2010
In Situ Identification of Putative Cancer Stem Cells by Multiplexing ALDH1, CD44, and Cytokeratin Identifies Breast Cancer Patients with Poor Prognosis
Neumeister V, Agarwal S, Bordeaux J, Camp RL, Rimm DL. In Situ Identification of Putative Cancer Stem Cells by Multiplexing ALDH1, CD44, and Cytokeratin Identifies Breast Cancer Patients with Poor Prognosis. American Journal Of Pathology 2010, 176: 2131-2138. PMID: 20228222, PMCID: PMC2861079, DOI: 10.2353/ajpath.2010.090712.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAldehyde DehydrogenaseAldehyde Dehydrogenase 1 FamilyBreast NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansHyaluronan ReceptorsIsoenzymesKeratinsMiddle AgedNeoplastic Stem CellsPrognosisRetinal DehydrogenaseRetrospective StudiesConceptsCancer stem cellsPutative cancer stem cellsBreast cancerIdentifies high-risk patientsPresence of CSCsNode-positive patientsHigh-risk patientsBreast cancer patientsAggressive tumor behaviorParaffin-embedded breast cancer tissuesBreast cancer tissuesFlow cytometric studyStem cellsMean followNodal statusRisk patientsTumor persistenceCD44 positivityPoor prognosisPrognostic valueTumor sizeHistological gradeALDH1 positivityCancer patientsWorse outcomes
2009
Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning
Drozdov I, Kidd M, Nadler B, Camp RL, Mane SM, Hauso O, Gustafsson BI, Modlin IM. Predicting neuroendocrine tumor (carcinoid) neoplasia using gene expression profiling and supervised machine learning. Cancer 2009, 115: 1638-1650. PMID: 19197975, PMCID: PMC2743551, DOI: 10.1002/cncr.24180.Peer-Reviewed Original ResearchAnalysis of Drosophila Segmentation Network Identifies a JNK Pathway Factor Overexpressed in Kidney Cancer
Liu J, Ghanim M, Xue L, Brown CD, Iossifov I, Angeletti C, Hua S, Nègre N, Ludwig M, Stricker T, Al-Ahmadie HA, Tretiakova M, Camp RL, Perera-Alberto M, Rimm DL, Xu T, Rzhetsky A, White KP. Analysis of Drosophila Segmentation Network Identifies a JNK Pathway Factor Overexpressed in Kidney Cancer. Science 2009, 323: 1218-1222. PMID: 19164706, PMCID: PMC2756524, DOI: 10.1126/science.1157669.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsApoptosisCarcinoma, Renal CellCell LineCompound Eye, ArthropodDrosophila melanogasterDrosophila ProteinsEmbryo, NonmammalianFushi Tarazu Transcription FactorsGene Expression ProfilingGene Regulatory NetworksHomeodomain ProteinsHumansJanus KinasesKidneyKidney NeoplasmsMolecular Sequence DataNervous SystemNuclear ProteinsPhosphoprotein PhosphatasesPhosphorylationRepressor ProteinsSignal TransductionTranscription FactorsTranscription, GeneticConceptsTranscription factorsClear cell renal cell carcinomaCell renal cell carcinomaKey transcription factorDrosophila segmentation networkConserved roleEmbryonic segmentationDrosophila melanogasterUbiquitin E3JNK signalingDependent apoptosisSPOPRenal cell carcinomaSPOP expressionKidney cancerTumor necrosis factorNew roleDrosophilaMelanogasterPuckeredGenesSignalingOverexpressedIdentificationApoptosis
2008
A Decade of Tissue Microarrays: Progress in the Discovery and Validation of Cancer Biomarkers
Camp RL, Neumeister V, Rimm DL. A Decade of Tissue Microarrays: Progress in the Discovery and Validation of Cancer Biomarkers. Journal Of Clinical Oncology 2008, 26: 5630-5637. PMID: 18936473, DOI: 10.1200/jco.2008.17.3567.Peer-Reviewed Original Research
2006
Utility of molecular genetic signatures in the delineation of gastric neoplasia
Kidd M, Modlin IM, Mane SM, Camp RL, Eick GN, Latich I, Zikusoka MN. Utility of molecular genetic signatures in the delineation of gastric neoplasia. Cancer 2006, 106: 1480-1488. PMID: 16502410, DOI: 10.1002/cncr.21758.Peer-Reviewed Original ResearchMeSH KeywordsAdaptor Proteins, Signal TransducingAdenocarcinomaAdultAgedAntigens, NeoplasmCarcinoid TumorChromogranin AChromograninsDiagnosis, DifferentialFemaleGene Expression ProfilingGenetic MarkersHistone DeacetylasesHumansImmunohistochemistryMaleMiddle AgedNeoplasm InvasivenessOligonucleotide Array Sequence AnalysisPhenotypeRepressor ProteinsReverse Transcriptase Polymerase Chain ReactionStomach NeoplasmsTrans-ActivatorsConceptsType III/IVGastric carcinoidsMAGE-D2Gastric neoplasiaMolecular genetic signaturesType I/IIGenetic signaturesTumor invasionSimilar expression patternsCgA protein levelsProtein expression levelsII tumorsStromal tumorsClinical behaviorGastric adenocarcinomaGastric neoplasmsMTA1 levelsNormal mucosaImmunohistochemical analysisMolecular basisExpression patternsGene expressionTumorsGene signatureBiological rationale
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 expressionCoexpression of β1,6-N-Acetylglucosaminyltransferase V Glycoprotein Substrates Defines Aggressive Breast Cancers with Poor Outcome
Siddiqui SF, Pawelek J, Handerson T, Lin CY, Dickson RB, Rimm DL, Camp RL. Coexpression of β1,6-N-Acetylglucosaminyltransferase V Glycoprotein Substrates Defines Aggressive Breast Cancers with Poor Outcome. Cancer Epidemiology Biomarkers & Prevention 2005, 14: 2517-2523. PMID: 16284372, DOI: 10.1158/1055-9965.epi-05-0464.Peer-Reviewed Original ResearchConceptsSubstrate proteinsEpidermal growth factor receptorGrowth factor receptorLAMP-1Glycoprotein substratesFactor receptorComplex oligosaccharide side chainsN-cadherin expressionTumor progressionOligosaccharide side chainsBeta1 integrin expressionGnT-VN-cadherinUnsupervised hierarchical clusteringN-acetylglucosaminyltransferaseMatriptaseDistinct clustersProteinProtein expressionTumor metastasisExpressionHigh expressionAggressive breast cancerLow expressionSide chainsEvaluating the Expression and Prognostic Value of TRAIL-R1 and TRAIL-R2 in Breast Cancer
McCarthy MM, Sznol M, DiVito KA, Camp RL, Rimm DL, Kluger HM. Evaluating the Expression and Prognostic Value of TRAIL-R1 and TRAIL-R2 in Breast Cancer. Clinical Cancer Research 2005, 11: 5188-5194. PMID: 16033835, DOI: 10.1158/1078-0432.ccr-05-0158.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBreast NeoplasmsCase-Control StudiesFemaleFollow-Up StudiesGene Expression ProfilingHumansMiddle AgedMultivariate AnalysisOligonucleotide Array Sequence AnalysisPrognosisReceptors, TNF-Related Apoptosis-Inducing LigandReceptors, Tumor Necrosis FactorSurvival AnalysisConceptsEarly-stage breast cancerTRAIL-R2 expressionBreast cancerPrognostic valueTRAIL-R2TRAIL-R1Normal breast specimensTumor necrosis factor-related apoptosis-inducing ligand receptor 1Lymph node involvementSubset of patientsBreast cancer patientsIndependent prognostic markerTRAIL-R1 expressionNormal breast epitheliumTRAIL receptor expressionLigand receptor 1Apoptosis-inducing ligand receptor 1Adjuvant treatmentNode involvementNodal statusPathologic variablesTumor sizeCancer patientsClinical trialsPrognostic markerUsing a Xenograft Model of Human Breast Cancer Metastasis to Find Genes Associated with Clinically Aggressive Disease
Kluger HM, Lev D, Kluger Y, McCarthy MM, Kiriakova G, Camp RL, Rimm DL, Price JE. Using a Xenograft Model of Human Breast Cancer Metastasis to Find Genes Associated with Clinically Aggressive Disease. Cancer Research 2005, 65: 5578-5587. PMID: 15994930, DOI: 10.1158/0008-5472.can-05-0108.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBreast NeoplasmsCell AdhesionCell Growth ProcessesCell Line, TumorDisease Models, AnimalFemaleGene Expression ProfilingHumansImmunohistochemistryMiceMice, NudeMultivariate AnalysisNeoplasm InvasivenessNeoplasm MetastasisNeoplasm TransplantationOligonucleotide Array Sequence AnalysisPredictive Value of TestsReproducibility of ResultsTissue Array AnalysisTransplantation, HeterologousConceptsBreast cancerXenograft modelHuman breast cancer metastasisLymph node involvementLymph node metastasisChemokine ligand 1Human breast cancer cell linesBreast cancer metastasisLeukocyte protease inhibitorBreast cancer cell linesBreast cancer tissuesHSP-70 expressionHeat shock protein 70Cancer cell linesShock protein 70Identification of genesNode involvementNode metastasisAggressive diseaseClinicopathologic variablesPrimary tumorPrognostic markerNovel therapiesCDNA microarray analysisCancer tissues
2001
Tissue microarray: a new technology for amplification of tissue resources.
Rimm DL, Camp RL, Charette LA, Costa J, Olsen DA, Reiss M. Tissue microarray: a new technology for amplification of tissue resources. The Cancer Journal 2001, 7: 24-31. PMID: 11269645.Peer-Reviewed Original Research