2024
High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)
Moutafi M, Bates K, Aung T, Milian R, Xirou V, Vathiotis I, Gavrielatou N, Angelakis A, Schalper K, Salichos L, Rimm D. High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC). Journal For ImmunoTherapy Of Cancer 2024, 12: e009039. PMID: 38857914, PMCID: PMC11168162, DOI: 10.1136/jitc-2024-009039.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerImmune checkpoint inhibitorsProgrammed cell death protein 1Associated with OSCell lung cancerTissue microarray spotsTissue microarrayValidation cohortLung cancerNon-small cell lung cancer treated with immune checkpoint inhibitorsAssociated with resistance to immunotherapyCell death protein 1Resistance to immunotherapyAssociated with PFSProgression-free survivalSecreted frizzled-related protein 2Cox proportional-hazards model analysisCheckpoint inhibitorsImmunotherapy strategiesTumor compartmentsRetrospective cohortDiscovery cohortLong-term benefitsPatientsCD68An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers
Xirou V, Moutafi M, Bai Y, Nwe Aung T, Burela S, Liu M, Kimple R, Shabbir Ahmed F, Schultz B, Flieder D, Connolly D, Psyrri A, Burtness B, Rimm D. An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers. Oral Oncology 2024, 152: 106750. PMID: 38547779, PMCID: PMC11060915, DOI: 10.1016/j.oraloncology.2024.106750.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesHead and neck cancerTILs evaluationHPV-positiveNeck cancerPrognostic valueHead and neck squamous cell cancer casesTIL variablesAssociated with favorable prognosisHPV-negative headHPV-negative populationHematoxylin-eosin-stained sectionsCox regression analysisPotential clinical implicationsInter-observer variabilityInfiltrating lymphocytesClinicopathological factorsFavorable prognosisValidation cohortTumor cellsCancer casesProspective settingQuPath softwareRetrospective collectionPredictive significance
2022
Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit.
Hayes D, Herbst R, Myles J, Topalian S, Yohe S, Aronson N, Bellizzi A, Basu Roy U, Bradshaw G, Edwards R, El-Gabry E, Elvin J, Gajewski T, McShane L, Oberley M, Philip R, Rimm D, Rosenbaum J, Rubin E, Schlager L, Sherwood S, Stewart M, Taube J, Thurin M, Vasalos P, Laser J. Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit. JCO Precision Oncology 2022, 6: e2200454. PMID: 36446042, PMCID: PMC10530621, DOI: 10.1200/po.22.00454.Peer-Reviewed Original ResearchConceptsICI therapyImmune checkpoint inhibition therapyDeath ligand 1 (PD-L1) expressionMultiple predictive biomarkersTumor biomarker testsCheckpoint inhibition therapyLigand 1 expressionDeath ligand 1Field of oncologyICI benefitPredictive factorsPredictive biomarkersInhibition therapyNeoantigen expressionBiomarker testsHealth insurance organizationsUS FoodDrug AdministrationAmerican PathologistsMedicaid ServicesTherapyBiomarker developmentNational InstituteLigand 1Clinical application
2021
Programmed Death-Ligand 1 Tumor Proportion Score and Overall Survival From First-Line Pembrolizumab in Patients With Nonsquamous Versus Squamous NSCLC
Doroshow DB, Wei W, Gupta S, Zugazagoitia J, Robbins C, Adamson B, Rimm DL. Programmed Death-Ligand 1 Tumor Proportion Score and Overall Survival From First-Line Pembrolizumab in Patients With Nonsquamous Versus Squamous NSCLC. Journal Of Thoracic Oncology 2021, 16: 2139-2143. PMID: 34455068, PMCID: PMC8612948, DOI: 10.1016/j.jtho.2021.07.032.Peer-Reviewed Original ResearchConceptsPD-L1 tumor proportion scoreTumor proportion scoreHigh PD-L1 tumor proportion scoreOverall survivalNonsquamous histologySquamous NSCLCNonsquamous NSCLCPredictive biomarkersProportion scoreDeath ligand 1 (PD-L1) tumor proportion scoreElectronic health record-derived databaseFirst-line pembrolizumab therapyPD-1 expression levelsPD-L1 expression levelsCommunity oncology clinicsMedian OS differenceSingle-agent pembrolizumabImmune checkpoint inhibitorsImproved overall survivalMedian overall survivalPrimary end pointFirst-line pembrolizumabFirst-line therapyPD-L1 expressionPD-L1 testingAnalysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade
Berry S, Giraldo NA, Green BF, Cottrell TR, Stein JE, Engle EL, Xu H, Ogurtsova A, Roberts C, Wang D, Nguyen P, Zhu Q, Soto-Diaz S, Loyola J, Sander IB, Wong PF, Jessel S, Doyle J, Signer D, Wilton R, Roskes JS, Eminizer M, Park S, Sunshine JC, Jaffee EM, Baras A, De Marzo AM, Topalian SL, Kluger H, Cope L, Lipson EJ, Danilova L, Anders RA, Rimm DL, Pardoll DM, Szalay AS, Taube JM. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science 2021, 372 PMID: 34112666, PMCID: PMC8709533, DOI: 10.1126/science.aba2609.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntigens, CDAntigens, Differentiation, MyelomonocyticAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorCD8 AntigensFemaleFluorescent Antibody TechniqueForkhead Transcription FactorsHumansImmune Checkpoint ProteinsMacrophagesMaleMelanomaMiddle AgedPrognosisProgrammed Cell Death 1 ReceptorProgression-Free SurvivalReceptors, Cell SurfaceSingle-Cell AnalysisSOXE Transcription FactorsT-Lymphocyte SubsetsTreatment OutcomeTumor MicroenvironmentConceptsAnti-programmed cell death 1Anti-PD-1 blockadePD-1 blockadeCell death 1Tissue-based biomarkersLong-term survivalTumor tissue sectionsDeath-1PD-1PD-L1Immunoregulatory moleculesT cellsIndependent cohortMyeloid cellsMelanoma specimensMultiple cell typesTissue sectionsLow/BlockadeCell typesDistinct expression patternsExpression patternsImagingCD8Foxp3BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort
Vassilakopoulou M, Won M, Curran WJ, Souhami L, Prados MD, Langer CJ, Rimm DL, Hanna JA, Neumeister VM, Melian E, Diaz AZ, Atkins JN, Komarnicky LT, Schultz CJ, Howard SP, Zhang P, Dicker AP, Knisely JPS. BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort. Oncology 2021, 99: 580-588. PMID: 33957633, PMCID: PMC8491475, DOI: 10.1159/000516168.Peer-Reviewed Original ResearchConceptsBRCA1 protein expressionTensin homolog (PTEN) tumor suppressor geneProtein expressionTumor suppressor geneQuantitative protein analysisDNA repairGenetic profiling studiesMolecular markersSuppressor geneProtein analysisProfiling studiesBRCA1 expressionSitu hybridizationExpression levelsTumor formationCommon malignant brain tumorCancer cellsTissue microarrayGlioblastoma tumorsExpressionPre-temozolomide eraGlioblastoma patientsComparison of programmed death-ligand 1 protein expression between primary and metastatic lesions in patients with lung cancer
Moutafi MK, Tao W, Huang R, Haberberger J, Alexander B, Ramkissoon S, Ross JS, Syrigos K, Wei W, Pusztai L, Rimm DL, Vathiotis IA. Comparison of programmed death-ligand 1 protein expression between primary and metastatic lesions in patients with lung cancer. Journal For ImmunoTherapy Of Cancer 2021, 9: e002230. PMID: 33833050, PMCID: PMC8039214, DOI: 10.1136/jitc-2020-002230.Peer-Reviewed Original ResearchConceptsPD-L1 expressionMetastatic lesionsLung cancer casesLung cancerCancer casesAdvanced stage non-small cell lung cancerNon-small cell lung cancerNon-squamous histologyCell lung cancerFuture patient managementDefinite diagnostic testSquamous histologyFoundation MedicineLymph nodesRoutine careHistologic subtypeMetastatic sitesPrimary lesionRetrospective studyAdrenal glandPrimary tumorPleural fluidPatient managementTrial designDrug AdministrationAn independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy
Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, Rothrock B, Raciti P, Klimstra D, Sinard J. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Modern Pathology 2021, 34: 1588-1595. PMID: 33782551, PMCID: PMC8295034, DOI: 10.1038/s41379-021-00794-x.Peer-Reviewed Original ResearchConceptsMemorial Sloan-Kettering Cancer CenterCore biopsyPredictive valueDiagnostic accuracyProstate core needle biopsiesCore needle biopsySurgical pathology practiceNegative predictive valueProstate core biopsiesPositive predictive valueProstate cancer detectionStrong diagnostic accuracyPoor quality scansCancer detectionCancer CenterProstate biopsyLeading causeNeedle biopsyTransrectal approachProstate cancerProstatic adenocarcinomaProstate carcinomaBiopsyPathology practiceProstateA new tool for technical standardization of the Ki67 immunohistochemical assay
Aung TN, Acs B, Warrell J, Bai Y, Gaule P, Martinez-Morilla S, Vathiotis I, Shafi S, Moutafi M, Gerstein M, Freiberg B, Fulton R, Rimm DL. A new tool for technical standardization of the Ki67 immunohistochemical assay. Modern Pathology 2021, 34: 1261-1270. PMID: 33536573, PMCID: PMC8222064, DOI: 10.1038/s41379-021-00745-6.Peer-Reviewed Original ResearchAutomated 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
Comparison of PD-L1 protein expression between primary tumors and metastatic lesions in triple negative breast cancers
Rozenblit M, Huang R, Danziger N, Hegde P, Alexander B, Ramkissoon S, Blenman K, Ross JS, Rimm DL, Pusztai L. Comparison of PD-L1 protein expression between primary tumors and metastatic lesions in triple negative breast cancers. Journal For ImmunoTherapy Of Cancer 2020, 8: e001558. PMID: 33239417, PMCID: PMC7689582, DOI: 10.1136/jitc-2020-001558.Peer-Reviewed Original ResearchConceptsPD-L1 positivity ratePD-L1 positivityPD-L1 expressionDifferent metastatic sitesPrimary tumorMetastatic sitesPositivity rateImmune cellsMetastatic lesionsTumor cellsPD-L1 protein expressionTriple-negative breast cancerMore primary tumorsTriple negative breast cancer tumorsPrimary breast lesionsPrimary outcome measureSoft tissueNegative breast cancerLow positivity rateBreast cancer tumorsBone metastasesFoundation MedicineLymph nodesPD-L1Spearman correlation coefficientA 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 cohortPD-L1 Protein Expression on Both Tumor Cells and Macrophages are Associated with Response to Neoadjuvant Durvalumab with Chemotherapy in Triple-negative Breast Cancer
Ahmed FS, Gaule P, McGuire J, Patel K, Blenman K, Pusztai L, Rimm DL. PD-L1 Protein Expression on Both Tumor Cells and Macrophages are Associated with Response to Neoadjuvant Durvalumab with Chemotherapy in Triple-negative Breast Cancer. Clinical Cancer Research 2020, 26: 5456-5461. PMID: 32709714, PMCID: PMC7572612, DOI: 10.1158/1078-0432.ccr-20-1303.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntibodies, MonoclonalAntigens, CDAntigens, Differentiation, MyelomonocyticAntineoplastic Combined Chemotherapy ProtocolsB7-H1 AntigenBiomarkers, TumorCell ProliferationFemaleGene Expression Regulation, NeoplasticHumansLymphocytes, Tumor-InfiltratingMacrophagesMiddle AgedNeoadjuvant TherapyProgrammed Cell Death 1 ReceptorTriple Negative Breast NeoplasmsConceptsTriple-negative breast cancerPD-L1 expressionNeoadjuvant durvalumabTumor cellsImmune cellsBreast cancerPretreatment core-needle biopsiesPhase I/II clinical trialsPD-L1 protein expressionIMpassion 130 trialCore needle biopsyAmount of CD68Neoadjuvant settingMetastatic settingPD-L1Clinical trialsNeedle biopsyInsufficient tissuePatientsCD68Stromal compartmentQuantitative immunofluorescenceChemotherapyFinal analysisProtein expressionBiomarkers Associated with Beneficial PD-1 Checkpoint Blockade in Non–Small Cell Lung Cancer (NSCLC) Identified Using High-Plex Digital Spatial Profiling
Zugazagoitia J, Gupta S, Liu Y, Fuhrman K, Gettinger S, Herbst RS, Schalper KA, Rimm DL. Biomarkers Associated with Beneficial PD-1 Checkpoint Blockade in Non–Small Cell Lung Cancer (NSCLC) Identified Using High-Plex Digital Spatial Profiling. Clinical Cancer Research 2020, 26: 4360-4368. PMID: 32253229, PMCID: PMC7442721, DOI: 10.1158/1078-0432.ccr-20-0175.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerPD-1 checkpoint blockadeCell lung cancerCheckpoint blockadeLung cancerAdvanced non-small cell lung cancerUnivariate unadjusted analysisProgression-free survivalImmune cell countsMinority of patientsRobust predictive biomarkersBiomarkers of responseLarge independent cohortsSpatial profiling technologyDigital spatial profilingDigital spatial profiling (DSP) technologyOverall survivalClinical outcomesImmune predictorsHigher CD56NSCLC casesPredictive biomarkersUnadjusted analysesImmune parametersTissue microarrayImmune 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 collectionMelanomaDeep 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 immunotherapyQuantitative 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 outcomesClosed 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