2024
Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial.
Rios-Hoyo A, Xiong K, Marczyk M, García-Millán R, Wolf D, Huppert L, Nanda R, Yau C, Hirst G, van 't Veer L, Esserman L, Pusztai L. Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial. Journal Of Clinical Oncology 2024, 42: 573-573. DOI: 10.1200/jco.2024.42.16_suppl.573.Peer-Reviewed Original ResearchGene expression dataGene expression analysisExpression dataExpressed genesExpression analysisTriple-negativeDistance analysisPathway analysisDifferential gene expression analysisCell cycle pathwayGene set enrichment analysisBreast cancerIngenuity Pathway AnalysisRate of pathological complete responseHigh-risk stage IIGlucocorticoid receptor signalingTriple negative breast cancerCycle pathwayPathological complete responseDNA repairEnrichment analysisOptimal treatment strategyNegative breast cancerI-SPY2 trialGenes
2015
Immune modulation of pathologic complete response after neoadjuvant HER2-directed therapies in the NeoSphere trial
Bianchini G, Pusztai L, Pienkowski T, Im YH, Bianchi GV, Tseng LM, Liu MC, Lluch A, Galeota E, Magazzù D, de la Haba-Rodríguez J, Oh DY, Poirier B, Pedrini JL, Semiglazov V, Valagussa P, Gianni L. Immune modulation of pathologic complete response after neoadjuvant HER2-directed therapies in the NeoSphere trial. Annals Of Oncology 2015, 26: 2429-2436. PMID: 26387142, DOI: 10.1093/annonc/mdv395.Peer-Reviewed Original Research
2013
Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data
Trentini F, Ji Y, Iwamoto T, Qi Y, Pusztai L, Müller P. Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data. PLOS ONE 2013, 8: e68071. PMID: 23874497, PMCID: PMC3709899, DOI: 10.1371/journal.pone.0068071.Peer-Reviewed Original Research
2012
Gene Expression, Molecular Class Changes, and Pathway Analysis after Neoadjuvant Systemic Therapy for Breast Cancer
Gonzalez-Angulo AM, Iwamoto T, Liu S, Chen H, Do KA, Hortobagyi GN, Mills GB, Meric-Bernstam F, Symmans WF, Pusztai L. Gene Expression, Molecular Class Changes, and Pathway Analysis after Neoadjuvant Systemic Therapy for Breast Cancer. Clinical Cancer Research 2012, 18: 1109-1119. PMID: 22235097, PMCID: PMC3288822, DOI: 10.1158/1078-0432.ccr-11-2762.Peer-Reviewed Original ResearchConceptsResidual cancerBreast cancerAdjuvant treatment strategiesNeoadjuvant systemic therapyLike breast cancerBasal-like cancersSmall G proteinsCalmodulin-dependent protein kinase IICancer stem cell signaturesEnergy metabolismFine-needle aspiration specimensGene expression differencesEpithelial-mesenchymal transitionSonic hedgehog (Shh) signalingNeedle aspiration specimensProtein kinase IIImmune-related pathwaysNew therapeutic insightsGene expression dataStem cell signatureSonic hedgehog pathwaySystemic therapy
2011
Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems
Hess KR, Wei C, Qi Y, Iwamoto T, Symmans WF, Pusztai L. Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems. BMC Bioinformatics 2011, 12: 463. PMID: 22132775, PMCID: PMC3245512, DOI: 10.1186/1471-2105-12-463.Peer-Reviewed Original ResearchConceptsPrediction problemCurrent statistical methodsClinical prediction problemsReal data setsMonte Carlo cross validationStatistical methodsData setsAccurate modelPerturbedInformative featuresPrediction modelCancer data setsPredictor performanceGene expression dataProblemBreast cancer data setsClassification problemSuch featuresMean expression valuesSet