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 benefitsPatientsCD68Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger H, Gerstein M, Rimm D. Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma. Clinical Cancer Research 2024, 30: 3520-3532. PMID: 38837895, PMCID: PMC11326985, DOI: 10.1158/1078-0432.ccr-23-3932.Peer-Reviewed Original ResearchGene signatureResistance to immunotherapyResponse to immunotherapyPrediction of treatment outcomeResistant to treatmentAccurate prediction of treatment outcomePredictive of responseImmunotherapy outcomesMelanoma patientsMelanoma specimensValidation cohortPatient stratificationDiscovery cohortTreatment outcomesImmunotherapyMelanomaTumorPatientsCohortS100BOutcomesGene expression dataGenesCD68+macrophagesExpression dataAn 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
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 expressionSubsets 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
Digital spatial profiling to uncover biomarkers of immunotherapy outcomes in head and neck squamous cell carcinoma.
Gavrielatou N, Vathiotis I, Aung T, Shafi S, Burela S, Fernandez A, Psyrri A, Rimm D. Digital spatial profiling to uncover biomarkers of immunotherapy outcomes in head and neck squamous cell carcinoma. Journal Of Clinical Oncology 2022, 40: 6050-6050. DOI: 10.1200/jco.2022.40.16_suppl.6050.Peer-Reviewed Original ResearchNeck squamous cell carcinomaM HNSCC patientsSquamous cell carcinomaB2M expressionOverall survivalHNSCC patientsValidation cohortCell carcinomaB2MPre-treatment biopsy samplesUnivariate Cox regression modelM expressionFunctional antigen presentationHigh beta2-microglobulinTreatment of recurrentImmune checkpoint expressionCox regression modelImmune cell markersStandard of careIndependent validation cohortSpatial profiling technologyDigital spatial profilingDigital spatial profiling (DSP) technologyImmune stromaM HNSCC
2021
Automated 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
A 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 cohort
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 collectionMelanoma
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
2014
A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue
Neumeister VM, Parisi F, England AM, Siddiqui S, Anagnostou V, Zarrella E, Vassilakopolou M, Bai Y, Saylor S, Sapino A, Kluger Y, Hicks DG, Bussolati G, Kwei S, Rimm DL. A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue. Laboratory Investigation 2014, 94: 467-474. PMID: 24535259, PMCID: PMC4030875, DOI: 10.1038/labinvest.2014.7.Peer-Reviewed Original ResearchConceptsCold ischemic timeIschemic timeQuantitative immunofluorescenceLevel of expressionTissue qualityER expression levelsBreast cancer casesExpression levelsFormalin fixationInformative epitopesValidation cohortCancer casesBreast cohortProtein statusGlobal assessmentPatient samplesTissue cohortPreanalytical variablesEpitope expressionCohortIntrinsic controlMeasurement of effectsQuality IndexFFPE tissuesEpitopes
2010
High expression of BCL-2 predicts favorable outcome in non-small cell lung cancer patients with non squamous histology
Anagnostou VK, Lowery FJ, Zolota V, Tzelepi V, Gopinath A, Liceaga C, Panagopoulos N, Frangia K, Tanoue L, Boffa D, Gettinger S, Detterbeck F, Homer RJ, Dougenis D, Rimm DL, Syrigos KN. High expression of BCL-2 predicts favorable outcome in non-small cell lung cancer patients with non squamous histology. BMC Cancer 2010, 10: 186. PMID: 20459695, PMCID: PMC2875218, DOI: 10.1186/1471-2407-10-186.Peer-Reviewed Original ResearchMeSH KeywordsAdenocarcinomaAgedBiomarkers, TumorCarcinoma, Large CellCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellCell DifferentiationCohort StudiesConnecticutFemaleGreeceHumansKaplan-Meier EstimateLung NeoplasmsMaleMiddle AgedNeoplasm StagingPredictive Value of TestsProportional Hazards ModelsProto-Oncogene Proteins c-bcl-2Reproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUp-RegulationConceptsNon-small cell lung cancer patientsCell lung cancer patientsNon-squamous tumorsLung cancer patientsBcl-2 expressionNSCLC patientsCancer patientsBcl-2Favorable outcomeIndependent cohortSmall cell lung cancer patientsIndependent lower riskNon-squamous histologySubgroup of patientsHigh expressersSquamous cell carcinomaHigh Bcl-2 expressionBcl-2 protein levelsSquamous histologyMedian survivalPrognostic factorsValidation cohortCell carcinomaPathological characteristicsPrognostic stratification
2009
High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma
Anagnostou VK, Bepler G, Syrigos KN, Tanoue L, Gettinger S, Homer RJ, Boffa D, Detterbeck F, Rimm DL. High Expression of Mammalian Target of Rapamycin Is Associated with Better Outcome for Patients with Early Stage Lung Adenocarcinoma. Clinical Cancer Research 2009, 15: 4157-4164. PMID: 19509151, DOI: 10.1158/1078-0432.ccr-09-0099.Peer-Reviewed Original ResearchConceptsLung cancer patientsMTOR expressionCancer patientsMammalian targetEarly-stage lung adenocarcinomaHigh mTOR expressionIndependent lower riskMedian overall survivalStage IA patientsProtein expressionSubgroup of patientsLung adenocarcinoma patientsStage lung adenocarcinomaMTOR protein expressionRole of mTOROverall survivalPathologic characteristicsPatient survivalValidation cohortAdenocarcinoma groupAdenocarcinoma patientsPrognostic stratificationLung cancerTraining cohortFavorable outcomeAssociation of expression of bcl-2 with outcome in non-small cell lung cancer
Anagnostou V, Lowery F, Syrigos K, Frangia K, Zolota V, Panagopoulos N, Dougenis D, Tanoue L, Detterbeck F, Homer R, Rimm D. Association of expression of bcl-2 with outcome in non-small cell lung cancer. Journal Of Clinical Oncology 2009, 27: e22039-e22039. DOI: 10.1200/jco.2009.27.15_suppl.e22039.Peer-Reviewed Original ResearchLung cancer patientsSquamous cell carcinomaBcl-2 expressionCancer patientsCell carcinomaBcl-2Non-small cell lung cancerYale-New Haven HospitalIndependent lower riskPatras University HospitalMedian overall survivalProtein expressionPotential prognostic parametersSubgroup of patientsCell lung cancerHigh Bcl-2 expressionNew Haven HospitalBcl-2 protein expressionAssociation of expressionBcl-2 protein levelsOverall survivalPatient survivalValidation cohortPathological characteristicsPrognostic parameters