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 ResearchMeSH KeywordsAgedAlgorithmsFemaleHead and Neck NeoplasmsHumansLymphocytes, Tumor-InfiltratingMaleMiddle AgedPrognosisRetrospective StudiesConceptsTumor-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
Automated 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 ResearchMeSH KeywordsDeep LearningHumansLymphocytes, Tumor-InfiltratingMelanomaNeoplasm Recurrence, LocalPrognosisSkin NeoplasmsConceptsTumor-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
An 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 ResearchAlgorithmsFemaleHumansLymphocytes, Tumor-InfiltratingMiddle AgedPrognosisRetrospective StudiesSurvival RateTriple Negative Breast NeoplasmsPutting the Microenvironment into the Immunotherapy Companion Diagnostic
Moutafi M, Rimm DL. Putting the Microenvironment into the Immunotherapy Companion Diagnostic. Clinical Cancer Research 2021, 27: 3812-3814. PMID: 33986024, DOI: 10.1158/1078-0432.ccr-21-1238.Peer-Reviewed Original ResearchMeSH KeywordsB7-H1 AntigenHumansImmunotherapyLymphocytes, Tumor-InfiltratingStomach NeoplasmsTumor MicroenvironmentAutomated 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 biomarkerSpatially Resolved and Quantitative Analysis of the Immunological Landscape in Human Meningiomas
Yeung J, Yaghoobi V, Aung TN, Vesely MD, Zhang T, Gaule P, Gunel M, Rimm DL, Chen L. Spatially Resolved and Quantitative Analysis of the Immunological Landscape in Human Meningiomas. Journal Of Neuropathology & Experimental Neurology 2021, 80: 150-159. PMID: 33393633, DOI: 10.1093/jnen/nlaa152.Peer-Reviewed Original ResearchMeSH KeywordsB7-H1 AntigenBiomarkers, TumorHumansLymphocytes, Tumor-InfiltratingMacrophagesMeningeal NeoplasmsMeningiomaProgrammed Cell Death 1 Ligand 2 ProteinConceptsPD-L1 expressionT cell infiltrationPD-L1PD-L2Human meningiomasTumor-infiltrating immune cell populationsHigh PD-L1 expressionT-cell activation/proliferationActivation/dysfunctionLevels of CD3Immune cell subsetsT-cell phenotypeImmune cell populationsHigh-grade tumorsActivation/proliferationHigher CD3TIL infiltrationCD8 ratioImmunotherapeutic strategiesCell subsetsImmunological statusGrade tumorsImmunological landscapeTissue microarrayMacrophage phenotype
2020
PD-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 expressionThe path to a better biomarker: application of a risk management framework for the implementation of PD‐L1 and TILs as immuno‐oncology biomarkers in breast cancer clinical trials and daily practice
Gonzalez‐Ericsson P, Stovgaard ES, Sua LF, Reisenbichler E, Kos Z, Carter JM, Michiels S, Le Quesne J, Nielsen TO, Lænkholm A, Fox SB, Adam J, Bartlett JM, Rimm DL, Quinn C, Peeters D, Dieci MV, Vincent‐Salomon A, Cree I, Hida AI, Balko JM, Haynes HR, Frahm I, Acosta‐Haab G, Balancin M, Bellolio E, Yang W, Kirtani P, Sugie T, Ehinger A, Castaneda CA, Kok M, McArthur H, Siziopikou K, Badve S, Fineberg S, Gown A, Viale G, Schnitt SJ, Pruneri G, Penault‐Llorca F, Hewitt S, Thompson EA, Allison KH, Symmans WF, Bellizzi AM, Brogi E, Moore DA, Larsimont D, Dillon DA, Lazar A, Lien H, Goetz MP, Broeckx G, Bairi K, Harbeck N, Cimino‐Mathews A, Sotiriou C, Adams S, Liu S, Loibl S, Chen I, Lakhani SR, Juco JW, Denkert C, Blackley EF, Demaria S, Leon‐Ferre R, Gluz O, Zardavas D, Emancipator K, Ely S, Loi S, Salgado R, Sanders M, Group I. The path to a better biomarker: application of a risk management framework for the implementation of PD‐L1 and TILs as immuno‐oncology biomarkers in breast cancer clinical trials and daily practice. The Journal Of Pathology 2020, 250: 667-684. PMID: 32129476, DOI: 10.1002/path.5406.Peer-Reviewed Original ResearchMeSH KeywordsB7-H1 AntigenBiomarkers, TumorHumansLymphocytes, Tumor-InfiltratingRisk ManagementTriple Negative Breast NeoplasmsConceptsTriple-negative breast cancerTumor-infiltrating lymphocytesPD-L1Breast cancerPatient selectionInter-reader reproducibilityEarly-stage triple-negative breast cancerPD-1/PD-L1 inhibitorsStage triple-negative breast cancerAdvanced triple-negative breast cancerPD-1/PD-L1High tumor-infiltrating lymphocytesImmune checkpoint inhibitor therapyAddition of atezolizumabPD-L1 assessmentSuboptimal patient selectionCheckpoint inhibitor therapyOptimal patient selectionPD-L1 expressionPD-L1 inhibitorsDaily practiceStandard of careImmuno-therapeutic approachesNegative breast cancerEosin-stained slides
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 collectionMelanomaHigh-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 analysisMultiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry
Carvajal-Hausdorf DE, Patsenker J, Stanton KP, Villarroel-Espindola F, Esch A, Montgomery RR, Psyrri A, Kalogeras KT, Kotoula V, Foutzilas G, Schalper KA, Kluger Y, Rimm DL. Multiplexed (18-Plex) Measurement of Signaling Targets and Cytotoxic T Cells in Trastuzumab-Treated Patients using Imaging Mass Cytometry. Clinical Cancer Research 2019, 25: 3054-3062. PMID: 30796036, PMCID: PMC6522272, DOI: 10.1158/1078-0432.ccr-18-2599.Peer-Reviewed Original ResearchConceptsTrastuzumab-treated patientsT cell infiltrationCD8 T cell infiltrationCohort of patientsCytotoxic T cellsMass cytometryCase-control seriesExtracellular domainMechanism of actionTrastuzumab benefitAdjuvant treatmentCD8 cellsRecurrence eventsT cellsAntibody panelImmune systemPatientsMetal-conjugated antibodiesQuantitative immunofluorescenceTrastuzumabImaging Mass CytometryHER2Signaling targetsObjective measurementsCytometryMultiplex 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 analysisExpression 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 ResearchMeSH KeywordsAgedCarcinoma, Non-Small-Cell LungFemaleHumansLung NeoplasmsLymphocytes, Tumor-InfiltratingMaleMiddle AgedNeoplasm MetastasisNeoplasm StagingPrognosisProportional Hazards ModelsRecurrence
2018
Immunological differences between primary and metastatic breast cancer
Szekely B, Bossuyt V, Li X, Wali VB, Patwardhan GA, Frederick C, Silber A, Park T, Harigopal M, Pelekanou V, Zhang M, Yan Q, Rimm DL, Bianchini G, Hatzis C, Pusztai L. Immunological differences between primary and metastatic breast cancer. Annals Of Oncology 2018, 29: 2232-2239. PMID: 30203045, DOI: 10.1093/annonc/mdy399.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorBiopsyBreast NeoplasmsDisease ProgressionDrug Resistance, NeoplasmFemaleGene Expression RegulationHumansImmunologic SurveillanceLymphocyte CountLymphocytes, Tumor-InfiltratingMiddle AgedMutation RateTumor EscapeTumor MicroenvironmentYoung AdultConceptsMetastatic breast cancerBreast cancerTherapeutic targetToll-like receptor pathway genesImmuno-oncology therapeutic targetsBreast cancer evolvesImmune proteasome expressionPD-L1 positivityCorresponding primary tumorsPotential therapeutic targetMHC class IImmune-related genesMetastatic cancer samplesLigand/receptor pairLymphocyte countT helperT-regsPD-L1Immune microenvironmentCytotoxic TPrimary tumorMastoid cellsDisease progressionTherapeutic combinationsMacrophage markersImmune Marker Profiling and Programmed Death Ligand 1 Expression Across NSCLC Mutations
Toki MI, Mani N, Smithy JW, Liu Y, Altan M, Wasserman B, Tuktamyshov R, Schalper K, Syrigos KN, Rimm DL. Immune Marker Profiling and Programmed Death Ligand 1 Expression Across NSCLC Mutations. Journal Of Thoracic Oncology 2018, 13: 1884-1896. PMID: 30267840, PMCID: PMC6251746, DOI: 10.1016/j.jtho.2018.09.012.Peer-Reviewed Original ResearchConceptsPD-L1 expressionPD-L1TIL activationHigh PD-L1 levelsDeath ligand 1 (PD-L1) expressionActivation statusKRAS wild-type tumorsKRAS mutantEGFR mutantsHigh PD-L1Multiplexed quantitative immunofluorescenceUnique immune profilePD-L1 levelsLigand 1 expressionDeath-1/EGFR-mutant tumorsImmunotherapy response ratesKRAS mutant tumorsWild-type tumorsHigher CD4NSCLC patientsImmune profileClinical efficacyKRAS WTLymphocyte populationsA dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers
Gettinger SN, Choi J, Mani N, Sanmamed MF, Datar I, Sowell R, Du VY, Kaftan E, Goldberg S, Dong W, Zelterman D, Politi K, Kavathas P, Kaech S, Yu X, Zhao H, Schlessinger J, Lifton R, Rimm DL, Chen L, Herbst RS, Schalper KA. A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers. Nature Communications 2018, 9: 3196. PMID: 30097571, PMCID: PMC6086912, DOI: 10.1038/s41467-018-05032-8.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsAntibodies, BlockingCarcinogenesisCarcinoma, Non-Small-Cell LungCell ProliferationCytotoxicity, ImmunologicHistocompatibility Antigens Class IHumansLung NeoplasmsLymphocyte ActivationLymphocytes, Tumor-InfiltratingMaleMice, Inbred NODMice, SCIDMutant ProteinsMutationPeptidesPhenotypeProgrammed Cell Death 1 ReceptorReproducibility of ResultsSurvival AnalysisTobaccoConceptsImmune checkpoint blockersCheckpoint blockersQuantitative immunofluorescenceNon-small cell lung carcinoma patientsCell lung carcinoma patientsNon-small cell lung carcinomaPatient-derived xenograft modelsIntratumoral T cellsMultiplexed quantitative immunofluorescencePD-1 blockadeLevels of CD3Lung carcinoma patientsCell lung carcinomaT cell proliferationPre-treatment samplesTIL phenotypeSurvival benefitCarcinoma patientsEffector capacityLung carcinomaT cellsWhole-exome DNA sequencingXenograft modelFavorable responseBlockersScoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
Klauschen F, Müller K, Binder A, Bockmayr M, Hägele M, Seegerer P, Wienert S, Pruneri G, de Maria S, Badve S, Michiels S, Nielsen TO, Adams S, Savas P, Symmans F, Willis S, Gruosso T, Park M, Haibe-Kains B, Gallas B, Thompson AM, Cree I, Sotiriou C, Solinas C, Preusser M, Hewitt SM, Rimm D, Viale G, Loi S, Loibl S, Salgado R, Denkert C, Group O. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. Seminars In Cancer Biology 2018, 52: 151-157. PMID: 29990622, DOI: 10.1016/j.semcancer.2018.07.001.Peer-Reviewed Original ResearchConceptsClassical image segmentationLearning-based approachImage analysis approachImage segmentationTraining dataConventional machineExplainable machineVisual approachPlausibility checksML resultsSegmentationMachineSuch approachesLimited precisionShape propertiesDecision-making processLarge amountScoring approachComplex propertiesAnalysis approachHeatmapsTIL quantificationObjectsBiomedical researchEstimation