2021
Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers
Li X, Kumar S, Harmanci A, Li S, Kitchen RR, Zhang Y, Wali VB, Reddy SM, Woodward WA, Reuben JM, Rozowsky J, Hatzis C, Ueno NT, Krishnamurthy S, Pusztai L, Gerstein M. Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers. Genome Medicine 2021, 13: 70. PMID: 33902690, PMCID: PMC8077918, DOI: 10.1186/s13073-021-00879-x.Peer-Reviewed Original ResearchConceptsSingle nucleotide variantsWhole-genome sequencingGermline single nucleotide variantsInternational Cancer Genome ConsortiumGenomic featuresGenomic alterationsGenome ConsortiumClonal architectureWhole Genomes (PCAWG) ConsortiumNon-coding regionsCancer-related pathwaysNon-IBC samplesCancer Genome Atlas ProgramMAST2 geneCopy number profilesPan-cancer analysisTGF-β pathwayGenomic architectureGenomic regionsSimilar genomic alterationsSimilar genomic characteristicsComplex SVsIBC samplesGenomic differencesOverall mutational load
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 regressionReproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments
Qi Y, Liu X, Liu CG, Wang B, Hess KR, Symmans WF, Shi W, Pusztai L. Reproducibility of Variant Calls in Replicate Next Generation Sequencing Experiments. PLOS ONE 2015, 10: e0119230. PMID: 26136146, PMCID: PMC4489803, DOI: 10.1371/journal.pone.0119230.Peer-Reviewed Original ResearchConceptsSingle nucleotide variantsEuropean Genome-phenome ArchiveProtein kinase geneMillions of nucleotidesSame genomic DNANext-generation sequencing experimentsVariant callsGenomic locationNext-generation sequencingSequence dataSNV callsKinase geneGenomic DNANucleotide substitutionsSequencing experimentsHigh stringencyVariant allele frequencyNucleotide variantsTrue biological changeNucleotide alterationsGeneration sequencingAllele countsSequencing errorsBreast cancer samplesAllele frequencies
2013
A 3-gene proliferation score (TOP-FOX-67) can re-classify histological grade-2, ER-positive breast cancers into low- and high-risk prognostic categories
Szekely B, Iwamoto T, Szasz AM, Qi Y, Matsuoka J, Symmans WF, Tokes AM, Kulka J, Swanton C, Pusztai L. A 3-gene proliferation score (TOP-FOX-67) can re-classify histological grade-2, ER-positive breast cancers into low- and high-risk prognostic categories. Breast Cancer Research And Treatment 2013, 138: 691-698. PMID: 23504136, DOI: 10.1007/s10549-013-2475-4.Peer-Reviewed Original ResearchMeSH KeywordsAntigens, NeoplasmBreast NeoplasmsCell ProliferationChemotherapy, AdjuvantCohort StudiesDatabases, GeneticDNA Topoisomerases, Type IIDNA-Binding ProteinsFemaleForkhead Box Protein M1Forkhead Transcription FactorsGene Expression Regulation, NeoplasticGenome, HumanHumansKi-67 AntigenPoly-ADP-Ribose Binding ProteinsPredictive Value of TestsPrognosisReceptors, EstrogenSurvival RateTamoxifenConceptsGenomic grade indexGrade 2 cancersPrognostic valueProliferation scoreBreast cancerDistant metastasis-free survival curvesGrade 2Metastasis-free survival curvesER-positive breast cancerSystemic adjuvant therapyHigh expressionCohort of patientsHistological grade 2Intermediate-risk cancerPositive breast cancerSimilar prognostic valueGrade 2 tumorsHigh-risk groupGrade 1 cancersHistological grade groupsNon-significant trendWorse DMFSAdjuvant tamoxifenAdjuvant therapyWorse survival
2010
Development 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
2004
Pharmacogenomics
Ross JS, Schenkein DP, Kashala O, Linette GP, Stec J, Symmans WF, Pusztai L, Hortobagyi GN. Pharmacogenomics. Advances In Anatomic Pathology 2004, 11: 211-220. PMID: 15220824, DOI: 10.1097/01.pap.0000131825.77317.ee.Peer-Reviewed Original ResearchConceptsDisease pathway genesGene expression patternsWhole-genome technologiesHuman genomeTranscriptional profilingGenome technologyPathway genesMolecular basisGenomic microarraysSNP technologySolid Tumors (RECIST) classificationExpression patternsTarget discoveryProteomics researchDrug targetsGenetic variantsNon-genetic factorsAnti-cancer drugsComputational biologyVariety of diseasesField of pharmacogeneticsDrug metabolizing enzymesToxic responseLeukemia/lymphomaGroup of drugs
2003
Clinical Application of cDNA Microarrays in Oncology
Pusztai L, Ayers M, Stec J, Hortobágyi GN. Clinical Application of cDNA Microarrays in Oncology. The Oncologist 2003, 8: 252-258. PMID: 12773747, DOI: 10.1634/theoncologist.8-3-252.Peer-Reviewed Original ResearchConceptsHundreds of genesGene expression patternsSingle-gene markersIndividual genesTranscriptional profilingCDNA microarrayMRNA speciesDNA microarraysExpression patternsComplex biologyNovel targetImportant new toolMicroarrayClinical outcomesGenesHuman tissuesImportant clinical outcomesDiseased tissuesDrug developmentTrue clinical utilityClinical utilityClinical OncologyNew toolBiologyExciting new technology