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 patternsImagingCD8Foxp3
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
2013
Construction and Analysis of Multiparameter Prognostic Models for Melanoma Outcome
Rothberg BE, Rimm DL. Construction and Analysis of Multiparameter Prognostic Models for Melanoma Outcome. Methods In Molecular Biology 2013, 1102: 227-258. PMID: 24258982, PMCID: PMC3912557, DOI: 10.1007/978-1-62703-727-3_13.Peer-Reviewed Original ResearchConceptsAdjuvant regimensNegative sentinel lymph node biopsyAdverse risk-benefit ratioPrognostic modelStage II melanoma patientsSentinel lymph node biopsyConventional clinicopathologic criteriaLymph node biopsyStage II melanomaMelanoma-specific survivalWide local excisionRisk-benefit ratioKi-67 assaysTumor molecular profilesComposite prognostic indicesMost patientsNode biopsyLocal excisionMelanoma patientsPrognostic indexRisk stratificationClinicopathologic criteriaMelanoma outcomesPrognostic biomarkerIndependent cohort
2012
Evaluation of the pattern of SOX2 expression in non-small cell lung cancer (NSCLC) and correlation with clinicopathologic (CP) features and prognosis.
Velcheti V, Cheng H, Yao X, Deng Y, Gettinger S, Rimm D. Evaluation of the pattern of SOX2 expression in non-small cell lung cancer (NSCLC) and correlation with clinicopathologic (CP) features and prognosis. Journal Of Clinical Oncology 2012, 30: e21106-e21106. DOI: 10.1200/jco.2012.30.15_suppl.e21106.Peer-Reviewed Original ResearchNon-small cell lung cancerSquamous cell carcinomaSOX2 expressionWorse median overall survivalYale-New Haven HospitalMedian overall survivalMore stage IIIPatras University HospitalKaplan-Meier analysisCell lung cancerLog-rank testHigh SOX2 expressionNew Haven HospitalMore effective markersSOX2 expression levelsOverall survivalCell carcinomaLung cancerUniversity HospitalIndependent cohortKruskal-Wallis testStage IIIBetter outcomesStem cell transcription factorsSurvival analysis
2011
Standardization of Estrogen Receptor Measurement in Breast Cancer Suggests False-Negative Results Are a Function of Threshold Intensity Rather Than Percentage of Positive Cells
Welsh AW, Moeder CB, Kumar S, Gershkovich P, Alarid ET, Harigopal M, Haffty BG, Rimm DL. Standardization of Estrogen Receptor Measurement in Breast Cancer Suggests False-Negative Results Are a Function of Threshold Intensity Rather Than Percentage of Positive Cells. Journal Of Clinical Oncology 2011, 29: 2978-2984. PMID: 21709197, PMCID: PMC3157961, DOI: 10.1200/jco.2010.32.9706.Peer-Reviewed Original ResearchConceptsER-positive patientsEstrogen receptorQuantitative immunofluorescenceBreast cancerTissue microarrayPositive cellsIndependent retrospective cohortsEstrogen receptor measurementsAssessment of survivalTMA cohortFalse-negative resultsRetrospective cohortER immunoreactivityTest discordancePrognostic outcomesIndependent cohortReceptor measurementsLimitations of immunohistochemistryPatientsDiscrepant casesCohortIHC methodPathologists' judgmentsDiscrepant resultsStandardized assays
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 stratificationNuclear Localization of Signal Transducer and Activator of Transcription 3 in Head and Neck Squamous Cell Carcinoma Is Associated with a Better Prognosis
Pectasides E, Egloff AM, Sasaki C, Kountourakis P, Burtness B, Fountzilas G, Dafni U, Zaramboukas T, Rampias T, Rimm D, Grandis J, Psyrri A. Nuclear Localization of Signal Transducer and Activator of Transcription 3 in Head and Neck Squamous Cell Carcinoma Is Associated with a Better Prognosis. Clinical Cancer Research 2010, 16: 2427-2434. PMID: 20371693, PMCID: PMC3030188, DOI: 10.1158/1078-0432.ccr-09-2658.Peer-Reviewed Original ResearchConceptsLonger progression-free survivalNeck squamous cell cancerNeck squamous cell carcinomaProgression-free survivalSquamous cell cancerSquamous cell carcinomaPittsburgh Medical CenterTranscription 3Early Detection Research NetworkCurative intentPrognostic roleSurgical resectionBetter prognosisSignal transducerCell cancerCell carcinomaFavorable outcomeSurvival prognosisClinicopathologic parametersMedical CenterIndependent cohortLower riskTest cohortHNSCCSurvival analysis
2008
Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation
Emerson JW, Dolled-Filhart M, Harris L, Rimm DL, Tuck DP. Quantitative Assessment of Tissue Biomarkers and Construction of a Model to Predict Outcome in Breast Cancer Using Multiple Imputation. Cancer Informatics 2008, 7: cin.s911. PMID: 19352457, PMCID: PMC2664700, DOI: 10.4137/cin.s911.Peer-Reviewed Original ResearchLymph node statusProtein expression levelsNode statusBreast cancerBaseline clinical modelCohort of patientsLack of tumorTissue microarray studyLarge independent cohortsExpression levelsMultiple imputationPatient survivalTraining cohortTissue biomarkersIndependent cohortClinical modelSelect markersCohortSimilar improvementsBiomarker analysisCancerClinical annotationProtein markersBiomarkersFuture studiesThyroid Transcription Factor 1 Is an Independent Prognostic Factor for Patients With Stage I Lung Adenocarcinoma
Anagnostou VK, Syrigos KN, Bepler G, Homer RJ, Rimm DL. Thyroid Transcription Factor 1 Is an Independent Prognostic Factor for Patients With Stage I Lung Adenocarcinoma. Journal Of Clinical Oncology 2008, 27: 271-278. PMID: 19064983, DOI: 10.1200/jco.2008.17.0043.Peer-Reviewed Original ResearchConceptsThyroid transcription factor-1Stage I lung adenocarcinomaTTF1 expressionTranscription factor 1Lung adenocarcinomaStage IIndependent lower riskMedian overall survivalProtein expressionIndependent prognostic factorPotential prognostic parametersSubgroup of patientsFactor 1Overall survivalPrognostic factorsPatient survivalPrognostic parametersPrognostic stratificationLung cancerFavorable outcomeSitu protein expressionIndependent cohortLower riskPatientsAdenocarcinoma
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 discovery