2021
PD-L1 as a biomarker of response to immune-checkpoint inhibitors
Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, Wistuba II, Rimm DL, Tsao MS, Hirsch FR. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nature Reviews Clinical Oncology 2021, 18: 345-362. PMID: 33580222, DOI: 10.1038/s41571-021-00473-5.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsSelection of patientsPD-L1L1 antibodyImmunohistochemistry assaysPD-L1 immunohistochemistry assaysOutcomes of patientsBiomarkers of responseCompanion diagnostic assayTypes of cancerPD-1Clinical outcomesSelection biomarkerProspective comparisonClinical challengeNew therapiesFuture treatmentPatientsSolid tumorsClinical useSpecific agentsInter-assay variabilityBiomarkersCurrent roleDiagnostic assaysAutomated 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 biomarker
2020
Advances in quantitative immunohistochemistry and their contribution to breast cancer
Yaghoobi V, Martinez-Morilla S, Liu Y, Charette L, Rimm DL, Harigopal M. Advances in quantitative immunohistochemistry and their contribution to breast cancer. Expert Review Of Molecular Diagnostics 2020, 20: 509-522. PMID: 32178550, DOI: 10.1080/14737159.2020.1743178.Peer-Reviewed Original Research
2019
Deep 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 immunotherapy
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 biomarkersExceptional Response to Pembrolizumab in a Metastatic, Chemotherapy/Radiation-Resistant Ovarian Cancer Patient Harboring a PD-L1-Genetic Rearrangement
Bellone S, Buza N, Choi J, Zammataro L, Gay L, Elvin J, Rimm DL, Liu Y, Ratner E, Schwartz PE, Santin AD. Exceptional Response to Pembrolizumab in a Metastatic, Chemotherapy/Radiation-Resistant Ovarian Cancer Patient Harboring a PD-L1-Genetic Rearrangement. Clinical Cancer Research 2018, 24: 3282-3291. PMID: 29351920, PMCID: PMC6050068, DOI: 10.1158/1078-0432.ccr-17-1805.Peer-Reviewed Original ResearchMeSH KeywordsAged, 80 and overAntibodies, Monoclonal, HumanizedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorBiopsyComputational BiologyDrug Resistance, NeoplasmExome SequencingFemaleGene RearrangementHLA AntigensHumansMolecular Targeted TherapyMutationOvarian NeoplasmsPositron Emission Tomography Computed TomographyProgrammed Cell Death 1 ReceptorReceptors, Cell SurfaceRetreatmentT-LymphocytesTreatment OutcomeConceptsImmune checkpoint inhibitor pembrolizumabCheckpoint inhibitor pembrolizumabComplete clinical responseClinical responsePD-L1Ovarian carcinomaAberrant PD-L1 expressionPD-L1 surface expressionAnti-PD1 inhibitorsPD-L1 expressionRemarkable clinical responsesHigh-grade ovarian carcinomaStandard treatment modalityAlternative therapeutic optionClear cell featuresNovel treatment optionsSignificant side effectsT-cell lymphocytesWhole exome sequencing techniqueClin Cancer ResMetastatic human tumorsRecurrent diseaseComplete responseHeavy infiltrationTherapeutic options
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
2009
Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features
Creighton CJ, Li X, Landis M, Dixon JM, Neumeister VM, Sjolund A, Rimm DL, Wong H, Rodriguez A, Herschkowitz JI, Fan C, Zhang X, He X, Pavlick A, Gutierrez MC, Renshaw L, Larionov AA, Faratian D, Hilsenbeck SG, Perou CM, Lewis MT, Rosen JM, Chang JC. Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proceedings Of The National Academy Of Sciences Of The United States Of America 2009, 106: 13820-13825. PMID: 19666588, PMCID: PMC2720409, DOI: 10.1073/pnas.0905718106.Peer-Reviewed Original ResearchConceptsBreast cancerConventional treatmentHigh tumor-initiating potentialResidual breast cancerBreast cancer patientsCell surface antigen profileLong-term survivalHuman breast tumorsBreast cancer cellsTumor-initiating cellsTumor-initiating potentialEndocrine therapyGene expression signaturesCancer patientsTumor cell populationClinical significanceMolecular subtypesTherapeutic strategiesMesenchymal markersMetalloproteinase-2Breast tumorsSubpopulation of cellsAntigen profileMesenchymal featuresTumor tissue
1997
Degree of dysplasia following diagnosis of atypical squamous cells of undetermined significance is influenced by patient history and type of follow‐up
Ghoussoub R, Rimm D. Degree of dysplasia following diagnosis of atypical squamous cells of undetermined significance is influenced by patient history and type of follow‐up. Diagnostic Cytopathology 1997, 17: 14-19. PMID: 9218897, DOI: 10.1002/(sici)1097-0339(199707)17:1<14::aid-dc3>3.0.co;2-p.Peer-Reviewed Original ResearchConceptsHigh-grade lesionsAtypical squamous cellsSelection of patientsGrade lesionsSquamous cellsUndetermined significanceDiagnosis of ASCUSSignificant past historyDegree of dysplasiaBethesda System criteriaASCUS diagnosisRetrospective studySignificant lesionsPatient historyVaginal smearsBiopsyCurrent literature findingsPatientsLesionsComparison of the costs of fine‐needle aspiration and open surgical biopsy as methods for obtaining a pathologic diagnosis
Rimm D, Stastny J, Rimm E, Ayer S, Frable W. Comparison of the costs of fine‐needle aspiration and open surgical biopsy as methods for obtaining a pathologic diagnosis. Cancer 1997, 81: 51-56. PMID: 9100542, DOI: 10.1002/(sici)1097-0142(19970225)81:1<51::aid-cncr11>3.0.co;2-b.Peer-Reviewed Original Research
1996
Atypical reparative change on cervical/vaginal smears may be associated with dysplasia
Rimm D, Gmitro S, Frable W. Atypical reparative change on cervical/vaginal smears may be associated with dysplasia. Diagnostic Cytopathology 1996, 14: 374-379. PMID: 8725141, DOI: 10.1002/(sici)1097-0339(199605)14:4<374::aid-dc17>3.0.co;2-h.Peer-Reviewed Original ResearchConceptsHigh-grade squamous intraepithelial lesionsLow-grade squamous intraepithelial lesionsAtypical reparative changesDiagnosis of ASCUSSquamous intraepithelial lesionsReparative changesIntraepithelial lesionsAtypical repairVaginal smearsSignificant histologic abnormalitiesUniversity Pathology DepartmentAtypical squamous cellsPercentage of casesChronic cervicitisSquamous cellsSquamous metaplasiaHistologic abnormalitiesHistologic findingsClinical managementHistologic changesCytologic diagnosisBethesda SystemClinical concernPathology departmentMedical College