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Introduction: Response assessment is used to evaluate effectiveness of hypomethylating agent (HMA)-based therapy in myelodysplastic syndromes/neoplasms (MDS) in clinical practice and trials. Recently, the clinical response criteria (IWG 2023) and the clinical-molecular risk assessment (IPSS-M) in MDS were published. However, it remains unknown whether the IWG 2023 response criteria have an independent impact on overall survival (OS) when adjusting for baseline IPSS-M risk. Additionally, the association of IWG 2023 response criteria with OS in patients (pts) undergoing allogeneic stem cell transplantation (allo-HCT) versus those who do not is unknown. Lastly, it is unclear if optimal time-to-transplant impacts OS in MDS pts who achieve a IWG 2023 defined remission with HMA therapy. We examined these questions in a large population of MDS pts within the multi-institutional international consortium for MDS (icMDS) VALIDATE database. Methods: High-risk MDS pts with IPSS ≥1.5 or IPSS-R >3.5 treated with HMA-based therapy were included in the study. Baseline IPSS-M risk was stratified into very low/low/moderate low (VL+L+ML), moderate-high/high (MH+H), and very high (VH). Response to treatment was evaluated using the IWG 2023 criteria and grouped into: composite complete response (cCR) and no cCR. cCR included: complete response (CR), CR equivalent (CRequ), CR bilineage (CRbi), CR unilineage (CRuni), and CR with partial hematologic recovery (CRh). We analyzed the differential impact of achieving cCR on HMA-based therapy prior to transplant on OS in pts who either did or did not undergo allo-HCT, while adjusting for their baseline IPSS-M risk in Cox regression analysis. Optimal time-to-transplant was determined based on age and IPSS-M category as described by Tentori et al. (JCO 2024). Pts proceeding to allo-HCT were stratified on whether they underwent transplant within the optimal time-to-transplant timeframe after MDS diagnosis or not. VALIDATE protocol was exempted, acknowledged, or approved by all centers' IRBs. VALIDATE is supported by an independent grant from AbbVie. Results: We analyzed 762 MDS pts. The median age at MDS diagnosis was 68 years (range: 19-95 years) and 64% of pts were male. Distribution of the baseline IPSS-M risk was: VL+L+ML in 110 (14%) pts, MH+H in 347 (46%) pts, VH in 305 (40%) pts. Pts received azacitidine monotherapy, decitabine monotherapy, and HMA-based combination therapy (e.g. HMA + venetoclax) in 52%, 21%, and 28% of cases, respectively. A total of 282/762 (37%) pts achieved cCR as their best response to HMA-based therapy. The median OS for all pts was 18.7 months (95% CI: 16.6-21.3 months). Pts who did not undergo allo-HCT (n=436, 57%) had an improved median OS if they achieved cCR when compared to pts in the no cCR group in all IPSS-M risk groups (VL+L+ML: 23.5 months vs. 10.7 months; MH+H: 20.8 months vs. 11.6 months; VH: 13.8 months vs. 7.5 months). Cox analysis demonstrated an independent effect of cCR on OS in these pts when adjusted for baseline IPSS-M risk (cCR vs. no cCR: HR 0.60, 95% CI 0.48-0.76, p<0.001). In contrast, in pts who subsequently received an allo-HCT (n=326, 43%), achieving cCR with HMA prior to allo-HCT did not provide an additional OS benefit, when adjusted for baseline IPSS-M risk (cCR vs. no cCR: HR 1.00, 95% CI 0.73-1.38, p=0.986). The median time-to-transplant was 6.7 months and 34% of transplanted MDS pts received their allo-HCT during the optimal timeframe. Among pts who achieved cCR with HMA-based therapy prior to allo-HCT, optimal time-to-transplant was associated with significantly improved OS (optimal vs. non-optimal timing: HR 0.55; p=0.033). Conclusion: Baseline IPSS-M risk and allo-HCT had an independent impact on OS, while IWG 2023 defined cCR had an independent effect on OS only in non-transplanted MDS pts. While this retrospective analysis occurred at large academic centers with specific expertise in the management of MDS, our data may have an impact on clinical practice: (i) failure to achieve cCR on HMA therapy prior to transplant should not impact the decision to proceed to allo-HCT, (ii) upfront allo-HCT without prior HMA therapy might also be an option if the clinical situation and availability of donors allow, and (iii) allo-HCT should ideally be performed during the timeframe that is considered optimal as defined by pt age and baseline IPSS-M.

Background. The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme. Methods. We analyzed retrospective international cohorts of patients with a diagnosis of MDS (n=7017) and AML (n=1002) according to WHO 2016 criteria. Hierarchical Dirichlet Processes were applied to define clusters capturing broad dependencies among all gene mutations and cytogenetic abnormalities. To investigate the features of importance and their impact on the clustering process, we employed the SHapley Additive exPlanations approach (SHAP). This allowed to define harmonized labels for each clinical entity. The clinical relevance of the unsupervised clustering was assessed through the analysis of phenotypic features and outcomes among each group. ( Blood 2022;140: 9828-9830) Results. Patients' characteristics are summarized in Table 1. We identified 9 clusters, defined by specific genomic features. The cluster of highest hierarchical importance was characterized by biallelic inactivation of TP53 (biTP53). According to SHAP analysis, bi TP53 was defined as 2 or more TP53 mutations, or 1 mutation with copy number loss or cnLOH. Most patients assigned to bi TP53 cluster had TP53 VAF>10% (77.9%) and complex karyotype (70.1%). Assignment to bi TP53 cluster was irrespective of blast count. Patients with monoallelic TP53 mutation segregated into other clusters. Hierarchically, the second cluster included patients with del(5q). SHAP analysis highlighted 5q deletion alone, or with one other chromosomal abnormality other than -7, and absence of bi TP53, as the most relevant features. Most of these patients had blast counts <5% (88.1%). The third distinct cluster included patients with SF3B1 mutations (in the absence of concurrent del(7q), abn3q26.2, complex karyotype or RUNX1 mutation). Most patients with MDS and SF3B1 mutation had <5% blasts (94.2%). Common co-mutated variants in the SF3B1 cluster included mutant DNMT3A (25.2%) and TET2 (38.3%). Morphologically defined MDS cases (i.e., not meeting criteria for bi TP53, del(5q) or SF3B1) were preferentially assigned to the following additional clusters: SF3B1 and concurrent higher-risk mutations (e.g., RUNX1 and ASXL1); SRSF2 and concomitant TET2 mutations; U2AF1 mutations with del(20q), del(7q) or -7; SRSF2 with TET2 mutations and co-mutational patterns including RUNX1 and ASXL1; and AML-like genomic signatures. Our analyses suggest that morphologically defined MDS is characterized by a large heterogeneity in terms of mutation profiles, not entirely captured by the presence of unilineage versus multilineage dysplasia, percentage of bone marrow blasts, and presence of hypocellularity and fibrosis. To better investigate the continuum between high risk MDS (i.e., patients with ≥10% blasts) and AML, an exploratory comparison was made using a cohort of AML (defined according to WHO 2016) patients analyzed using the same statistical methods. Only a partial overlap in genetic signatures was observed between MDS with ≥10% blasts and AML. However, similarities were observed between the AML-like MDS clusters (characterized by mutant NPM1, bZIP CEBPA, and Core Binding Factor abnormalities) and AML clusters defined by the same genetic signature, thus supporting the classification of these entities as AML, irrespective of blast count. Conclusion. Our study demonstrated the utility of a data-driven approach based on advanced statistical methods to generate a harmonized classification for MDS. Table 2 shows a provisional, hierarchical classification algorithm. Further refinement of entity labels and classification criteria is the subject of the ongoing modified Delphi Process consensus approach among icMDS experts.

Abstract Eosinophilia and eosinophil activation are recurrent features in various reactive states and certain hematologic malignancies. In patients with hypereosinophilia (HE), HE‐induced organ damage is often encountered and may lead to the diagnosis of a hypereosinophilic syndrome (HES). A number of known mechanisms and etiologies contribute to the development of HE and HES. Based on these etiologies and the origin of eosinophils, HE and HES are divided into primary forms where eosinophils are clonal cells, reactive forms where an underlying reactive or neoplastic condition is detected and eosinophils are considered to be “non‐clonal” cells, and idiopathic HE and HES in which neither a clonal nor a reactive underlying pathology is detected. Since 2012, this classification and the related criteria have been widely accepted and regarded as standard. However, during the past few years, new developments in the field and an increasing number of markers and targets have created a need to update these criteria and the classification of HE and HES. To address this challenge, a Working Conference on eosinophil disorders was organized in 2021. In this conference, a panel of experts representing the relevant fields, including allergy, dermatology, hematology, immunology, laboratory medicine, and pathology, met and discussed new markers and concepts as well as refinements in definitions, criteria and classifications of HE and HES. The outcomes of this conference are presented in this article and should assist in the diagnosis and management of patients with HE and HES in daily practice and in the preparation and conduct of clinical trials.

Abstract Myeloid neoplasms and acute leukemias derive from the clonal expansion of hematopoietic cells driven by somatic gene mutations. Although assessment of morphology plays a crucial role in the diagnostic evaluation of patients with these malignancies, genomic characterization has become increasingly important for accurate diagnosis, risk assessment, and therapeutic decision making. Conventional cytogenetics, a comprehensive and unbiased method for assessing chromosomal abnormalities, has been the mainstay of genomic testing over the past several decades and remains relevant today. However, more recent advances in sequencing technology have increased our ability to detect somatic mutations through the use of targeted gene panels, whole-exome sequencing, whole-genome sequencing, and whole-transcriptome sequencing or RNA sequencing. In patients with myeloid neoplasms, whole-genome sequencing represents a potential replacement for both conventional cytogenetic and sequencing approaches, providing rapid and accurate comprehensive genomic profiling. DNA sequencing methods are used not only for detecting somatically acquired gene mutations but also for identifying germline gene mutations associated with inherited predisposition to hematologic neoplasms. The 2022 International Consensus Classification of myeloid neoplasms and acute leukemias makes extensive use of genomic data. The aim of this report is to help physicians and laboratorians implement genomic testing for diagnosis, risk stratification, and clinical decision making and illustrates the potential of genomic profiling for enabling personalized medicine in patients with hematologic neoplasms.

Abstract Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease >1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

Abstract Lineage-ambiguous leukemias are high-risk malignancies of poorly understood genetic basis. Here, we describe a distinct subgroup of acute leukemia with expression of myeloid, T lymphoid, and stem cell markers driven by aberrant allele-specific deregulation of BCL11B, a master transcription factor responsible for thymic T-lineage commitment and specification. Mechanistically, this deregulation was driven by chromosomal rearrangements that juxtapose BCL11B to superenhancers active in hematopoietic progenitors, or focal amplifications that generate a superenhancer from a noncoding element distal to BCL11B. Chromatin conformation analyses demonstrated long-range interactions of rearranged enhancers with the expressed BCL11B allele and association of BCL11B with activated hematopoietic progenitor cell cis-regulatory elements, suggesting BCL11B is aberrantly co-opted into a gene regulatory network that drives transformation by maintaining a progenitor state. These data support a role for ectopic BCL11B expression in primitive hematopoietic cells mediated by enhancer hijacking as an oncogenic driver of human lineage-ambiguous leukemia. Significance: Lineage-ambiguous leukemias pose significant diagnostic and therapeutic challenges due to a poorly understood molecular and cellular basis. We identify oncogenic deregulation of BCL11B driven by diverse structural alterations, including de novo superenhancer generation, as the driving feature of a subset of lineage-ambiguous leukemias that transcend current diagnostic boundaries. This article is highlighted in the In This Issue feature, p. 2659

Abstract Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

Abstract Myeloproliferative disorders (MPD) are a group of heterogeneous diseases that include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). They are characterized by increased hematopoiesis leading to elevated numbers of non-lymphoid cells and/or platelets in the peripheral blood. Beside thrombotic and hemorrhagic complications, MPD may evolve into secondary acute myeloid leukemia (sAML). Recently characterized markers suggest an opportunity to diagnose and identify subpopulations of MPD patients. In particular, altered expression and point mutations of PRV-1, MPL and JAK2 were commonly found in MPD, as well as deletions on chromosome 20q (del20q). Acquired uniparental disomy (UPD) on chromosome 1p (1pUPD) and 9p (9pUPD) leading to copy-neutral loss of heterozygosity (LOH) is a further mechanism found in MPD which often leads to homozygous activated mutations of MPL and JAK2, respectively. However, the molecular mechanisms involved in the transformation process to sAML remains unclear. Using standard metaphase cytogenetics (MC), chromosomal abnormalities are found in only a proportion of patients with MPD. We hypothesized that with new precise methods more genomic lesions can be uncovered that may be associated with leukemic transformation. To address this issue, we used 250K single nucleotide polymorphisms (SNP) Chip arrays to study chromosomal lesions in 40 sAML samples from patients who evolved from MPD; 7 had preexisting PV, 25 PMF, and 8 ET. Moreover, 43 additional samples of MPD (10 PV, 17 ET, and 16 PMF) were included in this study. SNP-chip analysis showed major chromosomal changes in almost all the sAML samples including monosomy 16, deletions of 1q-, 3p-, 6p-, 5q-, 7q-, 9q-, 12p-, 6q-, 3q-, 17p-, 19q-, and 20q- as well as trisomy 2, 3, 8, 9, 12, 15, 19, 21, and 22. We validated these data by MC. However, numerous new genomic alterations which contained potentially interesting genes that might contribute to leukemic transformation were detected by SNP Chip Array in patient samples with normal karyotype. Moreover, UPD was very frequent: 44% (19/43) of MPD and 53 % (21/40) sAML samples. 1pUPD occurred in 5 patients with MPD (1PV, 4 PMF; 12 %) compared to 5 patients with sAML (1 PV, 4 PMF; 13 %). 9pUPD was found in 16 MPD patients (8 PV, 7 PMF, 1 ET; 37%) and 6 sAML patients (3 PV, 2 PMF, 1ET; 15 %). All patients with 9pUPD proved to be positive for the JAK2 V617F mutation seen by allele specific PCR. Interestingly, the MPD samples only had UPD on 1p, 9p, and 12q. In contrast, sAML samples showed additional UPD regions on 7q, 11q, 12q, 16p, 17p, 19q, and 21q. Beside the evaluation of the non-matching groups of patients with MPD and sAML, we also evaluated 4 patients during their PMF and sAML stages by SNP Chip. The sAML samples acquired additional genomic changes including trisomy 8, 10, 14, 19, duplication on 3q and 6p, and heterozygote deletion on 18q. In contrast, 1pUPD, 9pUPD, and 12qUPD were detected in both MPD and sAML matching samples, suggesting that these changes do not play an immediate role in causing transformation. In conclusion, we detected chromosomal regions possessing genes which may be involved in the leukemic transformation of MPD patients.