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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.)

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.

Introduction: Chronic myelomonocytic leukemia (CMML) and myelodysplastic syndromes (MDS) with ring sideroblasts (RS) or SF3B1 mutation (MDS-RS/ SF3B1 ) differ in many clinical features, but share others, such as anemia. RS and SF3B1 mutation can also be found in CMML., Methods: We compared CMML with and without RS/ SF3B1 and MDS-RS/ SF3B1 considering the criteria established by the 2022 World Health Organization classification., Results: A total of 815 patients were included (CMML, n=319, CMML-RS/ SF3B1 , n=172 and MDS-RS/ SF3B1 , n=324). The percentage of RS was ≥15% in almost all CMML-RS/ SF3B1 patients (169, 98.3%) and most (125, 72.7%) showed peripheral blood monocyte counts between 0.5 and 0.9 x10 9 /L and low risk prognostic categories. CMML-RS/ SF3B1 differed significantly from classical CMML in the main clinical characteristics, whereas it resembled MDS-RS/ SF3B1 . At a molecular level, CMML and CMML-RS/ SF3B1 had a significantly higher frequency of mutations in TET2 (mostly multi-hit) and ASXL1 (p=0.013) and CMML had a significantly lower frequency of DNMT3A and SF3B1 mutations compared to CMML/MDS-RS/ SF3B1 . Differences in the median overall survival among the three groups were statistically significant: 6.75 years (95% confidence interval [CI] 5.41-8.09) for CMML-RS/ SF3B1 vs. 3.17 years (95% CI 2.56-3.79) for CMML vs. 16.47 years (NA) for MDS-RS/ SF3B1 , p<0.001. Regarding patients with CMML and MDS, both with SF3B1 mutation, survival did not significantly differ. CMML had a higher risk of transformation to acute myeloid leukemia (24% at 8 years, 95%CI 19%-30%)., Discussion: CMML-RS/ SF3B1 mutation resembles MDS-RS/ SF3B1 in terms of phenotype and clearly differs from CMML. The presence of ≥15% RS and/or SF3B1 in CMML is associated with a low monocyte count. SF3B1 mutation clearly improves the prognosis of CMML., 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., (Copyright © 2024 Xicoy, Pomares, Morgades, Germing, Arnan, Tormo, Palomo, Orna, Della Porta, Schulz, Díaz-Beya, Esteban, Molero, Lanino, Avendaño, Hernández, Roldan, Ubezio, Pineda, Díez-Campelo and Zamora.)

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.

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.

Background: Patients affected by chronic kidney disease are at a risk of cardiovascular morbidity and mortality. Body fluids unbalance is one of the main characteristics of this condition, as fluid overload is highly prevalent in patients affected by the cardiorenal syndrome., Summary: We describe the state of the art and new insights into body volume evaluation. The mechanisms behind fluid balance are often complex, mainly because of the interplay of multiple regulatory systems. Consequently, its management may be challenging in clinical practice and even more so out-of-hospital. Availability of novel technologies offer new opportunities to improve the quality of care and patients' outcome. Development and validation of new technologies could provide new tools to reduce costs for the healthcare system, promote personalized medicine, and boost home care. Due to the current COVID-19 pandemic, a proper monitoring of chronic patients suffering from fluid unbalances is extremely relevant. Key Message: We discuss the main mechanisms responsible for fluid overload in different clinical contexts, including hemodialysis, peritoneal dialysis, and heart failure, emphasizing the potential impact provided by the implementation of the new technologies., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

The mutations of NPM1 and FLT3 -ITD represent the most frequent genetic aberration in acute myeloid leukemia. Indeed, the presence of an NPM1 mutation reduces the negative prognostic impact of FLT3 -ITD in patients treated with conventional "3+7" induction. However, little information is available on their prognostic role with intensified regimens. Here, we investigated the efficacy of a fludarabine, high-dose cytarabine and idarubicin induction (FLAI) in 149 consecutive fit AML patients (median age 52) carrying the NPM1 and/or FLT3 -ITD mutation, treated from 2008 to 2018. One-hundred-and-twenty-nine patients achieved CR (86.6%). After a median follow up of 68 months, 3-year overall survival was 58.6%. Multivariate analysis disclosed that both NPM1 mut ( p < 0.05) and ELN 2017 risk score ( p < 0.05) were significant predictors of survival. NPM1 -mutated patients had a favorable outcome, with no significant differences between patients with or without concomitant FLT3 -ITD ( p = 0.372), irrespective of FLT3 -ITD allelic burden. Moreover, in landmark analysis, performing allogeneic transplantation (HSCT) in first CR proved to be beneficial only in ELN 2017 high-risk patients. Our data indicate that FLAI exerts a strong anti-leukemic effect in younger AML patients with NPM1 mut and question the role of HSCT in 1st CR in NPM1 mut patients with concomitant FLT3 -ITD.