Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features
Lanino L, D'Amico S, Maggioni G, Al Ali N, Wang Y, Gurnari C, Gagelmann N, Bewersdorf J, Ball S, Guglielmelli P, Meggendorfer M, Hunter A, Kubasch A, Travaglino E, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Buizza A, Sauta E, Zampini M, Riva E, Asti G, Delleani M, Ficara F, Santoro A, Sala C, Dall'Olio D, Dall'Olio L, Kewan T, Casetti I, Awada H, Xicoy B, Vucinic V, Hou H, Chou W, Yao C, Lin C, Tien H, Consagra A, Sallman D, Kern W, Bernardi M, Chiusolo P, Borin L, Voso M, Pleyer L, Palomo L, Quintela D, Jerez A, Cornejo E, Martin P, Díaz-Beyá M, Pita A, Roldan V, Suarez D, Velasco E, Calabuig M, Garcia-Manero G, Loghavi S, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Kröger N, Fenaux P, Fontenay M, Santini V, Haferlach T, Germing U, Padron E, Robin M, Passamonti F, Solary E, Vannucchi A, Castellani G, Zeidan A, Komrokji R, Della Porta M. Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features. Blood 2024, 144: 1005. DOI: 10.1182/blood-2024-204826.Peer-Reviewed Original ResearchGenomic featuresSplicing mutationBiallelic inactivationAnalysis of genomic profilesBiallelic inactivation of TP53Clinical phenotypeGene expression profilesCNV analysisMorphological featuresInactivation of TP53Myeloid neoplasmsGenomic characterizationRNAseq dataMorphological dataMutation screeningExpression profilesMutationsJAK/STATGenomic profilingGenomeHierarchical importanceHeterogeneous phenotypesIntegrated analysisPhenotypeHematological phenotypeEnhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA)
Sauta E, Sartori F, Lanino L, Asti G, D'Amico S, Delleani M, Riva E, Zampini M, Zazzetti E, Bicchieri M, Maggioni G, Campagna A, Todisco G, Tentori C, Ubezio M, Russo A, Buizza A, Ficara F, Crisafulli L, Brindisi M, Ventura D, Pinocchio N, Rahal D, Lancellotti C, Bonometti A, Di Tommaso L, Savevski V, Santoro A, Derus N, Dall'Olio D, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Garcia-Manero G, Haferlach T, Kordasti S, Zeidan A, Castellani G, Sanavia T, Fariselli P, Della Porta M. Enhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA). Blood 2024, 144: 105-105. DOI: 10.1182/blood-2024-205413.Peer-Reviewed Original ResearchPersonalized medicine programsMyelodysplastic syndrome patientsMyelodysplastic syndromeOverall survivalConcordance indexClinical outcomesMay-Grunwald-GiemsaHypomethylating agentsBone marrowAnalysis of hematological malignanciesSomatic mutation screeningEvaluation of T lymphocytesResponse to hypomethylating agentsCD34+ bone marrowStudies of myelodysplastic syndromesGenomic featuresMDS populationRNA-seqPrediction of patient outcomeGenomic characterizationHarrell's concordance indexPredicting clinical outcomesHematoxylin and eosin (H&EMorphological dataMulti-omicsA 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 screeningData-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes
Komrokji R, Lanino L, Ball S, Bewersdorf J, Marchetti M, Maggioni G, Travaglino E, Al Ali N, Fenaux P, Platzbecker U, Santini V, Diez-Campelo M, Singh A, Jain A, Aguirre L, Tinsley-Vance S, Schwabkey Z, Chan O, Xie Z, Brunner A, Kuykendall A, Bennett J, Buckstein R, Bejar R, Carraway H, DeZern A, Griffiths E, Halene S, Hasserjian R, Lancet J, List A, Loghavi S, Odenike O, Padron E, Patnaik M, Roboz G, Stahl M, Sekeres M, Steensma D, Savona M, Taylor J, Xu M, Sweet K, Sallman D, Nimer S, Hourigan C, Wei A, Sauta E, D’Amico S, Asti G, Castellani G, Delleani M, Campagna A, Borate U, Sanz G, Efficace F, Gore S, Kim T, Daver N, Garcia-Manero G, Rozman M, Orfao A, Wang A, Foucar M, Germing U, Haferlach T, Scheinberg P, Miyazaki Y, Iastrebner M, Kulasekararaj A, Cluzeau T, Kordasti S, van de Loosdrecht A, Ades L, Zeidan A, Della Porta M, Syndromes I. Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes. The Lancet Haematology 2024, 11: e862-e872. PMID: 39393368, DOI: 10.1016/s2352-3026(24)00251-5.Peer-Reviewed Original ResearchGenomic featuresData-driven approachTP53 inactivationGenomic heterogeneityEntity labelsGenetic featuresDel(7q)/-7Myelodysplastic syndromeGenomic profilingData scientistsMutated SF3B1Cluster assignmentComplex karyotypeRUNX1 mutationsModified Delphi consensus processDel(5qIsolated del(5qAcute myeloid leukemiaData-drivenDelphi consensus processMarrow blasts