Kiwan, Ahmad

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: Higher-risk myelodysplastic syndromes/neoplasms (HR-MDS) and secondary acute myeloid leukemia (sAML) share overlapping molecular features but both remain clinically diverse with poor prognosis. Machine learning (ML)-based genomic clustering offers an opportunity to redefine disease subgroups beyond traditional clinical prognostic schemes or pathologic classifications. Prognostic scoring systems emphasize outcomes while pathologic classifications morphology resulting in inclusion of patients (pts) with different genetic background into same outcome or morphologic subgroup. We compared our ML-unsupervised clustering (UC) model named molecular nosology1 with the taxonomy model2 to evaluate differences in cluster architecture and clinical relevance in HR-MDS and sAML. Methods We analyzed HR-MDS (≥10% blasts) and sAML pts with available molecular data at diagnosis from a well-annotated cohort. Our molecular nosology model used UC via a binary latent-factor model to generate molecular clusters (MCs). The taxonomy model assigned pts to predefined genomic classes. We compared MC size, genomic enrichment, and overall survival (OS) across diagnostic categories. Concordance was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Purity and entropy were calculated to assess how concentrated (purity) or dispersed (entropy) each taxonomy group was across MCs. Results Among 898 pts, 367 (41%) had HR-MDS and 531 (59%) had sAML. Median age was 71 (IQR 65–77) years. Compared to sAML, HR-MDS pts had similar age (71 vs. 72, p=0.556) but higher hemoglobin (9.9 vs. 9.3, p&lt;0.001) and platelet counts (91 vs. 50, p&lt;0.001). Normal karyotype was more common in HR-MDS (55% vs. 42%, p&lt;0.001). TET2 (24%), ASXL1 (24%), RUNX1 (23%), and SRSF2 (22%) were the most frequently mutated genes. Using our nosology model, we identified 14 MCs.1 The most frequent were MC2 (normal karyotype) at 22% and MC13 (TP53-complex) at 17%, followed by MC1 (+8/ASXL1/TET2/RUNX1, 12%), MC6 (SRSF2/RAS-pathway, 9%), and MC9 and MC14 (each 8%). Less common clusters included MC12 (5%) and MC3–MC5 (2–3%). For comparison, the taxonomy model included 18 genomic groups: TP53-complex (20%), mNOS (13%), bi-TET2, IDH-STAG2, and no event (each 10%), CCUS-like, SETBP1/-7, and AML-like (each 6%), SRSF2 (4%), del(5q), BCOR/L1, EZH2-ASXL1, and SF3B1 (each 3%), and five rare subgroups (DDX41, der(1;7), U2AF1157, U2AF134, ZRSR2; all &lt;1%). Our MCs aligned closely with several taxonomy groups: 82% of TP53-complex cases were assigned to MC13, 99% of no event cases mapped to MC2, and 97% of del(5q) cases mapped to MC8. Bi-TET2 cases were distributed across MC6 and MC12 (both TET2-enriched); SETBP1/-7 to MC11; and EZH2-ASXL1 cases to MC1 and MC12. In contrast, mNOS, AML-like, and CCUS-like groups were genomically heterogeneous and spanned multiple MCs. ARI and NMI values were 0.391 and 0.458, indicating moderate concordance. Purity was highest in no event (0.989), del(5q) (0.967), and TP53-complex (0.823), and lowest in IDH-STAG2 (0.247), CCUS-like (0.241), and AML-like (0.347), all with high entropy (&gt;2.5), reflecting genomic diversity not captured by taxonomy groups alone. Median OS for the entire cohort was 17 mo (95%CI: 14–20). HR-MDS pts had longer OS vs. sAML (26 vs. 12 mo, p&lt;0.001). OS did not differ significantly between HR-MDS and sAML within most taxonomy groups or MCs, except for MC3 (NA vs. 11 mo, p=0.014), MC6 (111 vs. 15 mo, p&lt;0.001), and MC9 (18 vs. 9 mo, p=0.027) for the nosology model and SF3B1 (129 vs. 23 mo, p=0.033), del(5q) (33 vs. 6 mo, p=0.022), bi-TET2 (25 vs. 8 mo, p=0.019), mNOS (34 vs. 9 mo, p&lt;0.001) for taxonomy. Conclusions Some molecular taxonomy groups showed strong concordance with our nosology-defined clusters, which served as the gold standard for genomically distinct subsets (e.g., TP53-complex, del(5q), and no event), irrespective of diagnosis or blast percentage which may present the stage of the disease. In contrast, taxonomy-defined categories such as CCUS-like, AML-like, and mNOS were more genetically diverse and less aligned with specific clusters in the nosology model. Median OS within most clusters was not significantly influenced by blast count, underscoring the potential of molecular-driven clustering to reveal underlying disease biology not captured by morphology and to refine classification in MDS/AML and sAML with few exceptions (SF3B1 or biTET2). Ref 1.Kewan, Durmaz et al.Nat Commun 2023 2.Bernard, et al.Blood 2024

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.