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.Peer-Reviewed Original Research
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 frameworkProspective evaluation of an AI-powered clinical trial patient matching (CTPM) system in myelodysplastic syndromes (MDS) and multiple myeloma
Taborda C, Gong G, Douglas G, Liu J, Incoom A, Stahl M, Podoltsev N, Bewersdorf J, Getz T, Stempel J, Kewan T, Lanino L, Bidikian A, Parker T, Bar N, Browning S, Halene S, Neparidze N, Zeidan A, Mendez L. Prospective evaluation of an AI-powered clinical trial patient matching (CTPM) system in myelodysplastic syndromes (MDS) and multiple myeloma. Blood 2025, 146: 1086-1086. DOI: 10.1182/blood-2025-1086.Peer-Reviewed Original ResearchMDS trialsMyelodysplastic syndromeEligible patientsMyeloma trialNon-enrolleesCommunity sitesConcurrent malignancyElectronic medical record dataEarly initiationManual reviewLower-risk myelodysplastic syndromesRandomized phase 3 trialMedical record dataEthnically minoritized groupsNext-line therapyPatient-matchedPhase 1b/2 trialProgression to AMLLow-risk statusPhase 3 trialIdentification of patientsOlder adultsRelapsed/refractory patientsRelapsed/refractory myelomaScreening sensitivityValidation of the international working group (IWG) 2023 criteria for clinical benefit in higher risk Myelodysplastic Syndromes (HR-MDS) using a large, international, randomized Phase 3 clinical trial dataset
Stahl M, Bewersdorf J, Vedula R, Liu Y, Murdock H, Tsai H, Chen E, Rolles B, Podoltsev N, Mendez L, Lanino L, Mina A, Aguirre L, Sekeres M, Ades L, Lindsley R, Zeidan A. Validation of the international working group (IWG) 2023 criteria for clinical benefit in higher risk Myelodysplastic Syndromes (HR-MDS) using a large, international, randomized Phase 3 clinical trial dataset. Blood 2025, 146: 5651. DOI: 10.1182/blood-2025-5651.Peer-Reviewed Original ResearchPartial remissionOverall survivalAllo-SCTHR-MDSCount recoveryProgressive diseaseClinical trialsInternational Working GroupClinical benefitNo responseLandmark analysisDeletion of chromosome 17pAllogeneic stem cell transplantationHigh-risk myelodysplastic syndromeBone marrow blast percentageResponse criteriaBlood count recoveryMarrow blast percentageRisk myelodysplastic syndromesStem cell transplantationMolecular correlatesLog-rank testAZA monotherapyMedian OSIPSS-RLarge language models mediated extraction of clinical information from bone marrow biopsy pathology reports
Lanino L, Getz T, Kewan T, Kiwan A, Rolles B, Bidikian A, Sariipek N, Mendez L, Podoltsev N, Durant T, Meeker D, Perincheri S, Gershkovich P, Katz S, Siddon A, Xu M, Bewersdorf J, Della Porta M, Zeidan A, Stahl M. Large language models mediated extraction of clinical information from bone marrow biopsy pathology reports. Blood 2025, 146: 2555. DOI: 10.1182/blood-2025-2555.Peer-Reviewed Original ResearchMedical record numberElectronic health recordsLanguage modelExtraction accuracyBlast countAutomated extractionBiopsy pathology reportConfidence intervalsExtraction of clinical informationHallucination ratingsPathology reportsRule-based algorithmReal-world dataSystematically extract informationClinical researchCategorical variablesFibrosis gradeZero-ShotUnprocessed textRinged sideroblastsExpert systemHealth recordsContinuous variablesSpearman correlation coefficientCode adjustmentClinical implications of TP53 mutations (TP53MT) in patients (pts) with higher risk Myelodysplastic Syndromes (HR-MDS) treated with hypomethylating agents (HMA) and allogeneic hematopoietic transplantation (allo-HCT): An analysis from the international consortium of MDS (icMDS) validate database
Kewan T, Bewersdorf J, Lanino L, Blaha O, Stempel J, Al Ali N, DeZern A, Sekeres M, Uy G, Urrutia S, Carraway H, Desai P, Griffiths E, Stein E, Brunner A, McMahon C, Shallis R, Zeidner J, Savona M, Chandhok N, Logothetis C, Bidikian A, Getz T, Roboz G, Rolles B, Wang E, Harris A, Amaya M, Hawkins H, Ball S, Grenet J, Xie Z, Madanat Y, Abaza Y, Badar T, Campos J, Haferlach T, Maciejewski J, Enjeti A, Al-Rabi K, Halahleh K, Hiwase D, Diez-Campelo M, Valcarcel D, Haferlach C, Pleyer L, Kotsianidis I, Pappa V, Santini V, Consagra A, Al-Kali A, Ogawa S, Nannya Y, Komrokji R, Stahl M, Della Porta M, Sallman D, Zeidan A. Clinical implications of TP53 mutations (TP53MT) in patients (pts) with higher risk Myelodysplastic Syndromes (HR-MDS) treated with hypomethylating agents (HMA) and allogeneic hematopoietic transplantation (allo-HCT): An analysis from the international consortium of MDS (icMDS) validate database. Blood 2025, 146: 3863-3863. DOI: 10.1182/blood-2025-3863.Peer-Reviewed Original ResearchComposite complete remissionCleveland Clinic FoundationAllo-HCTHypomethylating agentsHR-MDSMedian OSComplex karyotypeCR rateTP53 mutationsCo-mutationsTreatment responseResponse to HMATreated with hypomethylating agentsHigh-risk myelodysplastic syndromeCopy neutral loss of heterozygositySurvival rateAllogeneic hematopoietic transplantationHypomethylating agent combinationsAssociated with poor outcomesBone marrow blastsRisk myelodysplastic syndromesPost-allo-HCTNegative prognostic factorYear survival rateTP53 wild-typeThe prognostic significance (or lack) of achieving marrow complete remission (mCR) with hypomethylating agent-based therapy in patients with myelodysplastic syndrome
Rolles B, Bewersdorf J, Kewan T, Blaha O, Stempel J, Lanino L, Al Ali N, DeZern A, Sekeres M, Uy G, Urrutia S, Carraway H, Desai P, Griffiths E, Stein E, Brunner A, McMahon C, Shallis R, Zeidner J, Savona M, Chandhok N, Logothetis C, Bidikian A, Getz T, Roboz G, Wang E, Amaya M, Hawkins H, Ball S, Grenet J, Xie Z, Madanat Y, Abaza Y, Badar T, Campos J, Haferlach T, Maciejewski J, Sallman D, Enjeti A, Alrabi K, Halahleh K, Hiwase D, Diez-Campelo M, Valcarcel D, Haferlach C, Pleyer L, Kotsianidis I, Pappa V, Santini V, Consagra A, Al-Kali A, Ogawa S, Nannya Y, Della Porta M, Komrokji R, Zeidan A, Stahl M. The prognostic significance (or lack) of achieving marrow complete remission (mCR) with hypomethylating agent-based therapy in patients with myelodysplastic syndrome. Blood 2025, 146: 3865-3865. DOI: 10.1182/blood-2025-3865.Peer-Reviewed Original ResearchMarrow complete remissionOverall response rateInternational Working Group criteriaComposite CRCount recoveryHematologic improvementAllo-HCTInternational Working GroupMedian OSComplete remissionHR-MDSMedian ageBone marrowClinical trialsAllogeneic stem cell transplantationMedian age of patientsPartial hematologic recoveryStem cell transplantationNon-transplant patientsHypomethylating agent-based therapyKaplan-Meier methodAge of patientsAgent-based therapyLog-rank testRoutine clinical practiceDefining evolutionary trajectories in myelodysplastic syndromes and integrating them into prognostic models to improve risk stratification: The progevo machine learning approach
Civettini I, Malighetti F, Villa M, Crippa V, Aroldi A, Cavalca F, Graudenzi A, Lanino L, Maggioni G, Haferlach T, Fenaux P, Platzbecker U, Diez-Campelo M, Borin L, Fumagalli M, Zappaterra A, Manghisi B, Al Ali N, Sallman D, Padron E, Xie Z, Chan O, Zeidan A, Mologni L, Piazza R, Komrokji R, Della Porta M, Gambacorti-Passerini C, Ramazzotti D. Defining evolutionary trajectories in myelodysplastic syndromes and integrating them into prognostic models to improve risk stratification: The progevo machine learning approach. Blood 2025, 146: 3847-3847. DOI: 10.1182/blood-2025-3847.Peer-Reviewed Original ResearchAssociated with leukemia-free survivalLeukemia-free survivalIPSS-MGene co-occurrenceMyelodysplastic syndromeOverall survivalMoffitt Cancer CenterEvolutionary trajectoriesRisk stratificationDriver mutationsPrognostic modelEvolutionary routesClonal hematopoietic stem cell disordersHematopoietic stem cell disordersMolecular evolutionary featuresStem cell disordersHematopoietic stem cellsAIC-based model selectionCancer cell fractionCo-occurring mutationsHigher C-indexTiming of driver mutationsVariant allele frequencyEvolutionary relationshipsCo-occurrenceImmune-based risk score derived from bulk and single-cell transcriptomics enhances prognostic stratification in MDS
Sompairac N, Zhang Z, Andres Ejarque R, Antunes Dos Reis R, Santaolalla A, Van Hemelrijck M, Zampini M, Riva E, Ficara F, Maggioni G, Lanino L, Campagna A, Ventura D, Walter W, Wang Y, Tien H, Chou W, Lin C, Rutella S, Kern W, Della Porta M, Kordasti S. Immune-based risk score derived from bulk and single-cell transcriptomics enhances prognostic stratification in MDS. Blood 2025, 146: 115-115. DOI: 10.1182/blood-2025-115.Peer-Reviewed Original ResearchRNA-seqSingle-cell RNA-seqRNA-seq measurementsCell-type proportionsCITE-seq datasetsHigh-throughput technologiesBulk RNA sequencingGene Set Enrichment AnalysisSingle-sample gene set enrichment analysisGene expression profilesComplex biological systemsRNA sequencingEnrichment analysisExpression profilesImmune-related dataMyelodysplastic syndromeSingle-cellCell typesHallmark pathwaysAverage expressionComputational workflowPatient stratificationCellular resolutionTherapeutic targetSequenceCMML2AML: Machine-learning discovery of co-mutations predictive of blast transformation in chronic myelomonocytic leukemia
Fathima S, Rokach L, Yousuf M, Faldu P, Csizmar C, Nakhleh M, Mangaonkar A, Alsugair A, Pardanani A, Lanino L, Campagna A, Maggioni G, Farnoud N, Rampal R, Reichard K, He R, Gangat N, Patnaik M, Della Porta M, Tefferi A. CMML2AML: Machine-learning discovery of co-mutations predictive of blast transformation in chronic myelomonocytic leukemia. Blood 2025, 146: 3844-3844. DOI: 10.1182/blood-2025-3844.Peer-Reviewed Original ResearchAllogeneic stem cell transplantationChronic myelomonocytic leukemiaHigh-risk mutationsBlast transformationPrognostic relevanceConcurrent mutationsMyelomonocytic leukemiaTime of allogeneic stem cell transplantationMayo ClinicStem cell transplantationCox regression analysisInternational consensus classificationMayo Clinic cohortCompeting Risk AnalysisOptimal timingCell transplantationProlonged survivalSurvival outcomesCause of deathPrognostic contributionTreatment modalitiesPatient cohortPrognostic interactionContemporary risk modelsPatient subgroups
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