2023
Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer
Barrera C, Corredor G, Viswanathan V, Ding R, Toro P, Fu P, Buzzy C, Lu C, Velu P, Zens P, Berezowska S, Belete M, Balli D, Chang H, Baxi V, Syrigos K, Rimm D, Velcheti V, Schalper K, Romero E, Madabhushi A. Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer. Npj Precision Oncology 2023, 7: 52. PMID: 37264091, PMCID: PMC10235089, DOI: 10.1038/s41698-023-00403-x.Peer-Reviewed Original ResearchNon-small cell lung cancerTumor-infiltrating lymphocytesLung cancerEffective adaptive immune responseImmune checkpoint blockersCell lung cancerLung cancer patientsTumor immune microenvironmentAdaptive immune responsesImmune-related biomarkersTreatment-specific outcomesCheckMate 057Histology variantsImmunotherapy resistanceCheckpoint blockersRegulatory cellsTumor rejectionTumor-immune interactionsClinical outcomesImmunosuppressive signalsClinical benefitInfluence prognosisImmune microenvironmentCancer patientsPatient outcomes
2016
miR-34a Silences c-SRC to Attenuate Tumor Growth in Triple-Negative Breast Cancer
Adams BD, Wali VB, Cheng CJ, Inukai S, Booth CJ, Agarwal S, Rimm DL, Győrffy B, Santarpia L, Pusztai L, Saltzman WM, Slack FJ. miR-34a Silences c-SRC to Attenuate Tumor Growth in Triple-Negative Breast Cancer. Cancer Research 2016, 76: 927-939. PMID: 26676753, PMCID: PMC4755913, DOI: 10.1158/0008-5472.can-15-2321.Peer-Reviewed Original ResearchConceptsTriple-negative breast cancerBreast cancerTumor growthMiR-34a replacement therapyTNBC cell linesDifferent TNBC subtypesPromising therapeutic strategyAttenuates tumor growthHuman clinical trialsMiRNA-profiling studiesMiR-34a levelsCell linesPotent antitumorigenic effectsMiR-34a targetsHuman tumor specimensC-SrcReplacement therapyTNBC subtypesAggressive subtypeTreatment optionsClinical trialsDisease progressionEffective therapyPatient outcomesC-Src inhibitor
2008
Estrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome
Harigopal M, Heymann J, Ghosh S, Anagnostou V, Camp RL, Rimm DL. Estrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome. Breast Cancer Research And Treatment 2008, 115: 77-85. PMID: 18521745, DOI: 10.1007/s10549-008-0063-9.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAutomationBiomarkers, TumorBreast NeoplasmsFemaleGene Expression Regulation, NeoplasticHumansMultivariate AnalysisNuclear Receptor Coactivator 3Oligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsReceptors, EstrogenReceptors, ProgesteroneRegression AnalysisTranscription FactorsTreatment OutcomeConceptsHigh AIB1 expressionTranscription intermediary factor 2Poor patient outcomesAIB1 expressionTissue microarrayPatient outcomesHER2/neu statusBreast cancer tissue microarrayFluorescent immunohistochemical stainingWorse overall survivalUnivariate survival analysisBreast cancer specimensCancer tissue microarrayHER2/neuCoregulatory proteinsCox univariate survival analysesBreast tissue microarraysOverall survivalER statusPR statusPrognostic significanceIndependent associationBreast cancerPrognostic biomarkerImmunohistochemical staining
2007
Melanophages reside in hypermelanotic, aberrantly glycosylated tumor areas and predict improved outcome in primary cutaneous malignant melanoma
Handerson T, Berger A, Harigopol M, Rimm D, Nishigori C, Ueda M, Miyoshi E, Taniguchi N, Pawelek J. Melanophages reside in hypermelanotic, aberrantly glycosylated tumor areas and predict improved outcome in primary cutaneous malignant melanoma. Journal Of Cutaneous Pathology 2007, 34: 679-686. PMID: 17696914, DOI: 10.1111/j.1600-0560.2006.00681.x.Peer-Reviewed Original ResearchConceptsCutaneous malignant melanomaPrimary cutaneous malignant melanomaImproved outcomesMalignant melanomaMelanoma cellsAnti-tumor roleMelanoma tissue microarrayFollow-upWorse outcomesPatient outcomesPoor survivalTissue microarrayBetter outcomesMyeloid cellsImmune systemMelanophagesTumor areaMelanomaCancer cellsMelanoma biologyOutcomesAberrant glycosylationCell typesCellsTumor regionPhosphorylation of Akt (Ser473) Predicts Poor Clinical Outcome in Oropharyngeal Squamous Cell Cancer
Yu Z, Weinberger PM, Sasaki C, Egleston BL, Speier WF, Haffty B, Kowalski D, Camp R, Rimm D, Vairaktaris E, Burtness B, Psyrri A. Phosphorylation of Akt (Ser473) Predicts Poor Clinical Outcome in Oropharyngeal Squamous Cell Cancer. Cancer Epidemiology Biomarkers & Prevention 2007, 16: 553-558. PMID: 17372251, DOI: 10.1158/1055-9965.epi-06-0121.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBiomarkers, TumorCarcinoma, Squamous CellChi-Square DistributionFemaleHumansImmunoenzyme TechniquesMaleMiddle AgedNeoplasm Recurrence, LocalOropharyngeal NeoplasmsPhosphorylationPredictive Value of TestsPrognosisProportional Hazards ModelsProtein Array AnalysisProto-Oncogene Proteins c-aktPTEN PhosphohydrolaseSurvival AnalysisConceptsNuclear p-AktAkt activationP-AktOropharyngeal squamous cell cancerSquamous cell carcinoma progressionPhosphorylated AktCohort of patientsLocal recurrence rateOverall survival rateSquamous cell cancerPoor clinical outcomeAdverse patient outcomesP-AKT levelsPromising molecular targetP-AKT expressionProtein expression levelsPhosphorylation of AktDisease recurrenceLocal recurrenceCell cancerClinical outcomesAdjusted analysisPrognostic significanceRecurrence ratePatient outcomes
2006
Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays
Dolled-Filhart M, Rydén L, Cregger M, Jirström K, Harigopal M, Camp RL, Rimm DL. Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays. Clinical Cancer Research 2006, 12: 6459-6468. PMID: 17085660, DOI: 10.1158/1078-0432.ccr-06-1383.Peer-Reviewed Original ResearchConceptsBreast cancerPatient outcomesTissue microarraySubset of patientsBreast cancer patientsTissue microarray platformInternal validation setRoutine pathology laboratoriesCancer patientsEstrogen receptorTissue biomarkersIndependent cohortTumor subtypesPredictive valueAcid-base analysisPathology laboratoryRNA expression studiesCancerTissue sectionsPatientsCohortOutcomesFurther validationObjective quantitative analysisBiomarker discoveryAutomated quantitative analysis of DCC tumor suppressor protein in ovarian cancer tissue microarray shows association with β-catenin levels and outcome in patients with epithelial ovarian cancer
Bamias A, Yu Z, Weinberger P, Markakis S, Kowalski D, Camp R, Rimm D, Dimopoulos M, Psyrri A. Automated quantitative analysis of DCC tumor suppressor protein in ovarian cancer tissue microarray shows association with β-catenin levels and outcome in patients with epithelial ovarian cancer. Annals Of Oncology 2006, 17: 1797-1802. PMID: 16971669, DOI: 10.1093/annonc/mdl310.Peer-Reviewed Original ResearchConceptsEpithelial ovarian cancerOvarian cancerPatient outcomesDCC expressionPlatinum-paclitaxel combination chemotherapyProgression-free survival ratesAdvanced stage ovarian cancerOvarian cancer tissue microarrayAssociation of DCCCancer tissue microarrayPoor patient outcomesBeta-catenin levelsDCC tumor suppressor geneColorectal cancer (DCC) proteinMedian followSurgical debulkingCombination chemotherapyPrognostic significanceEntire cohortPreclinical dataClinicopathological parametersAntitumor functionΒ-catenin levelsTissue microarraySufficient tissue
2005
Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis
McCabe A, Dolled-Filhart M, Camp RL, Rimm DL. Automated Quantitative Analysis (AQUA) of In Situ Protein Expression, Antibody Concentration, and Prognosis. Journal Of The National Cancer Institute 2005, 97: 1808-1815. PMID: 16368942, DOI: 10.1093/jnci/dji427.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntibodies, NeoplasmBiomarkers, TumorCell Line, TumorConfidence IntervalsEnzyme-Linked Immunosorbent AssayFemaleFluorescent Antibody TechniqueGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmunohistochemistryMaleMiddle AgedNeoplasmsOdds RatioPredictive Value of TestsPrognosisProtein Array AnalysisReceptor, ErbB-2Receptors, EstrogenSurvival AnalysisTreatment OutcomeTumor Suppressor Protein p53ConceptsDisease-specific mortalityHigh HER2 expressionHER2 expressionAntibody concentrationsHigh expressionPoor survivalRelative riskTissue microarrayCumulative disease-specific survivalBiomarker expressionLong-term survival dataLow expressionHER2 antibodyX-tile programDisease-specific survivalLow HER2 expressionKaplan-Meier methodBreast cancer patientsExpression of HER2Higher antibody concentrationsLow antibody concentrationsConcentration of antibodyCancer patientsPatient outcomesSitu protein expressionAltered Localization of p120 Catenin During Epithelial to Mesenchymal Transition of Colon Carcinoma Is Prognostic for Aggressive Disease
Bellovin DI, Bates RC, Muzikansky A, Rimm DL, Mercurio AM. Altered Localization of p120 Catenin During Epithelial to Mesenchymal Transition of Colon Carcinoma Is Prognostic for Aggressive Disease. Cancer Research 2005, 65: 10938-10945. PMID: 16322241, DOI: 10.1158/0008-5472.can-05-1947.Peer-Reviewed Original ResearchConceptsSurvival timeMesenchymal transitionLymph node metastasisColorectal cancer progressionPoor patient outcomesE-cadherinLate-stage tumorsPatient survival timePost-EMT cellsP120ctn expressionAltered localizationLymph nodesNode metastasisAggressive diseaseTumor stagePrimary tumorTumor necrosisColorectal carcinomaPatient outcomesColon carcinoma cellsE-cadherin lossCytoplasmic stainingColon carcinomaCancer progressionCarcinoma cells
2003
Tissue microarray analysis of hepatocyte growth factor/Met pathway components reveals a role for Met, matriptase, and hepatocyte growth factor activator inhibitor 1 in the progression of node-negative breast cancer.
Kang JY, Dolled-Filhart M, Ocal IT, Singh B, Lin CY, Dickson RB, Rimm DL, Camp RL. Tissue microarray analysis of hepatocyte growth factor/Met pathway components reveals a role for Met, matriptase, and hepatocyte growth factor activator inhibitor 1 in the progression of node-negative breast cancer. Cancer Research 2003, 63: 1101-5. PMID: 12615728.Peer-Reviewed Original ResearchConceptsHepatocyte growth factor activator inhibitor-1Breast carcinomaSeries of proteasesNode-negative breast cancerHigh-level expressionNode-negative breast carcinomaHGF/MET pathwayIndependent prognostic valueBreast cancer progressionPoor patient outcomesTissue microarray analysisPathway componentsMicroarray analysisExtracellular domainActivator inhibitor-1Expression of HGFOverexpression of METMet receptorHepatocyte growth factorCancer progressionMatriptasePrognostic valueBreast markersPatient followPatient outcomesFrequent alterations of Smad signaling in human head and neck squamous cell carcinomas: a tissue microarray analysis.
Xie W, Bharathy S, Kim D, Haffty BG, Rimm DL, Reiss M. Frequent alterations of Smad signaling in human head and neck squamous cell carcinomas: a tissue microarray analysis. Oncology Research Featuring Preclinical And Clinical Cancer Therapeutics 2003, 14: 61-73. PMID: 14649540, DOI: 10.3727/000000003108748612.Peer-Reviewed Original ResearchConceptsNeck squamous cell carcinomaSquamous cell carcinomaCell carcinomaHNSCC specimensTGF-beta type II receptorTGF-beta/Smad signalingTissue microarray analysisTGF-beta/SmadProgression of HNSCCCell linesType II receptorHuman SCC linesDistant recurrenceTGF-beta signalingFrequent cancerCell cycle arrestPatient outcomesMetastatic spreadTissue microarrayHNSCCII receptorsSmall seriesEvidence of lossSCC linesActivation of Smad
2002
Subjective Differences in Outcome Are Seen as a Function of the Immunohistochemical Method Used on a Colorectal Cancer Tissue Microarray
Chung GG, Kielhorn EP, Rimm DL. Subjective Differences in Outcome Are Seen as a Function of the Immunohistochemical Method Used on a Colorectal Cancer Tissue Microarray. Clinical Colorectal Cancer 2002, 1: 237-242. PMID: 12450422, DOI: 10.3816/ccc.2002.n.005.Peer-Reviewed Original ResearchConceptsTissue microarrayTissue sectionsColorectal cancer tissue microarraySemiquantitative grading systemColorectal cancer specimensCancer tissue microarrayPatient outcomesLarge cohortSubjective assessmentCancer specimensImmunohistochemical methodsGrading systemNuclear stainingPathology literatureProtein expressionTissue samplesCell preparationsExpression levelsBeta-catenin antibodyCurrent standardImmunohistochemistryCohortOutcomesApparent increaseExpression