2024
Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial.
Rios-Hoyo A, Xiong K, Marczyk M, García-Millán R, Wolf D, Huppert L, Nanda R, Yau C, Hirst G, van 't Veer L, Esserman L, Pusztai L. Correlation of hormone receptor positive HER2-negative/MammaPrint high-2 breast cancer with triple negative breast cancer: Results from gene expression data from the ISPY2 trial. Journal Of Clinical Oncology 2024, 42: 573-573. DOI: 10.1200/jco.2024.42.16_suppl.573.Peer-Reviewed Original ResearchGene expression dataGene expression analysisExpression dataExpressed genesExpression analysisTriple-negativeDistance analysisPathway analysisDifferential gene expression analysisCell cycle pathwayGene set enrichment analysisBreast cancerIngenuity Pathway AnalysisRate of pathological complete responseHigh-risk stage IIGlucocorticoid receptor signalingTriple negative breast cancerCycle pathwayPathological complete responseDNA repairEnrichment analysisOptimal treatment strategyNegative breast cancerI-SPY2 trialGenes
2023
Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms
Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Computational And Structural Biotechnology Journal 2023, 21: 4663-4674. PMID: 37841335, PMCID: PMC10568495, DOI: 10.1016/j.csbj.2023.09.035.Peer-Reviewed Original ResearchSingle-cell RNA-seq platformsSingle-cell RNA sequencingBulk RNA-seq dataRNA-seq platformsNumber of transcriptsLow-expression genesRNA-seq dataSingle-cell dataExpression levelsLow sequencing depthDiscordant genesRNA sequencingSequencing technologiesExpression shiftsPathway levelBiological pathwaysGene levelSequencing depthTranscriptomic platformsGenesIndividual cellsSingle cellsRNA integrityPathwayCellsChapter 3 Single-cell transcriptomics
Marczyk M, Kujawa T, Papiez A, Polanska J. Chapter 3 Single-cell transcriptomics. 2023, 67-84. DOI: 10.1016/b978-0-323-91810-7.00015-7.ChaptersSingle-cell transcriptomicsSingle-cell technologiesCell trajectory inferenceGene expression dataSimilar cell typesTranscriptional signalsCancer cell line dataRNAseq dataGene expressionCellular heterogeneitySequencing platformsBreast cancer cell line dataExpression dataCell line dataGroups of cellsTrajectory inferenceCell typesIndividual cellsMolecular characteristicsProper processingCellsTranscriptomicsGenesExpressionCertain steps
2022
Investigating Sources of Zeros in 10× Single-Cell RNAseq Data
Slowik H, Zyla J, Marczyk M. Investigating Sources of Zeros in 10× Single-Cell RNAseq Data. Lecture Notes In Computer Science 2022, 13347: 71-80. DOI: 10.1007/978-3-031-07802-6_6.Peer-Reviewed Original ResearchSingle-cell levelSingle-cell RNA sequencingSingle-cell RNAseq dataNumber of transcriptsMulti-omics dataGene expression estimatesRibosomal genesRNA sequencingExpression profilingEnrichment analysisRNAseq dataBiological pathwaysSequencing platformsExpression dataGenesExpression estimatesIndividual cellsBreast cancer cell linesCancer cell linesCell linesSingle experimentLow mappabilityTranscriptsSequencingProfilingCancer Relevance of Human Genes
Qing T, Mohsen H, Cannataro VL, Marczyk M, Rozenblit M, Foldi J, Murray M, Townsend J, Kluger Y, Gerstein M, Pusztai L. Cancer Relevance of Human Genes. Journal Of The National Cancer Institute 2022, 114: 988-995. PMID: 35417011, PMCID: PMC9275765, DOI: 10.1093/jnci/djac068.Peer-Reviewed Original ResearchConceptsCore cancer genesHuman genesFunctional importanceSomatic mutation frequencySelection pressureGene/protein networksCancer genesHigher somatic mutation frequencyNegative selection pressureGene-gene interaction networksMutation frequencyProtein-truncating variantsGenomic contextCell viabilityGenes decreasesCancer Genome AtlasInteraction networksProtein networkCancer relevanceCancer cell viabilityCell survivalGenesCancer biologyGenome AtlasSearch tools
2021
Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies
Marczyk M, Macioszek A, Tobiasz J, Polanska J, Zyla J. Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies. Frontiers In Genetics 2021, 12: 767358. PMID: 34956320, PMCID: PMC8696167, DOI: 10.3389/fgene.2021.767358.Peer-Reviewed Original ResearchGenome-wide association studiesOver-Representation AnalysisSingle nucleotide polymorphismsMeta-Analysis Gene-set EnrichmentSame geneTypical genome-wide association studyEnrichment analysisAssociation studiesGene-set analysisLD correctionGene set enrichmentGene expression dataGSEA-SNPCancer Genome Atlas (TCGA) databaseRelevant single nucleotide polymorphismsTranscriptomic levelGene levelExpression dataVariant associationsNonrandom associationGenesAssociation of SNPsMore lociAtlas databaseBreast cancer samples
2020
Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells
Marczyk M, Patwardhan GA, Zhao J, Qu R, Li X, Wali VB, Gupta AK, Pillai MM, Kluger Y, Yan Q, Hatzis C, Pusztai L, Gunasekharan V. Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells. Cancers 2020, 12: 2551. PMID: 32911681, PMCID: PMC7563413, DOI: 10.3390/cancers12092551.Peer-Reviewed Original ResearchTriple-negative breast cancer cellsCancer cellsBreast cancer cellsStress response genesMulti-omics landscapeCell population compositionDrug-induced cell deathMulti-omics investigationsCell linesBCL2 family inhibitorsSingle-cell analysisChromatin accessibilityGenome structureMDA-MB-231 triple-negative breast cancer cellsChromatin structureMethylation stateResponse genesFamily inhibitorsCell deathTNBC cell linesNumber variationsDefense mechanismsResistance mechanismsNew therapeutic strategiesGenes