2024
Comparative Analysis of Ficoll-Hypaque and CytoLyt® Techniques for Blood Removal in Breast Cancer Malignant Effusions: Effects on RNA Quality and Sequencing Outcomes
Sura G, Tran K, Trevarton A, Marczyk M, Fu C, Du L, Qu J, Lau R, Tasto A, Gould R, Tinnirello A, Sinn B, Pusztai L, Hatzis C, Symmans W. Comparative Analysis of Ficoll-Hypaque and CytoLyt® Techniques for Blood Removal in Breast Cancer Malignant Effusions: Effects on RNA Quality and Sequencing Outcomes. Journal Of The American Society Of Cytopathology 2024 DOI: 10.1016/j.jasc.2024.11.001.Peer-Reviewed Original ResearchRNA integrity numberRNA qualityRNA-seqMeasurement of gene expressionRNA-seq analysisMetastatic breast cancerFicoll-Hypaque methodDensity gradient enrichmentSequence dataRead-basedVariant detectionMalignant effusionsCytospin slidesFresh frozen samplesRNA fragmentsTranscript abundanceSequencing outcomesSequencing methodsBreast cancerRNA sequencingFicoll-HypaqueUMI-basedGene expressionRNAMalignant effusion specimens
2023
Pan-cancer analysis of antibody-drug conjugate targets and putative predictors of treatment response
Bosi C, Bartha Á, Galbardi B, Notini G, Naldini M, Licata L, Viale G, Mariani M, Pistilli B, Ali H, André F, Piras M, Callari M, Barreca M, Locatelli A, Viganò L, Criscitiello C, Pusztai L, Curigliano G, Győrffy B, Dugo M, Bianchini G. Pan-cancer analysis of antibody-drug conjugate targets and putative predictors of treatment response. European Journal Of Cancer 2023, 195: 113379. PMID: 37913680, DOI: 10.1016/j.ejca.2023.113379.Peer-Reviewed Original ResearchConceptsGenotype-Tissue Expression (GTEx) databaseAntibody-drug conjugatesADC targetPan-cancer analysisCancer Genome AtlasExpression distributionGene expressionNew therapeutic opportunitiesTreatment responseExpression of determinantsPrimary tissuesGenome AtlasExpression levelsNormal tissuesPotential downstreamProtein expressionRelative expressionExpression databaseGenesTherapeutic opportunitiesExpressionTarget expressionMRNA levelsCancer typesInter-patient heterogeneity
2022
Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies
Wolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher RI, Lee PRE, Zhu Z, Magbanua MJ, Sayaman R, O’Grady N, Basu A, Delson A, Coppé JP, Lu R, Braun J, Investigators I, Asare SM, Sit L, Matthews JB, Perlmutter J, Hylton N, Liu MC, Pohlmann P, Symmans WF, Rugo HS, Isaacs C, DeMichele AM, Yee D, Berry DA, Pusztai L, Petricoin EF, Hirst GL, Esserman LJ, van 't Veer LJ. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies. Cancer Cell 2022, 40: 609-623.e6. PMID: 35623341, PMCID: PMC9426306, DOI: 10.1016/j.ccell.2022.05.005.Peer-Reviewed Original ResearchConceptsBreast cancer subtypesHormone receptorsHuman epidermal growth factor receptor 2 (HER2) statusCancer subtypesEpidermal growth factor receptor 2 statusPathologic complete response rateTreatment prioritizationComplete response ratePatient selectionPredictive biomarkersTreatment allocationPlatform trialsClinical dataLuminal phenotypeTreatment selectionResponse rateTumor biologyNew treatmentsDrug responseSubtypesCancer therapyBiomarkersProtein/phosphoproteinGene expressionDiverse biologyPredictive Markers of Response to Neoadjuvant Durvalumab with Nab-Paclitaxel and Dose-Dense Doxorubicin/Cyclophosphamide in Basal-Like Triple-Negative Breast Cancer.
Blenman KRM, Marczyk M, Karn T, Qing T, Li X, Gunasekharan V, Yaghoobi V, Bai Y, Ibrahim EY, Park T, Silber A, Wolf DM, Reisenbichler E, Denkert C, Sinn BV, Rozenblit M, Foldi J, Rimm DL, Loibl S, Pusztai L. Predictive Markers of Response to Neoadjuvant Durvalumab with Nab-Paclitaxel and Dose-Dense Doxorubicin/Cyclophosphamide in Basal-Like Triple-Negative Breast Cancer. Clinical Cancer Research 2022, 28: 2587-2597. PMID: 35377948, PMCID: PMC9464605, DOI: 10.1158/1078-0432.ccr-21-3215.Peer-Reviewed Original ResearchConceptsBasal-like triple-negative breast cancerPathologic complete responseResidual diseaseNeoadjuvant durvalumabDNA damage repairSomatic mutationsBreast cancerWnt/β-cateninHigh expressionTriple-negative breast cancerBasal-Like TripleDoxorubicin/cyclophosphamideDNA repairTumor mutation burdenRNA sequencingEpithelial-mesenchymal transitionFive-gene signatureB-cell markersCancer driversEnrichment analysisNegative breast cancerDamage repairGene expressionJAK-STATCell cycle
2019
The impact of RNA extraction method on accurate RNA sequencing from formalin-fixed paraffin-embedded tissues
Marczyk M, Fu C, Lau R, Du L, Trevarton AJ, Sinn BV, Gould RE, Pusztai L, Hatzis C, Symmans WF. The impact of RNA extraction method on accurate RNA sequencing from formalin-fixed paraffin-embedded tissues. BMC Cancer 2019, 19: 1189. PMID: 31805884, PMCID: PMC6896723, DOI: 10.1186/s12885-019-6363-0.Peer-Reviewed Original ResearchConceptsBreast cancerFresh frozenParaffin-embedded tumor samplesFF samplesFFPE samplesConcordance correlation coefficientMixed-effects model analysisBreast cancer signaturesQiagen RNeasy kitWhole transcriptome RNA sequencingParaffin-embedded tissuesTranscriptome RNA sequencingLinear mixed-effects model analysisGene expression signaturesDifferent kitsClinical trialsGene signatureTumor samplesGene expressionTranslational researchCancerTissue samplesExpression signaturesArchival formalinSimilar concordance
2018
An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes
Győrffy B, Pongor L, Bottai G, Li X, Budczies J, Szabó A, Hatzis C, Pusztai L, Santarpia L. An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes. British Journal Of Cancer 2018, 118: 1107-1114. PMID: 29559730, PMCID: PMC5931099, DOI: 10.1038/s41416-018-0030-0.Peer-Reviewed Original ResearchConceptsHER2-negative tumorsBreast cancer patientsCancer patientsER-positive/HER2-negative tumorsBreast cancer molecular subtypesMETABRIC data setMolecular breast cancer subtypesCox regression analysisBreast cancer subtypesCancer molecular subtypesGene expression profilesMann-Whitney U testRegression analysisMultivariate regression analysisPrognostic valueKaplan-MeierBreast cancerClinical dataDisease outcomeTCGA cohortGene expressionMolecular subtypesCancer-associated genesCancer-related genesClinical relevance
2017
Integrated MicroRNA–mRNA Profiling Identifies Oncostatin M as a Marker of Mesenchymal-Like ER-Negative/HER2-Negative Breast Cancer
Bottai G, Diao L, Baggerly KA, Paladini L, Győrffy B, Raschioni C, Pusztai L, Calin GA, Santarpia L. Integrated MicroRNA–mRNA Profiling Identifies Oncostatin M as a Marker of Mesenchymal-Like ER-Negative/HER2-Negative Breast Cancer. International Journal Of Molecular Sciences 2017, 18: 194. PMID: 28106823, PMCID: PMC5297825, DOI: 10.3390/ijms18010194.Peer-Reviewed Original ResearchConceptsEpidermal growth factorExpression profilesMessenger RNA (mRNA) expression profilesMiRNA-regulated pathwaysAvailable gene expression profilesOncostatin M signalingMesenchymal-like breast cancer cellsGene expression profilesRNA expression profilesImmune-related pathwaysPathway regulationGlobal miRNAOncogenic networksGene expressionSpecific miRNAsPathway analysisBreast cancer cellsHuman estrogen receptorTriple-negative breast cancerEMT pathwayMesenchymal transitionMiRNAMRNA dataOncostatin MCancer cells
2015
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
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 ResearchConceptsRNA-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 regressionThe cell cycle regulator 14-3-3σ opposes and reverses cancer metabolic reprogramming
Phan L, Chou PC, Velazquez-Torres G, Samudio I, Parreno K, Huang Y, Tseng C, Vu T, Gully C, Su CH, Wang E, Chen J, Choi HH, Fuentes-Mattei E, Shin JH, Shiang C, Grabiner B, Blonska M, Skerl S, Shao Y, Cody D, Delacerda J, Kingsley C, Webb D, Carlock C, Zhou Z, Hsieh YC, Lee J, Elliott A, Ramirez M, Bankson J, Hazle J, Wang Y, Li L, Weng S, Rizk N, Wen YY, Lin X, Wang H, Wang H, Zhang A, Xia X, Wu Y, Habra M, Yang W, Pusztai L, Yeung SC, Lee MH. The cell cycle regulator 14-3-3σ opposes and reverses cancer metabolic reprogramming. Nature Communications 2015, 6: 7530. PMID: 26179207, PMCID: PMC4507299, DOI: 10.1038/ncomms8530.Peer-Reviewed Original ResearchMeSH Keywords14-3-3 ProteinsAdultAgedAged, 80 and overBiomarkers, TumorBreast NeoplasmsCell Line, TumorDisease-Free SurvivalEnergy MetabolismExoribonucleasesFemaleGene Expression Regulation, NeoplasticGene Knockout TechniquesGlutamineGlycolysisHCT116 CellsHumansMiddle AgedOrganelle BiogenesisPrognosisProteolysisProto-Oncogene Proteins c-mycUbiquitinationYoung AdultConceptsCancer metabolic reprogrammingMetabolic reprogrammingRecurrence-free survival ratesMetabolic gene expressionBreast cancer patientsCellular energy metabolismHallmarks of cancerMajor metabolic processesTumor glucose uptakeExtensive reprogrammingMetabolic programsMitochondrial biogenesisGene expressionTumorigenic transformationCancer glycolysisMolecular mechanismsReprogrammingCancer patientsMetabolic processesMetabolic shiftA prospective comparison of ER, PR, Ki67 and gene expression in paired sequential core biopsies of primary, untreated breast cancer.
Thompson A, Hadad S, Jordan L, Roy P, Purdie C, Iwamoto T, Pusztai L, Moulder S. A prospective comparison of ER, PR, Ki67 and gene expression in paired sequential core biopsies of primary, untreated breast cancer. Journal Of Clinical Oncology 2015, 33: 578-578. DOI: 10.1200/jco.2015.33.15_suppl.578.Peer-Reviewed Original Research
2014
Combined analysis of gene expression, DNA copy number, and mutation profiling data to display biological process anomalies in individual breast cancers
Shi W, Balazs B, Györffy B, Jiang T, Symmans WF, Hatzis C, Pusztai L. Combined analysis of gene expression, DNA copy number, and mutation profiling data to display biological process anomalies in individual breast cancers. Breast Cancer Research And Treatment 2014, 144: 561-568. PMID: 24619174, DOI: 10.1007/s10549-014-2904-z.Peer-Reviewed Original ResearchConceptsDNA copy numberBiological processesIndividual molecular eventsCopy numberGene expressionMolecular eventsMulticellular organismal processGene Ontology databaseGO biological processesSignal transduction pathwaysOrganismal processesGO termsMolecular dataTransduction pathwaysSiRNA screenComplex genomic abnormalitiesIndividual cancersOntology databaseFunctional roleDriver eventsCell growthSequence abnormalitiesBreast cancer cell linesCancer cell linesGenomic abnormalitiesGlobal gene expression changes induced by prolonged cold ischemic stress and preservation method of breast cancer tissue
Aktas B, Sun H, Yao H, Shi W, Hubbard R, Zhang Y, Jiang T, Ononye SN, Wali VB, Pusztai L, Symmans WF, Hatzis C. Global gene expression changes induced by prolonged cold ischemic stress and preservation method of breast cancer tissue. Molecular Oncology 2014, 8: 717-727. PMID: 24602449, PMCID: PMC4048748, DOI: 10.1016/j.molonc.2014.02.002.Peer-Reviewed Original ResearchConceptsGlobal gene expression changesGlobal gene expressionGene expression changesGenomic signaturesResponse genesGene expressionSensitive transcriptsExpression changesStress response genesCell cycle regulationSignificant transcriptional changesExpression levelsCycle regulationTranscriptional changesIndividual probe setsInduced transcriptsAffected transcriptsProtein processingEnrichment analysis
2013
Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients
Haibe-Kains B, Desmedt C, Di Leo A, Azambuja E, Larsimont D, Selleslags J, Delaloge S, Duhem C, Kains JP, Carly B, Maerevoet M, Vindevoghel A, Rouas G, Lallemand F, Durbecq V, Cardoso F, Salgado R, Rovere R, Bontempi G, Michiels S, Buyse M, Nogaret JM, Qi Y, Symmans F, Pusztai L, D'Hondt V, Piccart-Gebhart M, Sotiriou C. Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients. Data In Brief 2013, 1: 7-10. PMID: 26484051, PMCID: PMC4608867, DOI: 10.1016/j.gdata.2013.09.001.Peer-Reviewed Original ResearchBreast cancer patientsResponse/resistanceAnthracycline monotherapyNeoadjuvant trialsGene expression signaturesNegative tumorsCancer patientsBreast cancerClinical dataEstrogen receptorClinical OncologyPredictive valuePatientsAnthracyclinesGene expressionII alphaExpression signaturesGenome-wide gene expressionMonotherapyExpressionTumorsCancerOncologyTrialsBiomarkersBiomarker Analysis of Neoadjuvant Doxorubicin/Cyclophosphamide Followed by Ixabepilone or Paclitaxel in Early-Stage Breast Cancer
Horak CE, Pusztai L, Xing G, Trifan OC, Saura C, Tseng LM, Chan S, Welcher R, Liu D. Biomarker Analysis of Neoadjuvant Doxorubicin/Cyclophosphamide Followed by Ixabepilone or Paclitaxel in Early-Stage Breast Cancer. Clinical Cancer Research 2013, 19: 1587-1595. PMID: 23340299, DOI: 10.1158/1078-0432.ccr-12-1359.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Combined Chemotherapy ProtocolsATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily B, Member 1Biomarkers, TumorBreast NeoplasmsCyclophosphamideDoxorubicinEpothilonesFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicrofilament ProteinsMicrotubule-Associated ProteinsNeoadjuvant TherapyNeoplasm ProteinsNuclear ProteinsPaclitaxelPrognosisTubulinConceptsMDR1 protein expressionNeoadjuvant doxorubicin/cyclophosphamideEarly-stage breast cancerDoxorubicin/cyclophosphamidePositive patientsProtein expressionTreatment armsBreast cancerPathologic complete response rateEfficacy of ixabepiloneInvasive breast adenocarcinomaComplete response ratePhase II trialCore needle biopsyRates of pCRΒIII-tubulin proteinNeoadjuvant settingII trialNegative patientsGene expressionPrimary cancerPredictive biomarkersPredictive markerRisk ratioNeedle biopsy
2012
A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes
Dutta B, Pusztai L, Qi Y, André F, Lazar V, Bianchini G, Ueno N, Agarwal R, Wang B, Shiang CY, Hortobagyi GN, Mills GB, Symmans WF, Balázsi G. A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes. British Journal Of Cancer 2012, 106: 1107-1116. PMID: 22343619, PMCID: PMC3304402, DOI: 10.1038/bjc.2011.584.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsCell Line, TumorComputer SimulationDNA Copy Number VariationsEpithelial-Mesenchymal TransitionFemaleGene ExpressionGene Expression ProfilingGene Expression Regulation, NeoplasticGene Knockdown TechniquesGene Regulatory NetworksGenes, NeoplasmHumansModels, BiologicalProtein Interaction MapsReceptor, ErbB-2Receptors, EstrogenReceptors, ProgesteroneRNA InterferenceConceptsGenome-scale dataCore biological pathwaysTriple receptor-negative breast cancerProtein-protein interactionsCell line data setsGene knockdown experimentsGene copy number dataCopy number dataCopy number variation dataNumber variation dataMember genesGene networksTranscriptional disturbancesKnockdown experimentsBiological discoveryGene expressionFunctional specificityBiological pathwaysDifferential expressionIntegrative studyFunctional relevanceVariation dataLine data setsCell linesGenes
2011
Final analysis of the NEOMET trial of neoadjuvant metformin: Examining effects on Ki67, gene expression, and pathway analysis in primary operable breast cancer.
Thompson A, Iwamoto T, Jordan L, Purdie C, Bray S, Baker L, Hardie G, Pusztai L, Moulder S, Dewar J, Hadad S. Final analysis of the NEOMET trial of neoadjuvant metformin: Examining effects on Ki67, gene expression, and pathway analysis in primary operable breast cancer. Journal Of Clinical Oncology 2011, 29: 534-534. DOI: 10.1200/jco.2011.29.15_suppl.534.Peer-Reviewed Original Research
2010
Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?
Iwamoto T, Pusztai L. Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? Genome Medicine 2010, 2: 81. PMID: 21092148, PMCID: PMC3016623, DOI: 10.1186/gm202.Peer-Reviewed Original Research
2009
Correlations of estrogen receptor (ER) related genomic transcription and ER gene expression with increasing AJCC stage of ER-positive breast cancer
Andreopoulou E, Hatzis C, Booser D, Valero V, Wallace M, Sotiriou C, Hortobagyi G, Pusztai L, Symmans W. Correlations of estrogen receptor (ER) related genomic transcription and ER gene expression with increasing AJCC stage of ER-positive breast cancer. Journal Of Clinical Oncology 2009, 27: 1044-1044. DOI: 10.1200/jco.2009.27.15_suppl.1044.Peer-Reviewed Original ResearchER-positive breast cancerBreast cancerEstrogen receptorPathologic stageAJCC stageProgesterone receptorStage IIIAdvanced ER-positive breast cancerExpression levelsStage IV diseaseER gene expressionClinical samplesPatient clinical samplesPgR expression levelsInitial presentationTumor dependenceExpression of GAPDHAdvanced stageCancerGene expressionGenomic pathwayGAPDH gene expressionReceptor geneGene expression profilesFurther studiesMetastatic 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 ResearchConceptsMetastatic gene signatureGene expression studiesGene expression profilingDistinct neoplastic diseasesExpression profilingDNA microarraysExpression studiesGene expressionMRNA transcriptsGene signatureSingle experimentNovel diagnostic assaysTranscriptsDiagnostic assaysNovel prognostic testsMicroarrayProfilingExpression
2005
A single-gene biomarker identifies breast cancers associated with immature cell type and short duration of prior breastfeeding
Symmans W, Fiterman D, Anderson S, Ayers M, Rouzier R, Dunmire V, Stec J, Valero V, Sneige N, Albarracin C, Wu Y, Ross J, Wagner P, Theriault R, Arun B, Kuerer H, Hess K, Zhang W, Hortobagyi G, Pusztai L. A single-gene biomarker identifies breast cancers associated with immature cell type and short duration of prior breastfeeding. Endocrine Related Cancer 2005, 12: 1059-1069. PMID: 16322343, DOI: 10.1677/erc.1.01051.Peer-Reviewed Original ResearchConceptsReal-time reverse transcription-polymerase chain reactionInvasive breast cancerImmature cell typesBreast cancerPrimary invasive breast cancerHigh-grade breast cancerMultivariate linear regression analysisReverse transcription-polymerase chain reactionCell typesTranscription-polymerase chain reactionShorter lifetime durationGene expressionParous womenClinicopathologic characteristicsEstrogen receptorHispanic ethnicitySeparate cohortHispanic womenElevated gene expressionLinear regression analysisLifetime historyYounger ageNeu receptorCancerBreastfeeding