Molecular-Based Ecosystem to Improve Personalized Medicine in Chronic Myelomonocytic Leukemia.
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.
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.
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.
Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.
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.
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.
Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.
Authors
Lanino Lanino, Hunter Hunter, Gagelmann Gagelmann, Robin Robin, Dall'Olio Dall'Olio, Flamigni Flamigni, Sala Sala, Gurnari Gurnari, Wang Wang, Pleyer Pleyer, Xicoy Xicoy, Montalban-Bravo Montalban-Bravo, Shih Shih, Alsugair Alsugair, Fathima Fathima, Gregorio Gregorio, Rollo Rollo, Palomo Palomo, Platzbecker Platzbecker, Asti Asti, Itzykson Itzykson, Sallman Sallman, Fariselli Fariselli, Kern Kern, Garcia-Manero Garcia-Manero, Platzbecker Platzbecker, Solé Solé, Diez-Campelo Diez-Campelo, Maciejewski Maciejewski, Bejar Bejar, Thol Thol, Kroger Kroger, Savona Savona, Fenaux Fenaux, Sanz Sanz, Kordasti Kordasti, Santini Santini, Fontenay Fontenay, Zeidan Zeidan, Komrokji Komrokji, Haferlach Haferlach, Germing Germing, Castellani Castellani, D'Amico D'Amico, Patnaik Patnaik, Ieva Ieva, Solary Solary, Tefferi Tefferi, Padron Padron, Della Porta Della Porta,
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