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 benefitsPatientsCD68SACI-IO HR+: A randomized phase II trial of sacituzumab govitecan with or without pembrolizumab in patients with metastatic hormone receptor-positive/HER2-negative breast cancer.
Garrido-Castro A, Kim S, Desrosiers J, Nanda R, Carey L, Clark A, Sacks R, O'Connor T, Sinclair N, Lo K, Thomas A, Wrabel E, O'Meara T, Lin N, Burstein H, He M, Rimm D, Mittendorf E, Tayob N, Tolaney S. SACI-IO HR+: A randomized phase II trial of sacituzumab govitecan with or without pembrolizumab in patients with metastatic hormone receptor-positive/HER2-negative breast cancer. Journal Of Clinical Oncology 2024, 42: lba1004-lba1004. DOI: 10.1200/jco.2024.42.17_suppl.lba1004.Peer-Reviewed Original ResearchProgression-free survivalMetastatic breast cancerHR+/HER2- metastatic breast cancerPD-L1 expressionSacituzumab govitecanAntibody drug conjugatesOverall survivalArm BPD-L1Arm ASN-38Study therapyBreast cancerHormone receptor-positive/HER2-negative breast cancerOpen-label phase 2 studyFollow-upDeplete regulatory T cellsFrequent treatment-related toxicitiesHormone receptor-positive/HER2-negativeImproving progression-free survivalTopoisomerase I inhibitor payloadMedian progression-free survivalRandomized phase II trialUpregulated MHC class IT cell effector function
2023
B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma
Gavrielatou N, Fortis E, Spathis A, Anastasiou M, Economopoulou P, Foukas G, Lelegiannis I, Rusakiewicz S, Vathiotis I, Aung T, Tissot S, Kastrinou A, Kotsantis I, Vagia E, Panayiotides I, Rimm D, Coukos G, Homicsko K, Foukas P, Psyrri A. B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma. Annals Of Oncology 2023, 35: 340-350. PMID: 38159908, DOI: 10.1016/j.annonc.2023.12.011.Peer-Reviewed Original ResearchProlonged progression-free survivalTertiary lymphoid structuresPD-L1 expressionB cellsM HNSCCCell death protein 1 inhibitionPD-1-based immunotherapyNeck squamous cell cancerNeck squamous cell carcinomaHigher B cell countsIncreased B cellsB cell infiltrationB-cell countsPD-L1 positivityProgression-free survivalTreatment of recurrentSquamous cell cancerBlood immune cell compositionSquamous cell carcinomaBiomarkers of responseImmune cell compositionB-cell-associated genesProtein 1 inhibitionCell death proteinMetastatic headStress Keratin 17 Is a Predictive Biomarker Inversely Associated with Response to Immune Check-Point Blockade in Head and Neck Squamous Cell Carcinomas and Beyond
Lozar T, Laklouk I, Golfinos A, Gavrielatou N, Xu J, Flynn C, Keske A, Yu M, Bruce J, Wang W, Kuhar C, Bailey H, Harari P, Dinh H, Rimm D, Hu R, Lambert P, Fitzpatrick M. Stress Keratin 17 Is a Predictive Biomarker Inversely Associated with Response to Immune Check-Point Blockade in Head and Neck Squamous Cell Carcinomas and Beyond. Cancers 2023, 15: 4905. PMID: 37835599, PMCID: PMC10571921, DOI: 10.3390/cancers15194905.Peer-Reviewed Original ResearchImmune check-point blockadeNeck squamous cell carcinomaCheck-point blockadeSquamous cell carcinomaCK17 expressionDisease controlHNSCC patientsCell carcinomaPredictive biomarkersResponse rateKeratin 17Pembrolizumab-based therapyPembrolizumab-treated patientsPD-L1 expressionProgression-free survivalRNA expressionIndependent retrospective cohortsIndependent validation cohortDecreased response rateLow response rateREMARK criteriaOverall survivalProgressive diseaseRetrospective cohortCXCL10 chemokinesDigital spatial profiling of melanoma shows CD95 expression in immune cells is associated with resistance to immunotherapy
Martinez-Morilla S, Moutafi M, Fernandez A, Jessel S, Divakar P, Wong P, Garcia-Milian R, Schalper K, Kluger H, Rimm D. Digital spatial profiling of melanoma shows CD95 expression in immune cells is associated with resistance to immunotherapy. OncoImmunology 2023, 12: 2260618. PMID: 37781235, PMCID: PMC10540659, DOI: 10.1080/2162402x.2023.2260618.Peer-Reviewed Original ResearchConceptsDigital spatial profilingImmune checkpoint inhibitor therapyShorter progression-free survivalQuantitative immunofluorescenceCheckpoint inhibitor therapyProgression-free survivalMetastatic melanoma patientsPre-treatment specimensIndependent validation cohortMetastatic melanoma casesAdjuvant settingNanoString GeoMxMultivariable adjustmentAdverse eventsImmunotherapy cohortInhibitor therapyPD-L1Immune markersMelanoma patientsUnivariable analysisValidation cohortImmune cellsMelanoma casesMultiplex immunofluorescenceCD95 expressionDeep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
Chatziioannou E, Roßner J, Aung T, Rimm D, Niessner H, Keim U, Serna-Higuita L, Bonzheim I, Cuellar L, Westphal D, Steininger J, Meier F, Pop O, Forchhammer S, Flatz L, Eigentler T, Garbe C, Röcken M, Amaral T, Sinnberg T. Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases. EBioMedicine 2023, 93: 104644. PMID: 37295047, PMCID: PMC10363450, DOI: 10.1016/j.ebiom.2023.104644.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesMultiple Cox regressionMelanoma-specific survivalCox regressionTumor thicknessCutaneous melanomaPrimary melanomaAssessment of TILsPD-1 checkpoint inhibitionSignificant unfavourable prognostic factorLonger progression-free survivalDistant metastasis-free survivalSimple Cox regressionUnfavourable survival outcomeFirst-line therapyProgression-free survivalUnfavourable prognostic factorCutaneous melanoma patientsMetastasis-free survivalPresence of ulcerationPrimary cutaneous melanomaCox regression modelPrimary melanoma samplesPrimary tissuesOverall survival
2022
Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade
Vathiotis I, Salichos L, Martinez-Morilla S, Gavrielatou N, Aung T, Shafi S, Wong P, Jessel S, Kluger H, Syrigos K, Warren S, Gerstein M, Rimm D. Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade. Npj Precision Oncology 2022, 6: 92. PMID: 36522538, PMCID: PMC9755314, DOI: 10.1038/s41698-022-00330-3.Peer-Reviewed Original ResearchProgression-free survivalLong-term benefitsPredictive valueAnti-PD-1 therapyCell death protein 1Baseline tumor samplesImmune checkpoint inhibitorsAntitumor immune responseCohort of patientsDeath protein 1Gene expression profilesAdvanced diseaseCheckpoint inhibitorsAdvanced melanomaAxis blockadeImmunotherapy outcomesTreatment initiationEarly outcomesDisease progressionMalignant melanomaBaseline gene expressionImmune responseBaseline gene expression profilesExpression profilesTumor samplesQuantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer.
Shafi S, Aung T, Xirou V, Gavrielatou N, Vathiotis I, Fernandez A, Moutafi M, Yaghoobi V, Herbst R, Liu L, Langermann S, Rimm D. Quantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer. Laboratory Investigation 2022, 102: 1143-1149. PMID: 36775354, DOI: 10.1038/s41374-022-00796-6.Peer-Reviewed Original ResearchConceptsSiglec-15 expressionNon-small cell lung cancerNeck squamous cell carcinomaProgression-free survivalSquamous cell carcinomaCancer typesOverall survivalCell carcinomaBladder cancerImmune cellsSiglec-15PD-1/PD-L1 blockadePotential future clinical trialsQuantitative immunofluorescencePD-L1 blockadeStromal immune cellsImmune checkpoint blockadeCell lung cancerFuture clinical trialsNew potential targetsCheckpoint blockadePD-L1Lung cancerClinical trialsIntra-tumoral heterogeneityAssociation of PD-1/PD-L1 Co-location with Immunotherapy Outcomes in Non-Small Cell Lung Cancer
Gavrielatou N, Liu Y, Vathiotis I, Zugazagoitia J, Aung TN, Shafi S, Fernandez A, Schalper K, Psyrri A, Rimm DL. Association of PD-1/PD-L1 Co-location with Immunotherapy Outcomes in Non-Small Cell Lung Cancer. Clinical Cancer Research 2022, 28: clincanres.2649.2021. PMID: 34686497, PMCID: PMC8776595, DOI: 10.1158/1078-0432.ccr-21-2649.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerBest overall responsePD-L1 tumor proportion scorePD-1/PD-L1Immune checkpoint inhibitorsProgression-free survivalTumor proportion scoreCell lung cancerPD-L1Immunotherapy outcomesCheckpoint inhibitorsOverall survivalQuantitative immunofluorescenceLung cancerProportion scoreAdvanced non-small cell lung cancerLocal T cell responsesCell death protein 1Immunotherapy-treated patientsMultiplexed quantitative immunofluorescencePD-1 expressionPD-L1 expressionDeath protein 1Selection of patientsT cell responses
2021
Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes, Cancer-Associated Fibroblasts, and CD200 in Pancreatic Cancer
MacNeil T, Vathiotis IA, Shafi S, Aung TN, Zugazagoitia J, Gruver AM, Driscoll K, Rimm DL. Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes, Cancer-Associated Fibroblasts, and CD200 in Pancreatic Cancer. Cancers 2021, 13: 5501. PMID: 34771664, PMCID: PMC8583434, DOI: 10.3390/cancers13215501.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesPancreatic ductal adenocarcinomaCancer-associated fibroblastsImmune checkpoint blockadePancreatic cancerCheckpoint blockadePDAC patientsTumor microenvironmentQuantitative immunofluorescenceExpression levelsProgression-free survivalLarge retrospective cohortMajority of patientsPotential prognostic valueLow tumor immunogenicityPotential clinical utilityDesmoplastic tumor microenvironmentImmunoinhibitory proteinOverall survivalRetrospective cohortIndependent predictorsImmunotherapy drugsPrognostic significancePrognostic valueTumor expressionUsing 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
Biomarkers 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 microarray
2019
High-Plex Predictive Marker Discovery for Melanoma Immunotherapy–Treated Patients Using Digital Spatial Profiling
Toki MI, Merritt CR, Wong PF, Smithy JW, Kluger HM, Syrigos KN, Ong GT, Warren SE, Beechem JM, Rimm DL. High-Plex Predictive Marker Discovery for Melanoma Immunotherapy–Treated Patients Using Digital Spatial Profiling. Clinical Cancer Research 2019, 25: 5503-5512. PMID: 31189645, PMCID: PMC6744974, DOI: 10.1158/1078-0432.ccr-19-0104.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic Agents, ImmunologicalBiomarkers, TumorFemaleFluorescent Antibody TechniqueHumansImmunohistochemistryImmunotherapyLymphocytes, Tumor-InfiltratingMaleMelanomaMolecular Diagnostic TechniquesMolecular Targeted TherapyPrognosisProportional Hazards ModelsTissue Array AnalysisTreatment OutcomeConceptsNon-small cell lung cancerProlonged progression-free survivalDigital spatial profilingOverall survivalPD-L1Predictive markerPD-L1 expressionProgression-free survivalProtein expressionCell lung cancerNovel predictive markerCD68-positive cellsStromal CD3Melanoma immunotherapyImmune markersImmune therapyPrognostic valueLung cancerAntibody cocktailTissue microarrayQuantitative fluorescenceOutcome assessmentTumor cellsHigh concordanceMultiple biomarkersExpression Analysis and Significance of PD-1, LAG-3, and TIM-3 in Human Non–Small Cell Lung Cancer Using Spatially Resolved and Multiparametric Single-Cell Analysis
Datar I, Sanmamed MF, Wang J, Henick BS, Choi J, Badri T, Dong W, Mani N, Toki M, Mejías L, Lozano MD, Perez-Gracia JL, Velcheti V, Hellmann MD, Gainor JF, McEachern K, Jenkins D, Syrigos K, Politi K, Gettinger S, Rimm DL, Herbst RS, Melero I, Chen L, Schalper KA. Expression Analysis and Significance of PD-1, LAG-3, and TIM-3 in Human Non–Small Cell Lung Cancer Using Spatially Resolved and Multiparametric Single-Cell Analysis. Clinical Cancer Research 2019, 25: 4663-4673. PMID: 31053602, PMCID: PMC7444693, DOI: 10.1158/1078-0432.ccr-18-4142.Peer-Reviewed Original ResearchMeSH KeywordsAntigens, CDBiomarkers, TumorCarcinoma, Non-Small-Cell LungGene Expression Regulation, NeoplasticHepatitis A Virus Cellular Receptor 2HumansLung NeoplasmsLymphocyte ActivationLymphocyte Activation Gene 3 ProteinLymphocytes, Tumor-InfiltratingPrognosisProgrammed Cell Death 1 ReceptorRetrospective StudiesSingle-Cell AnalysisSurvival RateConceptsNon-small cell lung cancerHuman non-small cell lung cancerTumor-infiltrating lymphocytesAdvanced non-small cell lung cancerTim-3PD-1Cell lung cancerLAG-3Lung cancerPD-1 axis blockadeShorter progression-free survivalBaseline samplesTim-3 protein expressionMajor clinicopathologic variablesMultiplexed quantitative immunofluorescencePD-1 expressionProgression-free survivalTim-3 expressionLAG-3 expressionT-cell phenotypeTumor mutational burdenImmune inhibitory receptorsImmune evasion pathwaysTIM-3 proteinMass cytometry analysisMultiplex quantitative analysis of cancer-associated fibroblasts and immunotherapy outcome in metastatic melanoma
Wong PF, Wei W, Gupta S, Smithy JW, Zelterman D, Kluger HM, Rimm DL. Multiplex quantitative analysis of cancer-associated fibroblasts and immunotherapy outcome in metastatic melanoma. Journal For ImmunoTherapy Of Cancer 2019, 7: 194. PMID: 31337426, PMCID: PMC6651990, DOI: 10.1186/s40425-019-0675-0.Peer-Reviewed Original ResearchConceptsProgression-free survivalBest overall responseSmooth muscle actinOverall survivalCell countQuantitative immunofluorescenceImmune markersImmunotherapy outcomesMelanoma patientsSignificant progression-free survivalAnti-PD-1 therapyAbsence of immunotherapyPretreatment tumor specimensImmune checkpoint blockadeCell death 1Cancer-associated fibroblast (CAF) populationNegative prognostic biomarkerCancer-associated fibroblastsWhole tissue sectionsOverall responseOS associationCAF parametersCheckpoint blockadeImmune dysregulationDeath-1Multiplex 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 analysis
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
Quantitative spatial profiling of PD-1/PD-L1 interaction and HLA-DR/IDO1 to predict outcomes to anti-PD-1 in metastatic melanoma (MM).
Johnson D, Bordeaux J, Kim J, Vaupel C, Rimm D, Ho T, Joseph R, Daud A, Conry R, Gaughan E, Dimou A, Balko J, Smithy J, Witte J, McKee S, Dominiak N, Dabbas B, Hall J, Dakappagari N. Quantitative spatial profiling of PD-1/PD-L1 interaction and HLA-DR/IDO1 to predict outcomes to anti-PD-1 in metastatic melanoma (MM). Journal Of Clinical Oncology 2017, 35: 9517-9517. DOI: 10.1200/jco.2017.35.15_suppl.9517.Peer-Reviewed Original ResearchHLA-DRValidation cohortMetastatic melanomaPD-1/PD-L1Anti-PD-1 therapyPD-1/L1Pre-treatment tumor biopsiesPD-1/PD-L1 interactionPD-1 monotherapyPD-L1 expressionProgression-free survivalBiomarkers of responseFuture clinical trialsMultiple immune markersPD-L1 interactionImmune suppression mechanismsPrior therapyFree survivalDurable responsesOverall survivalPD-L1Immune markersClinical trialsTreatment responseTumor biopsiesNuclear IRF-1 expression as a mechanism to assess “Capability” to express PD-L1 and response to PD-1 therapy in metastatic melanoma
Smithy JW, Moore LM, Pelekanou V, Rehman J, Gaule P, Wong PF, Neumeister VM, Sznol M, Kluger HM, Rimm DL. Nuclear IRF-1 expression as a mechanism to assess “Capability” to express PD-L1 and response to PD-1 therapy in metastatic melanoma. Journal For ImmunoTherapy Of Cancer 2017, 5: 25. PMID: 28331615, PMCID: PMC5359951, DOI: 10.1186/s40425-017-0229-2.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedB7-H1 AntigenBiomarkers, PharmacologicalDisease-Free SurvivalFemaleGene Expression Regulation, NeoplasticHumansImmunotherapyInterferon Regulatory Factor-1IpilimumabMaleMelanomaMiddle AgedNeoplasm MetastasisNeoplasms, Second PrimaryNivolumabProgrammed Cell Death 1 ReceptorConceptsProgression-free survivalObjective radiographic responsePD-L1 expressionPD-L1IRF-1 expressionMetastatic melanomaAnti-PD-1 therapyCombination ipilimumab/nivolumabHigh PD-L1 expressionAnti-PD-1 immunotherapyYale-New Haven HospitalIpilimumab/nivolumabPD-1 therapyPR/CRPre-treatment formalinRECIST v1.1 criteriaDeath ligand 1Valuable predictive biomarkerMajor unmet needNew Haven HospitalInterferon regulatory factor 1Combination ipilimumabProgressive diseaseRadiographic responseComplete response
2016
Copy 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