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Publications

Journal Article Ahead of print

PubMed: 41894646 DOI: 10.1200/JCO-25-02116

Molecular-Based Ecosystem to Improve Personalized Medicine in Chronic Myelomonocytic Leukemia.

J Clin Oncol

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

Affiliations

  • Hematology Section, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
  • Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, GA.
  • University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Département Hématologie et Immunologie, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université de Paris Cité, Paris, France.
  • Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy.
  • IRCCS Azienda Ospedaliero-Universitaria di Bologna S. Orsola, Bologna, Italy.
  • MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy.
  • Translational Hematology and Oncology Research Department, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH.
  • Department of Biomedicine and Prevention, Tor Vergata University of Rome, Rome, Italy.
  • Department of Hematological Oncology, National Taiwan University Cancer Center, Taipei, Taiwan.
  • Division of Hematology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • 3rd Medical Department, Paracelsus Medical University Hospital, Salzburg, Austria.
  • Institut Català d'Oncologia, Hospital Germans Trias i Pujol, Josep Carreras Leukaemia Research Institute, Universitat Autònoma de Barcelona, Badalona, Spain.
  • Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.
  • Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN.
  • Department of Neurobiology, Aging Research Center, Care Sciences and Society, Karolinska Institutet and Stockholm University, Solna, Sweden.
  • Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Torino, Italy.
  • Universitätsklinikum Carl Gustav Carus, Dresden, Germany.
  • IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
  • Institut de Recherche Saint-Louis, UMR1342, Université Paris Cité, INSERM, Paris, France.
  • Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
  • MLL Munich Leukemia Laboratory, Munich, Germany.
  • MDS Research Group, Josep Carreras Leukaemia Research Institute, ICO-Hospital Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain.
  • Facultat de Biociències, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Hematology Department, Hospital Universitario de Salamanca, Salamanca, Spain.
  • University of California San Diego, La Jolla, CA.
  • Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.
  • Division of Hematology/Oncology, Program in Cancer Biology, Vanderbilt University School of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, TN.
  • Hematology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain.
  • Haematology, Guy's Hospital & Comprehensive Cancer Centre, King's College, London, United Kingdom.
  • Hematology Department & Stem Cell Transplant Unit, DISCLIMO-Università Politecnica delle Marche, Ancona, Italy.
  • MDS Unit, DMSC, Hematology, University of Florence, AOU Careggi, Florence, Italy.
  • Department of Hematology, AP-HP. Centre, Hôpital Cochin, Université Paris Cité, Paris, France.
  • Institut de la Leucémie Paris Saint-Louis, Paris, France.
  • Department of Hematology, Oncology, and Clinical Immunology, Heinrich-Heine-University, Düsseldorf, Germany.
  • Health Data Science Center, Human Technopole, Milan, Italy.
  • INSERM U1287, Gustave Roussy Cancer Center, Villejuif, France.
  • Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.

Abstract

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.

MeSH

Neoplasms, Leukemia, Myelomonocytic, Chronic, Classification, Ecosystem, Leukemia, Precision Medicine, Medical Oncology, Transplantation