2018
A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers
Gettinger SN, Choi J, Mani N, Sanmamed MF, Datar I, Sowell R, Du VY, Kaftan E, Goldberg S, Dong W, Zelterman D, Politi K, Kavathas P, Kaech S, Yu X, Zhao H, Schlessinger J, Lifton R, Rimm DL, Chen L, Herbst RS, Schalper KA. A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers. Nature Communications 2018, 9: 3196. PMID: 30097571, PMCID: PMC6086912, DOI: 10.1038/s41467-018-05032-8.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsAntibodies, BlockingCarcinogenesisCarcinoma, Non-Small-Cell LungCell ProliferationCytotoxicity, ImmunologicHistocompatibility Antigens Class IHumansLung NeoplasmsLymphocyte ActivationLymphocytes, Tumor-InfiltratingMaleMice, Inbred NODMice, SCIDMutant ProteinsMutationPeptidesPhenotypeProgrammed Cell Death 1 ReceptorReproducibility of ResultsSurvival AnalysisTobaccoConceptsImmune checkpoint blockersCheckpoint blockersQuantitative immunofluorescenceNon-small cell lung carcinoma patientsCell lung carcinoma patientsNon-small cell lung carcinomaPatient-derived xenograft modelsIntratumoral T cellsMultiplexed quantitative immunofluorescencePD-1 blockadeLevels of CD3Lung carcinoma patientsCell lung carcinomaT cell proliferationPre-treatment samplesTIL phenotypeSurvival benefitCarcinoma patientsEffector capacityLung carcinomaT cellsWhole-exome DNA sequencingXenograft modelFavorable responseBlockers
2016
Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer
McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelakanou V, Rehman J, Velcheti V, Herbst R, LoRusso P, Rimm DL. Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer. JAMA Oncology 2016, 2: 1-9. PMID: 26562159, PMCID: PMC4941982, DOI: 10.1001/jamaoncol.2015.3638.Peer-Reviewed Original ResearchMeSH KeywordsAgedAntibodies, MonoclonalAntibody SpecificityB7-H1 AntigenBiomarkers, TumorCarcinoma, Non-Small-Cell LungFemaleFluorescent Antibody TechniqueHumansImmunohistochemistryLung NeoplasmsMaleObserver VariationPredictive Value of TestsReproducibility of ResultsRetrospective StudiesTissue Array AnalysisConceptsTumor-infiltrating lymphocytesPD-L1 expressionPD-L1 antibodiesPD-L1 protein expressionCell lung cancerPD-L1Whole tissue sectionsQuantitative immunofluorescenceLung cancerChromogenic immunohistochemistryPoor concordanceDifferent PD-L1 antibodiesHigh tumor-infiltrating lymphocytesTumor PD-L1 expressionPD-L1 protein levelsCell lung cancer biopsiesMonoclonal antibodiesCurrent consensus guidelinesProtein expressionDurable clinical responsesMain outcome measuresEarly phase trialsLung cancer biopsiesRabbit monoclonal antibodyCorresponding tissue microarrays
2013
Programmed death ligand-1 expression in non-small cell lung cancer
Velcheti V, Schalper KA, Carvajal DE, Anagnostou VK, Syrigos KN, Sznol M, Herbst RS, Gettinger SN, Chen L, Rimm DL. Programmed death ligand-1 expression in non-small cell lung cancer. Laboratory Investigation 2013, 94: 107-116. PMID: 24217091, PMCID: PMC6125250, DOI: 10.1038/labinvest.2013.130.Peer-Reviewed Original ResearchMeSH KeywordsAgedB7-H1 AntigenBiomarkers, TumorCarcinoma, Non-Small-Cell LungCell Line, TumorChi-Square DistributionCohort StudiesConnecticutFemaleGreeceHumansImmunohistochemistryLung NeoplasmsLymphocytes, Tumor-InfiltratingMalePrognosisReproducibility of ResultsRNA, MessengerSurvival AnalysisTissue Array AnalysisConceptsNon-small cell lung cancerPD-L1 expressionCell lung cancerPD-L1Tissue microarrayBetter outcomesNSCLC casesLung cancerDeath ligand 1 (PD-L1) expressionCell death ligand 1PD-L1 protein expressionEarly phase clinical trialsLigand 1 expressionTumor-infiltrating lymphocytesDeath ligand 1Significant better outcomePD-L1 mRNAPD-L1 proteinPhase clinical trialsNormal human placentaPrediction of responseQuantitative fluorescence approachesFrequency of expressionPD-1Prognostic valueAn Epithelial–Mesenchymal Transition Gene Signature Predicts Resistance to EGFR and PI3K Inhibitors and Identifies Axl as a Therapeutic Target for Overcoming EGFR Inhibitor Resistance
Byers LA, Diao L, Wang J, Saintigny P, Girard L, Peyton M, Shen L, Fan Y, Giri U, Tumula PK, Nilsson MB, Gudikote J, Tran H, Cardnell RJ, Bearss DJ, Warner SL, Foulks JM, Kanner SB, Gandhi V, Krett N, Rosen ST, Kim ES, Herbst RS, Blumenschein GR, Lee JJ, Lippman SM, Ang KK, Mills GB, Hong WK, Weinstein JN, Wistuba II, Coombes KR, Minna JD, Heymach JV. An Epithelial–Mesenchymal Transition Gene Signature Predicts Resistance to EGFR and PI3K Inhibitors and Identifies Axl as a Therapeutic Target for Overcoming EGFR Inhibitor Resistance. Clinical Cancer Research 2013, 19: 279-290. PMID: 23091115, PMCID: PMC3567921, DOI: 10.1158/1078-0432.ccr-12-1558.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAxl Receptor Tyrosine KinaseCarcinoma, Non-Small-Cell LungCell Line, TumorCluster AnalysisDrug Resistance, NeoplasmEpithelial-Mesenchymal TransitionErbB ReceptorsGene Expression ProfilingHumansLung NeoplasmsMiceNeoplasm MetastasisPhosphoinositide-3 Kinase InhibitorsProtein Kinase InhibitorsProteomeProteomicsProto-Oncogene ProteinsReceptor Protein-Tyrosine KinasesRecurrenceReproducibility of ResultsConceptsEpithelial-mesenchymal transitionPotential therapeutic targetEGFR inhibitor resistanceTherapeutic targetEMT signatureInhibitor resistanceMesenchymal transition gene signatureMesenchymal cellsCell linesBiomarker-Integrated ApproachesPI3K/Akt pathway inhibitorNon-small cell lung carcinoma cell lineEGFR mutation statusReceptor tyrosine kinase AXLNSCLC cell linesPI3K/Akt inhibitorCell lung carcinoma cell lineGene expression profilesTyrosine kinase AXLLung carcinoma cell linePI3K inhibitorsDrug response analysisAkt pathway inhibitorCarcinoma cell linesErlotinib resistance
2009
Classification by Mass Spectrometry Can Accurately and Reliably Predict Outcome in Patients with Non-small Cell Lung Cancer Treated with Erlotinib-Containing Regimen
Salmon S, Chen H, Chen S, Herbst R, Tsao A, Tran H, Sandler A, Billheimer D, Shyr Y, Lee JW, Massion P, Brahmer J, Schiller J, Carbone D, Dang TP. Classification by Mass Spectrometry Can Accurately and Reliably Predict Outcome in Patients with Non-small Cell Lung Cancer Treated with Erlotinib-Containing Regimen. Journal Of Thoracic Oncology 2009, 4: 689-696. PMID: 19404214, PMCID: PMC3563261, DOI: 10.1097/jto.0b013e3181a526b3.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedAntineoplastic Combined Chemotherapy ProtocolsBevacizumabBiomarkers, TumorCarcinoma, Non-Small-Cell LungCase-Control StudiesCohort StudiesErlotinib HydrochlorideFemaleHumansLung NeoplasmsMaleMiddle AgedNeoplasm Recurrence, LocalPleural Effusion, MalignantPrognosisQuinazolinesReproducibility of ResultsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationSurvival RateTreatment OutcomeConceptsNon-small cell lung cancerCell lung cancerLung cancerRefractory non-small cell lung cancerPhase I/II studyUnivariate Cox proportional hazards modelProgression-free survival outcomesCox proportional hazards modelOutcomes of patientsCohort of patientsSelection of patientsVascular endothelial growth factorProportional hazards modelEndothelial growth factorReceptor kinase inhibitorEpidermal growth factor receptorGrowth factor receptorII studyOverall survivalPretreatment serumTreatment cohortsClinical outcomesSurvival outcomesEpidermal growth factor receptor kinase inhibitorsSuch therapyTumor Blood Flow Measured by Perfusion Computed Tomography and 15O-Labeled Water Positron Emission Tomography
Ng CS, Kodama Y, Mullani NA, Barron BJ, Wei W, Herbst RS, Abbruzzese JL, Charnsangavej C. Tumor Blood Flow Measured by Perfusion Computed Tomography and 15O-Labeled Water Positron Emission Tomography. Journal Of Computer Assisted Tomography 2009, 33: 460-465. PMID: 19478644, DOI: 10.1097/rct.0b013e318182d2e0.Peer-Reviewed Original ResearchConceptsWater positron emission tomographyPositron emission tomographyBlood flowEmission tomographyMean blood flow valuesBlood flow estimatesPerfusion Computed TomographyTumor blood flowPairs of examinationsBlood flow valuesMixed regression analysesUse of PCTBlood flow measurementsClinical gold standardIndex tumorClinical studiesComputed tomographySolid tumorsTumorsGold standardBland-AltmanTomographySignificant differencesRegression analysisT-test
2006
Development and Validation of a Drug Activity Biomarker that Shows Target Inhibition in Cancer Patients Receiving Enzastaurin, a Novel Protein Kinase C-β Inhibitor
Green LJ, Marder P, Ray C, Cook CA, Jaken S, Musib LC, Herbst RS, Carducci M, Britten CD, Basche M, Eckhardt SG, Thornton D. Development and Validation of a Drug Activity Biomarker that Shows Target Inhibition in Cancer Patients Receiving Enzastaurin, a Novel Protein Kinase C-β Inhibitor. Clinical Cancer Research 2006, 12: 3408-3415. PMID: 16740765, DOI: 10.1158/1078-0432.ccr-05-2231.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Combined Chemotherapy ProtocolsBiomarkersCapecitabineCell Line, TumorClinical Trials, Phase I as TopicDeoxycytidineEnzyme ActivatorsEnzyme InhibitorsFlow CytometryFluorouracilFollow-Up StudiesHumansIndolesLeukocytes, MononuclearMonocytesNeoplasmsProtein Kinase CProtein Kinase C betaReproducibility of ResultsSensitivity and SpecificitySignal TransductionStructure-Activity RelationshipTreatment OutcomeConceptsPeripheral blood mononuclear cellsDaily oral dosesBlood mononuclear cellsCancer patientsOral dosesMononuclear cellsFlow cytometryDrug activity biomarkerPKC activityTarget cellsActivity biomarkersPhorbol esterNormal donorsPatientsActivity of PKCU937 cell lineTarget inhibitionEnzastaurinKinase inhibitorsΒ inhibitorSignificant decreaseCell linesU937 cellsIntracellular phosphoproteinsProtein kinase C
2003
Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response.
Erasmus JJ, Gladish GW, Broemeling L, Sabloff BS, Truong MT, Herbst RS, Munden RF. Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. Journal Of Clinical Oncology 2003, 21: 2574-82. PMID: 12829678, DOI: 10.1200/jco.2003.01.144.Peer-Reviewed Original ResearchConceptsTumor sizeTumor responseLung tumorsSerial measurementsComputed tomographyWorld Health Organization criteriaResponse Evaluation CriteriaLung tumor sizeProgressive diseaseWHO criteriaOrganization criteriaLung lesionsSolid malignanciesStudy groupCT scanSolid tumorsIrregular tumorTumorsThoracic radiologistsResponse criteriaSignificant differencesPatientsInterobserver measurementsUnidimensional measurementBD measurements