2015
Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine
Zheng Y, Qing T, Song Y, Zhu J, Yu Y, Shi W, Pusztai L, Shi L. Standardization efforts enabling next-generation sequencing and microarray based biomarkers for precision medicine. Biomarkers In Medicine 2015, 9: 1265-1272. PMID: 26502353, DOI: 10.2217/bmm.15.99.Peer-Reviewed Original ResearchBiomarkersHigh-Throughput Nucleotide SequencingHumansOligonucleotide Array Sequence AnalysisPrecision MedicineReference StandardsTranslational Research, BiomedicalA genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients
Pongor L, Kormos M, Hatzis C, Pusztai L, Szabó A, Győrffy B. A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients. Genome Medicine 2015, 7: 104. PMID: 26474971, PMCID: PMC4609150, DOI: 10.1186/s13073-015-0228-1.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsDNA Copy Number VariationsFemaleGenome, HumanGenotypeHigh-Throughput Nucleotide SequencingHumansMutationOligonucleotide Array Sequence AnalysisSequence Analysis, RNAConceptsRNA-seq dataNext-generation sequencingBreast cancer patientsTranscriptomic fingerprintGenome-wide approachesGeneration sequencingClinical outcomesCancer patientsHuman gene mutationsTumor suppressor geneGene chip dataSuch genesRNA-seqGene mutationsLarge breast cancer cohortGene expressionChip dataSuppressor geneBreast cancer cohortGenesMicroarray dataMutationsSomatic mutationsClinical characteristicsCox regression
2014
Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer
Jiang T, Shi W, Natowicz R, Ononye SN, Wali VB, Kluger Y, Pusztai L, Hatzis C. Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer. BMC Genomics 2014, 15: 876. PMID: 25294321, PMCID: PMC4197225, DOI: 10.1186/1471-2164-15-876.Peer-Reviewed Original Research
2013
Ink4a/Arf−/− and HRAS(G12V) transform mouse mammary cells into triple-negative breast cancer containing tumorigenic CD49f− quiescent cells
Kai K, Iwamoto T, Kobayashi T, Arima Y, Takamoto Y, Ohnishi N, Bartholomeusz C, Horii R, Akiyama F, Hortobagyi GN, Pusztai L, Saya H, Ueno NT. Ink4a/Arf−/− and HRAS(G12V) transform mouse mammary cells into triple-negative breast cancer containing tumorigenic CD49f− quiescent cells. Oncogene 2013, 33: 440-448. PMID: 23376849, PMCID: PMC3957346, DOI: 10.1038/onc.2012.609.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCell Transformation, NeoplasticCyclin-Dependent Kinase Inhibitor p16FemaleFlow CytometryImmunohistochemistryIntegrin alpha6Mammary Neoplasms, ExperimentalMiceMice, Inbred C57BLMice, KnockoutNeoplastic Stem CellsOligonucleotide Array Sequence AnalysisProto-Oncogene Proteins p21(ras)Real-Time Polymerase Chain ReactionTriple Negative Breast NeoplasmsConceptsTriple-negative breast cancerHuman triple-negative breast cancerBreast cancerTumor-initiating potentialIntratumoral heterogeneityInk4a/Claudin-low breast cancerMouse mammary tumor modelNon-mammary tumorsHigh tumor-initiating potentialMouse mammary fat padMammary cellsMammary fat padMammary tumor modelIndividual breast tumorsTumor precursor cellsQuiescent cellsTumor-initiating cellsPathological featuresProgesterone receptorMammary tumorsEstrogen receptorAnimal modelsFat padBreast tumors
2012
Prognostic evaluation of the B cell/IL-8 metagene in different intrinsic breast cancer subtypes
Hanker LC, Rody A, Holtrich U, Pusztai L, Ruckhaeberle E, Liedtke C, Ahr A, Heinrich TM, Sänger N, Becker S, Karn T. Prognostic evaluation of the B cell/IL-8 metagene in different intrinsic breast cancer subtypes. Breast Cancer Research And Treatment 2012, 137: 407-416. PMID: 23242614, DOI: 10.1007/s10549-012-2356-2.Peer-Reviewed Original ResearchMeSH KeywordsB-LymphocytesBreast NeoplasmsDisease-Free SurvivalFemaleFollow-Up StudiesGene Expression Regulation, NeoplasticHumansInterleukin-8Middle AgedOligonucleotide Array Sequence AnalysisPredictive Value of TestsPrognosisProportional Hazards ModelsReceptor, ErbB-2Receptors, EstrogenReceptors, ProgesteroneConceptsTriple-negative breast cancerCell/ILNegative breast cancerBreast cancer subtypesPrognostic valueBreast cancerBetter prognosisB cellsCancer subtypesIntrinsic breast cancer subtypesPrimary breast cancer samplesER-negative subtypesEvent-free survivalB cell signaturesHigher B cellsSignificant prognostic valueTriple-negative samplesBreast cancer samplesRoutine clinicopathological variablesOnly significant predictorSubtype-specific analysesTNBC subtypesClinicopathological variablesOutcome predictorsPrognostic evaluation
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 ResearchGene Expression ProfilingHumansKaplan-Meier EstimateModels, GeneticMultiple MyelomaOligonucleotide Array Sequence AnalysisPredictive Value of TestsPrincipal Component AnalysisMaximizing biomarker discovery by minimizing gene signatures
Chang C, Wang J, Zhao C, Fostel J, Tong W, Bushel PR, Deng Y, Pusztai L, Symmans WF, Shi T. Maximizing biomarker discovery by minimizing gene signatures. BMC Genomics 2011, 12: s6. PMID: 22369133, PMCID: PMC3287502, DOI: 10.1186/1471-2164-12-s5-s6.Peer-Reviewed Original ResearchAlgorithmsBiomarkersBreast NeoplasmsDatabases, GeneticFemaleGene Expression Regulation, NeoplasticHumansOligonucleotide Array Sequence AnalysisLack 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 ResearchMeSH KeywordsBreast NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansModels, GeneticOligonucleotide Array Sequence AnalysisReceptors, EstrogenSoftwareConceptsPrediction 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 valuesSetA Genomic Predictor of Response and Survival Following Taxane-Anthracycline Chemotherapy for Invasive Breast Cancer
Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, Vidaurre T, Holmes F, Souchon E, Wang H, Martin M, Cotrina J, Gomez H, Hubbard R, Chacón JI, Ferrer-Lozano J, Dyer R, Buxton M, Gong Y, Wu Y, Ibrahim N, Andreopoulou E, Ueno NT, Hunt K, Yang W, Nazario A, DeMichele A, O’Shaughnessy J, Hortobagyi GN, Symmans WF. A Genomic Predictor of Response and Survival Following Taxane-Anthracycline Chemotherapy for Invasive Breast Cancer. JAMA 2011, 305: 1873-1881. PMID: 21558518, PMCID: PMC5638042, DOI: 10.1001/jama.2011.593.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlgorithmsAnthracyclinesAntineoplastic Agents, HormonalAntineoplastic Combined Chemotherapy ProtocolsBiopsy, NeedleBreast NeoplasmsBridged-Ring CompoundsDisease-Free SurvivalDrug Resistance, NeoplasmFemaleForecastingGene Expression ProfilingGenes, erbB-2Genes, NeoplasmGenomicsHumansMiddle AgedNeoadjuvant TherapyNeoplasm Recurrence, LocalOligonucleotide Array Sequence AnalysisPredictive Value of TestsPrognosisProspective StudiesReceptors, EstrogenRiskTaxoidsConceptsDistant relapse-free survivalInvasive breast cancerBreast cancerGenomic predictorsD. Anderson Cancer CenterAnthracycline-based regimensER-negative subsetExcellent pathologic responseProspective multicenter studyRelapse-free survivalAbsolute risk reductionStandard cancer treatmentPredictors of responseIndependent validation cohortAnderson Cancer CenterNegative breast cancerCancer treatment strategiesSequential taxaneNeoadjuvant chemotherapyPreoperative chemotherapyPathologic responseWorse survivalEndocrine sensitivityER statusMulticenter studyDistinct p53 Gene Signatures Are Needed to Predict Prognosis and Response to Chemotherapy in ER-Positive and ER-Negative Breast Cancers
Coutant C, Rouzier R, Qi Y, Lehmann-Che J, Bianchini G, Iwamoto T, Hortobagyi GN, Symmans WF, Uzan S, Andre F, de Thé H, Pusztai L. Distinct p53 Gene Signatures Are Needed to Predict Prognosis and Response to Chemotherapy in ER-Positive and ER-Negative Breast Cancers. Clinical Cancer Research 2011, 17: 2591-2601. PMID: 21248301, DOI: 10.1158/1078-0432.ccr-10-1045.Peer-Reviewed Original ResearchConceptsER- cancersPredictive valueBreast cancerP53 signatureWorse distant metastasis-free survivalDistant metastasis-free survivalER-negative breast cancerAdjuvant tamoxifen therapyDifferent molecular subsetsMetastasis-free survivalDifferent prognostic valueNegative breast cancerHigher chemotherapy sensitivityTamoxifen therapyFree survivalBetter prognosisER-positivePoor prognosisPrognostic valuePrognostic markerMolecular subsetsChemotherapy sensitivityMutation statusP53 mutationsMultivariate analysis
2010
Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer
Iwamoto T, Bianchini G, Qi Y, Cristofanilli M, Lucci A, Woodward WA, Reuben JM, Matsuoka J, Gong Y, Krishnamurthy S, Valero V, Hortobagyi GN, Robertson F, Symmans WF, Pusztai L, Ueno NT. Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer. Breast Cancer Research And Treatment 2010, 125: 785-795. PMID: 21153052, PMCID: PMC4109066, DOI: 10.1007/s10549-010-1280-6.Peer-Reviewed Original ResearchConceptsInflammatory breast cancerClinical subtypesBreast cancerNon-IBC patientsCase-control studyDistinct clinical subtypesDifferent molecular subtypesNon-IBC tumorsSignificant differencesNon-IBC specimensImmune system-related pathwaysLipid metabolism-related pathwaysHER2 statusReceptor phenotypeMetabolism-related pathwaysMolecular subtypesIBC tumorsSurvival curvesSubtypesTumor samplesHormone receptorsCancerPatientsT-testHER2Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer
Symmans WF, Hatzis C, Sotiriou C, Andre F, Peintinger F, Regitnig P, Daxenbichler G, Desmedt C, Domont J, Marth C, Delaloge S, Bauernhofer T, Valero V, Booser DJ, Hortobagyi GN, Pusztai L. Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer. Journal Of Clinical Oncology 2010, 28: 4111-4119. PMID: 20697068, PMCID: PMC2953969, DOI: 10.1200/jco.2010.28.4273.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Agents, HormonalAromatase InhibitorsBiomarkers, TumorBreast NeoplasmsChemotherapy, AdjuvantChi-Square DistributionDisease-Free SurvivalEstrogen Receptor alphaFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenomicsHumansMiddle AgedNeoplasm StagingOligonucleotide Array Sequence AnalysisPatient SelectionProportional Hazards ModelsRisk AssessmentRisk FactorsSurvival AnalysisTamoxifenTime FactorsTranscription, GeneticTreatment OutcomeConceptsAdjuvant endocrine therapyEndocrine therapyBreast cancerDistant relapseER-positive breast cancerChemo-endocrine therapyDistant relapse riskYears of tamoxifenAdjuvant systemic therapyEstrogen receptor αBreast cancer samplesPrior chemotherapyNeoadjuvant chemotherapyPathologic responseSurvival benefitSystemic therapyUntreated cohortRelapse riskDeath riskTherapy indexAromatase inhibitionESR1 levelsReceptor αTamoxifenTherapyPIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor–positive breast cancer
Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, Gonzalez-Angulo AM, Pusztai L, Symmans WF, Bardelli A, Ellis P, Tutt AN, Gillett CE, Hennessy BT, Mills GB, Phillips WA, Piccart MJ, Speed TP, McArthur GA, Sotiriou C. PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor–positive breast cancer. Proceedings Of The National Academy Of Sciences Of The United States Of America 2010, 107: 10208-10213. PMID: 20479250, PMCID: PMC2890442, DOI: 10.1073/pnas.0907011107.Peer-Reviewed Original ResearchMeSH KeywordsAntibiotics, AntineoplasticAntineoplastic Agents, HormonalBase SequenceBreast NeoplasmsCell Line, TumorClass I Phosphatidylinositol 3-KinasesDNA PrimersFemaleGene Expression ProfilingHumansMechanistic Target of Rapamycin Complex 1Multiprotein ComplexesMutationNeoplasms, Hormone-DependentOligonucleotide Array Sequence AnalysisPhosphatidylinositol 3-KinasesPrognosisProteinsProto-Oncogene Proteins c-aktReceptor, ErbB-2Receptors, EstrogenSignal TransductionSirolimusTamoxifenTOR Serine-Threonine KinasesTranscription FactorsConceptsBreast cancerPIK3CA mutationsClinical outcomesEstrogen receptor-positive breast cancerReceptor-positive breast cancerGene signaturePIK3CA mutation statusPI3K/mTOR inhibitorBetter clinical outcomesPI3K/mTOR inhibitionHuman breast cancerBC cell linesPIK3CA mutant breast cancersCommon genetic aberrationsTamoxifen monotherapyBetter prognosisMTOR inhibitorsBetter outcomesMutation statusMTOR inhibitionPathway activationExperimental modelGenetic aberrationsPrognosisCell linesBreast cancer prognostic markers in the post-genomic era
Pusztai L, Iwamoto T. Breast cancer prognostic markers in the post-genomic era. Breast Cancer Research And Treatment 2010, 125: 647-650. PMID: 20464478, DOI: 10.1007/s10549-010-0932-x.Peer-Reviewed Original ResearchDevelopment of Candidate Genomic Markers to Select Breast Cancer Patients for Dasatinib Therapy
Moulder S, Yan K, Huang F, Hess KR, Liedtke C, Lin F, Hatzis C, Hortobagyi GN, Symmans WF, Pusztai L. Development of Candidate Genomic Markers to Select Breast Cancer Patients for Dasatinib Therapy. Molecular Cancer Therapeutics 2010, 9: 1120-1127. PMID: 20423993, DOI: 10.1158/1535-7163.mct-09-1117.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic AgentsBiomarkers, PharmacologicalBiomarkers, TumorBreast NeoplasmsCarcinomaCell Line, TumorDasatinibDrug Resistance, NeoplasmFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Association StudiesGenome, HumanHumansMatched-Pair AnalysisOligonucleotide Array Sequence AnalysisPatient SelectionPrognosisPyrimidinesThiazolesConceptsClinical trialsCell linesPhase I/II trialIndependent breast cancer cell linesEarly phase clinical trialsDasatinib-resistant cellsPrimary breast cancerBreast cancer patientsDasatinib-resistant cell linesDifferent patient subsetsBreast cancer cell linesGenomic predictorsCancer cell linesDasatinib therapyDifferent potential predictorsII trialPatient subsetsPatient selectionCancer patientsBreast cancerDasatinib sensitivityMammary epithelial cellsDasatinib responseActivity indexPatient samplesProspective Comparison of Clinical and Genomic Multivariate Predictors of Response to Neoadjuvant Chemotherapy in Breast Cancer
Lee JK, Coutant C, Kim YC, Qi Y, Theodorescu D, Symmans WF, Baggerly K, Rouzier R, Pusztai L. Prospective Comparison of Clinical and Genomic Multivariate Predictors of Response to Neoadjuvant Chemotherapy in Breast Cancer. Clinical Cancer Research 2010, 16: 711-718. PMID: 20068086, PMCID: PMC2807997, DOI: 10.1158/1078-0432.ccr-09-2247.Peer-Reviewed Original ResearchAdultAgedAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, PharmacologicalBreast NeoplasmsCarcinomaCyclophosphamideDoxorubicinFemaleFluorouracilGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedNeoadjuvant TherapyOligonucleotide Array Sequence AnalysisPaclitaxelPrognosisSensitivity and Specificity
2009
Building Networks with Microarray Data
Broom BM, Rinsurongkawong W, Pusztai L, Do KA. Building Networks with Microarray Data. Methods In Molecular Biology 2009, 620: 315-343. PMID: 20652510, DOI: 10.1007/978-1-60761-580-4_10.Peer-Reviewed Original ResearchMeSH KeywordsAnalysis of VarianceBayes TheoremCluster AnalysisGene Expression ProfilingGene Regulatory NetworksHumansOligonucleotide Array Sequence AnalysisConceptsDetailed differential equation modelsDifferential equation modelAvailable breast cancer dataMathematical detailsNetwork modelBayesian network modelCo-expression network analysisMicroarray dataGene expression data setsFalse interactionsBayesian networkGene interaction networksData setsNumber of samplesPlausible networksRobust networkBreast cancer dataExpression data setsMicroarray data setsGene clusterGene shavingGene interactionsInteraction networksPreliminary clusteringSubsequent biological experimentsClinical evaluation of chemotherapy response predictors developed from breast cancer cell lines
Liedtke C, Wang J, Tordai A, Symmans WF, Hortobagyi GN, Kiesel L, Hess K, Baggerly KA, Coombes KR, Pusztai L. Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines. Breast Cancer Research And Treatment 2009, 121: 301-309. PMID: 19603265, DOI: 10.1007/s10549-009-0445-7.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorBreast NeoplasmsCell Line, TumorCyclophosphamideDoxorubicinDrug Resistance, NeoplasmFemaleFluorouracilGene Expression ProfilingHumansNeoplasm StagingOligonucleotide Array Sequence AnalysisPaclitaxelPredictive Value of TestsTreatment OutcomeConceptsBreast cancer cell linesCancer cell linesResponse predictorsBaseline gene expression dataCell linesChemotherapy drugsHuman breast cancer cell linesStandard chemotherapy drugsFine-needle aspiration specimensNeedle aspiration specimensPathologic responseAffymetrix U133A gene chipsClinical evaluationBreast cancerPharmacogenomic predictorsSame drugStage IPredictive valueAspiration specimensMultigene predictorsTumor samplesPatientsResistant cellsPatient dataDrugsMetastatic gene signatures and emerging novel prognostic tests in the management of early stage breast cancer
Tordai A, Liedtke C, Pusztai L. Metastatic gene signatures and emerging novel prognostic tests in the management of early stage breast cancer. Clinical & Experimental Metastasis 2009, 26: 625-632. PMID: 19381845, DOI: 10.1007/s10585-009-9261-z.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsEarly DiagnosisGene Expression ProfilingHumansNeoplasm MetastasisOligonucleotide Array Sequence AnalysisPrognosisConceptsMetastatic gene signatureGene expression studiesGene expression profilingDistinct neoplastic diseasesExpression profilingDNA microarraysExpression studiesGene expressionMRNA transcriptsGene signatureSingle experimentNovel diagnostic assaysTranscriptsDiagnostic assaysNovel prognostic testsMicroarrayProfilingExpressionGenomic Grade Index Is Associated With Response to Chemotherapy in Patients With Breast Cancer
Liedtke C, Hatzis C, Symmans WF, Desmedt C, Haibe-Kains B, Valero V, Kuerer H, Hortobagyi GN, Piccart-Gebhart M, Sotiriou C, Pusztai L. Genomic Grade Index Is Associated With Response to Chemotherapy in Patients With Breast Cancer. Journal Of Clinical Oncology 2009, 27: 3185-3191. PMID: 19364972, PMCID: PMC2716940, DOI: 10.1200/jco.2008.18.5934.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic Combined Chemotherapy ProtocolsArea Under CurveBreast NeoplasmsCyclophosphamideDoxorubicinDrug Resistance, NeoplasmFemaleFluorouracilHumansMiddle AgedNeoadjuvant TherapyOligonucleotide Array Sequence AnalysisPaclitaxelReceptors, EstrogenROC CurveTreatment OutcomeConceptsGenomic grade indexER-positive patientsRelapse-free survivalPathologic responseNeoadjuvant paclitaxelCyclophosphamide chemotherapyBreast cancerWorse distant relapse-free survivalDistant relapse-free survivalSystemic adjuvant therapyPathologic complete responseFine-needle aspiration biopsyGrade 3 tumorsER-negative cancersER-positive cancersGrade 1 tumorsGrade 2 tumorsMinimal residual diseaseHistological tumor gradeAdjuvant therapyNeoadjuvant chemotherapyComplete responseWorse survivalClinical parametersResidual disease