2020
Prospective multi-institutional evaluation of pathologist assessment of PD-L1 assays for patient selection in triple negative breast cancer
Reisenbichler ES, Han G, Bellizzi A, Bossuyt V, Brock J, Cole K, Fadare O, Hameed O, Hanley K, Harrison BT, Kuba MG, Ly A, Miller D, Podoll M, Roden AC, Singh K, Sanders MA, Wei S, Wen H, Pelekanou V, Yaghoobi V, Ahmed F, Pusztai L, Rimm DL. Prospective multi-institutional evaluation of pathologist assessment of PD-L1 assays for patient selection in triple negative breast cancer. Modern Pathology 2020, 33: 1746-1752. PMID: 32300181, PMCID: PMC8366569, DOI: 10.1038/s41379-020-0544-x.Peer-Reviewed Original ResearchConceptsTriple-negative breast cancerNegative breast cancerOverall percent agreementPD-L1Intraclass correlation coefficientBreast cancerAdvanced triple-negative breast cancerPD-L1 positive casesImmune cell stainingMultiple pathologistsPD-L1 scoringMulti-institutional evaluationLung cancer studiesAtezolizumab therapySP142 assaySP263 assaysPatient selectionSP263SP142US FoodDrug AdministrationPathologist's assessmentPositive casesReal-world settingPercent agreement
2019
Defining Risk of Late Recurrence in Early-Stage Estrogen Receptor–Positive Breast Cancer: Clinical Versus Molecular Tools
Foldi J, O'Meara T, Marczyk M, Sanft T, Silber A, Pusztai L. Defining Risk of Late Recurrence in Early-Stage Estrogen Receptor–Positive Breast Cancer: Clinical Versus Molecular Tools. Journal Of Clinical Oncology 2019, 37: jco.18.01933. PMID: 30943126, DOI: 10.1200/jco.18.01933.Peer-Reviewed Original Research
2018
CD68, CD163, and matrix metalloproteinase 9 (MMP-9) co-localization in breast tumor microenvironment predicts survival differently in ER-positive and -negative cancers
Pelekanou V, Villarroel-Espindola F, Schalper KA, Pusztai L, Rimm DL. CD68, CD163, and matrix metalloproteinase 9 (MMP-9) co-localization in breast tumor microenvironment predicts survival differently in ER-positive and -negative cancers. Breast Cancer Research 2018, 20: 154. PMID: 30558648, PMCID: PMC6298021, DOI: 10.1186/s13058-018-1076-x.Peer-Reviewed Original ResearchMeSH KeywordsAntigens, CDAntigens, Differentiation, MyelomonocyticAntineoplastic AgentsBiomarkers, TumorBreastBreast NeoplasmsDisease-Free SurvivalFemaleGene Expression Regulation, NeoplasticHumansMacrophagesMatrix Metalloproteinase 9Middle AgedPatient SelectionPrognosisReceptors, Cell SurfaceReceptors, EstrogenRetrospective StudiesSurvival AnalysisTissue Array AnalysisTumor MicroenvironmentConceptsTumor-associated macrophagesOverall survivalQuantitative immunofluorescenceMacrophage markersBreast cancerHigh expressionPan-macrophage marker CD68Triple-negative breast cancerCD163/CD68Multiplexed quantitative immunofluorescenceImproved overall survivalProtein expressionWorse overall survivalPoor overall survivalMMP-9 protein expressionSubclass of patientsMacrophage-targeted therapiesMatrix metalloproteinase-9Tissue microarray formatMMP-9 proteinBreast tumor microenvironmentModulator of responseParaffin-embedded tissuesBreast cancer biomarkersCohort BA framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT)
Mateo J, Chakravarty D, Dienstmann R, Jezdic S, Gonzalez-Perez A, Lopez-Bigas N, Ng CKY, Bedard PL, Tortora G, Douillard J, Van Allen EM, Schultz N, Swanton C, André F, Pusztai L. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). Annals Of Oncology 2018, 29: 1895-1902. PMID: 30137196, PMCID: PMC6158764, DOI: 10.1093/annonc/mdy263.Peer-Reviewed Original ResearchConceptsESMO ScaleMolecular targetsClinical actionabilityPrecision Medicine Working GroupGenomic alterationsPrecision medicineRoutine clinical decisionEvidence-based criteriaMedicine Working GroupLack of evidencePreclinical evidenceClinical benefitClinical evidencePatient populationClassification systemClinical managementCancer precision medicineInvestigational targetsPatient managementMolecular aberrationsTumor typesClinical decisionClinical targetsAvailable evidenceEuropean SocietyComparison of Residual Risk–Based Eligibility vs Tumor Size and Nodal Status for Power Estimates in Adjuvant Trials of Breast Cancer Therapies
Wei W, Kurita T, Hess KR, Sanft T, Szekely B, Hatzis C, Pusztai L. Comparison of Residual Risk–Based Eligibility vs Tumor Size and Nodal Status for Power Estimates in Adjuvant Trials of Breast Cancer Therapies. JAMA Oncology 2018, 4: e175092-e175092. PMID: 29372234, PMCID: PMC5885272, DOI: 10.1001/jamaoncol.2017.5092.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntineoplastic Combined Chemotherapy ProtocolsBreast NeoplasmsChemotherapy, AdjuvantEligibility DeterminationFemaleHumansLymph NodesLymphatic MetastasisMiddle AgedNeoplasm Recurrence, LocalNeoplasm, ResidualPatient SelectionPrognosisRandomized Controlled Trials as TopicReproducibility of ResultsResearch DesignRetrospective StudiesRisk FactorsSurvival AnalysisTrastuzumabTumor BurdenWatchful WaitingYoung AdultConceptsTumor sizeAdjuvant trialsEligibility criteriaNodal statusClinical trialsResidual riskEarly-stage breast cancerAdjuvant clinical trialsBaseline prognostic riskFuture adjuvant trialsResidual risk estimatesRisk of recurrenceBreast cancer therapyRisk thresholdTrial powerClinical trial powerTrial eligibilityAdjuvant therapyCare therapyConsecutive patientsPrognostic riskPatient eligibilityTrial populationPatient cohortControl arm
2011
Multifactorial Approach to Predicting Resistance to Anthracyclines
Desmedt C, Di Leo A, de Azambuja E, Larsimont D, Haibe-Kains B, 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. Multifactorial Approach to Predicting Resistance to Anthracyclines. Journal Of Clinical Oncology 2011, 29: 1578-1586. PMID: 21422418, DOI: 10.1200/jco.2010.31.2231.Peer-Reviewed Original ResearchMeSH KeywordsAntibiotics, AntineoplasticAntigens, NeoplasmBiomarkers, TumorBiopsyBreast NeoplasmsChemotherapy, AdjuvantDNA Topoisomerases, Type IIDNA-Binding ProteinsDrug Resistance, NeoplasmEpirubicinEuropeFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedNeoadjuvant TherapyOdds RatioPatient SelectionPoly-ADP-Ribose Binding ProteinsPredictive Value of TestsProspective StudiesReceptor, ErbB-2Receptors, EstrogenReproducibility of ResultsRisk AssessmentRisk FactorsTexasTreatment FailureConceptsPathologic complete responseHuman epidermal growth factor receptor 2Neoadjuvant trialsTOP trialPredictive valueEstrogen receptor-negative tumorsEpidermal growth factor receptor 2High negative predictive valuePrimary end pointGrowth factor receptor 2Receptor-negative tumorsResponse/resistanceFactor receptor 2Negative predictive valueUseful clinical toolER-negative samplesA scoresAnthracycline monotherapyEvaluable patientsGene expression signaturesComplete responseBreast cancerImmune responseReceptor 2Patients
2010
Genomic 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 αTamoxifenTherapyDevelopment 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 samples
2007
HER2 expression and efficacy of preoperative paclitaxel/FAC chemotherapy in breast cancer
Andre F, Mazouni C, Liedtke C, Kau SW, Frye D, Green M, Gonzalez-Angulo AM, Symmans WF, Hortobagyi GN, Pusztai L. HER2 expression and efficacy of preoperative paclitaxel/FAC chemotherapy in breast cancer. Breast Cancer Research And Treatment 2007, 108: 183-190. PMID: 17468948, DOI: 10.1007/s10549-007-9594-8.Peer-Reviewed Original ResearchMeSH KeywordsAntigens, NeoplasmAntineoplastic Combined Chemotherapy ProtocolsBreast NeoplasmsCyclophosphamideDisease-Free SurvivalDNA Topoisomerases, Type IIDNA-Binding ProteinsDoxorubicinDrug Administration ScheduleFemaleFluorouracilGene AmplificationGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedNeoadjuvant TherapyNeoplasm StagingOligonucleotide Array Sequence AnalysisPaclitaxelPatient SelectionPoly-ADP-Ribose Binding ProteinsReceptor, ErbB-2Receptors, EstrogenRetrospective StudiesRNA, MessengerTau ProteinsTime FactorsTreatment OutcomeConceptsPathologic complete responseHER2 overexpressionBreast cancerFAC chemotherapyPCR rateER statusHER2 expressionRelapse-free survival rateHER2-overexpressing breast cancerMicrotubule associated protein tauER-positive cancersEstrogen receptor statusPreoperative chemotherapyComplete responseHER2 tumorsMethodsRetrospective analysisReceptor statusPatientsSurvival rateMultivariate analysisWeekly scheduleMAP-tauProtein tauCancerChemotherapy
2006
Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy
Andre F, Pusztai L. Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy. Nature Reviews Clinical Oncology 2006, 3: 621-632. PMID: 17080180, DOI: 10.1038/ncponc0636.Peer-Reviewed Original ResearchConceptsAdjuvant chemotherapyMolecular classificationBreast cancerER-positive tumorsEarly-stage diseaseNew molecular classificationRoutine clinical useAdjuvant therapyNegative diseaseEstrogen receptorIndividual patientsChemotherapyClinical usePatientsCancerDiseaseMolecular characteristicsFuture promiseTherapyTumorsReceptorsHeterogeneity of Breast Cancer among Patients and Implications for Patient Selection for Adjuvant Chemotherapy
Andre F, Pusztai L. Heterogeneity of Breast Cancer among Patients and Implications for Patient Selection for Adjuvant Chemotherapy. Pharmaceutical Research 2006, 23: 1951-1958. PMID: 16906452, DOI: 10.1007/s11095-006-9075-5.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic AgentsBreast NeoplasmsChemotherapy, AdjuvantFemaleHumansPatient SelectionResearch DesignConceptsAdjuvant chemotherapyBreast cancerPredictive valuePatient selectionSuch therapySingle biomarkerHeterogeneity of cancerChemotherapyPatientsCancerDaily practiceBiomarkersAbsolute effectLarge-scale genomic analysisFunctional pathwaysDistinct subclassesLarge populationHomogeneous subclassesTherapy
2004
Changes in plasma levels of inflammatory cytokines in response to paclitaxel chemotherapy
Pusztai L, Mendoza TR, Reuben JM, Martinez MM, Willey JS, Lara J, Syed A, Fritsche HA, Bruera E, Booser D, Valero V, Arun B, Ibrahim N, Rivera E, Royce M, Cleeland CS, Hortobagyi GN. Changes in plasma levels of inflammatory cytokines in response to paclitaxel chemotherapy. Cytokine 2004, 25: 94-102. PMID: 14698135, DOI: 10.1016/j.cyto.2003.10.004.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic AgentsAntineoplastic Combined Chemotherapy ProtocolsAnxietyBloodBreast NeoplasmsCyclophosphamideCytokinesData Interpretation, StatisticalDoxorubicinFatigueFemaleFluorouracilHumansInterleukin-1Interleukin-10Interleukin-12Interleukin-6Interleukin-8Middle AgedNauseaPaclitaxelPainPatient SelectionQuality of LifeTumor Necrosis Factor-alphaConceptsFlu-like symptomsIL-10 levelsIL-6IL-8IL-10Paclitaxel groupHealthy volunteersPaclitaxel chemotherapyJoint painIL-12Inflammatory cytokinesPlasma levelsTNF-alphaDay 3Single-agent paclitaxelBaseline cytokine levelsIL-8 levelsTransient side effectsHigh-dose treatmentWeekly paclitaxelCytokine levelsFAC chemotherapyMusculoskeletal symptomsCancer patientsIL-1beta