Wang, Sa A.

Abstract Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort ( n = 5136) and transcriptomic data from a prospective cohort ( n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS‐M). We further assessed whether the taxonomy could guide management in patients receiving disease‐modifying therapies. Except for the “ TP53 ‐complex” subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS‐M and the taxonomy capture disease biology, other non‐genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML‐like, bi TET2 , SF3B1 , and TP53 ‐complex subgroups. Further integration of multi‐omics data may refine MDS classification, improving clinical decision‐making and guiding the development of targeted therapies.

Abstract Recent updates to monocyte count thresholds recognize oligomonocytic chronic myelomonocytic leukemia (OM-CMML) as an early form of CMML. However, the clinical validity of these changes remains uncertain without incorporating biological and genomic factors. In this study, we analyzed a cohort of 911 patients (249 with OM-CMML, 359 with overt CMML, and 303 with myelodysplastic syndromes) using unsupervised clustering to evaluate the role of genomic determinants in refining CMML diagnosis. Our findings show that CMML molecular signatures (biallelic TET2 mutations or SRSF2-TET2 comutations) are linked to a distinct transcriptome, monocytic bias, classical monocytosis, and a higher risk of progression to overt CMML in OM-CMML cases. We developed a weighted genomic model and diagnostic workflow showing that combining genomic signatures with bone marrow monocyte frequencies in OM-CMML more accurately predicts progression to overt CMML. These findings support integrating genomic determinants and our clinic-ready diagnostic workflow into the CMML diagnostic framework to improve accuracy. Significance: Through comprehensive clinical and genomic profiling of a large patient cohort, alongside immunophenotypic and transcriptional cellular analyses, this study provides evidence that incorporating genomic determinants into the diagnostic criteria for OM-CMML improves diagnostic accuracy and refines the identification of early-stage CMML, thereby preventing misclassification.

ABSTRACT Background Chronic myelomonocytic leukemia (CMML) is largely a disease of individuals older than 50 years; its occurrence in patients aged 50 years and younger is rare. The authors sought to characterize the clinicopathologic features and genetic profile of young‐onset (age 50 years and younger) CMML (y‐CMML) compared with older onset (older than 50 years) CMML (o‐CMML). Methods The authors conducted a multi‐institutional study of 32 patients with y‐CMML and 119 patients with o‐CMML, analyzing clinical, morphologic, cytogenetic, and molecular data. Results Patients who had y‐CMML frequently presented with splenomegaly (59% vs. 11%; p < .001) and hepatomegaly (25% vs. 2%; p < .001) compared with those who had o‐CMML. Although the overall incidence of cytogenetic abnormalities was similar, patients who had y‐CMML exhibited distinct mutational patterns. PTPN11 mutations were significantly more common in y‐CMML (13% vs. 3%; p = .034) than in o‐CMML, whereas TET2 (6% vs. 57%; p < .001) and SRSF2 (16% vs. 46%; p = .002) mutations were significantly less frequent. In addition, multi‐hit TET2 (6% vs. 34%; p = .002) and TET2‐SRSF2 (3% vs. 29%; p = .003) co‐mutations were rare in y‐CMML. However, the molecular differences did not affect the CMML‐specific risk‐stratification categories because the clinical outcomes, including overall survival and leukemia‐free survival, did not differ between the two groups. Conclusions CMML in patients aged 50 years and younger is rare and presents with unique clinical and mutational features, suggesting a distinct pathogenesis.

ABSTRACT The World Health Organization (WHO) 5th Edition and International Consensus Classification (ICC) have continued to delineate more genetically defined acute leukemias. Both recognize genetic aberrations associated with myelodysplastic syndromes/neoplasms (MDS) as defining MDS‐related AML (AML‐MR). Mutations in RUNX1 are included in this category in the ICC, but not WHO. The current classifications are unclear on how acute leukemias with ambiguous lineage (ALAL), including those of mixed lineage (MPAL) and those without a defined lineage (AUL), should be categorized in the presence of such mutations. Our multi‐institutional study aims to determine the significance of RUNX1 mutation in ALAL compared to de novo AML. Newly diagnosed MPAL, AUL, and AML with RUNX1 mutations were collected. ALAL with MDS‐related cytogenetics, BCR::ABL1 , KMT2A rearrangements, or ZNF384 or BCL11b abnormalities were excluded. We analyzed 17 cases of ALAL and 71 cases of AML that met inclusion criteria. ALAL‐ RUNX1 and AML‐ RUNX1 showed overlapping mutational landscapes with frequent comutations in MDS‐associated genes. No ALAL‐ RUNX1 or AML‐ RUNX1 cases showed concomitant TP53 mutation. ALAL‐ RUNX1 had more bone marrow blasts and more karyotypic abnormalities; however, neither impacted survival, nor did RUNX1 variant allele frequency (VAF). Survival analysis revealed similarly poor outcomes between them. AML‐directed therapies and allogeneic hematopoietic stem cell transplant trended toward improved survival in ALAL‐ RUNX1; however, it did not reach significance. Our results suggest ALAL‐ RUNX1 is associated with younger age, higher blasts, and more karyotypic abnormalities, but has similar clinical and genetic features and outcomes to AML‐ RUNX1 . Our findings suggest, like other MR mutations, RUNX1 ‐mutated ALALs should be included within AML‐MR.

Abstract Objective The successful diagnosis and classification of lymphoid neoplasms in blood and bone marrow is the responsibility of the practicing pathologist. This guide provides a general “roadmap” for this process, from initial case recognition to final classification. Methods The integration of hematologic, morphologic, immunophenotypic, and genetic features for the full spectrum of precursor and mature B-cell, T-cell, and natural killer–cell neoplasms that typically manifest in blood and bone marrow is included. Results Classification systems for lymphoid neoplasms provide criteria for pathologists to render a diagnosis that is optimal for patient care, treatment, and outcome prediction. Conclusions This guide provides diagnostic strategies for lymphoid neoplasms encountered in blood and bone marrow specimens using both the International Consensus Classification and the World Health Organization fifth edition classification systems. Key tips are provided for each entity along with testing requirements, differential diagnosis, nonneoplastic mimics, and other unique features based on the experience of the Bone Marrow Pathology Group members.

Abstract Objectives Myeloproliferative neoplasm, unclassifiable (MPN-U, revised to MPN, not otherwise specified in the fifth edition of the World Health Organization classification) is a heterogeneous category of primary marrow disorders with clinical, morphologic, and/or molecular features that preclude classification as a more specific MPN subtype due to stage at diagnosis, overlapping features between MPN subtypes, or the presence of coexisting disorders. Compared with other MPN subtypes, the contribution of the mutational landscape in MPN-U in conjunction with other clinical and morphologic biomarkers to prognosis has been less well investigated. Methods We performed a multicenter, retrospective study of MPN-U (94 cases) to better define the clinicopathologic features, genetic landscape, and clinical outcomes, including subgroups of early-stage, advanced-stage, and coexisting disorders. The Dynamic International Prognostic Scoring System (DIPSS) plus scoring system was applied to assess its relevance to MPN-U prognosis. Results Multivariate analysis demonstrated bone marrow blast count and DIPSS plus score as statistically significant in predicting overall survival. Univariate analysis identified additional potential poor prognostic markers, including abnormal karyotype and absence of JAK2 mutation. Secondary mutations were frequent in the subset analyzed by next-generation sequencing (26/37 cases, 70.3%) with a borderline association between high molecular risk mutations and overall survival. Conclusions This study, as one of the largest of MPN-U studies incorporating both clinicopathologic and molecular data, moves toward identification of biomarkers that better predict prognosis in this heterogeneous category.

Background. Some patients who present with clinical features consistent with idiopathic hypereosinophilic syndrome (HES; absolute eosinophil count (AEC) ≥1.5 x 10 9/L with related end organ damage) are ultimately diagnosed with a myeloid neoplasm. Although FIP1L1::PDGFRA is the most common molecular abnormality in this setting, many other fusion genes, point mutations and cytogenetic abnormalities have been implicated in driving primary (neoplastic) eosinophilia. Given the poor prognosis of these disorders in the absence of treatment and the availability of targeted therapies for many of the driver mutations, it is becoming increasingly important to rapidly identify the underlying genetic alteration. In this regard, the expense and variable sensitivity of molecular diagnostic tests remains problematic. Although clinical features have been reported to distinguish between PDGFRA-associated myeloid neoplasms and D816V KIT-positive systemic mastocytosis with hypereosinophilia, little is known about the clinical phenotypes of other subsets of patients. This multicenter retrospective study aimed to assemble a large cohort of patients with HES secondary to a myeloid neoplasm to explore the utility of clinical and laboratory features in predicting the underlying molecular abnormality. Methods. Retrospective, de-identified data from 16 centers were collected via an online Research Electronic Data Capture repository. Patients and associated data were obtained from in-house research databases and/or electronic medical record searches at each site. Clinical and laboratory data were entered in accordance with local Institutional Review Boards. Inclusion criteria were peripheral blood hypereosinophilia (AEC &gt;1.5 x 10 9/L) on at least two occasions and one of the following: ≥1 confirmed genetic abnormality recurrently associated with primary myeloid neoplasms, other evidence of clonal eosinophilia, or increased blasts (≥2% in the blood or &gt;5% but &lt;20% in the marrow). Patients with acute myeloid leukemia were excluded. Results. A total of 277 patients followed between 1990 and 2022 were included in the analysis. Median length of follow up was 31 months (range &lt;1-356), and 217/277 (78%) were alive at last follow up. The most common genetic abnormality was FIP1L1::PDGFRA (n=131) followed by D816V KIT mutations (n=39) and alterations involving JAK2 (n=22) (Figure 1). Five patients had genetic abnormalities involving two distinct genes recurrently associated with eosinophilic myeloid neoplasms. The overall median age at diagnosis was 55 years (range 12-89) and 79% of the patients were male. When patients with abnormalities involving PDGFRA and PDGFRB were excluded from the analysis, the male predominance was significantly lower at 62% (p&lt;0.001, Fisher's exact test as compared to the data set as a whole). The overall pattern of organ system involvement was similar irrespective of the underlying genetic abnormality with a few notable exceptions. Cardiac involvement was most common in patients with FIP1L1:: PDGFRA, abnormalities involving JAK2 or without pathologic alterations identified (reported in 22%, 32% and 30%, respectively); whereas skin and gastrointestinal involvement, lymphadenopathy and splenomegaly were most frequent in patients with KIT D816V. Laboratory abnormalities also differed between groups. Whereas anemia was the most common blood abnormality (reported in &gt;50% of patients), erythrocytosis was noted primarily in patients with abnormalities involving JAK2. In contrast, circulating blasts were identified in 70% of patient with BCR::ABL1 and 29% of patients with chromosomal abnormalities or KIT D816V, but in &lt;15% of patients in any of the other groups. Whereas peak AEC alone did not discriminate between the different patient groups, the AEC/tryptase ratio was increased in patients with PDGFRA-associated myeloid neoplasms, molecular abnormalities involving JAK2, or chromosomal abnormalities as compared to those with KIT mutations (Figure 2). Conclusions. Despite the limitations of retrospective data analysis, the data from this large multicenter cohort of patients with HES due to a chronic myeloid neoplasm suggest that routine demographic, clinical and laboratory features vary depending on the underlying molecular abnormality. This approach could ultimately help guide empiric therapy when molecular studies are negative or unavailable.

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&gt;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 &lt;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 &lt;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 The dismal outcome of acute leukemia undergoing immunophenotype-switch, compounded by the increased incidence in the era of immunotherapy, highlights the need for in-depth systemic studies. We investigated the clinicopathological, cytogenetic, and molecular features of 33 patients experiencing switch between acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). The median duration from the primary diagnosis to lineage switch was 7.8 months, with the subsequent median survival being < 3 months. The majority experienced a transition from B-ALL to AML. The AML cases demonstrated a spectrum of differentiation, ranging from minimal to erythroid, with monocytic being the most common. Cytogenetic analysis revealed 11q23/KMT2A fusions as the most recurrent abnormalities (n = 18); also observed were complex karyotype, 7/7q-, 9p-/CDKN2A deletion, t(5;14)/TCLX3::BCL11b, t(8;9)/PCM1::JAK2, t(9;22)/BCR::ABL1, and t(12;19)/TCF3::ZNF384. Remarkably, in 60% of patients lacking KMT2A fusions, a chronic myeloid neoplasm was observed preceding the first leukemia. In contrast, no prior hematological malignancies were observed in KMT2A cases. Deep mutational profiling reinforced the inherent clonal relationship between the initial and subsequent leukemias, evident from the overlapping mutational signatures. The presence of distinct additional mutations across the two phases supports the long-standing theory of lineage switch emerging via divergent evolution of a multipotential progenitor.