2021
Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
Moore MR, Friesner ID, Rizk EM, Fullerton BT, Mondal M, Trager MH, Mendelson K, Chikeka I, Kurc T, Gupta R, Rohr BR, Robinson EJ, Acs B, Chang R, Kluger H, Taback B, Geskin LJ, Horst B, Gardner K, Niedt G, Celebi JT, Gartrell-Corrado RD, Messina J, Ferringer T, Rimm DL, Saltz J, Wang J, Vanguri R, Saenger YM. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Scientific Reports 2021, 11: 2809. PMID: 33531581, PMCID: PMC7854647, DOI: 10.1038/s41598-021-82305-1.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiopsyChemotherapy, AdjuvantClinical Decision-MakingDeep LearningFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingMaleMelanomaMiddle AgedNeoplasm StagingPatient SelectionPrognosisRetrospective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsYoung AdultConceptsTumor-infiltrating lymphocytesDisease-specific survivalEarly-stage melanomaOpen-source deep learningCutoff valueMultivariable Cox proportional hazards analysisCox proportional hazards analysisDeep learningLow-risk patientsProportional hazards analysisKaplan-Meier analysisAccurate prognostic biomarkersEosin imagesAccuracy of predictionAdjuvant therapyRisk patientsSpecific survivalPrognostic valueValidation cohortReceiver operating curvesTraining cohortTIL analysisClinical trialsPrimary melanomaPrognostic biomarkerUsing Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma
Johannet P, Coudray N, Donnelly DM, Jour G, Illa-Bochaca I, Xia Y, Johnson DB, Wheless L, Patrinely JR, Nomikou S, Rimm DL, Pavlick AC, Weber JS, Zhong J, Tsirigos A, Osman I. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clinical Cancer Research 2021, 27: 131-140. PMID: 33208341, PMCID: PMC7785656, DOI: 10.1158/1078-0432.ccr-20-2415.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedDisease ProgressionDrug Resistance, NeoplasmFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedImmune Checkpoint InhibitorsMachine LearningMaleMelanomaMiddle AgedNeoplasm StagingPrognosisProgression-Free SurvivalProspective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsConceptsProgression-free survivalImmune checkpoint inhibitorsLower riskClinicodemographic characteristicsAdvanced melanomaClinical dataWorse progression-free survivalICI treatment outcomesKaplan-Meier curvesBiomarkers of responseStandard of careCheckpoint inhibitorsICI responseImmunotherapy responseValidation cohortTraining cohortDisease progressionProspective validationTreatment outcomesHigh riskClinical practicePatientsROC curveProgressionRisk
2020
A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study
Lu C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J, Choi H, Schmid RA, Berezowska S, Schalper K, Rimm D, Velcheti V, Madabhushi A. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet Digital Health 2020, 2: e594-e606. PMID: 33163952, PMCID: PMC7646741, DOI: 10.1016/s2589-7500(20)30225-9.Peer-Reviewed Original ResearchConceptsNon-small cell lung carcinomaEarly-stage non-small cell lung carcinomaOverall survivalRetrospective studyEarly-stage non-small cell lung cancerNon-small cell lung cancerMultivariable Cox regression analysisCell differentiation pathwayCox proportional hazards modelLung squamous cell carcinomaEarly-stage LUADOverall survival informationCox regression analysisPrognosis of patientsCell lung cancerRisk stratification modelSquamous cell carcinomaLung cancer pathogenesisIndependent validation cohortCell lung carcinomaProportional hazards modelComputer-extracted featuresAdjuvant therapyDifferentiation pathwayValidation cohortImmune Cell PD-L1 Colocalizes with Macrophages and Is Associated with Outcome in PD-1 Pathway Blockade Therapy
Liu Y, Zugazagoitia J, Ahmed FS, Henick BS, Gettinger S, Herbst RS, Schalper KA, Rimm DL. Immune Cell PD-L1 Colocalizes with Macrophages and Is Associated with Outcome in PD-1 Pathway Blockade Therapy. Clinical Cancer Research 2020, 26: 970-977. PMID: 31615933, PMCID: PMC7024671, DOI: 10.1158/1078-0432.ccr-19-1040.Peer-Reviewed Original ResearchConceptsPD-L1 expressionHigh PD-L1 expressionPD-L1 levelsPD-L1Immune cellsTumor cellsT cellsHigh PD-L1 levelsPredominant immune cell typeNon-small cell lung cancer (NSCLC) casesDifferent immune cell subsetsCell lung cancer casesElevated PD-L1High PD-L1Better overall survivalDeath ligand 1Natural killer cellsImmune cell subsetsMultiple immune cellsCytotoxic T cellsLung cancer casesImmune cell typesCD68 levelsCell typesBlockade therapy
2019
An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma
Acs B, Ahmed FS, Gupta S, Wong P, Gartrell RD, Sarin Pradhan J, Rizk EM, Gould Rothberg BE, Saenger YM, Rimm DL. An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma. Nature Communications 2019, 10: 5440. PMID: 31784511, PMCID: PMC6884485, DOI: 10.1038/s41467-019-13043-2.Peer-Reviewed Original ResearchConceptsOpen sourceOpen source softwareSource softwareTIL scoreTraining setDisease-specific overall survivalHigh TIL scorePoor prognosis cohortsSubset of patientsAlgorithmIndependent prognostic markerBroad adoptionAssessment of tumorOverall survivalFavorable prognosisMelanoma patientsMultivariable analysisValidation cohortIndependent associationPrognostic markerSeparate patientsPrognostic variablesIndependent cohortRetrospective collectionMelanomaQuantitative Assessment of CMTM6 in the Tumor Microenvironment and Association with Response to PD-1 Pathway Blockade in Advanced-Stage Non–Small Cell Lung Cancer
Zugazagoitia J, Liu Y, Toki M, McGuire J, Ahmed FS, Henick BS, Gupta R, Gettinger S, Herbst R, Schalper KA, Rimm DL. Quantitative Assessment of CMTM6 in the Tumor Microenvironment and Association with Response to PD-1 Pathway Blockade in Advanced-Stage Non–Small Cell Lung Cancer. Journal Of Thoracic Oncology 2019, 14: 2084-2096. PMID: 31605795, PMCID: PMC6951804, DOI: 10.1016/j.jtho.2019.09.014.Peer-Reviewed Original ResearchConceptsPD-L1CMTM6 expressionPathway blockadeAdvanced stage non-small cell lung cancerNon-small cell lung cancerPD-1 pathway blockadeTumor cellsAbsence of immunotherapyMultiplexed quantitative immunofluorescencePD-L1 coexpressionStromal immune cellsPD-L1 expressionT cell infiltrationLonger overall survivalCell lung cancerIndependent retrospective cohortsKRAS mutational statusExpression of CMTM6MARVEL transmembrane domainNSCLC cohortOverall survivalRetrospective cohortAxis blockadeClinical featuresImmunotherapy outcomesMultiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma
Wong PF, Wei W, Smithy JW, Acs B, Toki MI, Blenman K, Zelterman D, Kluger HM, Rimm DL. Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma. Clinical Cancer Research 2019, 25: 2442-2449. PMID: 30617133, PMCID: PMC6467753, DOI: 10.1158/1078-0432.ccr-18-2652.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAntineoplastic Agents, ImmunologicalBiomarkersBiomarkers, TumorFemaleFluorescent Antibody TechniqueHumansImmunohistochemistryImmunotherapyKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingMaleMelanomaMiddle AgedMolecular Targeted TherapyNeoplasm StagingROC CurveT-Lymphocyte SubsetsConceptsCell countTIL activationQuantitative immunofluorescenceLymphocytic infiltrationMelanoma patientsMetastatic melanomaAnti-PD-1 responseAnti-PD-1 therapyCell death 1 (PD-1) inhibitionAbsence of immunotherapyDeath-1 (PD-1) inhibitionDisease control rateProgression-free survivalCD8 cell countsTumor-Infiltrating LymphocytesNew predictive biomarkersWhole tissue sectionsRECIST 1.1Progressive diseaseDurable responsesObjective responsePartial responseImmunotherapy outcomesLymphocyte profilesMultivariable analysisExpression and clinical significance of PD-L1, B7-H3, B7-H4 and TILs in human small cell lung Cancer (SCLC)
Carvajal-Hausdorf D, Altan M, Velcheti V, Gettinger SN, Herbst RS, Rimm DL, Schalper KA. Expression and clinical significance of PD-L1, B7-H3, B7-H4 and TILs in human small cell lung Cancer (SCLC). Journal For ImmunoTherapy Of Cancer 2019, 7: 65. PMID: 30850021, PMCID: PMC6408760, DOI: 10.1186/s40425-019-0540-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overB7 AntigensB7-H1 AntigenBiomarkers, TumorFemaleFluorescent Antibody TechniqueHumansKaplan-Meier EstimateLung NeoplasmsLymphocytes, Tumor-InfiltratingMaleMiddle AgedNeoplasm GradingNeoplasm StagingPrognosisRetrospective StudiesSmall Cell Lung CarcinomaV-Set Domain-Containing T-Cell Activation Inhibitor 1ConceptsSmall cell lung cancerCell lung cancerB7-H4B7-H3Lung cancerPD-L1Non-small cell lung cancerBackgroundSmall cell lung cancerAnti-tumor immune responseHuman small cell lung cancerQuantitative immunofluorescenceB7 family ligandsLevels of TILsMultiplexed quantitative immunofluorescenceLevels of CD3Effector T cellsImmune checkpoint blockersPromising clinical activityTissue microarray formatLymphocyte subsetsCheckpoint blockersOverall survivalLung malignancyClinicopathological variablesMarker levelsSpatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer
Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer. Clinical Cancer Research 2019, 25: 1526-1534. PMID: 30201760, PMCID: PMC6397708, DOI: 10.1158/1078-0432.ccr-18-2013.Peer-Reviewed Original Research
2018
Quantitative Spatial Profiling of PD-1/PD-L1 Interaction and HLA-DR/IDO-1 Predicts Improved Outcomes of Anti–PD-1 Therapies in Metastatic Melanoma
Johnson DB, Bordeaux J, Kim J, Vaupel C, Rimm DL, Ho TH, Joseph RW, Daud AI, Conry RM, Gaughan EM, Hernandez-Aya LF, Dimou A, Funchain P, Smithy J, Witte JS, McKee SB, Ko J, Wrangle J, Dabbas B, Tangri S, Lameh J, Hall J, Markowitz J, Balko JM, Dakappagari N. Quantitative Spatial Profiling of PD-1/PD-L1 Interaction and HLA-DR/IDO-1 Predicts Improved Outcomes of Anti–PD-1 Therapies in Metastatic Melanoma. Clinical Cancer Research 2018, 24: 5250-5260. PMID: 30021908, PMCID: PMC6214750, DOI: 10.1158/1078-0432.ccr-18-0309.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorBiopsyCell Line, TumorFemaleHLA-DR AntigensHumansImmunohistochemistryIndoleamine-Pyrrole 2,3,-DioxygenaseMaleMelanomaMiddle AgedModels, BiologicalNeoplasm MetastasisNeoplasm StagingPrognosisProgrammed Cell Death 1 ReceptorProtein BindingRetreatmentTreatment OutcomeConceptsAnti-PD-1 responseHLA-DRValidation cohortPD-1/PD-L1PD-1 blockersPD-1 monotherapyPD-L1 expressionPretreatment tumor biopsiesProgression-free survivalSubset of patientsAcademic cancer centerBiomarkers of responseIndependent validation cohortClin Cancer ResImmunosuppression mechanismsClinical responseOverall survivalPD-L1Melanoma patientsCancer CenterTreatment outcomesTumor biopsiesDiscovery cohortPatientsIndividual biomarkers
2017
Implications of the tumor immune microenvironment for staging and therapeutics
Taube JM, Galon J, Sholl LM, Rodig SJ, Cottrell TR, Giraldo NA, Baras AS, Patel SS, Anders RA, Rimm DL, Cimino-Mathews A. Implications of the tumor immune microenvironment for staging and therapeutics. Modern Pathology 2017, 31: 214-234. PMID: 29192647, PMCID: PMC6132263, DOI: 10.1038/modpathol.2017.156.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorHumansLymphocytes, Tumor-InfiltratingNeoplasm StagingNeoplasmsPrognosisTumor MicroenvironmentConceptsTumor immune microenvironmentImmune microenvironmentTumor typesTumor microenvironmentAnti-PD-1/PD-L1Therapeutic targetPD-1/PD-L1 axisFirst line treatment algorithmHost antitumor immune responseEarly stage colorectal carcinomaLocal immune contextureImmune checkpoint inhibitorsPD-L1 axisAntitumor immune responseImmune-based therapiesPD-L1 antibodiesAbundance of CD8Th1 helper cellsNovel therapeutic targetPotential therapeutic targetPrimary organ siteNew candidate biomarkersNumerous tumor typesSpecific tumor typesCurrent TNMDifferential Expression and Significance of PD-L1, IDO-1, and B7-H4 in Human Lung Cancer
Schalper KA, Carvajal-Hausdorf D, McLaughlin J, Altan M, Velcheti V, Gaule P, Sanmamed MF, Chen L, Herbst RS, Rimm DL. Differential Expression and Significance of PD-L1, IDO-1, and B7-H4 in Human Lung Cancer. Clinical Cancer Research 2017, 23: 370-378. PMID: 27440266, PMCID: PMC6350535, DOI: 10.1158/1078-0432.ccr-16-0150.Peer-Reviewed Original ResearchMeSH KeywordsA549 CellsAgedB7-H1 AntigenBiomarkers, TumorCarcinoma, Non-Small-Cell LungDisease-Free SurvivalDrug Resistance, NeoplasmGene Expression Regulation, NeoplasticHumansIndoleamine-Pyrrole 2,3,-DioxygenaseInterferon-gammaInterleukin-10Lymphocytes, Tumor-InfiltratingMiddle AgedNeoplasm StagingRNA, MessengerV-Set Domain-Containing T-Cell Activation Inhibitor 1ConceptsNon-small cell lung cancerB7-H4PD-L1IDO-1Lung cancerLung carcinomaQuantitative immunofluorescenceIFNγ stimulationElevated PD-L1Major clinicopathologic variablesMultiplexed quantitative immunofluorescenceOptimal clinical trialsT-cell infiltratesCell lung cancerImmune evasion pathwaysHuman lung carcinomaLung adenocarcinoma A549Cancer Genome AtlasClinicopathologic variablesMarker levelsClinical trialsStage ITherapeutic resistanceTCGA datasetA549 cellsWhole-exome sequencing and immune profiling of early-stage lung adenocarcinoma with fully annotated clinical follow-up
Kadara H, Choi M, Zhang J, Parra ER, Rodriguez-Canales J, Gaffney SG, Zhao Z, Behrens C, Fujimoto J, Chow C, Yoo Y, Kalhor N, Moran C, Rimm D, Swisher S, Gibbons DL, Heymach J, Kaftan E, Townsend JP, Lynch TJ, Schlessinger J, Lee J, Lifton RP, Wistuba II, Herbst RS. Whole-exome sequencing and immune profiling of early-stage lung adenocarcinoma with fully annotated clinical follow-up. Annals Of Oncology 2017, 28: 75-82. PMID: 27687306, PMCID: PMC5982809, DOI: 10.1093/annonc/mdw436.Peer-Reviewed Original ResearchConceptsRecurrence-free survivalPoor recurrence-free survivalWhole-exome sequencingEarly-stage lung adenocarcinomaMutant lung adenocarcinomaLung adenocarcinomaImmune markersClinical outcomesExact testNatural killer cell infiltrationProportional hazards regression modelsGranzyme B levelsImmune marker analysisImmune profiling analysisPD-L1 expressionImmune-based therapiesTumoral PD-L1Hazards regression modelsKRAS mutant tumorsNormal lung tissuesMajority of deathsFisher's exact testHigh mutation burdenAnalysis of immunophenotypeRelevant molecular markersMutation profiles in early-stage lung squamous cell carcinoma with clinical follow-up and correlation with markers of immune function
Choi M, Kadara H, Zhang J, Parra ER, Rodriguez-Canales J, Gaffney SG, Zhao Z, Behrens C, Fujimoto J, Chow C, Kim K, Kalhor N, Moran C, Rimm D, Swisher S, Gibbons DL, Heymach J, Kaftan E, Townsend JP, Lynch TJ, Schlessinger J, Lee J, Lifton RP, Herbst RS, Wistuba II. Mutation profiles in early-stage lung squamous cell carcinoma with clinical follow-up and correlation with markers of immune function. Annals Of Oncology 2017, 28: 83-89. PMID: 28177435, PMCID: PMC6246501, DOI: 10.1093/annonc/mdw437.Peer-Reviewed Original ResearchConceptsLung squamous cell carcinomaEarly stage lung squamous cell carcinomaNon-small cell lung cancerSquamous cell carcinomaWhole-exome sequencingImmune markersClinical outcomesCell carcinomaPIK3CA mutationsExact testPoor recurrence-free survivalProportional hazards regression modelsTumoral PD-L1 expressionPD-L1 expressionRecurrence-free survivalCell lung cancerComprehensive immune profilingTP53 mutant tumorsHazards regression modelsNormal lung tissuesFisher's exact testLUSC cohortAdjuvant therapyImmune profilingPoor prognosis
2016
Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer
Mani NL, Schalper KA, Hatzis C, Saglam O, Tavassoli F, Butler M, Chagpar AB, Pusztai L, Rimm DL. Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Research 2016, 18: 78. PMID: 27473061, PMCID: PMC4966732, DOI: 10.1186/s13058-016-0737-x.Peer-Reviewed Original ResearchConceptsIntraclass correlation coefficientQuantitative immunofluorescenceBreast cancerSame cancerSingle biopsyMultiplexed quantitative immunofluorescenceTumor-infiltrating lymphocytesPotential predictive markerPrimary breast carcinomaCytokeratin-positive epithelial cellsCD20-positive lymphocytesCD8 levelsLymphocyte scoreQIF scoresLymphocyte countLymphocyte subpopulationsMultiple biopsiesSubpopulation countsPredictive markerPrognostic informationBreast carcinomaBiopsyB lymphocytesCD3Breast tumorsEvaluation of PD-L1 Expression and Associated Tumor-Infiltrating Lymphocytes in Laryngeal Squamous Cell Carcinoma
Vassilakopoulou M, Avgeris M, Velcheti V, Kotoula V, Rampias T, Chatzopoulos K, Perisanidis C, Kontos CK, Giotakis AI, Scorilas A, Rimm D, Sasaki C, Fountzilas G, Psyrri A. Evaluation of PD-L1 Expression and Associated Tumor-Infiltrating Lymphocytes in Laryngeal Squamous Cell Carcinoma. Clinical Cancer Research 2016, 22: 704-713. PMID: 26408403, DOI: 10.1158/1078-0432.ccr-15-1543.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overB7-H1 AntigenBiomarkers, TumorCarcinoma, Squamous CellFemaleFollow-Up StudiesGene ExpressionHumansImmunohistochemistryKaplan-Meier EstimateLaryngeal NeoplasmsLymphocytes, Tumor-InfiltratingMaleMiddle AgedNeoplasm GradingNeoplasm MetastasisNeoplasm StagingPrognosisProportional Hazards ModelsRetrospective StudiesRisk FactorsRNA, MessengerConceptsLaryngeal squamous cell carcinomaSquamous cell carcinomaPrimary laryngeal squamous cell carcinomaPD-L1 expressionTumor-infiltrating lymphocytesPD-L1 mRNA expressionTIL densityCell carcinomaAssessment of TILsLaryngeal squamous cell cancerStromal tumor-infiltrating lymphocytesSuperior disease-free survivalTumor PD-L1 expressionMRNA expressionPD-L1 protein expressionPD-L1 mRNA levelsHigher TIL densityImmune checkpoint inhibitorsPD-L1 levelsDisease-free survivalT cell infiltrationSquamous cell cancerSecond independent cohortAdjacent tissue specimensFresh-frozen tumorsCopy Number Changes Are Associated with Response to Treatment with Carboplatin, Paclitaxel, and Sorafenib in Melanoma
Wilson MA, Zhao F, Khare S, Roszik J, Woodman SE, D'Andrea K, Wubbenhorst B, Rimm DL, Kirkwood JM, Kluger HM, Schuchter LM, Lee SJ, Flaherty KT, Nathanson KL. Copy Number Changes Are Associated with Response to Treatment with Carboplatin, Paclitaxel, and Sorafenib in Melanoma. Clinical Cancer Research 2016, 22: 374-382. PMID: 26307133, PMCID: PMC4821426, DOI: 10.1158/1078-0432.ccr-15-1162.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Combined Chemotherapy ProtocolsCarboplatinDisease-Free SurvivalDNA Copy Number VariationsDNA Mutational AnalysisDouble-Blind MethodGenes, rasHumansMelanomaMutationNeoplasm StagingNiacinamidePaclitaxelPhenylurea CompoundsProto-Oncogene Proteins B-rafProto-Oncogene Proteins c-metSorafenibTreatment OutcomeConceptsProgression-free survivalGene copy gainOverall survivalImproved progression-free survivalCopy gainImproved overall survivalGenomic alterationsCancer Genome Atlas (TCGA) datasetImproved treatment responseClinical outcomesMET amplificationV600KCCND1 amplificationTreatment responseMelanoma pathogenesisV600E mutationCurrent FDAPretreatment samplesBRAF geneTumor samplesPatientsSorafenibTherapyTumorsAtlas dataset
2014
Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer
Brown JR, Wimberly H, Lannin DR, Nixon C, Rimm DL, Bossuyt V. Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer. Clinical Cancer Research 2014, 20: 5995-6005. PMID: 25255793, PMCID: PMC4252785, DOI: 10.1158/1078-0432.ccr-14-1622.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntigens, CD20Antineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorBreast NeoplasmsCD3 ComplexCD8 AntigensChemotherapy, AdjuvantFemaleHumansImmunophenotypingLymphocyte SubsetsLymphocytes, Tumor-InfiltratingMiddle AgedNeoadjuvant TherapyNeoplasm GradingNeoplasm StagingPrognosisReproducibility of ResultsTreatment OutcomeTumor BurdenConceptsTumor-infiltrating lymphocytesPathologic complete responseBreast cancerTonsil specimensPredictive valueAQUA scoreQuantitative immunofluorescenceFlow cytometryFuture larger studiesPathologist estimationNeoadjuvant cohortNeoadjuvant chemotherapyNeoadjuvant therapyLymphocyte infiltratesTIL countComplete responseNodal statusLymphocyte percentageLymphocyte subpopulationsStromal expressionNuclear gradeUnivariate analysisKi-67CD8Clinical utilityIdentification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error
Shipitsin M, Small C, Choudhury S, Giladi E, Friedlander S, Nardone J, Hussain S, Hurley AD, Ernst C, Huang YE, Chang H, Nifong TP, Rimm DL, Dunyak J, Loda M, Berman DM, Blume-Jensen P. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. British Journal Of Cancer 2014, 111: 1201-1212. PMID: 25032733, PMCID: PMC4453845, DOI: 10.1038/bjc.2014.396.Peer-Reviewed Original ResearchMeSH KeywordsActininAgedAlkyl and Aryl TransferasesArea Under CurveBiomarkers, TumorBiopsy, Fine-NeedleCullin ProteinsDNA-Binding ProteinsFollow-Up StudiesHSP70 Heat-Shock ProteinsHumansImage Processing, Computer-AssistedMaleMembrane ProteinsMiddle AgedMitochondrial ProteinsNeoplasm GradingNeoplasm StagingPhosphorylationProstateProstatic NeoplasmsProteomicsRibosomal Protein S6RNA-Binding Protein FUSROC CurveSelection BiasSmad2 ProteinSmad4 ProteinTissue Array AnalysisVoltage-Dependent Anion Channel 1Y-Box-Binding Protein 1ConceptsProstate cancer aggressivenessCancer aggressivenessLarge patient cohortLow Gleason gradePatient cohortTumor microarrayLethal outcomeProstatectomy samplesGleason gradeSignificant overtreatmentBiopsy interpretationProstatectomy tissuePatient samplesBiopsy testsProteomic biomarkersCancer biomarker discoveryExpert pathologistsMarker signaturesTumor heterogeneityBiomarkersAggressivenessProtein biomarkersBiomarker discoveryQuantitative proteomics approachInduction cetuximab, paclitaxel, and carboplatin followed by chemoradiation with cetuximab, paclitaxel, and carboplatin for stage III/IV head and neck squamous cancer: a phase II ECOG-ACRIN trial (E2303)
Wanebo HJ, Lee J, Burtness BA, Ridge JA, Ghebremichael M, Spencer SA, Psyrri D, Pectasides E, Rimm D, Rosen FR, Hancock MR, Tolba KA, Forastiere AA. Induction cetuximab, paclitaxel, and carboplatin followed by chemoradiation with cetuximab, paclitaxel, and carboplatin for stage III/IV head and neck squamous cancer: a phase II ECOG-ACRIN trial (E2303). Annals Of Oncology 2014, 25: 2036-2041. PMID: 25009013, PMCID: PMC4176450, DOI: 10.1093/annonc/mdu248.Peer-Reviewed Original ResearchConceptsEvent-free survivalStage III/IV headResponse/survivalInduction therapyComplete responseStage III/IV HNSCCNeck squamous cell carcinomaPrimary site biopsiesTreatment-related deathsPathologic complete responseNeck squamous cancerSquamous cell carcinomaProtein expression statusEligible patientsSite biopsiesOverall survivalCell carcinomaPromising survivalSquamous cancerDisease progressionChemoradiationRadiation therapyPatientsWeek 9Cetuximab