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The WHO and International Consensus Classification 2022 classifications of myelodysplastic syndromes enhance diagnostic precision and refine decision-making processes in these diseases. However, some discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. We adopted a data-driven approach to provide a harmonisation between these two classification systems. We investigated the importance of genomic features and their effect on the cluster assignment process to define harmonised entity labels. A panel of expert haematologists, haematopathologists, and data scientists who are members of the International Consortium for Myelodysplastic Syndromes was formed and a modified Delphi consensus process was adopted to harmonise morphologically defined categories without a distinct genomic profile. The panel held regular online meetings and participated in a two-round survey using an online voting tool. We identified nine clusters with distinct genomic features. The cluster of highest hierarchical importance was characterised by biallelic TP53 inactivation. Cluster assignment was irrespective of blast count. Individuals with monoallelic TP53 inactivation were assigned to other clusters. Hierarchically, the second most important group included myelodysplastic syndromes with del(5q). Isolated del(5q) and less than 5% of blast cells in the bone marrow were the most relevant label-defining features. The third most important cluster included myelodysplastic syndromes with mutated SF3B1. The absence of isolated del(5q), del(7q)/-7, abn3q26.2, complex karyotype, RUNX1 mutations, or biallelic TP53 were the basis for a harmonised label of this category. Morphologically defined myelodysplastic syndrome entities showed large genomic heterogeneity that was not efficiently captured by single-lineage versus multilineage dysplasia, marrow blasts, hypocellularity, or fibrosis. We investigated the biological continuum between myelodysplastic syndromes with more than 10% bone marrow blasts and acute myeloid leukaemia, and found only a partial overlap in genetic features. After the survey, myelodysplastic syndromes with low blasts (ie, less than 5%) and myelodysplastic syndromes with increased blasts (ie, 5% or more) were recognised as disease entities. Our data-driven approach can efficiently harmonise current classifications of myelodysplastic syndromes and provide a reference for patient management in a real-world setting., Competing Interests: Declaration of interests UG reports speaker honoraria from Novartis, AbbVie, and BMS; and institutional research support from BMS, AbbVie, and Jazz Pharmaceuticals. FE reports consultancy or advisory roles for AbbVie, Incyte, Syros, Novartis, and Jazz Pharmaceuticals. SH reports research support from STORM Therapeutics and AstraZeneca. EAG reports honoraria from AAMDS, MedscapeLIVE!, MediCom Worldwide, MJH Life Sciences, ASH, MDS International Foundation, and Physicians’ Education Resource; consulting fees from AbbVie, Alexion, Apellis, Takeda Oncology, Astex/Taiho Oncology, Alexion/AstraZeneca Rare Disease, Celgene/BMS, CTI BioPharma, Novartis, Partner Therapeutics, Picnic Health, and Servier; and research funding from Alexion, Apellis, Astex /Otsuka/Taiho Oncology, Blueprint Medicines, Celldex Therapeutics, Genentech, and NextCure. MAS reports participation on advisory boards for BMS, Kurome, Schrödinger, and Karyopharm. MD-C reports participation on a data safety monitoring board or advisory board for BMS, Novartis, Blueprint Medicines, GSK, Agios, Hemavan, Syros, Keros, Curis, and Astex/Otsuka; and payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing, or educational events for BMS, Novartis, and Keros. UP reports research support and honoraria from BMS, Geron, Curis, AbbVie, and Janssen. RBe reports employment or equity from Aptose Biosciences; participation on advisory boards for BMS, Servier, NeoGenomics, and Geron; being Data Monitoring Committee Chair for Gilead, Ipsen, and Keros; and consultancy for TenSixteen. YM reports honoraria from Nippon Shinyaku, BMS, Novartis, Sumitomo Pharma, Kyowa Kirin, AbbVie, Daiichi Sankyo, Takeda, Janssen, Astellas, Pfizer, Eisai, and Otsuka; and research funding from Chugai. AED reports participation on advisory boards, consultancy, or honoraria from Celgene/BMS, Agios, Novartis, Astellas, and Gilead; and participation on clinical trial committees or data safety monitoring boards for Novartis, AbbVie, Kura, Geron, Servier, Keros, and Celgene/BMS. DPS reports employment by Ajax Therapeutics; former employment by Novartis; and minor equity in Arrowhead and Bluebird. TKK reports consultancy for Agenus and ImmunoBiome. AK reports research support from Novartis and BMS; consulting fees from Alexion, Novartis, Amgen, Agios, Pfizer, Samsung, Celgene, F Hoffmann-La Roche, and Sobi; honoraria from Alexion, Novartis, Pfizer, Amgen, Samsung, Celgene, F Hoffmann-La Roche, BMS, Sobi, and Silence Therapeutics; and speakers fees from Alexion, Novartis, Amgen, Pfizer, Celgene, F Hoffmann-La Roche, and Sobi. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Lenalidomide (LEN) can induce red blood cell-transfusion independence (RBC-TI) in 60-70% of del(5q) myelodysplastic neoplasm (MDS) patients. Current recommendation is to continue LEN in responding patients until failure or progression, with likelihood of toxicity and a high cost for healthcare systems. This HARMONY Alliance study investigated the outcome of MDS del(5q) patients who discontinued LEN while RBC-transfusion independent. We enrolled 118 patients with IPSS-R low-intermediate risk. Seventy patients (59%) discontinued LEN for intolerance, 38 (32%) per their physician decision, nine (8%) per their own decision and one (1%) for unknown reasons. After a median follow-up of 49 months from discontinuation, 50/118 patients lost RBC-TI and 22/30 who underwent cytogenetic re-evaluation lost complete cytogenetic response. The median RBC-TI duration was 56 months. In multivariate analysis, RBC-TI duration after LEN discontinuation correlated with low transfusion burden before LEN therapy, treatment ≥ 12 LEN cycles, younger age and higher Hb level at LEN withdrawal. Forty-eight patients were re-treated with LEN for loss of response and 28 achieved again RBC-TI. These data show that stopping LEN therapy in MDS del(5q) patients who reached RBC-TI allows prolonged maintenance of TI in a large subset of patients., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 González, Chakraborty, Hernández-Sánchez, Diez Campelo, Park and Hernández Rivas.)

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.

Patients aged 50 or above diagnosed with myeloid neoplasms (MNs) are typically not candidates for germline testing. However, approximately 8% carry pathogenic germline variants. Allogeneic haematopoietic stem cell transplantation (alloHSCT) remains an option for those aged over 50; neglecting germline testing could mask the risk for relative donor cell-derived MN. We propose a germline-augmented somatic panel (GASP), combining MN predisposition genes with a myeloid somatic panel for timely germline variant identification when initial testing is not indicated. Out of our 133 whole-exome-sequenced MN cases aged over 50 years, 9% had pathogenic/likely variants. GASP detected 92%, compared to 50% with somatic-only panel. Our study highlights the relevance of germline screening in MN, particularly for alloHSCT candidates without established germline-testing recommendations., (© 2024 British Society for Haematology and John Wiley & Sons Ltd.)

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.

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.

Aim: To assess treatment patterns and outcomes in patients with non-del(5q) lower-risk myelodysplastic syndromes. Methods: Patient medical records were reviewed in the USA, Canada (CAN), UK and the EU. Results: Analysis included 119 patients in the USA/CAN (median age, 61.5 years) and 245 patients in the UK/EU (median age, 67.3 years). Most patients received erythropoiesis-stimulating agents (ESAs) as first-line (1L) therapy (USA/CAN: 89.0%; UK/EU: 90.2%). A substantial proportion of 1L erythropoiesis-stimulating agent-treated patients were transfusion dependent before 1L (USA/CAN: 37.1%; UK/EU: 51.2%); a small percentage of these patients achieved transfusion independence during 1L therapy (USA/CAN: 2.8%; UK/EU: 14.4%). Conclusion: These findings highlight an unmet need for more effective treatments among patients with non-del(5q) lower-risk myelodysplastic syndromes.

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association.)

Purpose: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology., Methods: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data., Results: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients., Conclusion: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.

Purpose: Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M., Methods: A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed., Results: IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score., Conclusion: IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may facilitate the clinical implementation of the score.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals., Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease., In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients., Competing Interests: MA, TE, NB, AA, AV, PS, DA, JL, MS, TJ, FL, SM, FS, AM, JM, GS, AD, ML, DG, MD, DV, MH, FP No competing interests declared, JR Employed by 10x Genomics since February 2021; this employment had no bearing on this work, (© 2023, Ainciburu, Ezponda et al.)