Rearrangements involving 11q23.3/KMT2A in adult AML: mutational landscape and prognostic implications - a HARMONY study.

Balanced rearrangements involving the KMT2A gene (KMT2Ar) are recurrent genetic abnormalities in acute myeloid leukemia (AML), but there is lack of consensus regarding the prognostic impact of different fusion partners. Moreover, prognostic implications of gene mutations co-occurring with KMT2Ar are not established. From the HARMONY AML database 205 KMT2Ar adult patients were selected, 185 of whom had mutational information by a panel-based next-generation sequencing analysis. Overall survival (OS) was similar across the different translocations, including t(9;11)(p21.3;q23.3)/KMT2A::MLLT3 (p = 0.756). However, independent prognostic factors for OS in intensively treated patients were age >60 years (HR 2.1, p = 0.001), secondary AML (HR 2.2, p = 0.043), DNMT3A-mut (HR 2.1, p = 0.047) and KRAS-mut (HR 2.0, p = 0.005). In the subset of patients with de novo AML < 60 years, KRAS and TP53 were the prognostically most relevant mutated genes, as patients with a mutation of any of those two genes had a lower complete remission rate (50% vs 86%, p < 0.001) and inferior OS (median 7 vs 30 months, p < 0.001). Allogeneic hematopoietic stem cell transplantation in first complete remission was able to improve OS (p = 0.003). Our study highlights the importance of the mutational patterns in adult KMT2Ar AML and provides new insights into more accurate prognostic stratification of these patients.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1.

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

Genetic studies in clonal haematopoiesis, myelodysplastic neoplasms and acute myeloid leukaemia - a practical guide to WHO-HAEM5.

In recent years, technology developments and increase in knowledge have led to profound changes in the diagnostics of haematologic neoplasms, particularly myeloid neoplasms. Therefore an updated, fifth edition of the World Health Organization (WHO) classification of haematolymphoid neoplasms (WHO-HAEM5) will be issued in 2024. In this context, we present a practical guide for analysing the genetic aspects of clonal haematopoiesis of indeterminate potential (CHIP), clonal cytopenia of undetermined significance (CCUS), myelodysplastic neoplasms (MDS), and acute myeloid leukaemia (AML) based on WHO-HAEM5. This guide navigates through the genetic abnormalities underlying myeloid neoplasms which are required to be detected for classification according to WHO-HAEM5 and provides diagnostic algorithms.

Overview on WHO-HAEM5 and the diagnostic relevance of genetic alterations for the classification.

The landscape of haematological malignancies is constantly evolving, driven by advances in our understanding of their genetic basis. This has cumulated within the 5th Edition of the World Health Organization (WHO) Classification of Haematolymphoid Tumours published in short form in 2022 [1, 2] and being available in full length both as "Blue Book" (in print expected early 2024) as well as web-based classification (see: https://tumourclassification.iarc.who.int/welcome/). Similarly, the importance of genetic alterations for the classification is highlighted in other classification systems related to haematologic neoplasms [3-5]. In this special issue of the Medizinische Genetik, we present a comprehensive overview of the genetic alterations contributing to the classification of haematolymphoid neoplasms in the 5th Edition of the WHO classification (WHO-HAEM5) and its diagnostic relevance in the context of various haematological malignancies.

Integrated genetic, epigenetic, and immune landscape of TP53 mutant AML and higher risk MDS treated with azacitidine.

Background: TP53 mutations are associated with an adverse prognosis in acute myeloid leukemia (AML) and higher-risk myelodysplastic syndromes (HR-MDS). However, the integrated genetic, epigenetic, and immunologic landscape of TP53-mutated AML/HR-MDS is not well defined. Objectives: To define the genetic, epigenetic, and immunologic landscape of TP53-mutant and TP53 wild-type AML and HR-MDS patients. Design: Post hoc analysis of TP53-mutant and TP53 wild-type patients treated on the randomized FUSION trial with azacitidine ± the anti-PD-L1 antibody durvalumab. Methods: We performed extensive molecular, epigenetic, and immunologic assays on a well-annotated clinical trial dataset of 61 patients with TP53-mutated disease (37 AML, 24 MDS) and 144 TP53 wild-type (89 AML, 55 MDS) patients, all of whom received azacitidine-based therapy. A 38 gene-targeted myeloid mutation analysis from screening bone marrow (BM) was performed. DNA methylation arrays, immunophenotyping and immune checkpoint expression by flow cytometry, and gene expression profiles by bulk RNA sequencing were assessed at baseline and serially during the trial. Results: Global DNA methylation from peripheral blood was independent of TP53 mutation and allelic status. AZA therapy led to a statistically significant decrease in global DNA methylation scores independent of TP53 mutation status. In BM from TP53-mutant patients, we found both a higher T-cell population and upregulation of inhibitory immune checkpoint proteins such as PD-L1 compared to TP53 wild-type. RNA sequencing analyses revealed higher expression of the myeloid immune checkpoint gene LILRB3 in TP53-mutant samples suggesting a novel therapeutic target. Conclusion: This integrated analysis of the genetic, epigenetic, and immunophenotypic landscape of TP53 mutant AML/HR-MDS suggests that differences in the immune landscape resulting in an immunosuppressive microenvironment rather than epigenetic differences contribute to the poor prognosis of TP53-mutant AML/HR-MDS with mono- or multihit TP53 mutation status. Trial registration: FUSION trial (NCT02775903).

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS: We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS: UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION: MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation.

PURPOSE: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner. METHODS: Retrospective real-world data are leveraged to emulate a target trial for treatment timing using multistate modeling and microsimulation. This case study focuses on myelodysplastic syndromes, serving as a prototype for rare cancers characterized by a heterogeneous clinical course and complex genomic background. A cohort of 7,118 patients treated according to conventional available treatments/evidence across Europe and United States is analyzed. The primary clinical objective is to determine the ideal timing for HSCT, the only curative option for these patients. RESULTS: This analysis enabled us to identify the most appropriate time frames for HSCT on the basis of each patient's unique profile, defined by a combination relevant patients' characteristics. CONCLUSION: The developed methodology offers a structured framework to address a relevant clinical issue in the field of hematology. It makes several valuable contributions: (1) novel insights into how to develop decision models to identify the most favorable HSCT timing, (2) evidence to inform clinical decisions in a real-world context, and (3) the incorporation of complex information into decision making. This framework can be applied to provide medical insights for clinical issues that cannot be adequately addressed through randomized clinical trials.

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients With Myelodysplastic Syndromes.

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M). PATIENTS AND METHODS: We studied a retrospective population of 7,118 patients, stratified into training and validation cohorts. A decision strategy was built to estimate the average survival over an 8-year time horizon (restricted mean survival time [RMST]) for each combination of clinical and genomic covariates and to determine the optimal transplantation policy by comparing different strategies. RESULTS: Under an IPSS-M based policy, patients with either low and moderate-low risk benefited from a delayed transplantation policy, whereas in those belonging to moderately high-, high- and very high-risk categories, immediate transplantation was associated with a prolonged life expectancy (RMST). Modeling decision analysis on IPSS-M versus conventional Revised IPSS (IPSS-R) changed the transplantation policy in a significant proportion of patients (15% of patient candidate to be immediately transplanted under an IPSS-R-based policy would benefit from a delayed strategy by IPSS-M, whereas 19% of candidates to delayed transplantation by IPSS-R would benefit from immediate HSCT by IPSS-M), resulting in a significant gain-in-life expectancy under an IPSS-M-based policy (P = .001). CONCLUSION: These results provide evidence for the clinical relevance of including genomic features into the transplantation decision making process, allowing personalizing the hazards and effectiveness of HSCT in patients with MDS.

The E592K variant of SF3B1 creates unique RNA missplicing and associates with high-risk MDS without ring sideroblasts.

Among the most common genetic alterations in the myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, and generally associate with more favorable prognosis. However, not all SF3B1 mutations are the same, and little is known about how distinct hotspots influence disease. Here we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct co-mutation pattern, and a lack of the favorable survival seen with other SF3B1 mutations. Moreover, compared to other hotspot SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data have implications for our understanding of the functional diversity of spliceosome mutations, as well as the pathobiology, classification, prognosis, and management of SF3B1-mutant MDS.

A case of familial donor-derived acute myeloid leukemia with underlying pre-leukemic mutations.

Not available.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes.

BACKGROUND: Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS: To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS: We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS: Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Hyperactive STAT5 hijacks T cell receptor signaling and drives immature T cell acute lymphoblastic leukemia.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive immature T cell cancer. Mutations in IL7R have been analyzed genetically, but downstream effector functions such as STAT5A and STAT5B hyperactivation are poorly understood. Here, we studied the most frequent and clinically challenging STAT5BN642H driver in T cell development and immature T cell cancer onset and compared it with STAT5A hyperactive variants in transgenic mice. Enhanced STAT5 activity caused disrupted T cell development and promoted an early T cell progenitor-ALL phenotype, with upregulation of genes involved in T cell receptor (TCR) signaling, even in absence of surface TCR. Importantly, TCR pathway genes were overexpressed in human T-ALL and mature T cell cancers and activation of TCR pathway kinases was STAT5 dependent. We confirmed STAT5 binding to these genes using ChIP-Seq analysis in human T-ALL cells, which were sensitive to pharmacologic inhibition by dual STAT3/5 degraders or ZAP70 tyrosine kinase blockers in vitro and in vivo. We provide genetic and biochemical proof that STAT5A and STAT5B hyperactivation can initiate T-ALL through TCR pathway hijacking and suggest similar mechanisms for other T cell cancers. Thus, STAT5 or TCR component blockade are targeted therapy options, particularly in patients with chemoresistant clones carrying STAT5BN642H.

Classical meets malignant hematology: a case of acquired εγδß-thalassemia in clonal hematopoiesis.

Hemoglobinopathies including thalassemias are among the most frequent genetic disorders worldwide. Primarily, these entities result from germline variants in the globin gene clusters and their cis-acting regulatory elements, and thus the WHO classifies thalassemias as inherited diseases. Non-inherited disorders of globin chain synthesis mimicking the phenotype of thalassemias have also been described and are referred to as acquired thalassemias. These forms mainly affect the alpha-globin genes and are observed at much lower frequencies...