2024
An 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
2023
Emerging Prognostic and Predictive Significance of Stress Keratin 17 in HPV-Associated and Non HPV-Associated Human Cancers: A Scoping Review
Lozar T, Wang W, Gavrielatou N, Christensen L, Lambert P, Harari P, Rimm D, Burtness B, Kuhar C, Carchman E. Emerging Prognostic and Predictive Significance of Stress Keratin 17 in HPV-Associated and Non HPV-Associated Human Cancers: A Scoping Review. Viruses 2023, 15: 2320. PMID: 38140561, PMCID: PMC10748233, DOI: 10.3390/v15122320.Peer-Reviewed Original ResearchConceptsSquamous cell carcinomaTriple-negative breast cancerCancer typesPredictive significancePrognostic factorsClinical outcomesPrognostic significanceCell carcinomaHuman cancersCervical squamous cell carcinomaNeck squamous cell carcinomaAvailable clinical evidenceCochrane Central RegisterInferior clinical outcomesPositive prognostic factorNegative predictive factorNegative prognostic factorWeb of ScienceCentral RegisterControlled TrialsCervical cancerClinical evidencePredictive factorsPancreatic cancerEligible studiesDeep 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
2021
What if the future of HER2‐positive breast cancer patients was written in miRNAs? An exploratory analysis from NeoALTTO study
Pizzamiglio S, Cosentino G, Ciniselli CM, De Cecco L, Cataldo A, Plantamura I, Triulzi T, El‐abed S, Wang Y, Bajji M, Nuciforo P, Huober J, Ellard SL, Rimm DL, Gombos A, Daidone MG, Verderio P, Tagliabue E, Di Cosimo S, Iorio MV. What if the future of HER2‐positive breast cancer patients was written in miRNAs? An exploratory analysis from NeoALTTO study. Cancer Medicine 2021, 11: 332-339. PMID: 34921525, PMCID: PMC8729061, DOI: 10.1002/cam4.4449.Peer-Reviewed Original ResearchConceptsHER2-positive breast cancer patientsEvent-free survivalBreast cancer patientsNeoadjuvant therapyCancer patientsPathological complete response rateSingle-agent trastuzumabTwo-miRNA signatureComplete response rateDifferential clinical outcomesPredictive miRNA signatureTrastuzumab armBaseline biopsiesClinical outcomesPathological variablesPrognostic valueUnivariate analysisAgent trastuzumabPrognostic signatureResponse ratePatientsTissue miRNAsMiRNA expression profilesMiRNA signatureMultivariate modelAnalysis 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 patternsImagingCD8Foxp3An Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
Bai Y, Cole K, Martinez-Morilla S, Ahmed FS, Zugazagoitia J, Staaf J, Bosch A, Ehinger A, Nimeus E, Hartman J, Acs B, Rimm DL. An Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer. Clinical Cancer Research 2021, 27: clincanres.0325.2021. PMID: 34088723, PMCID: PMC8530841, DOI: 10.1158/1078-0432.ccr-21-0325.Peer-Reviewed Original ResearchSpatial Analysis and Clinical Significance of HLA Class-I and Class-II Subunit Expression in Non–Small Cell Lung Cancer
Datar IJ, Hauc SC, Desai S, Gianino N, Henick B, Liu Y, Syrigos K, Rimm DL, Kavathas P, Ferrone S, Schalper KA. Spatial Analysis and Clinical Significance of HLA Class-I and Class-II Subunit Expression in Non–Small Cell Lung Cancer. Clinical Cancer Research 2021, 27: 2837-2847. PMID: 33602682, PMCID: PMC8734284, DOI: 10.1158/1078-0432.ccr-20-3655.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerNK cell infiltrationReduced T cellCell lung cancerHLA class IIT cellsLung cancerHLA classClinical significanceSubunit expressionHLA class II downregulationΒ2MClass IIHLA genesHuman leukocyte antigen classShorter overall survivalTumor microenvironment compositionClass II β chainImmune contextureOverall survivalLung malignancyNatural killerPatient survivalCell infiltrationClinicopathologic variablesAutomated 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
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 factorsAssessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group
Nielsen TO, Leung SCY, Rimm DL, Dodson A, Acs B, Badve S, Denkert C, Ellis MJ, Fineberg S, Flowers M, Kreipe HH, Laenkholm AV, Pan H, Penault-Llorca FM, Polley MY, Salgado R, Smith IE, Sugie T, Bartlett JMS, McShane LM, Dowsett M, Hayes DF. Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group. Journal Of The National Cancer Institute 2020, 113: 808-819. PMID: 33369635, PMCID: PMC8487652, DOI: 10.1093/jnci/djaa201.Peer-Reviewed Original ResearchConceptsBreast cancerClinical utilityKi67 immunohistochemistryInternational Ki67Breast Cancer Working GroupAnalytical validityAssessment of Ki67HER2-negative patientsBreast cancer careCancer Working GroupGroup consensus meetingAdjuvant chemotherapyVisual scoring methodPatient groupCancer careT1-2Prognostic markerPrognosis assessmentPrognosis estimationTreatment decisionsEstrogen receptorHER2 testingConsensus meetingCurrent evidenceClinical validityA 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 cohortEstrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update
Allison KH, Hammond MEH, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL, Hayes DF, Lakhani SR, Chavez-MacGregor M, Perlmutter J, Perou CM, Regan MM, Rimm DL, Symmans WF, Torlakovic EE, Varella L, Viale G, Weisberg TF, McShane LM, Wolff AC. Estrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update. Archives Of Pathology & Laboratory Medicine 2020, 144: 545-563. PMID: 31928354, DOI: 10.5858/arpa.2019-0904-sa.Peer-Reviewed Original ResearchConceptsClinical Oncology/CollegeProgesterone receptor testingEstrogen receptorBreast cancerEndocrine therapyReceptor testingExpert panelAmerican Pathologists (CAP) guideline updatesClinical practice guideline recommendationsMultidisciplinary international expert panelEndocrine therapy benefitPractice guideline recommendationsBreast cancer guidelinesER-positive cancersInvasive breast cancerFuture breast cancerAmerican SocietyBreast cancer samplesInternational expert panelReporting of casesPgR testingTumor cell nucleiCancer guidelinesGuideline recommendationsGuideline update
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 collectionMelanomaClosed 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 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 analysisReanalysis of the NCCN PD-L1 companion diagnostic assay study for lung cancer in the context of PD-L1 expression findings in triple-negative breast cancer
Rimm DL, Han G, Taube JM, Yi ES, Bridge JA, Flieder DB, Homer R, Roden AC, Hirsch FR, Wistuba II, Pusztai L. Reanalysis of the NCCN PD-L1 companion diagnostic assay study for lung cancer in the context of PD-L1 expression findings in triple-negative breast cancer. Breast Cancer Research 2019, 21: 72. PMID: 31196152, PMCID: PMC6567382, DOI: 10.1186/s13058-019-1156-6.Peer-Reviewed Original ResearchConceptsPD-L1 expressionImmune cell PD-L1 expressionLung cancerImmune cellsTriple-negative breast cancerEasy scoring methodCompanion diagnostic testsPD-L1Immune therapyBreast cancerImmunohistochemical testsBetter outcomesLarger studyTumor cellsDiagnostic testsCancerExpression findingsCellsExpressionPoor agreementScoring methodTherapyTrialsExpression and clinical significance of PD-L1, B7-H3, B7-H4 and TILs in human small cell lung Cancer (SCLC)
Carvajal-Hausdorf D, Altan M, Velcheti V, Gettinger SN, Herbst RS, Rimm DL, Schalper KA. Expression and clinical significance of PD-L1, B7-H3, B7-H4 and TILs in human small cell lung Cancer (SCLC). Journal For ImmunoTherapy Of Cancer 2019, 7: 65. PMID: 30850021, PMCID: PMC6408760, DOI: 10.1186/s40425-019-0540-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overB7 AntigensB7-H1 AntigenBiomarkers, TumorFemaleFluorescent Antibody TechniqueHumansKaplan-Meier EstimateLung NeoplasmsLymphocytes, Tumor-InfiltratingMaleMiddle AgedNeoplasm GradingNeoplasm StagingPrognosisRetrospective StudiesSmall Cell Lung CarcinomaV-Set Domain-Containing T-Cell Activation Inhibitor 1ConceptsSmall cell lung cancerCell lung cancerB7-H4B7-H3Lung cancerPD-L1Non-small cell lung cancerBackgroundSmall cell lung cancerAnti-tumor immune responseHuman small cell lung cancerQuantitative immunofluorescenceB7 family ligandsLevels of TILsMultiplexed quantitative immunofluorescenceLevels of CD3Effector T cellsImmune checkpoint blockersPromising clinical activityTissue microarray formatLymphocyte subsetsCheckpoint blockersOverall survivalLung malignancyClinicopathological variablesMarker levelsSpatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer
Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer. Clinical Cancer Research 2019, 25: 1526-1534. PMID: 30201760, PMCID: PMC6397708, DOI: 10.1158/1078-0432.ccr-18-2013.Peer-Reviewed Original Research