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 patternsImagingCD8Foxp3BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort
Vassilakopoulou M, Won M, Curran WJ, Souhami L, Prados MD, Langer CJ, Rimm DL, Hanna JA, Neumeister VM, Melian E, Diaz AZ, Atkins JN, Komarnicky LT, Schultz CJ, Howard SP, Zhang P, Dicker AP, Knisely JPS. BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort. Oncology 2021, 99: 580-588. PMID: 33957633, PMCID: PMC8491475, DOI: 10.1159/000516168.Peer-Reviewed Original ResearchConceptsBRCA1 protein expressionTensin homolog (PTEN) tumor suppressor geneProtein expressionTumor suppressor geneQuantitative protein analysisDNA repairGenetic profiling studiesMolecular markersSuppressor geneProtein analysisProfiling studiesBRCA1 expressionSitu hybridizationExpression levelsTumor formationCommon malignant brain tumorCancer cellsTissue microarrayGlioblastoma tumorsExpressionPre-temozolomide eraGlioblastoma patientsComparison of programmed death-ligand 1 protein expression between primary and metastatic lesions in patients with lung cancer
Moutafi MK, Tao W, Huang R, Haberberger J, Alexander B, Ramkissoon S, Ross JS, Syrigos K, Wei W, Pusztai L, Rimm DL, Vathiotis IA. Comparison of programmed death-ligand 1 protein expression between primary and metastatic lesions in patients with lung cancer. Journal For ImmunoTherapy Of Cancer 2021, 9: e002230. PMID: 33833050, PMCID: PMC8039214, DOI: 10.1136/jitc-2020-002230.Peer-Reviewed Original ResearchConceptsPD-L1 expressionMetastatic lesionsLung cancer casesLung cancerCancer casesAdvanced stage non-small cell lung cancerNon-small cell lung cancerNon-squamous histologyCell lung cancerFuture patient managementDefinite diagnostic testSquamous histologyFoundation MedicineLymph nodesRoutine careHistologic subtypeMetastatic sitesPrimary lesionRetrospective studyAdrenal glandPrimary tumorPleural fluidPatient managementTrial designDrug AdministrationA new tool for technical standardization of the Ki67 immunohistochemical assay
Aung TN, Acs B, Warrell J, Bai Y, Gaule P, Martinez-Morilla S, Vathiotis I, Shafi S, Moutafi M, Gerstein M, Freiberg B, Fulton R, Rimm DL. A new tool for technical standardization of the Ki67 immunohistochemical assay. Modern Pathology 2021, 34: 1261-1270. PMID: 33536573, PMCID: PMC8222064, DOI: 10.1038/s41379-021-00745-6.Peer-Reviewed Original ResearchAutomated 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
Comparison of PD-L1 protein expression between primary tumors and metastatic lesions in triple negative breast cancers
Rozenblit M, Huang R, Danziger N, Hegde P, Alexander B, Ramkissoon S, Blenman K, Ross JS, Rimm DL, Pusztai L. Comparison of PD-L1 protein expression between primary tumors and metastatic lesions in triple negative breast cancers. Journal For ImmunoTherapy Of Cancer 2020, 8: e001558. PMID: 33239417, PMCID: PMC7689582, DOI: 10.1136/jitc-2020-001558.Peer-Reviewed Original ResearchConceptsPD-L1 positivity ratePD-L1 positivityPD-L1 expressionDifferent metastatic sitesPrimary tumorMetastatic sitesPositivity rateImmune cellsMetastatic lesionsTumor cellsPD-L1 protein expressionTriple-negative breast cancerMore primary tumorsTriple negative breast cancer tumorsPrimary breast lesionsPrimary outcome measureSoft tissueNegative breast cancerLow positivity rateBreast cancer tumorsBone metastasesFoundation MedicineLymph nodesPD-L1Spearman correlation coefficientPD-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 expression
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 immunotherapyMultiplexed (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 measurementsCytometry
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 markersQuantitative 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 biomarkersKi67 reproducibility using digital image analysis: an inter-platform and inter-operator study
Acs B, Pelekanou V, Bai Y, Martinez-Morilla S, Toki M, Leung SCY, Nielsen TO, Rimm DL. Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study. Laboratory Investigation 2018, 99: 107-117. PMID: 30181553, DOI: 10.1038/s41374-018-0123-7.Peer-Reviewed Original ResearchNuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers
Lu C, Romo-Bucheli D, Wang X, Janowczyk A, Ganesan S, Gilmore H, Rimm D, Madabhushi A. Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation 2018, 98: 1438-1448. PMID: 29959421, PMCID: PMC6214731, DOI: 10.1038/s41374-018-0095-7.Peer-Reviewed Original ResearchConceptsEarly-stage estrogen receptor-positive breast cancerEstrogen receptor-positive breast cancerReceptor-positive breast cancerBreast cancerRank sum testHazard ratioHistomorphometric featuresShort-term overall survivalLymph node negativeTissue microarray cohortPoor survival outcomesUnivariate survival analysisWilcoxon rank sum testAdjuvant chemotherapyMicroarray cohortOverall survivalNode negativeT stageHistology gradePatient survivalSurvival outcomesPathological parametersNuclear gradeOutcome groupPoor survivalClinical Features and Management of Acquired Resistance to PD-1 Axis Inhibitors in 26 Patients With Advanced Non–Small Cell Lung Cancer
Gettinger SN, Wurtz A, Goldberg SB, Rimm D, Schalper K, Kaech S, Kavathas P, Chiang A, Lilenbaum R, Zelterman D, Politi K, Herbst R. Clinical Features and Management of Acquired Resistance to PD-1 Axis Inhibitors in 26 Patients With Advanced Non–Small Cell Lung Cancer. Journal Of Thoracic Oncology 2018, 13: 831-839. PMID: 29578107, PMCID: PMC6485248, DOI: 10.1016/j.jtho.2018.03.008.Peer-Reviewed Original ResearchConceptsPD-1 axis inhibitorsNon-small cell lung cancerAdvanced non-small cell lung cancerCell lung cancerInhibitor therapyLocal therapyLymph nodesLung cancerSurvival rateSolid Tumors v1.1Response Evaluation CriteriaSite of diseaseProgression of diseaseProgressive diseaseClinical patternLN metastasisSuch patientsClinical featuresMedian timeRadiographic featuresTumor regressionProlonged benefitPatientsTherapyResponse criteria
2017
B7-H3 Expression in NSCLC and Its Association with B7-H4, PD-L1 and Tumor-Infiltrating Lymphocytes
Altan M, Pelekanou V, Schalper KA, Toki M, Gaule P, Syrigos K, Herbst RS, Rimm DL. B7-H3 Expression in NSCLC and Its Association with B7-H4, PD-L1 and Tumor-Infiltrating Lymphocytes. Clinical Cancer Research 2017, 23: 5202-5209. PMID: 28539467, PMCID: PMC5581684, DOI: 10.1158/1078-0432.ccr-16-3107.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedB7 AntigensB7-H1 AntigenBiomarkers, TumorCarcinoma, Non-Small-Cell LungCell Line, TumorDisease-Free SurvivalFemaleGene Expression Regulation, NeoplasticHumansImmunohistochemistryLymphocytes, Tumor-InfiltratingMaleMiddle AgedPrognosisV-Set Domain-Containing T-Cell Activation Inhibitor 1ConceptsNon-small cell lung cancerTumor-infiltrating lymphocytesB7-H3 proteinB7-H4PD-L1B7-H3Majority of NSCLCQuantitative immunofluorescenceImmune checkpoints PD-1Major clinicopathologic variablesLevels of CD3Negative prognostic impactCell lung cancerPoor overall survivalSuccessful therapeutic targetsB7 family membersClin Cancer ResB7-H1NSCLC cohortOverall survivalPrognostic impactSmoking historyClinicopathologic characteristicsPD-1Clinical stageEffect of neoadjuvant chemotherapy on tumor-infiltrating lymphocytes and PD-L1 expression in breast cancer and its clinical significance
Pelekanou V, Carvajal-Hausdorf DE, Altan M, Wasserman B, Carvajal-Hausdorf C, Wimberly H, Brown J, Lannin D, Pusztai L, Rimm DL. Effect of neoadjuvant chemotherapy on tumor-infiltrating lymphocytes and PD-L1 expression in breast cancer and its clinical significance. Breast Cancer Research 2017, 19: 91. PMID: 28784153, PMCID: PMC5547502, DOI: 10.1186/s13058-017-0884-8.Peer-Reviewed Original ResearchConceptsStromal tumor-infiltrating lymphocytesPD-L1 expressionTumor-infiltrating lymphocytesRecurrence-free survivalNeoadjuvant chemotherapyResidual cancer tissueTIL countBreast cancerCancer tissuesDeath ligand 1 (PD-L1) protein expressionNode-positive breast cancerImproved recurrence-free survivalPD-L1 protein expressionHigher TIL countsPD-L1 statusProtein expressionBreast cancer patientsBreast cancer tissuesPost-treatment samplesPrechemotherapy samplesTIL infiltrationResidual cancerImmune markersResidual diseasePatient cohortPD-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 rateCalcium Sensor, NCS-1, Promotes Tumor Aggressiveness and Predicts Patient Survival
Moore LM, England A, Ehrlich BE, Rimm DL. Calcium Sensor, NCS-1, Promotes Tumor Aggressiveness and Predicts Patient Survival. Molecular Cancer Research 2017, 15: 942-952. PMID: 28275088, PMCID: PMC5500411, DOI: 10.1158/1541-7786.mcr-16-0408.Peer-Reviewed Original ResearchConceptsBreast cancer cellsNCS-1Breast cancer patient cohortsNCS-1 expressionLymph node statusCancer cellsShorter survival rateIndependent breast cancer cohortsCancer patient cohortsBreast cancer cohortMB-231 breast cancer cellsPaclitaxel-induced cell deathAggressive tumor phenotypeNeuronal model systemClinical outcomesClinicopathologic featuresNeuronal calcium sensor-1Node statusPatient cohortProgesterone receptorWorse outcomesBreast cancerCalcium-binding proteinsCancer cohortEstrogen receptor