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 therapy