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 head
2021
PD-L1 as a biomarker of response to immune-checkpoint inhibitors
Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, Wistuba II, Rimm DL, Tsao MS, Hirsch FR. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nature Reviews Clinical Oncology 2021, 18: 345-362. PMID: 33580222, DOI: 10.1038/s41571-021-00473-5.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsSelection of patientsPD-L1L1 antibodyImmunohistochemistry assaysPD-L1 immunohistochemistry assaysOutcomes of patientsBiomarkers of responseCompanion diagnostic assayTypes of cancerPD-1Clinical outcomesSelection biomarkerProspective comparisonClinical challengeNew therapiesFuture treatmentPatientsSolid tumorsClinical useSpecific agentsInter-assay variabilityBiomarkersCurrent roleDiagnostic assaysUsing 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
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 biopsies