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
Intratumor Heterogeneity: Seeing the Wood for the Trees
Yap TA, Gerlinger M, Futreal PA, Pusztai L, Swanton C. Intratumor Heterogeneity: Seeing the Wood for the Trees. Science Translational Medicine 2012, 4: 127ps10. PMID: 22461637, DOI: 10.1126/scitranslmed.3003854.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorDrug Resistance, NeoplasmGenetic HeterogeneityGenomicsHumansModels, GeneticMutationNeoplasms
2011
Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile
Zhao C, Shi L, Tong W, Shaughnessy JD, Oberthuer A, Pusztai L, Deng Y, Symmans WF, Shi T. Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile. BMC Genomics 2011, 12: s3. PMID: 22369035, PMCID: PMC3287499, DOI: 10.1186/1471-2164-12-s5-s3.Peer-Reviewed Original ResearchLack 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
2010
Assessment of an RNA interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials
Juul N, Szallasi Z, Eklund AC, Li Q, Burrell RA, Gerlinger M, Valero V, Andreopoulou E, Esteva FJ, Symmans WF, Desmedt C, Haibe-Kains B, Sotiriou C, Pusztai L, Swanton C. Assessment of an RNA interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials. The Lancet Oncology 2010, 11: 358-365. PMID: 20189874, DOI: 10.1016/s1470-2045(10)70018-8.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic Combined Chemotherapy ProtocolsArea Under CurveBreast NeoplasmsCeramidesDrug Screening Assays, AntitumorFemaleHumansLogistic ModelsMetagenomicsMiddle AgedMitosisModels, GeneticMultivariate AnalysisNeoadjuvant TherapyPaclitaxelPredictive Value of TestsRetrospective StudiesRNA InterferenceConceptsTriple-negative breast cancerPathological complete responseMultivariate logistic regressionBreast cancerClinical trialsPrimary triple-negative breast cancerEpidermal growth factor receptor 2Logistic regressionBreast Cancer Research FoundationAddition of taxanesPaclitaxel-containing chemotherapyClinical trial cohortProportion of patientsCohort of patientsGrowth factor receptor 2Paclitaxel combination chemotherapyUK Medical Research CouncilAlternative treatment regimensPredictors of responseCancer Research UKBreast cancer cell linesTriple-negative breast cancer cell linesFactor receptor 2Cancer Research FoundationCell lines
2006
RefSeq Refinements of UniGene-Based Gene Matching Improve the Correlation of Expression Measurements Between Two Microarray Platforms
Ji Y, Coombes K, Zhang J, Wen S, Mitchell J, Pusztai L, Symmans WF, Wang J. RefSeq Refinements of UniGene-Based Gene Matching Improve the Correlation of Expression Measurements Between Two Microarray Platforms. Applied Bioinformatics 2006, 5: 89-98. PMID: 16722773, DOI: 10.2165/00822942-200605020-00003.Peer-Reviewed Original Research