Molecular-Based Ecosystem to Improve Personalized Medicine in Chronic Myelomonocytic Leukemia.
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Lanino Luca, Hunter Anthony M, Gagelmann Nico, Robin Marie, Dall'Olio Daniele, Flamigni Alice, Sala Claudia, Gurnari Carmelo, Wang Yu-Hung, Pleyer Lisa, Xicoy Blanca, Montalban-Bravo Guillermo, Shih Lee-Yung, Alsugair Ali, Fathima Saubia, Gregorio Caterina, Rollo Cesare, Palomo Laura, Platzbecker Anne Sophie, Asti Gianluca, Itzykson Raphael, Sallman David A, Fariselli Piero, Kern Wolfgang, Garcia-Manero Guillermo, Platzbecker Uwe, Solé Francesc, Diez-Campelo Maria, Maciejewski Jaroslaw, Bejar Rafael, Thol Felicitas, Kroger Nicolaus, Savona Michael, Fenaux Pierre, Sanz Guillermo, Kordasti Shahram, Santini Valeria, Fontenay Michaela, Zeidan Amer M, Komrokji Rami S, Haferlach Torsten, Germing Ulrich, Castellani Gastone, D'Amico Saverio, Patnaik Mrinal, Ieva Francesca, Solary Eric, Tefferi Ayalew, Padron Eric, Della Porta Matteo Giovanni, iCPSS Alliance
Аннотация
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.