Landscape of Immune Cell States and Ecosystems in Patients with Myelodysplastic Syndrome to Refine Prognostic Assessment and Predict Treatment Response. a Study By i4MDS Consortium
Riva E, Calvi M, Zampini M, Dall'Olio L, Merlotti A, Russo A, Maggioni G, Orlandi L, Frigo A, Ficara F, Crisafulli L, Sauta E, D'Amico S, Lugli E, Campagna A, Ubezio M, Tentori C, Todisco G, Lanino L, Buizza A, Ventura D, Pinocchio N, Saba E, Santoro A, Santini V, van de Loosdrecht A, Komrokji R, Garcia-Manero G, Fenaux P, Ades L, Platzbecker U, Haferlach T, Almeida A, Zeidan A, Kordasti S, Remondini D, Castellani G, Di Vito C, Mavilio D, Della Porta M. Landscape of Immune Cell States and Ecosystems in Patients with Myelodysplastic Syndrome to Refine Prognostic Assessment and Predict Treatment Response. a Study By i4MDS Consortium. Blood 2024, 144: 665-665. DOI: 10.1182/blood-2024-200184.Peer-Reviewed Original ResearchMyelodysplastic syndromeImmune dysfunctionClinical work-upIPSS-MHypomethylating agentsBone marrowImmune ecosystemNatural killerNK cellsImmune monitoringPeripheral bloodT cellsAntibody panelClinical heterogeneity of myelodysplastic syndromesPatients treated with hypomethylating agentsCohort of MDS patientsLevel of immune dysfunctionRisk of leukemic transformationResponse to hypomethylating agentsHeterogeneity of myelodysplastic syndromesMulti-color flow cytometryWork-up of patientsClinical work-up of patientsImmune monitoring approachesMDS microenvironmentA Molecular-Based Ecosystem to Improve Personalized Medicine in Patients with Chronic Myelomonocytic Leukemia (CMML)
Lanino L, Hunter A, Gagelmann N, Robin M, Sala C, Dall'Olio D, Gurnari C, Dall'Olio L, Wang Y, Pleyer L, Xicoy B, Montalban-Bravo G, Shih L, Haque T, Abdel-Wahab O, Geissler K, Bataller A, Bazinet A, Meggendorfer M, Casetti I, Sauta E, Travaglino E, Palomo L, Zamora L, Quintela D, Jerez A, Cornejo E, Garcia Martin P, Díaz-Beyá M, Avendaño Pita A, Roldan V, Fiallo Suarez D, Cerezo Velasco E, Calabuig M, Such E, Sanz G, Kubasch A, Castilla-Llorente C, Bulabois C, Souchet L, Awada H, Bernardi M, Chiusolo P, Curti A, Giaccone L, Onida F, Borin L, Passamonti F, Diral E, Vucinic V, Bergonzi G, Voso M, Hou H, Chou W, Yao C, Lin C, Tien H, Campagna A, Ubezio M, Russo A, Todisco G, Maggioni G, Tentori C, Buizza A, Asti G, Zampini M, Riva E, Delleani M, Consagra A, Ficara F, Santoro A, Carota L, Sanavia T, Rollo C, Kiwan A, VanOudenhove J, Fariselli P, Al Ali N, Sallman D, Kern W, Garcia-Manero G, Thota S, Griffiths E, Follo M, Finelli C, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Bejar R, Thol F, Kröger N, Fenaux P, Itzykson R, Graubert T, Fontenay M, Zeidan A, Komrokji R, Santini V, Haferlach T, Germing U, D'Amico S, Castellani G, Patnaik M, Solary E, Padron E, Della Porta M. A Molecular-Based Ecosystem to Improve Personalized Medicine in Patients with Chronic Myelomonocytic Leukemia (CMML). Blood 2024, 144: 1003-1003. DOI: 10.1182/blood-2024-200104.Peer-Reviewed Original ResearchChronic myelomonocytic leukemiaLeukemia-free survivalMyeloid neoplasmsProportion of patientsOverall survivalMolecular-based toolsMolecular informationEvaluation of mutation statusInfluence disease phenotypeGenomic overlapScoring systemGenomic associationsGenomic featuresSplicing machineryConcordance indexGenomic characterizationChronic myelomonocytic leukemia patientsMedian leukemia-free survivalProbability of disease relapseAllogeneic stem cell transplantationSignal transductionGenomic heterogeneityRisk of disease progressionMulti-color flow cytometryMutation screening