 37.8%), myelodysplastic syndrome (MDS) with excess blasts-2 (EB-2, BM 10-19% or PB 5%-19%, n=92, 15.9%), MDS with excess blasts-1 (EB-1, BM 5-9% or PB 2-4%, n=75, 12.9%), and the remaining were MDS with low blasts (-LB, BM <5% and PB ≤1%, n=194, 33.4%). Of the 578 cases evaluable for allelic status, 437 (75.6%) and 141 (24.4%) had biallelic and monoallelic TP53 loss, respectively. Higher frequency of biallelic loss was observed with increasing blasts. 63.2%, 73.3%, 78% and 86.3% of cases with MDS-LB, MDS-EB1, MDS-EB2, and AML had bi-allelic TP53 loss (P<0.0001). In a multivariable analysis, WHO-4R categories, VAF ≥10%, and biallelic loss were independent predictors of poor survival. CIT analysis further stratified the cohort into 4 groups with distinct survival: (1) MDS-LB (n=194, 33.4%); (2) MDS-EB1/EB2 and AML with VAF <10% (n=38, 6.6%); (3) MDS-EB1/EB2 with VAF ≥10% (n=149, 25.7%); and (4) AML with VAF ≥10% (n=199, 34.3%). MDS-LB cases could be further stratified based on allelic status and complex karyotype (CK): OS was poor for cases with biallelic TP53 loss and monoallelic TP53mut with CK compared to monoallelic TP53mut without CK, who had the most favorable survival (12.7 vs. 13 vs. 34.8 months, P<0.0001).MDS-EB1/EB2/AML with VAF<10% could also be stratified by CK: MDS-EB1/EB2/AML with VAF<10% cases without CK had the favorable survival compared to their counterparts with CK and MDS-EB1/EB2 with VAF ≥10% cases (26.2 vs. 5.6 vs. 6.3 months, P=0.003).MDS-EB1/EB2 with VAF ≥10% was a homogenous subgroup: Biological characteristics including the proportion of cases with biallelic TP53 loss, CK (P>0.9), monosomal karyotype (MK, P>0.9), del 5q (P=0.6), del 7q (P=0.4), and del 17p (P=0.2) were comparable between MDS-EB1 and -EB2 with VAF ≥10%. Importantly, median OS of MDS-EB1 and EB2 with VAF ≥10% was comparable (9.6 vs. 7.2 months, P=0.12). OS in this group was comparable regardless of the blast %, allelic status, or presence of CK.AML with VAF ≥10% had extremely poor survival: Median OS was 3.9 months. Survival of AML was equally poor irrespective of the mono- or biallelic (3.6 vs. 3.9, P=0.89) and non-CK vs. CK (1.8 vs. 3.9 months, P=0.09). Combined, our model would acknowledge poor survival of 514 (89.1%) cases. In contrast, retrospective application of the 5th edition of the WHO classification (WHO-5) and International Consensus Classification (ICC) would classify only 211 (36.4%) and 468 (80.7%) cases as biallelic/multi-hit TP53mut MN, respectively. Finally, the Harrell's c-index of the proposed model, WHO-5, and ICC was 0.66, 0.571, and 0.617, respectively. Conclusion: We provide evidence for subsequent refinements in classifying MN harboring TP53mut. Our salient findings include: (i) acknowledgement of CK as ‘biallelic equivalent’ in MDS-LB; as well as EB1/EB2/AML with VAF 2 to <10% cases; and (ii) demonstration of poor survival of MDS-EB1 and -EB2 with VAF ≥10% regardless of the allelic status. In conclusion, our proposed model acknowledged poor survival of a higher proportion of cases as TP53mut MN compared to the current models with a higher c- index.

Introduction: DDX41, DEAD-box RNA helicase 41 gene located on chromosome 5q25.3, is one of the most mutated genes in patients with germline predisposition to myeloid neoplasms. Germline and somatic mutations often have different locations and patterns of mutation, with some hot-spots displaying diversity based on ethnicity. We aimed to explore clinical outcomes in patients with various DDX41 hot-spots. Methods: A retrospective study of patients at Mayo Clinic with DDX41 mutation identified through Next Generation Sequencing (NGS) between 2016 and 2024. Patients classified as germline DDX41 variants or somatic variants based on confirmed germline testing results. Data regarding mutations recorded at time of patients' first in-house NGS. We completed unadjusted comparisons using continuous or categorical variables, and survival rates assessed using Kaplan-Meier method and cox regression analysis. Median overall survival calculated from time of NGS to last follow-up or death using BlueSky Statistics V.10.3.4 Results: Clinical Characteristics: Eighty-two patients analyzed, predominantly male (69.5%), with median age of diagnosis of 68 years (IQR 62-75). Most common diagnosis was MDS (50%), followed by AML (22%) and myeloproliferative neoplasm (MPN) (7.3%). Fourteen percent were DDX41 mutation carriers, 4.9% clonal cytopenia of undetermined significance (CCUS), and 1.2% chronic neutrophilic leukemia (CNL). MDS-IB2 was the prevalent MDS subtype (56.1%), and 83.3% of AML patients were intermediate risk. Cytogenetics normal in 73.8% of patients, with del20 being the most common abnormality (7.5%). Twenty-five (30.5%) of patients underwent allogeneic hematopoietic cell transplantation (alloHCT). Forty-nine patients (59.8%) had pathogenic DDX41 mutation only, 15 (18.3%) had only VUS, and 18 (22.0%) had both. Molecular Characteristic s: Mutation hot-spots included 12.2% with p.Arg525His only, 28% p.M1I only, 9.8% p.Asp140GlyFs*2 only, 9.8% p.Arg525 with co-mutation other than p.M1I/p.Asp140. Two patients (2.4%) had p.M1I co-mutation with non-p.Arg525 and p.Asp140, while 1 patient (1.2%) had p.M1I and p.Arg525, and 1 patient (1.2%) had p.Asp140 and p.Arg525. Lastly, 33 patients (40.2%) had mutations in hot-spots other than specified in this study. Median hemoglobin (Hg) was lowest in patients with p.Arg525 hotspot mutation (9.9 g/dl), compared to median Hg of 11.2 g/dl in group with p.M1I and 10.4 g/dl in p.Asp140 only group (p=0.87). The most common cytogenetics in patients with only p.Arg525 and only p.M1I hot-spot mutation was normal cytogenetics (70% and 90.9%, p=0.5). In the 16 patients with proven germline DDX41 mutation, 7 (43.8%) had mutations in only p.M1I. Lastly, median (m) VAF was significantly lower in the group with only p.Arg525 hot-spot mutation (9.5%, IQR 5.5-13.8), compared to mVAF of 48% in patients with only p.M1I (IQR 48.0-49.5, p<0.001). Co-mutations: The most frequent co-mutation was DNMT3A (11.0%), followed by JAK2 (9.8%).Mean number of co-mutations in p.Arg525 only was 0.6, 0.3 in p.M1I only, and 0.5 in p.Asp140 only (p=0.8). Most common co-mutation in p.M1I was ASXL1, with 13.0% of patients with p.M1I only having ASXL1 co-mutation (p=0.062). The most common co-mutation in p.Asp140 was DNMT3A (p=0.7). Survival: Median overall survival (mOS) was 21.5 months (IQR 12.4-38.6). mOS for p.Arg525 only group was 31.3, compared to 41.2 months in p.M1I only (p=0.197). At 12- and 24-months, survival rate was 88.9% and 59.3% in the p.Arg525 only group, compared to p.M1I group with 100% survival rate at 12 months and 89.5% at 24 months. p.Asp140 had 87.5% survival rate at both 12 months and 24 months. Males in p.M1I group had lower survival rate at 24 months (81.8%), compared to 100% survival in females. p.M1I only group with alloHCT also had lower 24 month survival at 60% compared to 100% in group who did not undergo alloHCT. Conclusions: The p.M1I hot-spot was commonly seen in patients with germline DDX41 mutation, likely resulting in significantly higher VAF percentage. Co-mutations frequency differed by hot spot mutation while p.Arg525 as second mutation was seen mainly in cases other than p.M1I/pArg140Gly. Overall survival appears to be higher in p.M1I/pArg140Gly compared to p.Arg525His hot-spot mutation, though males with p.M1I and those who underwent alloHCT had lower survival. Findings suggest implications for clinical outcomes based on DDX41 mutation hot-spots.