2024
BCAM (basal cell adhesion molecule) protein expression in different tumor populations
Burela S, He M, Trontzas I, Gavrielatou N, Schalper K, Langermann S, Flies D, Rimm D, Aung T. BCAM (basal cell adhesion molecule) protein expression in different tumor populations. Discover Oncology 2024, 15: 381. PMID: 39207605, PMCID: PMC11362396, DOI: 10.1007/s12672-024-01244-1.Peer-Reviewed Original ResearchPD-L1 expressionBasal cell adhesion moleculePD-L1Quantitative immunofluorescenceAssociated with better OSPD-L1 protein expressionCancer typesBladder urothelial tumorsProtein expressionMultiple immune checkpointsHead and neckMultiple tumor typesEvidence of hypermethylationImmune checkpointsImmunotherapy responseCell adhesion moleculesTumor typesValidation cohortTumor populationCancer patientsTumorPredictive valueAdhesion moleculesNovel biomarkersWidespread expression
2023
Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial
Fanucci K, Bai Y, Pelekanou V, Nahleh Z, Shafi S, Burela S, Barlow W, Sharma P, Thompson A, Godwin A, Rimm D, Hortobagyi G, Liu Y, Wang L, Wei W, Pusztai L, Blenman K. Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial. Npj Breast Cancer 2023, 9: 38. PMID: 37179362, PMCID: PMC10182981, DOI: 10.1038/s41523-023-00535-0.Peer-Reviewed Original ResearchPathologic complete responseBreast cancerComplete responseTIL scoreBreast Cancer Pathologic Complete ResponseTumor-infiltrating lymphocyte scoresEvent-free survivalNeoadjuvant chemotherapy trialsLymphocyte measurementsLymphocyte scoreNeoadjuvant chemotherapyChemotherapy trialsMean pretreatmentResidual diseaseTIL quantificationPredictive valuePretreatment samplesResponse discriminationScoresStrong positive correlationPositive correlationMulti-Institutional Study of Pathologist Reading of the Programmed Cell Death Ligand-1 Combined Positive Score Immunohistochemistry Assay for Gastric or Gastroesophageal Junction Cancer
Fernandez A, Robbins C, Gaule P, Agostini-Vulaj D, Anders R, Bellizzi A, Chen W, Chen Z, Gopal P, Zhao L, Lisovsky M, Liu X, Shia J, Wang H, Yang Z, McCann L, Chan Y, Weidler J, Bates M, Zhang X, Rimm D. Multi-Institutional Study of Pathologist Reading of the Programmed Cell Death Ligand-1 Combined Positive Score Immunohistochemistry Assay for Gastric or Gastroesophageal Junction Cancer. Modern Pathology 2023, 36: 100128. PMID: 36889057, PMCID: PMC10198879, DOI: 10.1016/j.modpat.2023.100128.Peer-Reviewed Original ResearchConceptsOverall percent agreementCut pointsReal-world settingHigher cut pointsCell death ligand 1Percent agreementGastroesophageal junction cancerPD-L1 immunohistochemistryDeath ligand 1Companion diagnostic testsMessenger RNA measurementsJunction cancerCancer casesImmunohistochemistry assaysIHC resultsDrug AdministrationPredictive valueScoring systemRange of assaysDiagnostic testsInstitutional studyRNA measurementsImmunohistochemistryPoor specificityPathologist's reading
2022
Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade
Vathiotis I, Salichos L, Martinez-Morilla S, Gavrielatou N, Aung T, Shafi S, Wong P, Jessel S, Kluger H, Syrigos K, Warren S, Gerstein M, Rimm D. Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade. Npj Precision Oncology 2022, 6: 92. PMID: 36522538, PMCID: PMC9755314, DOI: 10.1038/s41698-022-00330-3.Peer-Reviewed Original ResearchProgression-free survivalLong-term benefitsPredictive valueAnti-PD-1 therapyCell death protein 1Baseline tumor samplesImmune checkpoint inhibitorsAntitumor immune responseCohort of patientsDeath protein 1Gene expression profilesAdvanced diseaseCheckpoint inhibitorsAdvanced melanomaAxis blockadeImmunotherapy outcomesTreatment initiationEarly outcomesDisease progressionMalignant melanomaBaseline gene expressionImmune responseBaseline gene expression profilesExpression profilesTumor samples
2021
An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy
Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, Rothrock B, Raciti P, Klimstra D, Sinard J. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Modern Pathology 2021, 34: 1588-1595. PMID: 33782551, PMCID: PMC8295034, DOI: 10.1038/s41379-021-00794-x.Peer-Reviewed Original ResearchConceptsMemorial Sloan-Kettering Cancer CenterCore biopsyPredictive valueDiagnostic accuracyProstate core needle biopsiesCore needle biopsySurgical pathology practiceNegative predictive valueProstate core biopsiesPositive predictive valueProstate cancer detectionStrong diagnostic accuracyPoor quality scansCancer detectionCancer CenterProstate biopsyLeading causeNeedle biopsyTransrectal approachProstate cancerProstatic adenocarcinomaProstate carcinomaBiopsyPathology practiceProstate
2017
Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors
Hendry S, Salgado R, Gevaert T, Russell PA, John T, Thapa B, Christie M, van de Vijver K, Estrada MV, Gonzalez-Ericsson PI, Sanders M, Solomon B, Solinas C, Van den Eynden GGGM, Allory Y, Preusser M, Hainfellner J, Pruneri G, Vingiani A, Demaria S, Symmans F, Nuciforo P, Comerma L, Thompson EA, Lakhani S, Kim SR, Schnitt S, Colpaert C, Sotiriou C, Scherer SJ, Ignatiadis M, Badve S, Pierce RH, Viale G, Sirtaine N, Penault-Llorca F, Sugie T, Fineberg S, Paik S, Srinivasan A, Richardson A, Wang Y, Chmielik E, Brock J, Johnson DB, Balko J, Wienert S, Bossuyt V, Michiels S, Ternes N, Burchardi N, Luen SJ, Savas P, Klauschen F, Watson PH, Nelson BH, Criscitiello C, O’Toole S, Larsimont D, de Wind R, Curigliano G, André F, Lacroix-Triki M, van de Vijver M, Rojo F, Floris G, Bedri S, Sparano J, Rimm D, Nielsen T, Kos Z, Hewitt S, Singh B, Farshid G, Loibl S, Allison KH, Tung N, Adams S, Willard-Gallo K, Horlings HM, Gandhi L, Moreira A, Hirsch F, Dieci MV, Urbanowicz M, Brcic I, Korski K, Gaire F, Koeppen H, Lo A, Giltnane J, Rebelatto MC, Steele KE, Zha J, Emancipator K, Juco JW, Denkert C, Reis-Filho J, Loi S, Fox SB. Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors. Advances In Anatomic Pathology 2017, 24: 311-335. PMID: 28777143, PMCID: PMC5638696, DOI: 10.1097/pap.0000000000000161.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorBiopsyBrain NeoplasmsCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellEndometrial NeoplasmsFemaleGastrointestinal NeoplasmsHead and Neck NeoplasmsHumansImmunohistochemistryLung NeoplasmsLymphocytes, Tumor-InfiltratingMelanomaMesotheliomaOvarian NeoplasmsPathologyPhenotypePredictive Value of TestsSkin NeoplasmsSquamous Cell Carcinoma of Head and NeckUrogenital NeoplasmsConceptsTumor-infiltrating lymphocytesDifferent tumor typesSolid tumorsTumor typesTIL assessmentImmune responsePrimary brain tumorsCommon solid tumorsInvasive breast carcinomaRoutine clinical biomarkersWorking Group guidelinesPrognostic implicationsBreast carcinomaGroup guidelinesGynecologic systemGastrointestinal tractSimple biomarkerBrain tumorsGenitourinary systemPredictive valueClinical biomarkersStandardized methodologyTumorsAvailable evidenceImmunotherapy
2015
Development and Clinical Validation of an In Situ Biopsy-Based Multimarker Assay for Risk Stratification in Prostate Cancer
Blume-Jensen P, Berman DM, Rimm DL, Shipitsin M, Putzi M, Nifong TP, Small C, Choudhury S, Capela T, Coupal L, Ernst C, Hurley A, Kaprelyants A, Chang H, Giladi E, Nardone J, Dunyak J, Loda M, Klein EA, Magi-Galluzzi C, Latour M, Epstein JI, Kantoff P, Saad F. Development and Clinical Validation of an In Situ Biopsy-Based Multimarker Assay for Risk Stratification in Prostate Cancer. Clinical Cancer Research 2015, 21: 2591-2600. PMID: 25733599, DOI: 10.1158/1078-0432.ccr-14-2603.Peer-Reviewed Original ResearchConceptsBiomarker risk scoreRisk scoreRisk groupsPredictive valueNational Comprehensive Cancer NetworkComprehensive Cancer NetworkCurrent risk stratification systemsIndependent prognostic informationRisk stratification systemProstate cancer aggressivenessRisk classification groupsAccurate risk predictionCoprimary endpointsFavorable pathologyAppropriate therapyCurative therapyRisk stratificationPathologic parametersPrognostic informationProstate biopsyProstate pathologyProstate cancerBlinded studyProstatectomy specimensCancer Network
2014
Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer
Brown JR, Wimberly H, Lannin DR, Nixon C, Rimm DL, Bossuyt V. Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer. Clinical Cancer Research 2014, 20: 5995-6005. PMID: 25255793, PMCID: PMC4252785, DOI: 10.1158/1078-0432.ccr-14-1622.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntigens, CD20Antineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorBreast NeoplasmsCD3 ComplexCD8 AntigensChemotherapy, AdjuvantFemaleHumansImmunophenotypingLymphocyte SubsetsLymphocytes, Tumor-InfiltratingMiddle AgedNeoadjuvant TherapyNeoplasm GradingNeoplasm StagingPrognosisReproducibility of ResultsTreatment OutcomeTumor BurdenConceptsTumor-infiltrating lymphocytesPathologic complete responseBreast cancerTonsil specimensPredictive valueAQUA scoreQuantitative immunofluorescenceFlow cytometryFuture larger studiesPathologist estimationNeoadjuvant cohortNeoadjuvant chemotherapyNeoadjuvant therapyLymphocyte infiltratesTIL countComplete responseNodal statusLymphocyte percentageLymphocyte subpopulationsStromal expressionNuclear gradeUnivariate analysisKi-67CD8Clinical utilityThe evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014
Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, Wienert S, Van den Eynden G, Baehner FL, Penault-Llorca F, Perez EA, Thompson EA, Symmans WF, Richardson AL, Brock J, Criscitiello C, Bailey H, Ignatiadis M, Floris G, Sparano J, Kos Z, Nielsen T, Rimm DL, Allison KH, Reis-Filho JS, Loibl S, Sotiriou C, Viale G, Badve S, Adams S, Willard-Gallo K, Loi S. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Annals Of Oncology 2014, 26: 259-271. PMID: 25214542, PMCID: PMC6267863, DOI: 10.1093/annonc/mdu450.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesBreast cancerInternational TILs Working Group 2014Human epidermal growth factor receptor 2Epidermal growth factor receptor 2Growth factor receptor 2Evaluation of hematoxylinFactor receptor 2Immunological biomarkersLymphocytic infiltrationClinical trialsReceptor 2Clinical relevanceClinical validityTumor sectionsPredictive valueTumor tissueStandardized methodologyHistopathological practiceMorphological evaluationLymphocytesCancerCurrent dataFuture studiesVisual assessmentIn Situ Quantitative Measurement of HER2mRNA Predicts Benefit from Trastuzumab-Containing Chemotherapy in a Cohort of Metastatic Breast Cancer Patients
Vassilakopoulou M, Togun T, Dafni U, Cheng H, Bordeaux J, Neumeister VM, Bobos M, Pentheroudakis G, Skarlos DV, Pectasides D, Kotoula V, Fountzilas G, Rimm DL, Psyrri A. In Situ Quantitative Measurement of HER2mRNA Predicts Benefit from Trastuzumab-Containing Chemotherapy in a Cohort of Metastatic Breast Cancer Patients. PLOS ONE 2014, 9: e99131. PMID: 24968015, PMCID: PMC4072595, DOI: 10.1371/journal.pone.0099131.Peer-Reviewed Original ResearchConceptsBreast cancer patientsMetastatic breast cancer patientsFISH HER2Cancer patientsHER2 mRNAPrognostic factorsTrastuzumab-treated metastatic breast cancer patientsMultivariate Cox regression modelECD HER2Log rank pMetastatic breast cancerStrong prognostic factorCox regression modelKaplan-Meier estimatesHER2 mRNA levelsHER2-ICDChemotherapy regimensMetastatic cohortTrastuzumab treatmentBreast cancerTissue microarrayMRNA statusPerformance of markersHER2 receptorPredictive valueQuantitative assessment of CD3, CD8, and CD20 in tumor-infiltrating lymphocytes and predictive value for response to neoadjuvant chemotherapy in breast cancer.
Brown J, Bai Y, Bossuyt V, Nixon C, Lannin D, Rimm D. Quantitative assessment of CD3, CD8, and CD20 in tumor-infiltrating lymphocytes and predictive value for response to neoadjuvant chemotherapy in breast cancer. Journal Of Clinical Oncology 2014, 32: 1027-1027. DOI: 10.1200/jco.2014.32.15_suppl.1027.Peer-Reviewed Original Research
2012
Association between the nuclear to cytoplasmic ratio of p27 and the efficacy of adjuvant polychemotherapy in early breast cancer
Andre F, Conforti R, Moeder CB, Mauguen A, Arnedos M, Berrada N, Delaloge S, Tomasic G, Spielmann M, Esteva FJ, Rimm DL, Michiels S. Association between the nuclear to cytoplasmic ratio of p27 and the efficacy of adjuvant polychemotherapy in early breast cancer. Annals Of Oncology 2012, 23: 2059-2064. PMID: 22241898, DOI: 10.1093/annonc/mdr569.Peer-Reviewed Original ResearchConceptsAnthracycline-based chemotherapyEarly breast cancerNuclear/cytoplasmic (N/C) ratioCytoplasmic ratioAdjuvant chemotherapyHazard ratioBreast cancerP27 expressionNuclear expressionAdjusted hazard ratioNuclear p27 expressionAdjuvant polychemotherapyUntreated armPrognostic parametersTissue microarrayChemotherapyPatientsPredictive valueImmunofluorescence assaysQuantitative immunofluorescence assaysEfficacyP27CancerExpressionPolychemotherapy
2011
Evaluation of prognostic and predictive value of microtubule associated protein tau in two independent cohorts
Baquero MT, Lostritto K, Gustavson MD, Bassi KA, Appia F, Camp RL, Molinaro AM, Harris LN, Rimm DL. Evaluation of prognostic and predictive value of microtubule associated protein tau in two independent cohorts. Breast Cancer Research 2011, 13: r85. PMID: 21888627, PMCID: PMC3262195, DOI: 10.1186/bcr2937.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorBreast NeoplasmsCohort StudiesCyclophosphamideCytoplasmDocetaxelDoxorubicinEpithelial CellsFemaleFluorouracilHumansKaplan-Meier EstimateMiddle AgedPredictive Value of TestsPrognosisRandomized Controlled Trials as TopicRetrospective StudiesSurvival RateTau ProteinsTaxoidsConceptsOverall survivalBreast cancer cohortTreatment armsPredictive markerCancer cohortPredictive valueResponse rateConventional whole tissue sectionsMAP-tauImproved overall survivalHigh expressionMicrotubule associated protein tauTaxane-based chemotherapyKaplan-Meier analysisLonger median timeUseful predictive markerCox univariate analysisIndependent breast cancer cohortsWhole tissue sectionsFAC chemotherapyLonger TTPMedian timeMeier analysisPrognostic valueClinicopathologic variables
2009
Predictive value of p27 for the benefit of adjuvant anthracycline-based chemotherapy in early breast cancer.
Conforti R, Moeder C, Tomasic G, Boulet T, Nahta R, Yuan L, Spielmann M, Delaloge S, Michiels S, Rimm D, Esteva F, Andre F. Predictive value of p27 for the benefit of adjuvant anthracycline-based chemotherapy in early breast cancer. Cancer Research 2009, 69: 6063. DOI: 10.1158/0008-5472.sabcs-6063.Peer-Reviewed Original ResearchEarly breast cancerHazard ratioInstitut Gustave RoussyPredictive valueOverall survivalControl armBreast cancerAdjuvant anthracycline-based chemotherapyHR of deathCytoplasmic expressionTumor samplesAdjusted hazard ratioAnthracycline-based chemotherapyDisease-free survivalBreast cancer patientsCox regression modelInteraction p valueUnit increaseAdjuvant anthracyclinesAdjuvant treatmentCyclin-dependent kinase inhibitorEGFR tumorsER expressionRandomized trialsPredictive factors
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
2003
JAKs and STATs as Biomarkers of Disease
Dolled-Filhart M, Rimm D. JAKs and STATs as Biomarkers of Disease. 2003, 697-720. DOI: 10.1007/978-94-017-3000-6_44.Peer-Reviewed Original ResearchClinical practice todayPathways of tumorigenesisPrecise disease classificationPrognosticate outcomesClinical trialsDisease progressionDisease outcomeLarge cohortTumor specimensBiomarkers of diseaseSmall studyNew biomarkersPredictive valueBiomarker expressionHuman malignanciesPatient samplesLevel of expressionTherapeutic agentsTumor biomarkersTherapyProtein expressionBiomarkersPatientsOutcomesDisease
2000
The utility of Ki-ras mutation analysis in the cytologic diagnosis of pancreatobiliary neoplasma.
Dillon DA, Johnson CC, Topazian MD, Tallini G, Rimm DL, Costa JC. The utility of Ki-ras mutation analysis in the cytologic diagnosis of pancreatobiliary neoplasma. The Cancer Journal 2000, 6: 294-301. PMID: 11079168.Peer-Reviewed Original ResearchConceptsFine needle aspiratesBile duct brushingsCytologic diagnosisPositive predictive valueCommon bile duct brushingsDuct brushingsPancreatobiliary carcinomaPredictive valueMutation patternsBiliary tract carcinomaPrevious retrospective studyAvailable clinical informationConsecutive clinical specimensDefinitive cytologic diagnosisPolymerase chain reaction/single-strand conformation polymorphism analysisRoutine cytologic diagnosisPositive cytologyRetrospective studySuspicious morphologyCytologic evaluationSuspicious cytologyPancreatobiliary tractClinical informationMorphologic diagnosisNeoplastic cells
1998
Expression of c‐met is a strong independent prognostic factor in breast carcinoma
Ghoussoub R, Dillon D, D'Aquila T, Rimm E, Fearon E, Rimm D. Expression of c‐met is a strong independent prognostic factor in breast carcinoma. Cancer 1998, 82: 1513-1520. PMID: 9554529, DOI: 10.1002/(sici)1097-0142(19980415)82:8<1513::aid-cncr13>3.0.co;2-7.Peer-Reviewed Original ResearchConceptsBreast carcinomaIndependent predictorsStrong independent prognostic factorCox proportional hazards modelGrowth factorIndependent prognostic factorLymph node statusSubset of patientsInvasive ductal carcinomaUseful prognostic markerProportional hazards modelBreast tumor specimensHepatocyte growth factorNegative patientsPrognostic factorsAggressive diseaseDuctal carcinomaNode statusPrognostic valuePrognostic markerTumor specimensHazards modelPatientsPredictive valueSurvival rate