2021
Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler
Bergholtz H, Carter JM, Cesano A, Cheang MCU, Church SE, Divakar P, Fuhrman CA, Goel S, Gong J, Guerriero JL, Hoang ML, Hwang ES, Kuasne H, Lee J, Liang Y, Mittendorf EA, Perez J, Prat A, Pusztai L, Reeves JW, Riazalhosseini Y, Richer JK, Sahin Ö, Sato H, Schlam I, Sørlie T, Stover DG, Swain SM, Swarbrick A, Thompson EA, Tolaney SM, Warren SE, Consortium O. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler. Cancers 2021, 13: 4456. PMID: 34503266, PMCID: PMC8431590, DOI: 10.3390/cancers13174456.Peer-Reviewed Original ResearchWhole transcriptome levelDigital Spatial ProfilerSpatial profilingTranscriptome levelRNA profilingRNA transcriptsTumor microenvironmentMolecular diversityProtein profilingCancer researchIntegration of datasetsGeoMx Digital Spatial ProfilerProfilingCell populationsMouse samplesBiomarker discoveryBreast tumor microenvironmentBreast Cancer ConsortiumCancer researchersTreatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations
Patwardhan GA, Marczyk M, Wali VB, Stern DF, Pusztai L, Hatzis C. Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations. Npj Breast Cancer 2021, 7: 60. PMID: 34040000, PMCID: PMC8154902, DOI: 10.1038/s41523-021-00270-4.Peer-Reviewed Original ResearchHeterogeneous cancer cell populationsCancer cell populationsTriple-negative breast cancerSingle-cell RNA sequencingCell populationsFitness advantageRNA sequencingMDA-MB-231 TNBC cellsDrug resistanceMechanisms of resistanceVitro screening assaysClonal dynamicsTNBC cellsScreening assaysResistant clonesPatterns of resistanceConcomitant treatmentTherapy combinationsBreast cancerClinical studiesTreatment doseTreatment scheduleBarcodesSequencingTreatment
2018
Incorporating Genomics Into the Care of Patients With Advanced Breast Cancer
Kratz J, Burkard M, O'Meara T, Pusztai L, Veitch Z, Bedard PL. Incorporating Genomics Into the Care of Patients With Advanced Breast Cancer. American Society Of Clinical Oncology Educational Book 2018, 38: 56-64. PMID: 30231387, DOI: 10.1200/edbk_200731.Peer-Reviewed Original ResearchConceptsBreast cancerGenomic alterationsTumor genomic heterogeneityAdvanced breast cancerMetastatic breast cancerRecurrent genomic alterationsCare of patientsGenetic diversityMetastatic tumor sitesImproved clinical careGenomic sequencingLaboratory-developed testsClinical trialsGenomic heterogeneityDrug treatmentPatient tumorsClinical careSame patientBlood samplesHeterogeneous diseaseClinical relevanceTumor sitePatientsClonal evolutionCell populations
2010
Higher parity and shorter breastfeeding duration
Shinde SS, Forman MR, Kuerer HM, Yan K, Peintinger F, Hunt KK, Hortobagyi GN, Pusztai L, Symmans WF. Higher parity and shorter breastfeeding duration. Cancer 2010, 116: 4933-4943. PMID: 20665494, DOI: 10.1002/cncr.25443.Peer-Reviewed Original ResearchConceptsTriple-negative BCInvasive breast cancerDuration of breastfeedingBreast cancer phenotypeHigher parityOdds ratioBreast cancerTriple-negative breast cancer (TNBC) phenotypeConsecutive case seriesMultivariate logistic regressionConfidence intervalsAfrican American ethnicityCancer phenotypeShort durationCase seriesFamily historyNegative BCProgenitor cell populationsYounger ageLogistic regressionBreastfeedingAmerican ethnicityDemographic informationCell populationsAge
2003
Total RNA yield and microarray gene expression profiles from fine‐needle aspiration biopsy and core‐needle biopsy samples of breast carcinoma
Symmans WF, Ayers M, Clark EA, Stec J, Hess KR, Sneige N, Buchholz TA, Krishnamurthy S, Ibrahim NK, Buzdar AU, Theriault RL, Rosales MF, Thomas ES, Gwyn KM, Green MC, Syed AR, Hortobagyi GN, Pusztai L. Total RNA yield and microarray gene expression profiles from fine‐needle aspiration biopsy and core‐needle biopsy samples of breast carcinoma. Cancer 2003, 97: 2960-2971. PMID: 12784330, DOI: 10.1002/cncr.11435.Peer-Reviewed Original ResearchConceptsGene expression profilesTranscriptional profilesExpression profilesFine-needle aspiration biopsyGenomic databasesStromal gene expressionGene expressionTotal RNA yieldTotal RNABreast carcinomaTumor cell populationSubset of genesCDNA microarray analysisStromal cellsBiopsy samplesGene expression profilingCell populationsMicroscopic cell countsRNA yieldAspiration biopsyGenomic studiesTranscriptional profilingCDNA microarrayNonlymphoid stromal cellsExpression profiling
1998
Physiologic and Pathologic Drug Resistance in Ovarian Carcinoma: A Hypothesis Based on a Clonal Progression Model
Pusztai L, Siddik Z, Mills G, Bast R. Physiologic and Pathologic Drug Resistance in Ovarian Carcinoma: A Hypothesis Based on a Clonal Progression Model. Acta Oncologica 1998, 37: 629-640. PMID: 10050979, DOI: 10.1080/028418698429964.Peer-Reviewed Original ResearchConceptsEarly-stage ovarian cancerDrug-resistant cellsDrug resistanceDrug sensitivityOvarian cancerTumor progressionDisease-free survivalHigh-dose chemotherapyNumerous clinical studiesDifferent treatment strategiesCell populationsHigh response rateCorresponding normal tissuesInsufficient chemotherapyAdjuvant chemotherapyIntensive chemotherapyAdvanced diseaseClinical responseCombination chemotherapyConventional dosesMolecular biological observationsOvarian carcinomaPhysiological drug resistanceClinical failureClinical studies