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ASXL transcriptional regulator 1 (ASXL1) is one of the three most frequently mutated genes in age-related clonal hematopoiesis (CH), alongside DNA methyltransferase 3 alpha (DNMT3A) and Tet methylcytosine dioxygenase 2 ( TET2) . CH can progress to myeloid malignancies including chronic monomyelocytic leukemia (CMML) and is also strongly associated with inflammatory cardiovascular disease and all-cause mortality in humans. DNMT3A and TET2 regulate DNA methylation and demethylation pathways, respectively, and loss-of-function mutations in these genes reduce DNA methylation in heterochromatin, allowing derepression of silenced elements in heterochromatin. In contrast, the mechanisms that connect mutant ASXL1 and CH are not yet fully understood. CH/CMML-associated ASXL1 mutations encode C-terminally truncated proteins that enhance the deubiquitinase activity of the ASXL-BAP1 “PR-DUB” deubiquitinase complex, which removes monoubiquitin from H2AK119Ub. Here, we show that ASXL1 mutant proteins interact with the euchromatic histone lysine methyltransferases 1 and 2 (EHMT1–EHMT2) complex, which generates H3K9me1 and me2, the latter a repressive modification in constitutive heterochromatin. Compared to cells from age-matched wild-type mice, we found that expanded myeloid cells from old (≥18-mo-old) Asxl1tm/+ mice, a heterozygous knock-in mouse model of CH, display genome-wide decreases of H3K9me2, H3K9me3, and H2AK119Ub as well as an associated increase in expression of transposable elements (TEs) and satellite repeats. Increased TE expression was also observed in monocytes from ASXL1 -mutant CMML patients compared to monocytes from healthy controls. Our data suggest that mutant ASXL1 proteins compromise the integrity of both constitutive and facultative heterochromatin in an age-dependent manner by reducing the levels of H3K9me2/3 and H2AK119Ub. This increase in TE expression correlated with increased expression of nearby genes, including many interferon-inducible (inflammation-associated) genes (ISGs).

Introduction Chronic myelomonocytic leukemia (CMML) is associated with a 15-30% risk for blast transformation (BT) over 3-5 years. Post-BT survival remains dismal with a median overall survival (mOS) of ~6 months (mo), even with intensive therapies. Although genetic drivers are identified in >40% of BT-CMML cases (Carr, 2021), mechanisms of BT remain to be elucidated. This multicenter study examines risk factors for BT in CMML and predictors of post-BT survival. Methods CMML patients at Mayo Clinic (n = 400, 47%) and MD Anderson Cancer Center (n = 449, 53%) were included with IRB approval. Analyses used clinical and molecular data at diagnosis or first referral in chronic phase (CP) and again at BT. Categorical variables were compared by Chi squared and continuous by Mann-Whitney U or Kruskal-Wallis tests. Multivariate analyses used Cox proportional hazards models with binarization via receiver operator curves. Kaplan-Meier analyses were censored at hematopoietic cell transplantation (HCT). P < 0.05 was considered significant. Calculations used BlueSky (v10.3.1) or Prism (v10.2.3). Results Of 849 CMML patients (median age 71 years, 32% female, 90% white) included in the study, 133 (16%) underwent BT. In comparison to CP-CMML without BT, those with BT were more likely (p < 0.05) to have had higher WBC counts (15.1 vs 11.4 x109/L), higher absolute monocyte counts (2.9 vs 2.2 x109/L), lower platelet counts (84 vs 102 x109/L), have circulating immature myeloid cells (52% vs 37%), and be classified as myeloproliferative (MP)-CMML (58% vs 44%) or CMML-2 (33% vs 17%) at diagnosis/first referral in CP. More mutations were detected in BT-CMML than in CP-CMML (mean 3.2 vs 2.8, p = 0.002). Mutations in RAS (NRAS, KRAS, BRAF, CBL, PTPN11; 51% vs 40%, p = 0.02) and signaling (CSF3R, FLT3, JAK2, SH2B3; 15% vs 7%, p = 0.004) pathway genes were more frequent in BT-CMML. Conversely, there were fewer BT-CMML patients with the favorable ASXL1WT/TET2MT genotype compared to CP-CMML (22% vs 33%, p = 0.004). Multivariate analysis identified MP-CMML (hazard ratio [HR] 1.5, p = 0.04), CMML-2 (HR 2.4, p < 0.001), and the presence of ≥4 somatic gene mutations (HR 1.8, p = 0.002) as risk factors for BT whereas the ASXL1WT/TET2MTgenotype was protective (HR 0.45, p = 0.002). The ASXL1WT/TET2MT genotype was also protective in the MP-CMML (HR 0.49, p = 0.04) and RAS-mutated (HR 0.35, p = 0.005) subgroups. Amongst BT-CMML cases, the median time to BT was 10 mo from diagnosis/referral. Compared to their CP assessment, patients in an overlapping Mayo Clinic BT-CMML cohort (n = 156, median age 69 years, 35% female) were more likely to have NPM1 (24% vs 3%, p < 0.001) and IDH2 (14% vs 1%, p = 0.01) mutations at BT. Amongst cases with paired molecular evaluations (n = 44), 7 (16%) acquired new mutations, including FLT3-ITD (n = 2) and NPM1 (n = 2). Conversely, 6 (14%) lost mutations, including TET2 (n = 3), SRSF2 (n = 2), CBL, NRAS, or PTPN11 (n = 1 each). There were no differences in variant allele fractions in driver genes present at CP and BT. Abnormal karyotypes were more prevalent at BT than in CP (60% vs 34%, p < 0.001), and 26 (25%) patients with a normal karyotype in CP acquired structural (65%) and/or numerical (69%) changes at BT, including 6 (23%) that acquired a complex karyotype. Acute myeloid leukemia defining translocations were seen in 4 (4%) cases: 2 with t(8;21) and 1 each with inv(3) and t(10;11)(KMT2A::TET1). After considering mutation and karyotype changes, only bone marrow blasts ≥30% (HR 1.7, p = 0.04) were independently prognostic for OS after BT (mOS 4 mo). Compared to supportive care (n = 50, mOS 1.3 mo), OS was improved by hypomethylating agents (HMA, n = 14, mOS 16 mo, HR 0.3, p < 0.001) or induction chemotherapy (n = 56, mOS 8 mo, HR 0.3, p < 0.001), but not by HMA + venetoclax (n = 11, mOS 6 mo, HR 0.7, p = 0.4). Allogeneic HCT (n = 28) improved mOS to 46 vs 3 mo (p < 0.001). Amongst 4 patients with paired whole genome sequencing before and after BT, 3 had increased copy number alterations (including 2 with loss of heterozygosity, 2 with gains, and 3 with deletions) while the 4th acquired novel likely pathogenic somatic mutations (MUC4, BAX, WNK2). Conclusions Survival in CMML after BT remains exceptionally poor. In this multicenter cohort, MP-CMML, CMML-2, and the presence of ≥4 somatic gene mutations were identified as risk factors for BT, with the ASXL1WT/TET2MT genotype being protective. Following BT, bone marrow blast burden ≥30% was an independent adverse predictor of survival.

Introduction: Somatic mutations in RAS pathway genes are present in ~30% of patients with chronic myelomonocytic leukemia (CMML) and are usually associated with myeloproliferative features. Whether mutations in distinct RAS pathway genes share similar genomic context and have similar implications remains unknown. Such an understanding might inform future studies evaluating genotype-specific therapies (RAS-codon specific and pan-RAS inhibitors) or cooperative leukemogenic mechanisms. This multicenter study examines the landscape of RAS pathway mutations (RASMT) in CMML. Methods: Newly diagnosed CMML patients evaluated at MD Anderson Cancer Center (n=449, 54%) and Mayo Clinic (n=383, n=46%) were included after IRB approval. Clonal relationships were tested using Pearson goodness-of-fit. Clones with the highest variant allele frequency (VAF), or with VAF close to 40% were defined as dominant, and those present at VAF <20% in the presence of another dominant clone were defined as subclonal. RASMT genes included NRAS, KRAS, BRAF, CBL, CBLC, PTPN11 and NF1. Standard statistical measures were applied. Results: Among 832 CMML patients (median age 71 years, 68% male) 387 (47%) had myeloproliferative (MP) and 445 (53%) had myelodysplastic (MD) CMML. A total of 350 (42%) patients had RASMT with higher prevalence among MP-CMML compared to MD-CMML (55% vs 31%, p<0.001). Among patients with RASMT, 262 (32%), 59 (7%), 20 (2%), 6 (<1%) and 3 (<1%) had 1, 2, 3, 4 or 5 distinct RASMT, respectively. Importantly, 17 patients harbored a RASmt as the only detectable somatic mutation, with KRASMT being the most frequent (n=10, 59%), including 3 (18%) patients with >1 RASMT. Among RASMT patients, a trend for higher hemoglobin (12.7 vs 10.8 g/dL, p=0.054) was observed in NF1MT CMML and lower WBC (14.4 vs 27.9 x109/L, p=0.067) values in CBLMT CMML. In total, 479 somatic mutations in RAS pathway genes were identified the most frequent involving NRAS (n=169, 35%) followed by CBL (n=120, 25%), KRAS (n=102, 21%), PTPN11 (n=38, 8%), NF1 (n=34, 7%), BRAF (n=11, 2%) and CBLC (n=5, 1%). Most frequently involved codons for each RAS pathway gene were as follows: G594 (n=3) for BRAF, R420 (n=22) and C404 (n=15) for CBL, G12 (n=41; G12D: 13, G12C: 3) and A146 (n=16) for KRAS, G12 (n=116; G12D: 74, G12C: 5) for NRAS and A72 (n=8) for PTPN11. CBLMT CMML had higher ASXL1 (58% vs 44%, p=0.011), BRAF (8% vs 4%, p=0.017), IDH2 (10% vs 4%, p=0.024), KIT (5% vs 1%, p=0.038), SRSF2 (54% vs 40%, p=0.004), TET2 (59% vs 47%, p=0.019) and ZRSR2 (10% vs 4%, p=0.014) co-mutation frequency. Of note, DNMT3A mutations were mutually exclusive of CBLMT CMML (0% vs 7%, p=0.007), and multihit TET2 was more common among TET2MT/CBLMT vs TET2MT/CBLWT CMML (82% vs 67%, p =0.039). CMML with NF1 mutations had higher BCORL1 (8% vs 0%, p=0.005), PTPN11 (15% vs 3%, p=0.014) and SETBP1 (23% vs 8%, p=0.022) mutational cooccurrences. NRASMT CMML was enriched for ASXL1 (53% vs 43%, p=0.042), ETNK1 (5% vs 1%, p=0.004), GATA2 (4% vs 1%, p=0.034) and STAG2 (4% vs 1 %, p=0.023) mutations but had lower JAK2 (1% vs 6%, p=0.036) and TP53 (1% vs 5%, p=0.022) mutational frequency. PTPN11 mutations were associated with higher NF1 (29% vs 7%, p=0.014), SETBP1 (27% vs 9%, p=0.01) and WT1 (6% vs <1%, p=0.014) mutational cooccurrences and lower SRSF2 (23% vs 42%, p=0.05) frequency. We evaluated RAS mutant clonal dominance in 253 (72%) RASMT patients. NRAS (63% vs 38%), KRAS (63% vs 38%) and CBL (65% vs 35%) mutations were more frequently observed as dominant clones while PTPN11 (44% vs 56%), BRAF (25% vs 75%) and NF1 (35% vs 65%) mutations were more frequently subclonal events, particularly of TET2, ASXL1 or SRSF2 mutant clones. Presence of a RAS pathway mutation was associated with shorter LFS (36 vs 53 months, HR 1.5, 95% CI 1.1-2.2, p=0.020), with presence of multiple RAS mutations shortening LFS further (53, 43, 27 months for 0, 1 and >1, p=0.017). PTPN11 mutations predicted for shorter OS (18 vs 39 months, HR 1.7, 95% CI 1.0-2.8, p=0.035) and LFS (14 vs 36 months, HR 1.9, 95% CI 1.2-2.9, p=0.008). Presence of >1 RAS pathway mutation was associated with shorter OS in ASXL1MT/TET2MT CMML compared to those with no RAS mutations (24 vs 53 months, HR 4.2, 95% CI 1.6-10.8, p=0.004). Conclusions: RAS mutations exhibit distinct genomic features and clonal hierarchy in CMML and cooperate with other somatic mutations to drive clinical outcomes. This study forms the foundation for assessment of novel RAS pathway inhibitors in CMML.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Introduction Somatic mutations in TET2 are present in ~60% of chronic myelomonocytic leukemia (CMML) patients and are associated with improved median overall (mOS) and acute leukemia free survival (mLFS). Multiple TET2 mutations are detected in ~50% of such cases and are thought to confer additional benefit. Whether these benefits apply to the molecularly distinct myelodysplastic (MD) and myeloproliferative (MP) CMML subtypes is unknown. This study assesses the impact of the number and type of TET2 mutations across the MD and MP-CMML subtypes. Methods CMML patients seen at Mayo Clinic (n = 400, 47%) and MD Anderson Cancer Center (n = 449, 53%) were included with IRB approval. Analyses considered the clinical and molecular data at diagnosis or first referral. Truncating (frameshift or nonsense) mutations in TET2 were further annotated by location in either the proximal genome targeting domain (aa 1 - 1128) or distal catalytic domain (aa 1129 - 1936) as described (Coltro, 2020). Categorical variables were compared by Pearson Chi squared and continuous by Mann-Whitney U or Kruskal-Wallis tests. Univariate and multivariate analyses utilized Cox proportional hazards models with continuous variables binarized by receiver operator curves. Kaplan-Meier analyses were censored at hematopoietic cell transplantation (HCT). P < 0.05 was considered significant. Calculations used BlueSky (v10.3.1) or Prism (v10.2.3). Results In a cohort of 849 CMML patients (median age 71 years, 32% female, 90% white), 454 (54%) were classified as MD-CMML and 394 (46%) as MP-CMML. Cumulatively, 664 somatic TET2 mutations were identified across the MD (n = 415, 63%) and MP-CMML (n = 249, 38%) subgroups, with ≥1 TET2 mutation in 57% vs 44% (p < 0.001) and ≥2 TET2mutations in 33% vs 20% (p = 0.001) , respectively. The median (range) number of TET2 mutations in MD vs MP-CMML was 1 (0 - 4) vs 0 (0 - 3; p < 0.001). Truncating frameshift or nonsense mutations comprised 86% vs 78% of mutations in each group with hypomorphic missense mutations comprising 13% vs 20% (p = 0.03). Proximal (48% vs 43%) and distal (38% vs 35%) truncations were evenly distributed between MD and MP-CMML (p = 0.91). In both the MD and MP subgroups as well as the overall cohort (p < 0.01 each), increasing number of TET2 mutations (0 vs 1 vs ≥2) was associated with higher median hemoglobin (10.3 vs 11.4 vs 12.0 g/dL), fewer mean peripheral blasts (1.3% vs 0.5% vs 0.3%), and a higher proportion of normal karyotypes (60% vs 76% vs 79%). Likewise, co-mutations were increasingly frequent in SRSF2 (36% vs 45% vs 53%) and less frequent in IDH2 (9% vs 1% vs 1%) and SETBP1 (14% vs 4% vs 0%) throughout the cohort. TET2 mutations were mutually exclusive with ETV6, GATA2, IDH1, and WT1 mutations. Uniquely, MD-CMML cases with TET2 mutations (0 vs 1 vs ≥2) were enriched for co-mutations in ZRSR2 (5% vs 13% vs 9%) and had fewer co-mutations in both ASXL1 (47% vs 34% vs 28%) and U2AF1 (11% vs 6% vs 3%). In MP-CMML, co-mutations in CBL (11% vs 20% vs 27%) were increased while co-mutations in BCOR (6% vs 1% vs 0%) were decreased. Accordingly, the favorable ASXL1WT/TET2MT genotype was more common in MD (39% vs 22%), while the adverse ASXL1MT/TET2WT genotype was more common in MP-CMML (20% vs 33%; p < 0.001). In multivariate analyses, the presence of ≥2 TET2 mutations was favorably associated with OS and LFS in both MD and MP-CMML (hazard ratios 0.53 - 0.67, p < 0.04) independent of several clinical and genetic features including ASXL1 mutations and abnormal karyotype. Beyond the number of TET2 mutations, however, neither the mutation class (truncating vs hypomorphic) nor location (proximal vs distal) further affected OS or LFS. Likewise, although the median variant allele fraction (VAF) was lower in MD vs MP-CMML (42% vs 46%, p < 0.001), VAF did not impact survival. Accordingly, the mOS for patients with 0, 1, or ≥2 TET2 mutations was 29, 58, and 75 months in MD-CMML and 19, 26, and 39 months in MP-CMML, respectively (p < 0.001 each). The mLFS was similarly improved to 26, 52, and 67 months in MD-CMML (p < 0.001) and 16, 22, and 31 months in MP-CMML (p = 0.003). CONCLUSIONS This multicenter study demonstrates that the favorable impact of multiple TET2 mutations in CMML extends to both the MD and MP subtypes. In MD-CMML, multiple TET2 mutations segregate against adverse ASXL1 mutations while, in MP-CMML, they appear to offset the impact of adverse RAS pathway mutations. Thus, multiple TET2 mutations may identify a lower-risk subgroup of MP-CMML patients.

Introduction Oligomonocytic chronic myelomonocytic leukemia (OM-CMML) is defined by relative monocytosis ≥ 10% and an absolute monocyte count (AMC) between ≥ 0.5 and < 1.0 x109/L. The 5th ed WHO and International Consensus Classifications lowered the AMC threshold to ≥ 0.5 x10/9L for CMML diagnosis, thereby incorporating OM-CMML (Khoury, 2022; Arber, 2022). However, prior reports differ as to whether OM-CMML is a distinct CMML subtype. This multi-center study compares the clinical, molecular, and survival features of OM-CMML with myelodysplastic (MD, WBC < 13.0 x109/L) and myeloproliferative (MP, WBC ≥ 13.0 x109/L) CMML with an AMC ≥1.0. Methods After IRB approval, patients with CMML from Mayo Clinic (n = 400, 47%) and MD Anderson Cancer Center (n = 449, 53%) were identified retrospectively. Analyses considered clinical, laboratory, and next generation sequencing parameters at diagnosis or first referral (in chronic phase). Categorical variables were compared by Fisher exact or Pearson χ2 tests and continuous variables by Mann-Whitney U or ANOVA tests. Survival was assessed via the Kaplan-Meier method with outcomes censored at allogeneic stem cell transplantation (alloSCT). P < 0.05 was considered significant. Calculations used BlueSky Statistics (v10.3.1). Results A molecularly annotated cohort of 849 patients with CMML (median age 71 years, 32% female, 90% white, median follow up 31 months [mo]) was categorized as OM-CMML (n = 57, 7%), MD-CMML (n = 394, 46%), and MP-CMML (n = 391, 46%). There were no differences in age or sex across subtypes. No clinical differences were observed between OM-CMML and MD-CMML, with similar hematologic parameters, karyotype distributions, Mayo-Molecular (Patnaik, 2014) or CPSS-Molecular (Elena, 2016) risk stratifications, and rates of leukemic transformation (12% vs 12%, p = 0.98) and death (35% vs 43%, p = 0.27). Cases of OM-CMML had lower WBC counts (median 4.4 vs 24.7 x109/L, p < 0.0001), fewer circulating immature myeloid cells (23% vs 57%, p < 0.0001) and fewer marrow blasts (median 3% vs 4%, p = 0.0065) than MP-CMML. Comparatively, OM-CMML cases were overrepresented in the lower-risk groups of the CPSS-Molecular and Mayo-Molecular models (p < 0.0001). Leukemic transformation (12% vs 20%, p = 0.015) and death (35% vs 52%, p = 0.034) rates were higher in the MP-CMML cohort. Molecular profiles were similar between OM-CMML and MD-CMML, including median number of genes mutated (3 vs 3, p = 0.88). Frequencies of ASXL1 (39% vs 38%, p = 0.91), SRSF2 (48% vs 43%, p = 0.49), DNMT3A (7% vs 5%, p = 0.60), and SF3B1 (4% vs 7%, p = 0.48) mutations were also similar. TET2 mutation frequency was higher in OM-CMML (70% vs 55%, p = 0.031) and occurred in isolation more frequently (12% vs 6%, p = 0.048) compared to MD-CMML. Neither median number of TET2 mutations (1 vs 2, p = 0.87), nor TET2 variant allele fractions (41% vs 44%, p = 0.19) were different between the two groups. Compared to OM-CMML, patients with MP-CMML were more likely to have mutations in ASXL1 (39% vs 56%, p = 0.017), EZH2 (0% vs 9%, p = 0.019), JAK2 (0% vs 9%, p = 0.019), NRAS (7% vs 25%, p = 0.0024), and SETBP1 (2% vs 13%, p = 0.021). RAS family (CBL, BRAF, KRAS, NRAS, PTPN11; 25% vs 54%, p < 0.0001) and signaling pathway (CSF3R, FLT3, JAK2, SH2B3; 2% vs 14%, p = 0.0078) mutations were also more frequent whereas spliceosome mutations (SF3B1, SRSF2, U2AF1, ZRSR2; 61% vs 46%, p = 0.033) were less frequent in MP-CMML. The median (95% CI) overall survival (OS) of OM-CMML patients was 44 (38 - not reached) mo compared to 45 (34 - 60) mo in MD-CMML (p = 0.76) and 23 (20 - 29) mo in MP-CMML (p < 0.0032). Similar results were obtained when comparing the acute leukemia-free survival (LFS) of the OM-CMML (41 mo), MD-CMML (39 mo), and MP-CMML (19 mo) groups. At last follow up (medians 42 and 62 mo, respectively), 33% of evaluable OM-CMML patients had progressed to MD-CMML and 28% of MD-CMML patients had progressed to MP-CMML. No patient with OM-CMML progressed directly to MP-CMML. Conclusions These data show that OM-CMML and MD-CMML are a relatively homogenous subset of patients with similar clinicomolecular profiles and survival outcomes. Although OM-CMML patients are more likely to have TET2 mutations in comparison to other CMML subtypes, they have similar rates of disease progression as MD-CMML. This validates recent modifications to CMML classification criteria, in which OM-CMML cases are included as bona fide CMML.

ObjectiveDe novo variants in cullin‐3 ubiquitin ligase (CUL3) have been strongly associated with neurodevelopmental disorders (NDDs), but no large case series have been reported so far. Here, we aimed to collect sporadic cases carrying rare variants in CUL3, describe the genotype–phenotype correlation, and investigate the underlying pathogenic mechanism.MethodsGenetic data and detailed clinical records were collected via multicenter collaboration. Dysmorphic facial features were analyzed using GestaltMatcher. Variant effects on CUL3 protein stability were assessed using patient‐derived T‐cells.ResultsWe assembled a cohort of 37 individuals with heterozygous CUL3 variants presenting a syndromic NDD characterized by intellectual disability with or without autistic features. Of these, 35 have loss‐of‐function (LoF) and 2 have missense variants. CUL3 LoF variants in patients may affect protein stability leading to perturbations in protein homeostasis, as evidenced by decreased ubiquitin‐protein conjugates in vitro. Notably, we show that 4E‐BP1 (EIF4EBP1), a prominent substrate of CUL3, fails to be targeted for proteasomal degradation in patient‐derived cells.InterpretationOur study further refines the clinical and mutational spectrum of CUL3‐associated NDDs, expands the spectrum of cullin RING E3 ligase‐associated neuropsychiatric disorders, and suggests haploinsufficiency via LoF variants is the predominant pathogenic mechanism. ANN NEUROL 2025;97:76–89

601 Background: EOP is an I-SPY2 neoadjuvant sub-study designed to test the tolerability and impact of novel endocrine-based strategies in stage 2/3 BC pts predicted to have lower benefit from chemotherapy. Amcenestrant (A) is an oral selective estrogen receptor degrader (SERD) with activity in hormone receptor positive (HR+) HER2- metastatic BC. Methods: EOP eligibility included pts screened for the main I-SPY2 trial with HR+ HER2-, MammaPrint (MP) low risk Stage 2/3 BC. Pts with MP High risk tumor signatures were eligible if clinically node-negative (cN-). Pts were stratified by SET index and menopausal status. Pts were randomized to 3 arms: A 200 mg qd (A), A + abemaciclib 150 mg bid (AA), A + letrozole 2.5 mg qd (AL). Premenopausal pts received concurrent ovarian function suppression. Treatment was for 6 mos followed by surgery. Baseline (BL) and 3-wk biopsies were performed. Breast MRIs were conducted at BL (T0), 3 wks (T1), 12 wks (T2), 6 mos (T3). The primary endpoint was feasibility, defined as >75% of pts completing >75% study therapy. Results: Between May 2021 and Aug 2022, 74 pts were randomized: 31 A, 21 AA, 22 AL. Median age 50 years, 54% premenopausal, 38% clinically node-positive (cN+). 41% pts had ductal, 38% lobular, 11% mixed ductal/lobular histology. Mean BL tumor Ki67 (central testing) was 16.9%, 88% MP low, 76% SET index Hi. Pt and tumor characteristics were balanced across the 3 arms. Treatment in all 3 arms was feasible with 95% of pts completing >75% study therapy. Most common AEs include grade 1-2 hot flashes (69%) and fatigue (66%). Grade 3 AEs include neutropenia (2 pts) and diarrhea (1 pt) in AA. Table summarizes key response endpoints, which were similar between pre- and postmenopausal pts. Of 28 cN+ pts, 2 (7%) were pathologically node-negative (pN-). Mean MRI Functional Tumor Volume (cc) change between T0->T1, T1->T2, T2->T3, T0->T3 was -6.06 (-34.2%), -3.55 (-29.2%), -0.829 (-20.1%), -11.3 (-68.4%), respectively, similar across arms (p=0.396).ctDNA was detected in 10/59 (17%) pts at T0. ctDNA became undetectable in 6 pts, decreased in 4 pts, and went from ctDNA undetectable at baseline to detectable at T3 in 2 pts. Conclusions: Six months of neoadjuvant endocrine therapy (NET) with the oral SERD amcenestrant is feasible, well-tolerated, and demonstrates anti-proliferative and anti-tumor activity in pre- and postmenopausal pts. NET provides a rich platform for biomarker discovery and investigating response to endocrine therapy. Clinical trial information: NCT01042379 . [Table: see text]

Background Prognostic markers and biological pathways linked to detrimental clinical outcomes in heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. Methods and Results We measured serum levels of 4123 unique proteins in 1117 patients with HFpEF enrolled in the PARAGON‐HF (Efficacy and Safety of LCZ696 Compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction) trial using a modified aptamer proteomic assay. Baseline circulating protein concentrations significantly associated with the primary end point and the timing and occurrence of total heart failure hospitalization and cardiovascular death were identified by recurrent events regression, accounting for multiple testing, adjusted for age, sex, treatment, and anticoagulant use, and compared with published analyses in 2515 patients with heart failure with reduced ejection fraction from the PARADIGM‐HF (Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) and ATMOSPHERE (Efficacy and Safety of Aliskiren and Aliskiren/Enalapril Combination on Morbidity–Mortality in Patients With Chronic Heart Failure) clinical trials. We identified 288 proteins that were robustly associated with the risk of heart failure hospitalization and cardiovascular death in patients with HFpEF. The baseline proteins most strongly related to outcomes included B2M (β‐2 microglobulin), TIMP1 (tissue inhibitor of matrix metalloproteinase 1), SERPINA4 (serpin family A member 4), and SVEP1 (sushi, von Willebrand factor type A, EGF, and pentraxin domain containing 1). Overall, the protein–outcome associations in patients with HFpEF did not markedly differ as compared with patients with heart failure with reduced ejection fraction. A proteomic risk score derived in patients with HFpEF was not superior to a previous proteomic score derived in heart failure with reduced ejection fraction nor to clinical risk factors, NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), or high‐sensitivity cardiac troponin. Conclusions Numerous serum proteins linked to metabolic, coagulation, and extracellular matrix regulatory pathways were associated with worse HFpEF prognosis in the PARAGON‐HF proteomic substudy. Our results demonstrate substantial similarities among serum proteomic risk markers for heart failure hospitalization and cardiovascular death when comparing clinical trial participants with heart failure across the ejection fraction spectrum. Registration URL: https://www.clinicaltrials.gov ; Unique Identifiers: NCT01920711, NCT01035255, NCT00853658.

LBA509 Background: I-SPY2.2 is a multicenter phase 2 platform sequential multiple assignment randomized trial (SMART) evaluating novel experimental regimens in the neoadjuvant breast cancer setting. The novel therapy is given as first in a sequence (Block A), followed by standard chemo/targeted therapies (Block B/C) if indicated. The goal is to identify agents that lead to pCR after novel targeted agents alone, or in sequence with optimal therapy assigned based on the tumor response predictive subtype (RPS). RPS incorporates expression-based immune, DNA repair deficiency (DRD), and luminal signatures with hormone receptor (HR) and HER2 status to classify patients by subtype: S1: HR+HER2-Immune-DRD-; S2: HR-HER2-Immune-DRD-; S3: HER2-Immune+; S4: HER2-Immune-DRD+; S5: HER2+/non-Luminal; S6: HER2+/Luminal. Methods: RPS S1, S2, S3 and S4 were eligible for assignment to Dato in Block A. Patients (pts) were followed by MRI during treatment (at 3, 6 and 12 weeks after start of Blocks A and B). Predicted responders by MRI and biopsy at the end of Block A or B have the option of going to surgery early, otherwise they proceed to next treatment Block (B +/- C). Randomization to Block B includes a taxane-based regimen specific to the RPS, and options include S1: paclitaxel; S2 and S3: paclitaxel + carboplatin + pembrolizumab; S4: paclitaxel + carboplatin vs. paclitaxel + carboplatin + pembrolizumab. Patients who did not go to surgery after Block B proceeded to Block C (AC or AC + Pembrolizumab if HR-HER2-). The primary endpoint is pCR. Efficacy is evaluated within each RPS and HR+HER2- and HR-HER2- signatures. To estimate the arm's efficacy as a stand-alone therapy, we use a Bayesian covariate-adjusted model to estimate the pCR rate and compare the posterior distribution to a subtype-specific fixed threshold. This model uses pCR data when available and MRI data when pCR is not. To estimate pCR rate in the context of a multi-decision treatment regimen, we use a Bayesian model based on if and when a pCR occurred in the trial. The posterior is compared to a subtype-specific dynamic control generated from historical I-SPY data. Results: 103 pts were randomly assigned to the Dato arm between August 2022 and August 2023. All patients have proceeded beyond Block A; 33 went to surgery after Dato alone. The efficacy results for Dato as a stand-alone therapy are summarized in Table. Conclusions: Dato monotherapy was active, particularly in the HR-HER2- signature, but did not meet the pre-specified threshold for graduation in I-SPY 2.2. Clinical trial information: NCT01042379 . [Table: see text]