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
2019
Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death
Kulkarni PM, Robinson EJ, Pradhan J, Gartrell-Corrado RD, Rohr BR, Trager MH, Geskin LJ, Kluger HM, Wong PF, Acs B, Rizk EM, Yang C, Mondal M, Moore MR, Osman I, Phelps R, Horst BA, Chen ZS, Ferringer T, Rimm DL, Wang J, Saenger YM. Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death. Clinical Cancer Research 2019, 26: 1126-1134. PMID: 31636101, PMCID: PMC8142811, DOI: 10.1158/1078-0432.ccr-19-1495.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAlgorithmsArea Under CurveBiopsyDeep LearningDisease ProgressionFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMaleMelanomaMiddle AgedNeoplasm Recurrence, LocalNeural Networks, ComputerRetrospective StudiesRisk FactorsStaining and LabelingSurvival RateYoung AdultConceptsDeep neural network architectureNeural network architectureDeep learningNetwork architectureComputational modelImage sequencesDigital imagesVote aggregationDisease-specific survivalDSS predictionPractical advancesComputational methodsIHC-based methodsImagesGeisinger Health SystemNovel methodGHS patientsArchitectureLearningKaplan-Meier analysisPrimary melanoma tumorsEarly-stage melanomaClinical trial designModelAdjuvant immunotherapyClosed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma
Gupta S, McCann L, Chan YGY, Lai EW, Wei W, Wong PF, Smithy JW, Weidler J, Rhees B, Bates M, Kluger HM, Rimm DL. Closed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma. Journal For ImmunoTherapy Of Cancer 2019, 7: 254. PMID: 31533832, PMCID: PMC6751819, DOI: 10.1186/s40425-019-0731-9.Peer-Reviewed Original ResearchMeSH KeywordsAgedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorCD8 AntigensFemaleFollow-Up StudiesGene Expression ProfilingHumansInterferon Regulatory Factor-1MaleMelanomaMiddle AgedMonitoring, ImmunologicPrognosisProgrammed Cell Death 1 Ligand 2 ProteinProgression-Free SurvivalReal-Time Polymerase Chain ReactionRetrospective StudiesReverse Transcriptase Polymerase Chain ReactionRNA, MessengerSkin NeoplasmsConceptsCompanion diagnostic testsImmunotherapy outcomesMelanoma patientsClinical benefitAnti-PD-1 therapyImmune checkpoint inhibitor therapyMRNA expressionQuantitative immunofluorescenceDiagnostic testsCheckpoint inhibitor therapyReal-time quantitative reverse transcription polymerase chain reactionMetastatic melanoma patientsQuantitative reverse transcription polymerase chain reactionReverse transcription-polymerase chain reactionTranscription-polymerase chain reactionYale Pathology archivesParaffin-embedded tissue sectionsAdjuvant settingICI therapyOS associationInhibitor therapyBaseline variablesMetastatic melanomaPredictive biomarkersPolymerase chain reaction
2018
Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma
Hartman DJ, Ahmad F, Ferris R, Rimm D, Pantanowitz L. Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma. Oral Oncology 2018, 86: 278-287. PMID: 30409313, PMCID: PMC6260977, DOI: 10.1016/j.oraloncology.2018.10.005.Peer-Reviewed Original ResearchConceptsCD8 T cellsImmune cell densityOropharyngeal HNSCCT cellsNeck squamous cell carcinomaCD8 cell densityImmune cell infiltratesSquamous cell carcinomaWhole tissue sectionsEntire tumor sectionHPV infectionMedian survivalCell infiltrateHNSCC patientsCell carcinomaHNSCC casesClinicopathologic parametersOnly predictorTumor sectionsBetter outcomesClinical practiceTumor microenvironmentCell densityClinical validationCells/Immune Marker Profiling and Programmed Death Ligand 1 Expression Across NSCLC Mutations
Toki MI, Mani N, Smithy JW, Liu Y, Altan M, Wasserman B, Tuktamyshov R, Schalper K, Syrigos KN, Rimm DL. Immune Marker Profiling and Programmed Death Ligand 1 Expression Across NSCLC Mutations. Journal Of Thoracic Oncology 2018, 13: 1884-1896. PMID: 30267840, PMCID: PMC6251746, DOI: 10.1016/j.jtho.2018.09.012.Peer-Reviewed Original ResearchConceptsPD-L1 expressionPD-L1TIL activationHigh PD-L1 levelsDeath ligand 1 (PD-L1) expressionActivation statusKRAS wild-type tumorsKRAS mutantEGFR mutantsHigh PD-L1Multiplexed quantitative immunofluorescenceUnique immune profilePD-L1 levelsLigand 1 expressionDeath-1/EGFR-mutant tumorsImmunotherapy response ratesKRAS mutant tumorsWild-type tumorsHigher CD4NSCLC patientsImmune profileClinical efficacyKRAS WTLymphocyte populations
2017
Whole-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
Evaluation 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 tumors
2014
EGFR expression is associated with decreased benefit from trastuzumab in the NCCTG N9831 (Alliance) trial
Cheng H, Ballman K, Vassilakopoulou M, Dueck AC, Reinholz MM, Tenner K, Gralow J, Hudis C, Davidson NE, Fountzilas G, McCullough AE, Chen B, Psyrri A, Rimm DL, Perez EA. EGFR expression is associated with decreased benefit from trastuzumab in the NCCTG N9831 (Alliance) trial. British Journal Of Cancer 2014, 111: 1065-1071. PMID: 25117817, PMCID: PMC4453859, DOI: 10.1038/bjc.2014.442.Peer-Reviewed Original ResearchConceptsNorth Central Cancer Treatment GroupMetastatic breast cancer cohortEpidermal growth factor receptorBreast cancer cohortHigh EGFR expressionEGFR expressionConcurrent trastuzumabGrowth factor receptorCancer cohortEGFR antibodyNCCTG N9831 trialsAnti-HER2 therapyCancer Treatment GroupDisease-free survivalFactor receptorN9831 trialsSequential trastuzumabAdditive therapyArm AClinical outcomesTreatment optionsWorse outcomesArm CTissue microarrayTreatment groupsIdentification 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 approachCorrelation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib
Wilson MA, Zhao F, Letrero R, D'Andrea K, Rimm DL, Kirkwood JM, Kluger HM, Lee SJ, Schuchter LM, Flaherty KT, Nathanson KL. Correlation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib. Clinical Cancer Research 2014, 20: 3328-3337. PMID: 24714776, PMCID: PMC4058354, DOI: 10.1158/1078-0432.ccr-14-0093.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorCarboplatinDouble-Blind MethodFemaleFollow-Up StudiesGenotypeGTP PhosphohydrolasesHumansMaleMelanomaMembrane ProteinsMiddle AgedMutationNeoplasm StagingNiacinamidePaclitaxelPhenylurea CompoundsPrognosisProto-Oncogene Proteins B-rafSkin NeoplasmsSorafenibSurvival RateConceptsProgression-free survivalNRAS-mutant melanomaPlatelet-derived growth factor receptorPerformance statusClinical outcomesNRAS mutationsCox proportional hazards modelVEGF receptorsSomatic mutationsWorse performance statusGood performance statusImproved clinical responseKaplan-Meier methodClinical trial populationsPretreatment tumor samplesSite of diseaseProportional hazards modelEffect of sorafenibBRAF-mutant melanomaFisher's exact testGrowth factor receptorClinical responseOverall survivalClinicopathologic featuresMelanoma patients
2013
Sarcomatoid Lung Carcinomas Show High Levels of Programmed Death Ligand-1 (PD-L1)
Velcheti V, Rimm DL, Schalper KA. Sarcomatoid Lung Carcinomas Show High Levels of Programmed Death Ligand-1 (PD-L1). Journal Of Thoracic Oncology 2013, 8: 803-805. PMID: 23676558, PMCID: PMC3703468, DOI: 10.1097/jto.0b013e318292be18.Peer-Reviewed Original ResearchConceptsDeath ligand 1Sarcomatoid carcinomaCell lung carcinomaLung carcinomaPD-L1PD-1/PD-L1 axisPD-1/PD-L1 pathwayProgrammed Death Ligand 1PD-L1 protein expressionEffector immune responsesPD-L1 axisPD-L1 pathwayLung sarcomatoid carcinomaLung cancer cohortSarcomatoid lung carcinomasLigand 1Mouse monoclonal antibodyDeath-1Lymphocytic infiltrationRare subtypeSuch therapyCancer cohortT cellsCarcinomaTumor types
2009
Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer
Nadler Y, González AM, Camp RL, Rimm DL, Kluger HM, Kluger Y. Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer. Annals Of Oncology 2009, 21: 466-473. PMID: 19717535, PMCID: PMC2826097, DOI: 10.1093/annonc/mdp346.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiomarkers, TumorBlotting, WesternBreast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, LobularFemaleFluorescent Antibody TechniqueFollow-Up StudiesGRB7 Adaptor ProteinHumansImage Processing, Computer-AssistedMiddle AgedPrognosisReceptor, ErbB-2Survival RateTissue Array AnalysisTumor Cells, CulturedYoung AdultConceptsHER2/neuBreast cancerPrognostic markerHER2/neu-positive breast cancerGRB7 expressionHigh HER2/neuNeu-positive breast cancerHER2/neu overexpressionPrimary breast cancerBreast cancer patientsIndependent prognostic markerNode-positive subsetValuable prognostic markerProtein 7Cy5-conjugated antibodiesMultivariable analysisWorse prognosisEntire cohortCancer patientsNeu overexpressionTissue microarrayTherapeutic targetCancerNeuPatientsHigh Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma
Anagnostou VK, Bepler G, Syrigos KN, Tanoue L, Gettinger S, Homer RJ, Boffa D, Detterbeck F, Rimm DL. High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma. Clinical Cancer Research 2009, 15: 4157-4164. PMID: 19509151, DOI: 10.1158/1078-0432.ccr-09-0099.Peer-Reviewed Original ResearchConceptsLung cancer patientsMTOR expressionCancer patientsMammalian targetEarly-stage lung adenocarcinomaHigh mTOR expressionIndependent lower riskMedian overall survivalStage IA patientsProtein expressionSubgroup of patientsLung adenocarcinoma patientsStage lung adenocarcinomaMTOR protein expressionRole of mTOROverall survivalPathologic characteristicsPatient survivalValidation cohortAdenocarcinoma groupAdenocarcinoma patientsPrognostic stratificationLung cancerTraining cohortFavorable outcome
2008
Expression patterns and prognostic value of Bag-1 and Bcl-2 in breast cancer
Nadler Y, Camp RL, Giltnane JM, Moeder C, Rimm DL, Kluger HM, Kluger Y. Expression patterns and prognostic value of Bag-1 and Bcl-2 in breast cancer. Breast Cancer Research 2008, 10: r35. PMID: 18430249, PMCID: PMC2397537, DOI: 10.1186/bcr1998.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntineoplastic AgentsBiomarkers, TumorBreast NeoplasmsDNA-Binding ProteinsDrug Resistance, NeoplasmFemaleFluorescent Antibody TechniqueFollow-Up StudiesGene Expression Regulation, NeoplasticHumansImmunohistochemistryKaplan-Meier EstimateLymphatic MetastasisMiddle AgedPredictive Value of TestsPrognosisProportional Hazards ModelsProtein Array AnalysisProto-Oncogene Proteins c-bcl-2Receptors, EstrogenReceptors, ProgesteroneTranscription FactorsTreatment OutcomeConceptsNode-positive subsetHER2/neuProgesterone receptorBreast cancerEstrogen receptorBcl-2 expressionBAG-1 expressionImproved survivalBcl-2Anti-apoptotic mediator Bcl-2Breast tumorsSteroid receptor positivitySubset of patientsBAG-1Antihormonal therapyFavorable prognosisReceptor positivityMultivariable analysisPathological variablesEntire cohortPrognostic valuePrognostic markerImproved outcomesLarge cohortClinical development
2005
Immunohistochemical Biomarkers in Patients with Early-Onset Breast Carcinoma by Tissue Microarray
Choi DH, Kim S, Rimm DL, Carter D, Haffty BG. Immunohistochemical Biomarkers in Patients with Early-Onset Breast Carcinoma by Tissue Microarray. The Cancer Journal 2005, 11: 404-411. PMID: 16259871, DOI: 10.1097/00130404-200509000-00008.Peer-Reviewed Original ResearchMeSH KeywordsAdenocarcinoma, MucinousAdultBiomarkers, TumorBreast NeoplasmsDisease-Free SurvivalFemaleFollow-Up StudiesHumansImmunohistochemistryKi-67 AntigenMiddle AgedMultivariate AnalysisNeoplasm Recurrence, LocalNeoplasms, Ductal, Lobular, and MedullaryProtein Array AnalysisProto-Oncogene Proteins c-bcl-2Receptor, ErbB-2Receptors, EstrogenReceptors, ProgesteroneTime FactorsTumor Suppressor Protein p53Women's HealthConceptsHER-2/neuBreast cancerProgesterone receptorKi-67Local relapseNodal statusYoung womenDistant metastasisTumor stageBiologic markersEstrogen receptorRelapse-free survival rateDistant metastasis-free rateDistant relapse-free survival ratesEarly-stage breast cancerEarly-onset breast cancerEarly-onset breast carcinomaProgesterone receptor negativityGroup of patientsMetastasis-free rateBcl-2Ki-67 positivityParaffin-embedded specimensTissue microarray methodConservative surgeryEvaluating the Expression and Prognostic Value of TRAIL-R1 and TRAIL-R2 in Breast Cancer
McCarthy MM, Sznol M, DiVito KA, Camp RL, Rimm DL, Kluger HM. Evaluating the Expression and Prognostic Value of TRAIL-R1 and TRAIL-R2 in Breast Cancer. Clinical Cancer Research 2005, 11: 5188-5194. PMID: 16033835, DOI: 10.1158/1078-0432.ccr-05-0158.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBreast NeoplasmsCase-Control StudiesFemaleFollow-Up StudiesGene Expression ProfilingHumansMiddle AgedMultivariate AnalysisOligonucleotide Array Sequence AnalysisPrognosisReceptors, TNF-Related Apoptosis-Inducing LigandReceptors, Tumor Necrosis FactorSurvival AnalysisConceptsEarly-stage breast cancerTRAIL-R2 expressionBreast cancerPrognostic valueTRAIL-R2TRAIL-R1Normal breast specimensTumor necrosis factor-related apoptosis-inducing ligand receptor 1Lymph node involvementSubset of patientsBreast cancer patientsIndependent prognostic markerTRAIL-R1 expressionNormal breast epitheliumTRAIL receptor expressionLigand receptor 1Apoptosis-inducing ligand receptor 1Adjuvant treatmentNode involvementNodal statusPathologic variablesTumor sizeCancer patientsClinical trialsPrognostic markerUpstaging based solely on positive peritoneal washing does not affect outcome in endometrial cancer
Fadare O, Mariappan MR, Hileeto D, Wang, Mcalpine JN, Rimm DL. Upstaging based solely on positive peritoneal washing does not affect outcome in endometrial cancer. Modern Pathology 2005, 18: 673-680. PMID: 15578078, DOI: 10.1038/modpathol.3800342.Peer-Reviewed Original ResearchConceptsEndometrial carcinomaControl groupEndometrial cancerPeritoneal washingsPrognostic significanceStage IAge-matched control groupPositive peritoneal washingsSame histologic subtypeEndometrial carcinoma patientsPeritoneal washing cytologyProgression of diseaseExtra-uterine tumoursSingle-site studyExtrauterine diseasePositive washingsAbnormal cytologyAdjuvant therapySurgical stagingPositive cytologyCarcinoma patientsHistologic subtypeWashing cytologyHistologic evidenceIntraperitoneal disease
2001
Diagnosis of “ASCUS” in women over age 50 is less likely to be associated with dysplasia
Flynn K, Rimm D. Diagnosis of “ASCUS” in women over age 50 is less likely to be associated with dysplasia. Diagnostic Cytopathology 2001, 24: 132-136. PMID: 11169895, DOI: 10.1002/1097-0339(200102)24:2<132::aid-dc1026>3.0.co;2-n.Peer-Reviewed Original Research