Featured Publications
Paenibacillus infection with frequent viral coinfection contributes to postinfectious hydrocephalus in Ugandan infants
Paulson J, Williams B, Hehnly C, Mishra N, Sinnar S, Zhang L, Ssentongo P, Mbabazi-Kabachelor E, Wijetunge D, von Bredow B, Mulondo R, Kiwanuka J, Bajunirwe F, Bazira J, Bebell L, Burgoine K, Couto-Rodriguez M, Ericson J, Erickson T, Ferrari M, Gladstone M, Guo C, Haran M, Hornig M, Isaacs A, Kaaya B, Kangere S, Kulkarni A, Kumbakumba E, Li X, Limbrick D, Magombe J, Morton S, Mugamba J, Ng J, Olupot-Olupot P, Onen J, Peterson M, Roy F, Sheldon K, Townsend R, Weeks A, Whalen A, Quackenbush J, Ssenyonga P, Galperin M, Almeida M, Atkins H, Warf B, Lipkin W, Broach J, Schiff S. Paenibacillus infection with frequent viral coinfection contributes to postinfectious hydrocephalus in Ugandan infants. Science Translational Medicine 2020, 12 PMID: 32998967, PMCID: PMC7774825, DOI: 10.1126/scitranslmed.aba0565.Peer-Reviewed Original ResearchConceptsPostinfectious hydrocephalusCSF samplesPIH casesPotential causative organismsCerebrospinal fluid accumulationCytomegalovirus coinfectionUgandan infantsNeonatal sepsisSurgical palliationNeonatal infectionInfant casesOptimal treatmentInfant cohortCommon causeCausative organismPediatric hydrocephalusFluid accumulationHydrocephalusAnaerobic bacterial isolatesControl casesInfectionFacultative anaerobic bacterial isolatesInfantsParasitic DNADiseaseDifferential abundance analysis for microbial marker-gene surveys
Paulson J, Stine O, Bravo H, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods 2013, 10: 1200-1202. PMID: 24076764, PMCID: PMC4010126, DOI: 10.1038/nmeth.2658.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArea Under CurveCluster AnalysisComputer SimulationDatabases, GeneticGene Expression ProfilingGenetic MarkersGenetic VariationHumansIntestinesMetagenomicsMiceMicrobiotaModels, GeneticModels, StatisticalNormal DistributionPhenotypeRNA, Ribosomal, 16SSequence Analysis, DNASoftware
2024
Lubricating gel influence on vaginal microbiome sampling
Amitai Komem D, Hadar R, Paulson J, Mordechai Y, Eskandarian H, Efroni G, Amir A, Haberman Y, Tsur A. Lubricating gel influence on vaginal microbiome sampling. Scientific Reports 2024, 14: 18223. PMID: 39107405, PMCID: PMC11303677, DOI: 10.1038/s41598-024-68948-w.Peer-Reviewed Original ResearchConceptsMicrobial compositionVaginal samplesVaginal microbiome samplesBeta diversityVaginal microbiome studiesMicrobiome studiesMicrobiome samplesTaxa abundanceGynecological examinationPregnant womenLubricant gelReduce painMicrobial dataGel exposureEmergency roomSterile swabsEffect of gelMicrobial analysisPrediction of Benefit From Adjuvant Pertuzumab by 80-Gene Signature in the APHINITY (BIG 4-11) Trial
Krop I, Mittempergher L, Paulson J, Andre F, Bonnefoi H, Loi S, Loibl S, Gelber R, Caballero C, Bhaskaran R, Dreezen C, Menicucci A, Bernards R, van ’t Veer L, Piccart M. Prediction of Benefit From Adjuvant Pertuzumab by 80-Gene Signature in the APHINITY (BIG 4-11) Trial. JCO Precision Oncology 2024, 8: e2200667. PMID: 38237097, DOI: 10.1200/po.22.00667.Peer-Reviewed Original ResearchConceptsInvasive disease-free survivalHER2 typeLuminal typeInvasive disease-free survival eventsHazard ratioEarly-stage breast cancerLuminal-type tumorsMolecular subtype signaturesAnti-HER2 therapyHER2-positive tumorsDisease-free survivalStandard adjuvant chemotherapyBasal-type tumorsBasal typePredictive of benefitAdjuvant pertuzumabAPHINITY trialHER2-typeHER2-positiveInferior prognosisAdjuvant chemotherapyTumor subtypesNo significant differenceAnti-HER2Breast tumors
2023
Transcriptional immunogenomic analysis reveals distinct immunological clusters in paediatric nervous system tumours
Nabbi A, Beck P, Delaidelli A, Oldridge D, Sudhaman S, Zhu K, Yang S, Mulder D, Bruce J, Paulson J, Raman P, Zhu Y, Resnick A, Sorensen P, Sill M, Brabetz S, Lambo S, Malkin D, Johann P, Kool M, Jones D, Pfister S, Jäger N, Pugh T. Transcriptional immunogenomic analysis reveals distinct immunological clusters in paediatric nervous system tumours. Genome Medicine 2023, 15: 67. PMID: 37679810, PMCID: PMC10486055, DOI: 10.1186/s13073-023-01219-x.Peer-Reviewed Original ResearchConceptsChimeric antigen receptorNervous system tumorsImmune clustersImmunogenomic analysisSystem tumorsCancer typesImmune checkpoint inhibitorsT-cell therapyPaediatric solid tumoursTumor mutational burdenImmune cell frequenciesPotential immune biomarkersImmune infiltration levelsB cell repertoireDeterminants of immune responseVariable response ratesBackgroundCancer immunotherapyImmunogenomic profilingMyeloid predominanceCheckpoint inhibitorsImmunotherapeutic strategiesImmunotherapy approachesImmune compositionImmune neutralizationImmune desertmbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation
Movassagh M, Schiff S, Paulson J. mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation. Bioinformatics 2023, 39: btad565. PMID: 37707523, PMCID: PMC10516520, DOI: 10.1093/bioinformatics/btad565.Peer-Reviewed Original ResearchConceptsSingle nucleotide variationsRNA sequencingMicrobial abundance dataQuantitative trait lociSingle nucleotide polymorphism dataRibosomal RNA sequencingField of genomicsWhole-genome sequencingEvidence of interplayMutational profileTrait lociMicrobial communitiesMicrobial abundancePolymorphism dataMicrobial populationsGenome sequencingAbundance dataFirst R packageHuman geneticsBioconductor packageGenetic variantsMicrobiome dataSequencingR packageAbundanceTranscriptomic profiles and 5-year results from the randomized CLL14 study of venetoclax plus obinutuzumab versus chlorambucil plus obinutuzumab in chronic lymphocytic leukemia
Al-Sawaf O, Zhang C, Jin H, Robrecht S, Choi Y, Balasubramanian S, Kotak A, Chang Y, Fink A, Tausch E, Schneider C, Ritgen M, Kreuzer K, Chyla B, Paulson J, Pallasch C, Frenzel L, Peifer M, Eichhorst B, Stilgenbauer S, Jiang Y, Hallek M, Fischer K. Transcriptomic profiles and 5-year results from the randomized CLL14 study of venetoclax plus obinutuzumab versus chlorambucil plus obinutuzumab in chronic lymphocytic leukemia. Nature Communications 2023, 14: 2147. PMID: 37072421, PMCID: PMC10113251, DOI: 10.1038/s41467-023-37648-w.Peer-Reviewed Original ResearchConceptsProgression-free survivalChronic lymphocytic leukemiaUntreated chronic lymphocytic leukemiaLymphocytic leukemiaAssociated with longer progression-free survivalParallel-group phase 3 studyProgression-free survival ratesTreatment of chronic lymphocytic leukemiaLonger progression-free survivalDepth of remissionMedian follow-upPhase 3 studyAssociated with increased expressionLong-term efficacyLong-term outcomesExploratory post hoc analysisPost hoc analysisMRD statusBCL2 inhibitionOverall survivalPrimary endpointSecondary endpointsInflammatory response pathwaysVenetoclax-obinutuzumabFollow-upMultimodal immunogenomic biomarker analysis of tumors from pediatric patients enrolled to a phase 1-2 study of single-agent atezolizumab
Nabbi A, Danesh A, Espin-Garcia O, Pedersen S, Wellum J, Fu L, Paulson J, Geoerger B, Marshall L, Trippett T, Rossato G, Pugh T, Hutchinson K. Multimodal immunogenomic biomarker analysis of tumors from pediatric patients enrolled to a phase 1-2 study of single-agent atezolizumab. Nature Cancer 2023, 4: 502-515. PMID: 37038005, PMCID: PMC10132976, DOI: 10.1038/s43018-023-00534-x.Peer-Reviewed Original ResearchConceptsPediatric patientsAssociated with progression-free survivalCD8+ T cellsT cell receptor repertoireProgression-free survivalImmune-checkpoint inhibitionB cell infiltrationTertiary lymphoid structuresBiomarker analysisAtezolizumab therapyAntitumor immunityStable diseasePD-L1Partial responseTumor neoantigensRefractory tumorsPatient tumorsLymphoid structuresT cellsB cellsReceptor repertoireClinical trialsClinical activityTumorImmune responseThe Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks
Ben Guebila M, Wang T, Lopes-Ramos C, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson J, Altenbuchinger M, Shutta K, Sonawane A, Lim J, Calderer G, van IJzendoorn D, Morgan D, Marin A, Chen C, Song Q, Saha E, DeMeo D, Padi M, Platig J, Kuijjer M, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biology 2023, 24: 45. PMID: 36894939, PMCID: PMC9999668, DOI: 10.1186/s13059-023-02877-1.Peer-Reviewed Original Research
2022
A tumor volume and performance status model to predict outcome before treatment in diffuse large B-cell lymphoma
Thieblemont C, Chartier L, Dührsen U, Vitolo U, Barrington S, Zaucha J, Vercellino L, Silva M, Patrocinio-Carvalho I, Decazes P, Viailly P, Tilly H, Berriolo-Riedinger A, Casasnovas O, Hüttmann A, Ilyas H, Mikhaeel N, Dunn J, Cottereau A, Schmitz C, Kostakoglu L, Paulson J, Nielsen T, Meignan M. A tumor volume and performance status model to predict outcome before treatment in diffuse large B-cell lymphoma. Blood Advances 2022, 6: 5995-6004. PMID: 36044385, PMCID: PMC9691911, DOI: 10.1182/bloodadvances.2021006923.Peer-Reviewed Original ResearchConceptsLarge B-cell lymphomaAggressive large B-cell lymphomaB-cell lymphomaRisk factorsTumor volumePerformance statusDiffuse large B-cell lymphomaInternational Prognostic IndexMetabolic tumor volumeProgression-free survivalReal-world seriesReal-world clinicsREMARC trialOverall survivalRefractory diseaseECOG-PSPrognostic indexIntermediate riskInitial treatmentTreatment initiationRisk stratificationC-indexOlder patientsPrognostic toolClinical trialsFull automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments
Jemaa S, Paulson J, Hutchings M, Kostakoglu L, Trotman J, Tracy S, de Crespigny A, Carano R, El-Galaly T, Nielsen T, Bengtsson T. Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments. Cancer Imaging 2022, 22: 39. PMID: 35962459, PMCID: PMC9373298, DOI: 10.1186/s40644-022-00476-0.Peer-Reviewed Original ResearchConceptsDiffuse large B-cell lymphomaProgression-free survivalOverall survivalFDG-PETUntreated diffuse large B-cell lymphomaHazard ratioHigh-risk DLBCL patientsCentral nervous system relapseLarge B-cell lymphomaMetabolic tumor volumeImaging-based risk factorsB-cell lymphomaEnhanced risk stratificationHigh-risk populationImaging metricsRisk increaseDLBCL patientsFDG-PET/CTSystemic relapseTumor volumeOrgan involvementPrognostic improvementRisk stratificationInvasive proceduresClinical dataGameRank: R package for feature selection and construction
Henneges C, Paulson J. GameRank: R package for feature selection and construction. Bioinformatics 2022, 38: 4840-4842. PMID: 35951761, PMCID: PMC9563696, DOI: 10.1093/bioinformatics/btac552.Peer-Reviewed Original ResearchmirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis
Movassagh M, Morton S, Hehnly C, Smith J, Doan T, Irizarry R, Broach J, Schiff S, Bailey J, Paulson J. mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis. BMC Genomics 2022, 23: 439. PMID: 35698050, PMCID: PMC9191533, DOI: 10.1186/s12864-022-08558-w.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionLung epithelial cellsEpithelial cellsHuman lung epithelial cellsSARS-CoV-2NK cellsStatistical PackageEBV miRNAsT cellsImmune pathwaysB cellsClinical relevanceSample cohortCD34 cellsStomach adenocarcinomaEndothelial cellsTime pointsInfectionCOVID-19CellsCD4AdenocarcinomaPatientsCD19CytokinesPatient‐reported outcomes provide prognostic information for survival in patients with diffuse large B‐cell lymphoma: Analysis of 1239 patients from the GOYA study
Huang H, Datye A, Fan M, Knapp A, Nielsen T, Bottos A, Paulson J, Trask P, Efficace F. Patient‐reported outcomes provide prognostic information for survival in patients with diffuse large B‐cell lymphoma: Analysis of 1239 patients from the GOYA study. Cancer Medicine 2022, 11: 3312-3322. PMID: 35322932, PMCID: PMC9468432, DOI: 10.1002/cam4.4692.Peer-Reviewed Original ResearchConceptsDiffuse large B-cell lymphomaInternational Prognostic IndexProgression-free survivalLarge B-cell lymphomaB-cell lymphomaOverall survivalPrognostic valuePatient-reported outcomesPrognostic informationCox regression analysis of overall survivalClinical variablesAnalysis of overall survivalPhase III studyFifty-nine patientsCox regression analysisPatient risk stratificationPopulation of patientsEuropean Organization for Research and Treatment of Cancer Quality of LifeEuropean Organization for Research and TreatmentPlus chemotherapyIII studiesPrognostic indexTreatment of Cancer Quality of LifeGlobal health status/quality of lifeGlobal health status/qualityRisk profiling of patients with relapsed/refractory diffuse large B-cell lymphoma by measuring circulating tumor DNA
Herrera A, Tracy S, Croft B, Opat S, Ray J, Lovejoy A, Musick L, Paulson J, Sehn L, Jiang Y. Risk profiling of patients with relapsed/refractory diffuse large B-cell lymphoma by measuring circulating tumor DNA. Blood Advances 2022, 6: 1651-1660. PMID: 35086141, PMCID: PMC8941482, DOI: 10.1182/bloodadvances.2021006415.Peer-Reviewed Original ResearchConceptsCirculating tumor DNAInternational Prognostic IndexProgression-free survivalBaseline circulating tumour DNAEnd of treatmentOverall survivalR/R DLBCLComplete responseTumor DNARelapsed/refractory diffuse large B-cell lymphomaDiffuse large B-cell lymphomaMeasuring circulating tumor DNAShorter progression-free survivalLarge B-cell lymphomaHigh risk of progressionCirculating tumour DNA assaysLactate dehydrogenaseBaseline ctDNA levelRisk profile of patientsB-cell lymphomaRisk of relapseRisk of progressionSerum lactate dehydrogenaseProfile of patientsCtDNA assessmentPrognostic mutational subtyping in de novo diffuse large B-cell lymphoma
Kim E, Jiang Y, Xu T, Bazeos A, Knapp A, Bolen C, Humphrey K, Nielsen T, Penuel E, Paulson J. Prognostic mutational subtyping in de novo diffuse large B-cell lymphoma. BMC Cancer 2022, 22: 231. PMID: 35236331, PMCID: PMC8892802, DOI: 10.1186/s12885-022-09237-5.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic Combined Chemotherapy ProtocolsBridged Bicyclo Compounds, HeterocyclicClinical Trials, Phase II as TopicClinical Trials, Phase III as TopicEnhancer of Zeste Homolog 2 ProteinExome SequencingFemaleHumansLymphoma, Large B-Cell, DiffuseMaleMiddle AgedMutationPrognosisProto-Oncogene Proteins c-bcl-2RNA-SeqSulfonamidesTreatment OutcomeConceptsSequence dataWhole-exome sequencing dataExome-sequencing dataTargeted sequencing platformsExome sequencing dataTargeted sequencing dataBackgroundDiffuse large B-cell lymphomaLarge B-cell lymphomaSequencing platformsRNA-seqDe novo DLBCLImproved overall survivalTarget sequenceB-cell lymphomaVenetoclax therapyMutation subtypesPrognostic subsetsMutationsOverall survivalMolecular subsetsSubset distributionMutation groupSurvival outcomesMutation profilesClinical associationsCytomegalovirus infections in infants in Uganda: Newborn-mother pairs, neonates with sepsis, and infants with hydrocephalus
Hehnly C, Ssentongo P, Bebell L, Burgoine K, Bazira J, Fronterre C, Kumbakumba E, Mulondo R, Mbabazi-Kabachelor E, Morton S, Ngonzi J, Ochora M, Olupot-Olupot P, Mugamba J, Onen J, Roberts D, Sheldon K, Sinnar S, Smith J, Ssenyonga P, Kiwanuka J, Paulson J, Meier F, Ericson J, Broach J, Schiff S. Cytomegalovirus infections in infants in Uganda: Newborn-mother pairs, neonates with sepsis, and infants with hydrocephalus. International Journal Of Infectious Diseases 2022, 118: 24-33. PMID: 35150915, PMCID: PMC9058984, DOI: 10.1016/j.ijid.2022.02.005.Peer-Reviewed Original ResearchConceptsNewborn-mother pairsCMV prevalenceClinical sepsisCytomegalovirus infectionCerebrospinal fluidPrevalence of CMVQuantitative PCRCMV positivityPostinfectious hydrocephalusVaginal sheddingCMV infectionHIV seropositivityNeonatal ageMaternal ageMaternal vaginalRisk factorsMedical CenterLong-term consequencesMother pairsSepsisNeonatesInfantsPrevalenceHydrocephalusCMVTRAIL Score: A Simple Model to Predict Immunochemotherapy Tolerability in Patients With Diffuse Large B-Cell Lymphoma
Harris W, Bataillard E, Choi Y, El-Galaly T, Cuchelkar V, Henneges C, Kwan A, Schneider D, Paulson J, Nielsen T. TRAIL Score: A Simple Model to Predict Immunochemotherapy Tolerability in Patients With Diffuse Large B-Cell Lymphoma. JCO Clinical Cancer Informatics 2022, 6: e2100121. PMID: 35044836, DOI: 10.1200/cci.21.00121.Peer-Reviewed Original ResearchConceptsDiffuse large B-cell lymphomaLarge B-cell lymphomaB-cell lymphomaR-CHOPFirst-line treatment of diffuse large B-cell lymphomaDiagnosed DLBCLTreatment of diffuse large B-cell lymphomaCourses of R-CHOPPresence of cardiovascular diseaseFirst-line treatmentProportion of patientsArea under the curveRoutine clinical settingCharlson Comorbidity IndexStandard of careHigh-risk categoryStandard chemoimmunotherapyChemotherapy toxicityCreatinine clearanceInferior outcomesBaseline characteristicsLogistic regression modelsComorbidity indexPatient frailtyEnd points
2021
Polatuzumab vedotin plus obinutuzumab and lenalidomide in patients with relapsed or refractory follicular lymphoma: a cohort of a multicentre, single-arm, phase 1b/2 study
Diefenbach C, Kahl B, McMillan A, Briones J, Banerjee L, Cordoba R, Miall F, Burke J, Hirata J, Jiang Y, Paulson J, Chang Y, Musick L, Abrisqueta P. Polatuzumab vedotin plus obinutuzumab and lenalidomide in patients with relapsed or refractory follicular lymphoma: a cohort of a multicentre, single-arm, phase 1b/2 study. The Lancet Haematology 2021, 8: e891-e901. PMID: 34826412, DOI: 10.1016/s2352-3026(21)00311-2.Peer-Reviewed Original ResearchConceptsRecommended phase 2 doseRefractory follicular lymphomaPhase 2 dosePolatuzumab vedotinFollicular lymphomaAdverse eventsDay 1Dose escalationResponse rateRefractory diffuse large B-cell lymphomaCohort 3Treatment due to disease progressionCohort 2Diffuse large B-cell lymphomaEastern Cooperative Oncology Group performance statusCycles of induction treatmentDose-limiting toxicity eventsLarge B-cell lymphomaTyrosine kinase inhibitor treatmentComponent drugsPhase 1b/2 studyPhase 1b/2 trialComplete response rateDose-escalation phaseDose-limiting toxicityMultivariable association discovery in population-scale meta-omics studies
Mallick H, Rahnavard A, McIver L, Ma S, Zhang Y, Nguyen L, Tickle T, Weingart G, Ren B, Schwager E, Chatterjee S, Thompson K, Wilkinson J, Subramanian A, Lu Y, Waldron L, Paulson J, Franzosa E, Bravo H, Huttenhower C. Multivariable association discovery in population-scale meta-omics studies. PLOS Computational Biology 2021, 17: e1009442. PMID: 34784344, PMCID: PMC8714082, DOI: 10.1371/journal.pcbi.1009442.Peer-Reviewed Original ResearchConceptsMicrobial community measurementsMeta-omics studiesMicrobial community featuresMulti-omics datasetsControlling false discoveriesLandscape of inflammatory bowel diseaseHuman microbiomeMaAsLinMulti-omicsOmics profilesFalse discoveriesCommunity measuresMicrobiomeEnvironmental conditionsInflammatory bowel diseaseCommunity featuresHealth outcomesAssociation discoveryMultivariate associationsMultiple covariatesEpidemiological studiesLongitudinal designObservational studyStatistical powerZero-inflation