2024
Digital spatial proteomic profiling reveals immune checkpoints as biomarkers in lymphoid aggregates and tumor microenvironment of desmoplastic melanoma
Su D, Schoenfeld D, Ibrahim W, Cabrejo R, Djureinovic D, Baumann R, Rimm D, Khan S, Halaban R, Kluger H, Olino K, Galan A, Clune J. Digital spatial proteomic profiling reveals immune checkpoints as biomarkers in lymphoid aggregates and tumor microenvironment of desmoplastic melanoma. Journal For ImmunoTherapy Of Cancer 2024, 12: e008646. PMID: 38519058, PMCID: PMC10961546, DOI: 10.1136/jitc-2023-008646.Peer-Reviewed Original ResearchConceptsCTLA-4 expression levelsCancer-associated fibroblastsAssociated with worse survivalExpression of immune checkpointsLAG-3 expressionDesmoplastic melanomaLymphoid aggregatesCTLA-4PD-1Immune checkpointsIntratumoral leukocytesLAG-3Tumor compartmentsWorse survivalCD20+B cellsIncreased expression of immune checkpointsProgrammed cell death protein 1Macrophage/monocyte markerSentinel lymph node positivityCell death protein 1Associated with poor prognosisLymph node positivityDense fibrous stromaPotential prognostic significanceCore of tumors
2023
Digital 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 expressionAutomated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study
Tan S, Aung T, Claeson M, Acs B, Zhou C, Brown S, Lambie D, Baade P, Pandeya N, Soyer H, Smithers B, Whiteman D, Rimm D, Khosrotehrani K. Automated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study. Journal Of The American Academy Of Dermatology 2023, 90: 179-182. PMID: 37730017, DOI: 10.1016/j.jaad.2023.09.026.Peer-Reviewed Original ResearchDeep 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 survivalSubsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma.
Horowitch B, Lee D, Ding M, Martinez-Morilla S, Aung T, Ouerghi F, Wang X, Wei W, Damsky W, Sznol M, Kluger H, Rimm D, Ishizuka J. Subsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma. Clinical Cancer Research 2023, 29: 2908-2918. PMID: 37233452, PMCID: PMC10524955, DOI: 10.1158/1078-0432.ccr-23-0215.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsHuman melanoma cell linesMelanoma cell linesPD-L1Validation cohortYale-New Haven HospitalCombination of ipilimumabPD-L1 markersImmune checkpoint blockadePD-L1 biomarkerNew Haven HospitalSTAT1 levelsCell linesWestern blot analysisCheckpoint inhibitorsCheckpoint blockadeClinical responseOverall survivalImproved survivalResistance of cancersMetastatic melanomaMelanoma responsePredict responseTreatment responseDistinct patterns
2022
Inhibition of renalase drives tumour rejection by promoting T cell activation
Guo X, Jessel S, Qu R, Kluger Y, Chen TM, Hollander L, Safirstein R, Nelson B, Cha C, Bosenberg M, Jilaveanu LB, Rimm D, Rothlin CV, Kluger HM, Desir GV. Inhibition of renalase drives tumour rejection by promoting T cell activation. European Journal Of Cancer 2022, 165: 81-96. PMID: 35219026, PMCID: PMC8940682, DOI: 10.1016/j.ejca.2022.01.002.Peer-Reviewed Original ResearchConceptsPD-1 inhibitorsMurine melanoma modelMelanoma-bearing miceMelanoma modelTumor microenvironmentTumor rejectionCell death protein 1 (PD-1) inhibitorsAnti-PD-1 activityEnhanced T cell infiltrationT cell-dependent fashionMelanoma cellsMelanoma tumor regressionPreclinical melanoma modelsT cell infiltrationNatural killer cellsForkhead box P3Expression of IFNγWild-type miceProtein 1 inhibitorT cell activationTumor cell contentWild-type melanoma cellsCD4 cellsAdvanced melanomaAntibody treatment
2021
Analysis 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 patternsImagingCD8Foxp3Automated 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 biomarkerBiomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass CytometryMultiplex Discovery with Imaging Mass Cytometry
Martinez-Morilla S, Villarroel-Espindola F, Wong PF, Toki MI, Aung TN, Pelekanou V, Bourke-Martin B, Schalper KA, Kluger HM, Rimm DL. Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass CytometryMultiplex Discovery with Imaging Mass Cytometry. Clinical Cancer Research 2021, 27: 1987-1996. PMID: 33504554, PMCID: PMC8026677, DOI: 10.1158/1078-0432.ccr-20-3340.Peer-Reviewed Original ResearchUsing 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
Quantitative analysis of CMTM6 expression in tumor microenvironment in metastatic melanoma and association with outcome on immunotherapy
Martinez-Morilla S, Zugazagoitia J, Wong PF, Kluger HM, Rimm DL. Quantitative analysis of CMTM6 expression in tumor microenvironment in metastatic melanoma and association with outcome on immunotherapy. OncoImmunology 2020, 10: 1864909. PMID: 33457084, PMCID: PMC7781756, DOI: 10.1080/2162402x.2020.1864909.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsPD-L1CMTM6 expressionControl patientsLonger survivalTissue microarrayQuantitative immunofluorescenceEffectiveness of immunotherapyMetastatic melanoma patientsDeath ligand 1Like MARVEL transmembrane domainCancer Genome Atlas (TCGA) databaseExpression of CMTM6MARVEL transmembrane domainExpression of mRNAChemokine-like factorICI treatmentCheckpoint inhibitorsPretreatment biopsiesCD68 markersImmune compartmentMultivariable analysisMelanoma patientsImmune-related proteinsPredictive factors
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 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 reactionHigh-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 biomarkersMultiplex 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
Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors
Hendry S, Salgado R, Gevaert T, Russell PA, John T, Thapa B, Christie M, van de Vijver K, Estrada MV, Gonzalez-Ericsson PI, Sanders M, Solomon B, Solinas C, Van den Eynden GGGM, Allory Y, Preusser M, Hainfellner J, Pruneri G, Vingiani A, Demaria S, Symmans F, Nuciforo P, Comerma L, Thompson EA, Lakhani S, Kim SR, Schnitt S, Colpaert C, Sotiriou C, Scherer SJ, Ignatiadis M, Badve S, Pierce RH, Viale G, Sirtaine N, Penault-Llorca F, Sugie T, Fineberg S, Paik S, Srinivasan A, Richardson A, Wang Y, Chmielik E, Brock J, Johnson DB, Balko J, Wienert S, Bossuyt V, Michiels S, Ternes N, Burchardi N, Luen SJ, Savas P, Klauschen F, Watson PH, Nelson BH, Criscitiello C, O’Toole S, Larsimont D, de Wind R, Curigliano G, André F, Lacroix-Triki M, van de Vijver M, Rojo F, Floris G, Bedri S, Sparano J, Rimm D, Nielsen T, Kos Z, Hewitt S, Singh B, Farshid G, Loibl S, Allison KH, Tung N, Adams S, Willard-Gallo K, Horlings HM, Gandhi L, Moreira A, Hirsch F, Dieci MV, Urbanowicz M, Brcic I, Korski K, Gaire F, Koeppen H, Lo A, Giltnane J, Rebelatto MC, Steele KE, Zha J, Emancipator K, Juco JW, Denkert C, Reis-Filho J, Loi S, Fox SB. Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors. Advances In Anatomic Pathology 2017, 24: 311-335. PMID: 28777143, PMCID: PMC5638696, DOI: 10.1097/pap.0000000000000161.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorBiopsyBrain NeoplasmsCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellEndometrial NeoplasmsFemaleGastrointestinal NeoplasmsHead and Neck NeoplasmsHumansImmunohistochemistryLung NeoplasmsLymphocytes, Tumor-InfiltratingMelanomaMesotheliomaOvarian NeoplasmsPathologyPhenotypePredictive Value of TestsSkin NeoplasmsSquamous Cell Carcinoma of Head and NeckUrogenital NeoplasmsConceptsTumor-infiltrating lymphocytesDifferent tumor typesSolid tumorsTumor typesTIL assessmentImmune responsePrimary brain tumorsCommon solid tumorsInvasive breast carcinomaRoutine clinical biomarkersWorking Group guidelinesPrognostic implicationsBreast carcinomaGroup guidelinesGynecologic systemGastrointestinal tractSimple biomarkerBrain tumorsGenitourinary systemPredictive valueClinical biomarkersStandardized methodologyTumorsAvailable evidenceImmunotherapyPD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors
Kluger HM, Zito CR, Turcu G, Baine M, Zhang H, Adeniran A, Sznol M, Rimm DL, Kluger Y, Chen L, Cohen JV, Jilaveanu LB. PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clinical Cancer Research 2017, 23: 4270-4279. PMID: 28223273, PMCID: PMC5540774, DOI: 10.1158/1078-0432.ccr-16-3146.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerPD-L1 expressionRenal cell carcinomaPD-1 inhibitorsCell carcinomaImmune-infiltrating cellsMelanoma patientsPD-L1Tumor cellsTumor typesTumor-associated inflammatory cellsCTLA-4 inhibitorsCell lung cancerRenal cell carcinoma cellsHigh response rateClin Cancer ResCell linesMelanoma tumor cellsPD-1Multivariable analysisNSCLC specimensInflammatory cellsLung cancerTissue microarrayResponse rate