Luca Lanino
Postdoctoral AssociateAbout
Research
Publications
2026
Performance of molecular scoring systems in hypomethylating agent-treated myelodysplastic neoplasms
Chien K, Li Z, Lanino L, Ali N, Urrutia S, Bataller A, Kanagal-Shamanna R, Loghavi S, Lyu Y, Abdelhakeem A, Abuasab T, Almanza E, Ricos G, Bazinet A, Campagna A, Maggioni G, Padron E, Xie Z, Montalban-Bravo G, Short N, Jabbour E, Kadia T, Ravandi F, Borthakur G, DiNardo C, Hammond D, Swaminathan M, Sasaki K, Dong X, Pierce S, Sallman D, Kantarjian H, Della Porta M, Garcia-Manero G, Komrokji R. Performance of molecular scoring systems in hypomethylating agent-treated myelodysplastic neoplasms. Leukemia 2026, 1-4. PMID: 41792442, DOI: 10.1038/s41375-026-02895-5.Commentaries, Editorials and Letters
2025
Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer
Zazzetti E, D'Amico S, Jacobs F, De Sanctis R, Chiudinelli L, Gaudio M, Asti G, Delleani M, Sauta E, Quintavalla M, Bruseghini A, Lanino L, Maggioni G, Campagna A, Savevski V, Della Porta M, Zambelli A. Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer. JCO Clinical Cancer Informatics 2025, 9: e2500033. PMID: 41197110, PMCID: PMC12614387, DOI: 10.1200/cci-25-00033.Peer-Reviewed Original ResearchConceptsReal-world dataAdvanced generative modelsGenerative adversarial networkSynthetic dataLanguage modelVariational autoencoderGenerative modelRobust privacy protectionSynthetic data generatorSynthetic data setsData setsPrivacy protectionAdversarial networkPrivacy concernsData fragmentationI2b2 platformData generationPrivacyI2b2Uniform Manifold ApproximationBreast cancerReal dataProjective embeddingManifold approximationValidation frameworkArtificial intelligence in myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid Leukemia
Al-Nusair J, Lanino L, Durmaz A, Porta M, Zeidan A, Kewan T. Artificial intelligence in myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid Leukemia. Blood Reviews 2025, 74: 101340. PMID: 41109825, DOI: 10.1016/j.blre.2025.101340.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsAcute myeloid leukemiaMyelodysplastic syndromeApplication of machine learningMachine learningMyeloid leukemiaResponse to hypomethylating agentsMDS managementVenetoclax-based regimensPersonalized survival predictionPeripheral blood filmBone marrow smearsClinical applicationHypomethylating agentsMyeloid malignanciesPrognostic valueGenomic subtypesMarrow smearsDeep learningClinical decision-makingNeural networkArtificial intelligenceConsensus classificationBlood filmsFlow cytometryPrognostic modelValidation of BLAST and BLAST‐Mol Risk Models in Chronic Myelomonocytic Leukemia: Mayo–Humanitas Collaborative Project Involving 1101 Patients
Fathima S, Alsugair A, Yousuf M, Faldu P, Csizmar C, Nakhleh M, Mangaonkar A, Pardanani A, Lanino L, Campagna A, Maggioni G, Reichard K, He R, Gangat N, Patnaik M, Della Porta M, Tefferi A. Validation of BLAST and BLAST‐Mol Risk Models in Chronic Myelomonocytic Leukemia: Mayo–Humanitas Collaborative Project Involving 1101 Patients. American Journal Of Hematology 2025, 100: 2426-2430. PMID: 40996347, DOI: 10.1002/ajh.70080.Peer-Reviewed Original ResearchCharacterization and Clinical Implications of p53 Dysfunction in Patients With Myelodysplastic Syndromes
Zampini M, Riva E, Lanino L, Sauta E, Dos Reis R, Ejarque R, Maggioni G, Termanini A, Merlotti A, Campagna A, Dall’Olio L, Kulasekararaj A, Calvi M, Di Vito C, Bonometti A, Rahal D, Croci G, Boveri E, Gianelli U, Ponzoni M, Caselli R, Albertazzi S, Todisco G, Ubezio M, Crisafulli L, Frigo A, Lugli E, Mosca E, Acha P, Ghisletti S, Nicassio F, Santoro A, Diez-Campelo M, Solé F, Ades L, Platzbecker U, Santini V, Fenaux P, Haferlach T, Sallman D, Garcia-Manero G, Mavilio D, Remondini D, Castellani G, D'Amico S, Zeidan A, Komrokji R, Kordasti S, Ficara F, Della Porta M, Consortium C, Russo A, Travaglino E, Delleani M, Asti G, Ventura D, Tentori C, Buizza A, Brindisi M, Pinocchio N, Pesce F. Characterization and Clinical Implications of p53 Dysfunction in Patients With Myelodysplastic Syndromes. Journal Of Clinical Oncology 2025, 43: 2069-2083. PMID: 40315418, PMCID: PMC12169866, DOI: 10.1200/jco-24-02394.Peer-Reviewed Original ResearchConceptsMyelodysplastic syndromeP53 dysfunctionPhenotype of immune cellsClassification of myeloid neoplasmsIdentified high-risk patientsImpaired antigen presentationOptimal timing of therapeutic interventionsAbnormal p53 proteinHigh-risk patientsSubsets of patientsBone marrow progenitorsTiming of therapeutic interventionDesign of clinical trialsRecognition of patientsTumor protein 53Variant allele frequencyNF-kB pathwayInnovative immunotherapyMyeloid neoplasmsDismal outcomeMarrow progenitorsImmune dysregulationBiallelic inactivationImmune cellsPoor prognosisResponse to luspatercept can be predicted and improves overall survival in the real‐life treatment of LR‐MDS
Consagra A, Lanino L, Al Ali N, Aguirre L, Xie Z, Chan O, Andreossi G, Raddi M, Rigodanza L, Sanna A, Mattiuz G, Tofacchi E, Amato C, Tanturli M, De Pourcq S, Walker A, Kuykendall A, Lancet J, Padron E, Sallman D, Restuccia F, Perego A, Ubezio M, Fattizzo B, Riva M, Maggioni G, Campagna A, Della Porta M, Santini V, Komrokji R. Response to luspatercept can be predicted and improves overall survival in the real‐life treatment of LR‐MDS. HemaSphere 2025, 9: e70086. PMID: 39944234, PMCID: PMC11814532, DOI: 10.1002/hem3.70086.Peer-Reviewed Original ResearchHematologic responseOverall survivalReal-life treatmentRed blood cellsTransfusion burdenTransfusion independenceLR-MDSMedian durationPredictive markerHb increaseMolecular International Prognostic Scoring SystemRed blood cell transfusion burdenMedian duration of responseInternational Prognostic Scoring SystemMedian time to responsePredictive markers of responseLow riskMedian treatment durationDuration of responsePrognostic scoring systemMedian Follow-UpLR-MDS patientsMarkers of responseTime to responsePredictors of responseVAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes
Rollo C, Pancotti C, Sartori F, Caranzano I, D'Amico S, Carota L, Casadei F, Birolo G, Lanino L, Sauta E, Asti G, Buizza A, Delleani M, Zazzetti E, Bicchieri M, Maggioni G, Fenaux P, Platzbecker U, Diez-Campelo M, Haferlach T, Castellani G, Della Porta M, Fariselli P, Sanavia T. VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes. Computer Methods And Programs In Biomedicine 2025, 261: 108605. PMID: 39874934, DOI: 10.1016/j.cmpb.2025.108605.Peer-Reviewed Original ResearchConceptsVariational autoencoderAutomatic identificationState-of-the-artHigh-dimensional spaceK-means clusteringDeep survival modelsSurvival prediction taskLatent representationFeature spaceLatent spaceClustering performancePrediction taskUse casesHigh-dimensionalBiomedical dataComputational frameworkInput dataLocal coherencePredictive performancePenalized versionMolecular dataCoxPH modelMedian C-indexComputational pipelineInformation
2024
Data-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 Reviews, Practice Guidelines, Standards, and Consensus StatementsGenomic 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 blastsChronic myelomonocytic leukemia with ring sideroblasts/SF3B1 mutation presents with low monocyte count and resembles myelodysplastic syndromes with-RS/SF3B1 mutation in terms of phenotype and prognosis
Xicoy B, Pomares H, Morgades M, Germing U, Arnan M, Tormo M, Palomo L, Orna E, Della Porta M, Schulz F, Díaz-Beya M, Esteban A, Molero A, Lanino L, Avendaño A, Hernández F, Roldan V, Ubezio M, Pineda A, Díez-Campelo M, Zamora L. Chronic myelomonocytic leukemia with ring sideroblasts/SF3B1 mutation presents with low monocyte count and resembles myelodysplastic syndromes with-RS/SF3B1 mutation in terms of phenotype and prognosis. Frontiers In Oncology 2024, 14: 1385987. PMID: 39011475, PMCID: PMC11246989, DOI: 10.3389/fonc.2024.1385987.Peer-Reviewed Original ResearchChronic myelomonocytic leukemiaSF3B1,Ringed sideroblastsMyelodysplastic syndromeMonocyte countMyelomonocytic leukemiaHigh risk of transformation to acute myeloid leukemiaPrognosis of chronic myelomonocytic leukemiaRisk of transformation to acute myeloid leukemiaTransformation to acute myeloid leukemiaPeripheral blood monocyte countWorld Health Organization classificationHigh riskMedian overall survivalAcute myeloid leukemiaBlood monocyte countFrequency of mutationsOverall survivalPrognostic categoriesOrganization classificationMyeloid leukemiaClinical characteristicsClinical featuresLeukemiaPatientsMOSAIC: An Artificial Intelligence–Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers
D'Amico S, Dall’Olio L, Rollo C, Alonso P, Prada-Luengo I, Dall’Olio D, Sala C, Sauta E, Asti G, Lanino L, Maggioni G, Campagna A, Zazzetti E, Delleani M, Bicchieri M, Morandini P, Savevski V, Arroyo B, Parras J, Zhao L, Platzbecker U, Diez-Campelo M, Santini V, Fenaux P, Haferlach T, Krogh A, Zazo S, Fariselli P, Sanavia T, Della Porta M, Castellani G. MOSAIC: An Artificial Intelligence–Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers. JCO Clinical Cancer Informatics 2024, 8: e2400008. PMID: 38875514, PMCID: PMC11371092, DOI: 10.1200/cci.24.00008.Peer-Reviewed Original ResearchConceptsHierarchical Dirichlet processMyelodysplastic syndromePrognostic assessmentRare cancersArtificial intelligence (AI)-based frameworkDeep learning methodsValidation cohortDensity-based spatial clusteringPatient subgroupsAI-based approachesArtificial intelligence-based frameworkHierarchical Density-Based Spatial ClusteringFederated LearningExplainable AIAverage silhouette coefficientSurvival predictionData protectionInnovative treatment strategiesEffective classificationAI methodsFederal implementationLearning methodsSilhouette coefficientHigher balanced accuracyUnmet medical need
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