Meggendorfer, Manja

Myelodysplastic syndromes with isolated deletion of chromosome 5q [MDS-del(5q)] constitute a distinct biological entity traditionally associated with favorable outcomes, although up to one quarter of patients progress to acute myeloid leukemia (AML). Existing prognostic models, developed in heterogeneous MDS populations, may not adequately capture risk within this subgroup. We assembled an international cohort of 682 patients with MDS-del(5q) to evaluate the performance of the IPSS-R and IPSS-M, identify prognostic variables, and develop a disease-specific prognostic tool, the IPSS-del(5q). Most patients were classified as lower-risk by IPSS-R (94.4%) and IPSS-M (85.5%), yet both systems showed limited discriminatory ability (C-indices ≈0.5). Independent adverse prognostic factors included age ≥70 years, male sex, anemia (hemoglobin ≤10 g/dL), thrombocytopenia (platelets ≤100×10⁹/L), the presence of one additional chromosomal abnormality, ≥2 gene mutations, SF3B1 mutations, and high-risk TP53 status. Six variables were included in the IPSS-del(5q), stratifying patients into standard-risk (74.3%) and high-risk (25.7%) groups with significantly different LFS (69.2 vs. 32.0 months; p<0.01). Moreover, this model reclassified 19.1% of lower-risk IPSS-R and 14.6% of lower-risk IPSS-M patients into the high-risk IPSS-del(5q) group. However, its discriminative power remained modest, with a C-index of 0.60. Overall, this study provides the most comprehensive prognostic evaluation of MDS-del(5q) to date, demonstrates the limited discriminatory capacity of existing MDS scores in this entity, and underscores the need to develop refined disease-specific prognostic approaches for this MDS subtype.

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Abstract Background & aim: Myelodysplastic syndromes with isolated deletion of chromosome 5q (MDS-del(5q)) are associated with low-risk features and favorable prognosis. In contrast, TP53 multihit alterations, typically reflecting biallelic inactivation, are associated with high-risk features and adverse outcomes across MDS subtypes. Although current classification systems exclude MDS-del(5q) cases with TP53 multihit from low-risk categories, the prognostic significance of these alterations in this specific context remains unclear. Methods: We conducted an international, multicenter, retrospective study comparing MDS-del(5q) per 2017 WHO criteria with TP53 multihit alterations (MDS-del(5q) TP53 multihit) from 21 international institutions, to MDS with low blast counts with TP53 multihit alterations without isolated del(5q) (MDS-LB TP53 multihit) from the public data from the study of Bernard et al., Nat Med 2020. Clinical and biological data were collected at diagnosis. Genetic profiling included G-banding and NGS of myeloid genes. TP53 multihit was defined as &gt;1 TP53 mutation or a single mutation with 17p deletion, copy-neutral loss of heterozygosity (LOH), or variant allele frequency (VAF) ≥50%. Patients were stratified as lower- or higher-risk based on IPSS-R and IPSS-M scores. Lower-risk included those with IPSS-R ≤3.5 points or very-low, low, or moderate-low IPSS-M, while higher-risk included those with IPSS-R &gt;3.5 points or moderate-high, high, or very-high IPSS-M. Clinical, molecular features, overall survival (OS), and AML progression were compared between MDS-del(5q) TP53 multihit and MDS-LB TP53 multihit groups. Finally, outcomes between patients with likely biallelic TP53 multihit status, defined as harboring a single TP53 mutation accompanied by a co-occurring deletion/LOH of 17p or a TP53 VAF ≥50%, versus those considered less likely to be biallelic, thus still classified as multihit, defined as having &gt;1 TP53 mutation but a combined VAF &lt;50%, were compared in both MDS-del(5q) and MDS-LB groups. Statistical analysis was performed using R (version 4.2.2). Results: We included 113 patients with a median follow-up of 81.1 months (95% CI 74.9–NR). A total of 45 (39.8%) were MDS-del(5q) TP53 multihit, and 68 (60.2%) MDS-LB TP53 multihit. Compared to MDS-LB TP53 multihit, patients with MDS-del(5q) TP53 multihit had significantly higher platelet counts (209 ×10⁹/L vs. 77 ×10⁹/L) and were more frequently classified as lower-risk by both IPSS-R (75.4% vs. 9.8%) and IPSS-M (90.2% vs. 24.6%). The median number of mutations per patient, excluding TP53, was 1 in both groups, and only SF3B1 mutations were significantly more frequent in the MDS-del(5q) TP53 multihit group compared to MDS-LB TP53 multihit (20.5% vs. 2.9%; p&lt;0.01). Regarding survival, MDS-del(5q) TP53 multihit patients showed an improved OS compared to MDS-LB TP53 multihit (median OS of 57.0 months (95%CI, 43.0–112.0) vs 14.0 months (95%CI, 8.6–19.0); p&lt;0.01). Moreover, while the risk of progression to AML at 48 months were comparable between groups (34.9% for MDS-del(5q) TP53 multihit and 33.0% for MDS-LB TP53 multihit; p=0.8), a significant difference was observed in the timing of AML progression: median time to AML evolution of 31.7 months (IQR 23.4–55.3) in the MDS-del(5q) TP53 multihit group and 7.2 months (IQR 4.1–12.9) in the MDS-LB TP53 multihit group (p&lt;0.01). Notably, no significant differences in outcomes were found between TP53 multihit and most likely biallelic cases in both MDS-del(5q) and MDS-LB cohorts. The impact of complex karyotype (CK) in MDS-LB TP53 multihit was also explored. MDS-LB TP53 multihitwith CK showed a significantly worse OS compared to MDS-LB TP53 multihitwithout CK (median OS of 10.0 months (95% CI 7.5–17.0) vs 40.0 months (95% CI 14.0–NA); respectively; p&lt;0.001. However, the incidence of AML and median time to AML evolution were comparable between both groups. Finally, OS between MDS-LB TP53 multihit patients without CK and the MDS-del(5q) TP53 multihit cohort was compared. Median OS was significantly shorter in the former group (median of 40 months) compared to the latter (median of 61 months) (p &lt;0.01) Conclusions: These findings underscore that, in contrast to other TP53-multihit MDS subtypes, MDS-del(5q) with TP53 multihit alterations constitute a clinically distinct entity with better outcomes, thereby warranting reconsideration of its place in current classification systems.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Abstract Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease &gt;1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

Abstract Chronic lymphocytic leukemia (CLL) is characterized by the existence of subsets of patients with (quasi)identical, stereotyped B-cell receptor (BcR) immunoglobulins. Patients in certain major stereotyped subsets often display remarkably consistent clinicobiological profiles, suggesting that the study of BcR immunoglobulin stereotypy in CLL has important implications for understanding disease pathophysiology and refining clinical decision-making. Nevertheless, several issues remain open, especially pertaining to the actual frequency of BcR immunoglobulin stereotypy and major subsets, as well as the existence of higher-order connections between individual subsets. To address these issues, we investigated clonotypic IGHV-IGHD-IGHJ gene rearrangements in a series of 29 856 patients with CLL, by far the largest series worldwide. We report that the stereotyped fraction of CLL peaks at 41% of the entire cohort and that all 19 previously identified major subsets retained their relative size and ranking, while 10 new ones emerged; overall, major stereotyped subsets had a cumulative frequency of 13.5%. Higher-level relationships were evident between subsets, particularly for major stereotyped subsets with unmutated IGHV genes (U-CLL), for which close relations with other subsets, termed “satellites,” were identified. Satellite subsets accounted for 3% of the entire cohort. These results confirm our previous notion that major subsets can be robustly identified and are consistent in relative size, hence representing distinct disease variants amenable to compartmentalized research with the potential of overcoming the pronounced heterogeneity of CLL. Furthermore, the existence of satellite subsets reveals a novel aspect of repertoire restriction with implications for refined molecular classification of CLL.

Abstract Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.