Influence of TP53 Gene Mutations and its Allelic Status in Myelodysplastic Syndromes with Isolated 5q Deletion.

Mutations in the TP53 gene, particularly multihit alterations, have been associated with unfavorable clinical features and prognosis in patients diagnosed with myelodysplastic syndrome (MDS). Despite this, the role of TP53 gene aberrations in MDS with isolated deletion of chromosome 5 [MDS-del(5q)] remains unclear. This study aimed to assess the impact of TP53 gene mutations and their allelic state in patients with MDS-del(5q). To that end, a comprehensive analysis of TP53 abnormalities, examining both TP53 mutations and allelic imbalances, in 682 patients diagnosed with MDS-del(5q) was conducted. Twenty-four percent of TP53-mutated patients exhibited multihit alterations, while the remaining patients displayed monoallelic mutations. TP53 multihit alterations were predictive of an increased risk of leukemic transformation. The impact of monoallelic alterations was dependent on the variant allele frequency (VAF); patients with TP53 monoallelic mutations and VAF <20% exhibited behavior similar to TP53 wild type, and those with TP53 monoallelic mutations and VAF ≥20% presented outcomes equivalent to TP53 multihit patients. This study underscores the importance of considering TP53 allelic state and VAF in the risk stratification and treatment decision-making process for patients with MDS-del(5q).

Molecular and clinical presentation of UBA1-mutated myelodysplastic syndromes.

Mutations in UBA1, which are disease-defining for VEXAS syndrome, have been reported in patients diagnosed with myelodysplastic syndromes (MDS). Here, we define the prevalence and clinical associations of UBA1 mutations in a representative cohort of patients with MDS. Digital droplet PCR profiling of a selected cohort of 375 male patients lacking MDS disease-defining mutations or established WHO disease classification identified 28 patients (7%) with UBA1 p.M41T/V/L mutations. Using targeted sequencing of UBA1 in a representative MDS cohort (n=2,027), we identified an additional 27 variants in 26 patients (1%), which we classified as likely/pathogenic (n=12) and unknown significance (n=15). Among the total 40 patients with likely/pathogenic variants (2%), all were male and 63% were classified by WHO2016 as MDS-MLD/SLD. Patients had a median of one additional myeloid gene mutation, often in TET2 (n=12), DNMT3A (n=10), ASXL1 (n=3), or SF3B1 (n=3). Retrospective clinical review where possible showed that 83% (28/34) UBA1-mutant cases had VEXAS-associated diagnoses or inflammatory clinical presentation. The prevalence of UBA1-mutations in MDS patients argues for systematic screening for UBA1 in the management of MDS.

Molecular Taxonomy of Myelodysplastic Syndromes and its Clinical Implications.

Myelodysplastic syndromes/neoplasms (MDS) are clonal hematologic disorders characterized by morphologic abnormalities of myeloid cells and peripheral cytopenias. While genetic abnormalities underlie the pathogenesis of these disorders and their heterogeneity, current classifications of MDS rely predominantly on morphology. We performed genomic profiling of 3,233 patients with MDS or related disorders to delineate molecular subtypes and define their clinical implications. Gene mutations, copy-number alterations (CNAs), and copy-neutral loss of heterozygosity (cnLOH) 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 LOH 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 blast percentage across groups ranged from 1.5 to 10%, and the median overall survival 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 bone marrow blasts depended on the genetic subtypes. Within genetic subgroups, therapy-related MDS and myelodysplastic/myeloproliferative neoplasms (MDS/MPN) had comparable clinical and outcome profiles to primary MDS. In conclusion, genetically-derived subgroups of MDS are clinically relevant and may inform future classification schemas and translational therapeutic research.

Approaches for the diagnosis and treatment of VEXAS syndrome: the importance of clinical suspicion and the use of methotrexate.

Vacuoles, E1-enzyme, X-linked, Autoinflammatory, Somatic (VEXAS) syndrome is caused by mutations in the UBA1 gene in myeloid precursors, leading to systemic inflammatory manifestations. We present the case of a 75-year-old man presenting with fever, panniculitis, and macrocytic anemia testing repeatedly negative for UBA1 mutations in peripheral blood samples, but ultimately found positive on bone marrow mononuclear cell DNA. The man has been successfully treated with prednisone and methotrexate.

Using real-world evidence in haematology.

Most new drug approvals are based on data from large randomized clinical trials (RCTs). However, there are sometimes contradictory conclusions from seemingly similar trials and generalizability of conclusions from these trials is limited. These considerations explain, in part, the gap between conclusions from data of RCTs and those from registries termed real world data (RWD). Recently, real-world evidence (RWE) from RWD processed by artificial intelligence has received increasing attention. We describe the potential of using RWD in haematology concluding RWE from RWD may complement data from RCTs to support regulatory decisions.

A practical algorithm for acute myeloid leukaemia diagnosis following the updated 2022 classifications.

Disease classification of complex and heterogenous diseases, such as acute myeloid leukaemia (AML), is continuously updated to define diagnoses, appropriate treatments, and assist research and education. Recent availability of molecular profiling techniques further benefits the classification of AML. The World Health Organization (WHO) classification of haematolymphoid tumours and the International Consensus Classification of myeloid neoplasms and acute leukaemia from 2022 are two updated versions of the WHO 2016 classification. As a consequence, the European LeukemiaNet 2022 recommendations on the diagnosis and management of AML in adults have been also updated. The current review provides a practical interpretation of these guidelines to facilitate the diagnosis of AML and discusses genetic testing, disease genetic heterogeneity, and FLT3 mutations. We propose a practical algorithm for the speedy diagnosis of AML. Future classifications may need to incorporate gene mutation combinations to enable personalised treatment regimens in the management of patients with AML.

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.

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.

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.

Toward a more patient-centered drug development process in clinical trials for patients with myelodysplastic syndromes/neoplasms (MDS): Practical considerations from the International Consortium for MDS (icMDS).

Notable treatment advances have been made in recent years for patients with myelodysplastic syndromes/neoplasms (MDS), and several new drugs are under development. For example, the emerging availability of oral MDS therapies holds the promise of improving patients' health-related quality of life (HRQoL). Within this rapidly evolving landscape, the inclusion of HRQoL and other patient-reported outcomes (PROs) is critical to inform the benefit/risk assessment of new therapies or to assess whether patients live longer and better, for what will likely remain a largely incurable disease. We provide practical considerations to support investigators in generating high-quality PRO data in future MDS trials. We first describe several challenges that are to be thoughtfully considered when designing an MDS-focused clinical trial with a PRO endpoint. We then discuss aspects related to the design of the study, including PRO assessment strategies. We also discuss statistical approaches illustrating the potential value of time-to-event analyses and their implications within the estimand framework. Finally, based on a literature review of MDS randomized controlled trials with a PRO endpoint, we note the PRO items that deserve special attention when reporting future MDS trial results. We hope these practical considerations will facilitate the generation of rigorous PRO data that can robustly inform MDS patient care and support treatment decision-making for this patient population.

Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

BACKGROUND: Risk stratification and therapeutic decision-making for myelodysplastic syndromes (MDS) are based on the International Prognostic Scoring System­Revised (IPSS-R), which considers hematologic parameters and cytogenetic abnormalities. Somatic gene mutations are not yet used in the risk stratification of patients with MDS. METHODS: To develop a clinical-molecular prognostic model (IPSS-Molecular [IPSS-M]), pretreatment diagnostic or peridiagnostic samples from 2957 patients with MDS were profiled for mutations in 152 genes. Clinical and molecular variables were evaluated for associations with leukemia-free survival, leukemic transformation, and overall survival. Feature selection was applied to determine the set of independent IPSS-M prognostic variables. The relative weights of the selected variables were estimated using a robust Cox multivariable model adjusted for confounders. The IPSS-M was validated in an external cohort of 754 Japanese patients with MDS. RESULTS: We mapped at least one oncogenic genomic alteration in 94% of patients with MDS. Multivariable analysis identified TP53multihit, FLT3 mutations, and MLLPTD as top genetic predictors of adverse outcomes. Conversely, SF3B1 mutations were associated with favorable outcomes, but this was modulated by patterns of comutation. Using hematologic parameters, cytogenetic abnormalities, and somatic mutations of 31 genes, the IPSS-M resulted in a unique risk score for individual patients. We further derived six IPSS-M risk categories with prognostic differences. Compared with the IPSS-R, the IPSS-M improved prognostic discrimination across all clinical end points and restratified 46% of patients. The IPSS-M was applicable in primary and secondary/therapy-related MDS. To simplify clinical use of the IPSS-M, we developed an open-access Web calculator that accounts for missing values. CONCLUSIONS: Combining genomic profiling with hematologic and cytogenetic parameters, the IPSS-M improves the risk stratification of patients with MDS and represents a valuable tool for clinical decision-making. (Funded by Celgene Corporation through the MDS Foundation, the Josie Robertson Investigators Program, the Edward P. Evans Foundation, the Projects of National Relevance of the Italian Ministry of University and Research, Associazione Italiana per la Ricerca sul Cancro, the Japan Agency for Medical Research and Development, Cancer Research UK, the Austrian Science Fund, the MEXT [Japanese Ministry of Education, Culture, Sports, Science and Technology] Program for Promoting Research on the Supercomputer Fugaku, the Japan Society for the Promotion of Science, the Taiwan Department of Health, and Celgene Corporation through the MDS Foundation.)