2022
BAL Transcriptomes Characterize Idiopathic Pulmonary Fibrosis Endotypes With Prognostic Impact
De Sadeleer LJ, Verleden SE, Schupp JC, McDonough JE, Goos T, Yserbyt J, Bargagli E, Rottoli P, Kaminski N, Prasse A, Wuyts WA. BAL Transcriptomes Characterize Idiopathic Pulmonary Fibrosis Endotypes With Prognostic Impact. CHEST Journal 2022, 161: 1576-1588. PMID: 35063449, PMCID: PMC9424328, DOI: 10.1016/j.chest.2021.12.668.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisIPF samplesIndependent validation cohortAvailable gene expression datasetsClinical characteristicsPrognostic impactWorse survivalPathophysiologic mechanismsPulmonary fibrosisClinical evolutionClinical variablesValidation cohortEnrichment analysisBAL samplesSurvival-associated genesBlood samplesEndotypesStudy designControl participantsMitochondrial dysfunctionPatientsFibrosisSurvivalTranscription factorsNumeric trends
2018
S100A12 as a marker of worse cardiac output and mortality in pulmonary hypertension
Tzouvelekis A, Herazo‐Maya J, Ryu C, Chu J, Zhang Y, Gibson KF, Adonteng‐Boateng P, Li Q, Pan H, Cherry B, Ahmad F, Ford HJ, Herzog EL, Kaminski N, Fares WH. S100A12 as a marker of worse cardiac output and mortality in pulmonary hypertension. Respirology 2018, 23: 771-779. PMID: 29611244, PMCID: PMC6047907, DOI: 10.1111/resp.13302.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsPH patientsPH cohortCardiac outputWorld Health Organization group 1Idiopathic pulmonary fibrosis patientsPulmonary hypertension patientsPulmonary fibrosis patientsBlood mononuclear cellsProtein serum concentrationsHigher S100A12Pulmonary hypertensionS100A12 levelsOverall mortalityHypertension patientsPrognostic valueValidation cohortMononuclear cellsPeripheral bloodSerum concentrationsInflammatory diseasesGroup 1PatientsFibrosis patientsS100A12
2017
Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study
Herazo-Maya JD, Sun J, Molyneaux PL, Li Q, Villalba JA, Tzouvelekis A, Lynn H, Juan-Guardela BM, Risquez C, Osorio JC, Yan X, Michel G, Aurelien N, Lindell KO, Klesen MJ, Moffatt MF, Cookson WO, Zhang Y, Garcia JGN, Noth I, Prasse A, Bar-Joseph Z, Gibson KF, Zhao H, Herzog EL, Rosas IO, Maher TM, Kaminski N. Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study. The Lancet Respiratory Medicine 2017, 5: 857-868. PMID: 28942086, PMCID: PMC5677538, DOI: 10.1016/s2213-2600(17)30349-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleGene Expression ProfilingGenetic MarkersGenetic TestingHumansIdiopathic Pulmonary FibrosisLeukocytes, MononuclearLinear ModelsMaleMiddle AgedOligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsRisk AssessmentRisk FactorsTime FactorsVital CapacityConceptsIdiopathic pulmonary fibrosisTransplant-free survivalRisk profilePulmonary fibrosisAntifibrotic drugsPeripheral blood mononuclear cellsCox proportional hazards modelClinical prediction toolGroup of patientsBlood mononuclear cellsHigh-risk groupProportional hazards modelPulmonary Fibrosis FoundationPittsburgh cohortUntreated patientsCohort studyClinical courseIPF diagnosisBlood InstituteProspective studyVital capacityMononuclear cellsPeripheral bloodUS National InstitutesNational Heart
2016
Validation of the prognostic value of MMP‐7 in idiopathic pulmonary fibrosis
Tzouvelekis A, Herazo‐Maya J, Slade M, Chu J, Deiuliis G, Ryu C, Li Q, Sakamoto K, Ibarra G, Pan H, Gulati M, Antin‐Ozerkis D, Herzog EL, Kaminski N. Validation of the prognostic value of MMP‐7 in idiopathic pulmonary fibrosis. Respirology 2016, 22: 486-493. PMID: 27761978, PMCID: PMC5352520, DOI: 10.1111/resp.12920.Peer-Reviewed Original ResearchConceptsTransplant-free survivalIdiopathic pulmonary fibrosisMMP-7 concentrationsMatrix metalloproteinase-7IPF patientsCause mortalityPulmonary fibrosisHealthy controlsMultivariate Cox proportional hazards modelCox proportional hazards modelPulmonary function parametersVariable clinical courseBaseline pulmonary function parametersProportional hazards modelIPF biomarkersProgressive diseaseClinical coursePoor prognosisPrognostic valueVital capacityIndependent biomarkerLung capacityPrognostic thresholdPlasma concentrationsMortality riskAcute Exacerbation of Idiopathic Pulmonary Fibrosis. An International Working Group Report
Collard HR, Ryerson CJ, Corte TJ, Jenkins G, Kondoh Y, Lederer DJ, Lee JS, Maher TM, Wells AU, Antoniou KM, Behr J, Brown KK, Cottin V, Flaherty KR, Fukuoka J, Hansell DM, Johkoh T, Kaminski N, Kim DS, Kolb M, Lynch DA, Myers JL, Raghu G, Richeldi L, Taniguchi H, Martinez FJ. Acute Exacerbation of Idiopathic Pulmonary Fibrosis. An International Working Group Report. American Journal Of Respiratory And Critical Care Medicine 2016, 194: 265-275. PMID: 27299520, DOI: 10.1164/rccm.201604-0801ci.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisAcute exacerbationPulmonary fibrosisRespiratory deteriorationAcute respiratory deteriorationWorking Group ReportEvidence-based updateRisk factorsUnidentifiable causeDiagnostic criteriaExacerbationFibrosisGroup ReportComprehensive updateEtiologyText publicationsWorking GroupLiterature reviewReportPrognosisEpidemiologyDeterioration
2015
A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis
Huang Y, Ma SF, Vij R, Oldham JM, Herazo-Maya J, Broderick SM, Strek ME, White SR, Hogarth DK, Sandbo NK, Lussier YA, Gibson KF, Kaminski N, Garcia JG, Noth I. A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis. BMC Pulmonary Medicine 2015, 15: 147. PMID: 26589497, PMCID: PMC4654815, DOI: 10.1186/s12890-015-0142-8.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisPrognostic indexIPF patientsPulmonary fibrosisValidation cohortTraining cohortMultivariate Cox regression survival analysisPrognostic modelPeripheral blood mononuclear cellsUnivariate Cox regression analysisCox regression survival analysisLow-risk patientsWeighted gene co-expression network analysisCox regression analysisBlood mononuclear cellsCourse of diseaseIndependent validation cohortRegression survival analysisNovel prognostic modelPredictor genesT cell biologyT cell receptorCurrent prognostic toolsFunctional pathway analysisFold change
2014
T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES
Altman R, Dunker A, Hunter L, Ritchie M, Murray T, Klein T, HUANG G, TSAMARDINOS I, RAGHU V, KAMINSKI N, BENOS P. T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES. Biocomputing 2014, 20: 431-42. PMID: 25592602, PMCID: PMC4299881, DOI: 10.1142/9789814644730_0041.Peer-Reviewed Original ResearchConceptsTraditional feature selection methodsFeature selection methodCohort of patientsPersonalized medicine strategiesReal expression dataFeature selectionClassification accuracyCluster selectionBiological datasetsClinical outcomesCluster featuresLung diseaseBreast cancerSelection methodPatient classificationStructured natureMedicine strategiesSurvival dataTarget variablesEfficient selectionCohortStable selectionImportant featuresC-X-C Motif Chemokine 13 (CXCL13) Is a Prognostic Biomarker of Idiopathic Pulmonary Fibrosis
Vuga LJ, Tedrow JR, Pandit KV, Tan J, Kass DJ, Xue J, Chandra D, Leader JK, Gibson KF, Kaminski N, Sciurba FC, Duncan SR. C-X-C Motif Chemokine 13 (CXCL13) Is a Prognostic Biomarker of Idiopathic Pulmonary Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2014, 189: 966-974. PMID: 24628285, PMCID: PMC4098096, DOI: 10.1164/rccm.201309-1592oc.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overBiomarkersCase-Control StudiesChemokine CXCL13Disease ProgressionEnzyme-Linked Immunosorbent AssayFemaleHumansIdiopathic Pulmonary FibrosisImmunohistochemistryMaleMiddle AgedOligonucleotide Array Sequence AnalysisPredictive Value of TestsPrognosisPulmonary Disease, Chronic ObstructiveRisk FactorsSensitivity and SpecificitySeverity of Illness IndexConceptsChronic obstructive pulmonary diseaseC motif chemokine 13IPF lungsPrognostic biomarkerB cellsIdiopathic pulmonary fibrosis (IPF) pathogenesisB cell-targeted therapiesAntibody-mediated syndromeDysregulated B cellsPulmonary fibrosis pathogenesisPulmonary artery hypertensionObstructive pulmonary diseaseIdiopathic pulmonary fibrosisSix-month survivalB-cell traffickingAcute exacerbationArtery hypertensionCXCL13 mRNAPlasma CXCL13IPF pathogenesisRespiratory failureLung injuryCXCL13 concentrationsPulmonary diseaseRadiographic emphysema
2013
Patients with Idiopathic Pulmonary Fibrosis with Antibodies to Heat Shock Protein 70 Have Poor Prognoses
Kahloon RA, Xue J, Bhargava A, Csizmadia E, Otterbein L, Kass DJ, Bon J, Soejima M, Levesque MC, Lindell KO, Gibson KF, Kaminski N, Banga G, Oddis CV, Pilewski JM, Sciurba FC, Donahoe M, Zhang Y, Duncan SR. Patients with Idiopathic Pulmonary Fibrosis with Antibodies to Heat Shock Protein 70 Have Poor Prognoses. American Journal Of Respiratory And Critical Care Medicine 2013, 187: 768-775. PMID: 23262513, PMCID: PMC3678112, DOI: 10.1164/rccm.201203-0506oc.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisAnti-HSP70 autoantibodiesPulmonary fibrosisAntigen-specific immune responsesAntigen-specific immunoassaysLung function deteriorationCD4 T cellsInterstitial lung diseaseIL-4 productionIL-8 productionUseful clinical informationHeat shock protein 70Acute exacerbationDiverse autoantibodiesIPF cohortIPF outcomesShock protein 70Function deteriorationMost patientsSpecific autoantibodiesIPF lungsIgG autoantibodiesClinical progressionPoor prognosisIPF progression
2012
Biomarkers in idiopathic pulmonary fibrosis
Zhang Y, Kaminski N. Biomarkers in idiopathic pulmonary fibrosis. Current Opinion In Pulmonary Medicine 2012, 18: 441-446. PMID: 22847105, PMCID: PMC4165635, DOI: 10.1097/mcp.0b013e328356d03c.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisPeripheral blood biomarkersPulmonary fibrosisBlood biomarkersDisease presenceMultiple clinical contextsPeripheral bloodPredictive biomarkersGene polymorphismsLarger studyDrug studiesOutcome trajectoriesPrediction ruleClinical contextBiomarkersMolecular biomarkersMultiple studiesPatientsFibrosisSufficient evidenceOutcomesProtein markersConvincing evidenceRecent studiesMarkers
2010
CD28 Down-Regulation on Circulating CD4 T-Cells Is Associated with Poor Prognoses of Patients with Idiopathic Pulmonary Fibrosis
Gilani SR, Vuga LJ, Lindell KO, Gibson KF, Xue J, Kaminski N, Valentine VG, Lindsay EK, George MP, Steele C, Duncan SR. CD28 Down-Regulation on Circulating CD4 T-Cells Is Associated with Poor Prognoses of Patients with Idiopathic Pulmonary Fibrosis. PLOS ONE 2010, 5: e8959. PMID: 20126467, PMCID: PMC2813297, DOI: 10.1371/journal.pone.0008959.Peer-Reviewed Original ResearchMeSH KeywordsAdultCD28 AntigensCD4-Positive T-LymphocytesDown-RegulationHumansMalePrognosisPulmonary FibrosisConceptsIdiopathic pulmonary fibrosisCD4 T cellsPro-inflammatory cytokinesIPF patientsT cellsPulmonary fibrosisClinical eventsManifestations of IPFPeripheral blood CD4 T cellsCirculating CD4 T cellsMajor adverse clinical eventsBlood CD4 T cellsRegulatory T-cell marker FOXP3Proliferative T cell responsesAdaptive immune activationConfidence interval (CI) 1.6Cytotoxic mediators perforinOne-year freedomMajor adverse eventsAdverse clinical eventsT cell responsesAntigen-driven proliferationAntigen-induced proliferationCytokine multiplex assayClinical deterioration
2009
Systemic Inhibition of NF-κB Activation Protects from Silicosis
Di Giuseppe M, Gambelli F, Hoyle GW, Lungarella G, Studer SM, Richards T, Yousem S, McCurry K, Dauber J, Kaminski N, Leikauf G, Ortiz LA. Systemic Inhibition of NF-κB Activation Protects from Silicosis. PLOS ONE 2009, 4: e5689. PMID: 19479048, PMCID: PMC2682759, DOI: 10.1371/journal.pone.0005689.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCytokinesEpithelial CellsFemaleGene Expression RegulationGenes, DominantHumansI-kappa B ProteinsLungLung TransplantationMacrophagesMaleMiceMice, Inbred C57BLMiddle AgedNF-kappa BNF-KappaB Inhibitor alphaNitrilesPrognosisRNA, MessengerSilicon DioxideSilicosisSulfonesTumor Necrosis Factor-alphaConceptsNF-kappaB activationLung transplantationSystemic inhibitionLung injuryCollagen depositionLung transplant databaseIdiopathic pulmonary fibrosisComplex lung diseaseNecrosis factor alphaPathogenesis of silicosisIkappaB-alpha phosphorylationInnate immune responsePotential therapeutic strategyNF-kappaB inhibitionMouse experimental modelIPF patientsLung graftsGraft rejectionOverall survivalSurvival benefitTransplant databasePulmonary fibrosisPoor outcomeInflammatory cellsLung disease
2005
Can Blood Gene Expression Predict Which Patients with Multiple Sclerosis Will Respond to Interferon?
Kaminski N, Achiron A. Can Blood Gene Expression Predict Which Patients with Multiple Sclerosis Will Respond to Interferon? PLOS Medicine 2005, 2: e33. PMID: 15736992, PMCID: PMC549584, DOI: 10.1371/journal.pmed.0020033.Peer-Reviewed Original Research
2004
Interferon-γ 1b in Idiopathic Pulmonary Fibrosis
Dauber JH, Gibson KF, Kaminski N. Interferon-γ 1b in Idiopathic Pulmonary Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2004, 170: 107-108. PMID: 15242850, DOI: 10.1164/rccm.2405001.Peer-Reviewed Original ResearchGene Expression Patterns, Prognostic and Diagnostic Markers, and Lung Cancer Biology
Kaminski N, Krupsky M. Gene Expression Patterns, Prognostic and Diagnostic Markers, and Lung Cancer Biology. CHEST Journal 2004, 125: 111s-115s. PMID: 15136452, DOI: 10.1378/chest.125.5_suppl.111s-a.Peer-Reviewed Original Research