Molecular patterns identify distinct subclasses of myeloid neoplasia

Clonal hematopoiesis in large granular lymphocytic leukemia

Past studies described occasional patients with myeloid neoplasms (MN) and coexistent large granular lymphocytic leukemia (LGLL) or T-cell clonopathy of unknown significance (TCUS), which may represent expansion of myeloid clonal hematopoiesis (CH) as triggers or targets of clonal cytotoxic T cell reactions. We retrospectively analyzed 349 LGLL/TCUS patients, 672 MN patients, and 1443 CH individuals to establish the incidence, genetic landscape, and clinical phenotypes of CH in LGLL. We identified 8% of cases overlapping with MN, while CH was found in an additional 19% of cases (CH + /LGLL) of which TET2 (23%) and DNMT3A (14%) were the most common. In MN cohort, 3% of cases showed coexistent LGLL. The incidence of CH in LGLL was exceedingly higher than age-matched CH controls (P < 0.0001). By multivariate analysis, the presence of CH in LGLL (P = 0.026) was an independent risk factor for cytopenia in addition to older age (P = 0.003), splenomegaly (P = 0.015) and STAT3/5B mutations (P = 0.001). CH + /LGLL cases also showed a higher progression rate to MN than CH-/LGLL (10% vs. 2% at 5 years; P = 0.02). A close relationship between CH and LGLL suggests that cytopenia in LGLL may be not only related to LGLL but be also secondary to coexisting clonal cytopenia of unclear significance.

Can molecular patterns help to classify overlapping entities in myeloid neoplasms?

Myeloid neoplasms include myeloproliferative and myelodysplastic neoplasms and acute myeloid leukaemia. Historically, these diseases have been diagnosed based on clinicopathological features with sometimes arbitrary thresholds that have persisted even as molecular features were gradually incorporated into their classification. As such, although current diagnostic approaches can classify the majority of myeloid neoplasms accurately using a combination of molecular and clinicopathological features, some areas of overlap persist and occasionally pose diagnostic challenges. These include overlap across BCR::ABL1-negative myeloproliferative neoplasms; between clonal cytopenia of undetermined significance and myelodysplastic neoplasms; myelodysplastic/myeloproliferative neoplasms; and, detection of KIT mutations in myeloid neoplasms other than mastocytosis, raising the prospect of systemic mastocytosis. Molecular testing has become state of the art in the diagnostic work-up of myeloid neoplasms, and molecular patterns can inherently help to classify overlapping entities if considered within a framework of haematological presentations. For future development, molecular testing will likely include whole genome and transcriptome sequencing, and primarily molecular classifications of myeloid neoplasms have already been suggested. As such, genetically defined groups should still constitute the basis for our understanding of disease development from early onset to progression, while clinicopathological features could then be used to describe the stage of the disease rather than the specific type of myeloid neoplasm.

Future directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology

Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.

Validation and improvement of the molecular international prognostic scoring system in Chinese patients with myelodysplastic syndromes

Various prognostic models have been proposed to improve the accuracy of prognostic assessment for Myelodysplastic syndromes (MDS). Recently, the Molecular International Prognostic Scoring System (IPSS-M) has been developed. Here, we validated the accuracy of IPSS-M in Chinese MDS patients, and proposed a prognostic model more suitable for Chinese patients. We analyzed the clinical, molecular and cytogenetic data of 798 primary MDS patients, and compared the accuracy of IPSS-R and IPSS-M in predicting overall survival (OS) and acute myeloid leukemia (AML) transformation. Using Cox proportional hazards model, we screened out 14 genes that had significant impacts on OS and AML progression. In our study, 44.86% of individuals were reclassified from IPSS-R to IPSS-M, of whom 64.80% were upstaged and 35.2% were downstaged. IPSS-M showed better performance than IPSS-R in predicting AML transformation (C-index: 0.84 vs. 0.81), but it was similar to IPSS-R in OS (C-index: 0.77 vs. 0.76). By combining age, mutational data and IPSS-R, we developed a new prognostic model more suitable for the Chinese patients (c-index was 0.81 for OS and 0.89 for AML transformation, respectively).

Classification and Prognostic Stratification Based on Genomic Features in Myelodysplastic and Myeloproliferative Neoplasm- and Their Overlapping Conditions

Background/Objectives: Myeloid neoplasms encompass a diverse group of disorders. In this study, we aimed to analyze the clinical and genomic data of patients with myeloproliferative neoplasm (MPN), myelodysplastic neoplasm (MDS), and their overlapping conditions, such as MDS/MPN and aplastic anemia (AA), to help redefine the disease classification. Methods: Clinico-genomic data of 1585 patients diagnosed with MPN (n = 715), MDS (n = 698), MDS/MPN (n = 78), and AA (n = 94) were collected. Patterns of 53 recurrent genomic abnormalities were compartmentalized into 10 groups using a Dirichlet process (DP). Results: These genomic groups were correlated with specific genomic features, survival outcomes, and disease subtypes. Groups DP1 and DP5, characterized by JAK2 and CALR mutations, respectively, showed very favorable prognoses among the patients with MPN. Groups DP2, DP7, and DP9 demonstrated very adverse prognoses across the disease subtypes. DP2 included patients with MDS harboring TP53 mutations and complex karyotypes; DP9 comprised patients with acute myeloid leukemia-related mutations, including NPM1; and DP7 included patients with SETBP1 mutations. Groups DP10 and DP8, linked to SF3B1 and DDX41 mutations or chromosome 1q derivatives, presented a favorable risk profile. Improved survival was observed with transplantation in groups DP2, DP7, and DP9. Conclusions: These findings highlight the role of genomic classifications in guiding personalized treatment strategies, ultimately enhancing the understanding and management of myeloid neoplasms.

Dissecting Out a Rare Mutation, STAT: Features of STAT3-Mutant Myeloid Neoplasms

The transcription factor STAT3 drives the expression of genes promoting cellular proliferation, survival, and pluripotency. The description of STAT3 mutations and their clinical correlates in myeloid neoplasms, such as acute myeloid leukemia and myelodysplastic syndromes, raises new insights into both the pathogenesis and the targeted therapy of these diseases. See related article by Ye et al., p. 4681.

Molecular taxonomy of myelodysplastic syndromes and its clinical implications

ABSTRACT

Myelodysplastic syndromes (MDS) are clonal hematologic disorders characterized by morphologic abnormalities of myeloid cells and peripheral cytopenias. Although genetic abnormalities underlie the pathogenesis of these disorders and their heterogeneity, current classifications of MDS rely predominantly on morphology. We performed genomic profiling of 3233 patients with MDS or related disorders to delineate molecular subtypes and define their clinical implications. Gene mutations, copy-number alterations, and copy-neutral loss of heterozygosity were derived from targeted sequencing of a 152-gene panel, with abnormalities identified in 91%, 43%, and 11% of patients, respectively. We characterized 16 molecular groups, encompassing 86% of patients, using information from 21 genes, 6 cytogenetic events, and loss of heterozygosity at the TP53 and TET2 loci. Two residual groups defined by negative findings (molecularly not otherwise specified, absence of recurrent drivers) comprised 14% of patients. The groups varied in size from 0.5% to 14% of patients and were associated with distinct clinical phenotypes and outcomes. The median bone marrow (BM) blast percentage across groups ranged from 1.5% to 10%, and the median overall survival ranged from 0.9 to 8.2 years. We validated 5 well-characterized entities, added further evidence to support 3 previously reported subsets, and described 8 novel groups. The prognostic influence of BM blasts depended on the genetic subtypes. Within genetic subgroups, therapy-related MDS and myelodysplastic/myeloproliferative neoplasms had comparable clinical and outcome profiles to primary MDS. In conclusion, genetically-derived subgroups of MDS are clinically relevant and might inform future classification schemas and translational therapeutic research.

Clonal hematopoiesis-derived therapy-related myeloid neoplasms after autologous hematopoietic stem cell transplant for lymphoid and non-lymphoid disorders

Therapy-related myeloid neoplasms (tMN) are complications of cytotoxic therapies. Risk of tMN is high in recipients of autologous hematopoietic stem cell transplantation (aHSCT). Acquisition of genomic mutations represents a key pathogenic driver but the origins, timing and dynamics, particularly in the context of preexisting or emergent clonal hematopoiesis (CH), have not been sufficiently clarified. We studied a cohort of 1507 patients undergoing aHSCT and a cohort of 263 patients who developed tMN without aHSCT to determine clinico-molecular features unique to post-aHSCT tMN. We show that tMN occurs in up to 2.3% of patients at median of 2.6 years post-AHSCT. Age ≥ 60 years, male sex, radiotherapy, high treatment burden ( ≥ 3 lines of chemotherapy), and graft cellularity increased the risk of tMN. Time to evolution and overall survival were shorter in post-aHSCT tMN vs. other tMN, and the earlier group's mutational pattern was enriched in PPM1D and TP53 lesions. Preexisting CH increased the risk of adverse outcomes including post-aHSCT tMN. Particularly, antecedent lesions affecting PPM1D and TP53 predicted tMN evolution post-transplant. Notably, CH-derived tMN had worse outcomes than non CH-derived tMN. As such, screening for CH before aHSCT may inform individual patients' prognostic outcomes and influence their prospective treatment plans. Presented in part as an oral abstract at the 2022 American Society of Hematology Annual Meeting, New Orleans, LA, 2022.

Using machine learning to unravel the intricacy of acute myeloid leukemia

Not available.

Diagnosis and classification of myelodysplastic syndromes

ABSTRACT

Myelodysplastic syndromes (MDSs) are neoplastic myeloid proliferations characterized by ineffective hematopoiesis resulting in peripheral blood cytopenias. MDS is distinguished from nonneoplastic clonal myeloid proliferations by the presence of morphologic dysplasia and from acute myeloid leukemia by a blast threshold of 20%. The diagnosis of MDS can be challenging because of the myriad other causes of cytopenias: accurate diagnosis requires the integration of clinical features with bone marrow and peripheral blood morphology, immunophenotyping, and genetic testing. MDS has historically been subdivided into several subtypes by classification schemes, the most recent of which are the International Consensus Classification and World Health Organization Classification (fifth edition), both published in 2022. The aim of MDS classification is to identify entities with shared genetic underpinnings and molecular pathogenesis, and the specific subtype can inform clinical decision-making alongside prognostic risk categorization. The current MDS classification schemes incorporate morphologic features (bone marrow and blood blast percentage, degree of dysplasia, ring sideroblasts, bone marrow fibrosis, and bone marrow hypocellularity) and also recognize 3 entities defined by genetics: isolated del(5q) cytogenetic abnormality, SF3B1 mutation, and TP53 mutation. It is anticipated that with advancing understanding of the genetic basis of MDS pathogenesis, future MDS classification will be based increasingly on genetic classes. Nevertheless, morphologic features in MDS reflect the phenotypic expression of the underlying abnormal genetic pathways and will undoubtedly retain importance to inform prognosis and guide treatment.