2023
Automated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study
Tan S, Aung T, Claeson M, Acs B, Zhou C, Brown S, Lambie D, Baade P, Pandeya N, Soyer H, Smithers B, Whiteman D, Rimm D, Khosrotehrani K. Automated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study. Journal Of The American Academy Of Dermatology 2023, 90: 179-182. PMID: 37730017, DOI: 10.1016/j.jaad.2023.09.026.Peer-Reviewed Original ResearchDeep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
Chatziioannou E, Roßner J, Aung T, Rimm D, Niessner H, Keim U, Serna-Higuita L, Bonzheim I, Cuellar L, Westphal D, Steininger J, Meier F, Pop O, Forchhammer S, Flatz L, Eigentler T, Garbe C, Röcken M, Amaral T, Sinnberg T. Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases. EBioMedicine 2023, 93: 104644. PMID: 37295047, PMCID: PMC10363450, DOI: 10.1016/j.ebiom.2023.104644.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningHumansLymphocytes, Tumor-InfiltratingMelanomaNeoplasm Recurrence, LocalPrognosisSkin NeoplasmsConceptsTumor-infiltrating lymphocytesMultiple Cox regressionMelanoma-specific survivalCox regressionTumor thicknessCutaneous melanomaPrimary melanomaAssessment of TILsPD-1 checkpoint inhibitionSignificant unfavourable prognostic factorLonger progression-free survivalDistant metastasis-free survivalSimple Cox regressionUnfavourable survival outcomeFirst-line therapyProgression-free survivalUnfavourable prognostic factorCutaneous melanoma patientsMetastasis-free survivalPresence of ulcerationPrimary cutaneous melanomaCox regression modelPrimary melanoma samplesPrimary tissuesOverall survival
2022
Not all well-differentiated cutaneous squamous cell carcinomas are equal: Tumors with disparate biologic behavior have differences in protein expression via digital spatial profiling
Vesely M, Martinez-Morilla S, Gehlhausen JR, McNiff JM, Whang PG, Rimm D, Ko CJ. Not all well-differentiated cutaneous squamous cell carcinomas are equal: Tumors with disparate biologic behavior have differences in protein expression via digital spatial profiling. Journal Of The American Academy Of Dermatology 2022, 87: 695-698. PMID: 35398219, DOI: 10.1016/j.jaad.2022.03.057.Peer-Reviewed Original Research
2021
Automated 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 biomarkerUsing Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma
Johannet P, Coudray N, Donnelly DM, Jour G, Illa-Bochaca I, Xia Y, Johnson DB, Wheless L, Patrinely JR, Nomikou S, Rimm DL, Pavlick AC, Weber JS, Zhong J, Tsirigos A, Osman I. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clinical Cancer Research 2021, 27: 131-140. PMID: 33208341, PMCID: PMC7785656, DOI: 10.1158/1078-0432.ccr-20-2415.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedDisease ProgressionDrug Resistance, NeoplasmFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedImmune Checkpoint InhibitorsMachine LearningMaleMelanomaMiddle AgedNeoplasm StagingPrognosisProgression-Free SurvivalProspective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsConceptsProgression-free survivalImmune checkpoint inhibitorsLower riskClinicodemographic characteristicsAdvanced melanomaClinical dataWorse progression-free survivalICI treatment outcomesKaplan-Meier curvesBiomarkers of responseStandard of careCheckpoint inhibitorsICI responseImmunotherapy responseValidation cohortTraining cohortDisease progressionProspective validationTreatment outcomesHigh riskClinical practicePatientsROC curveProgressionRisk
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 collectionMelanomaClosed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma
Gupta S, McCann L, Chan YGY, Lai EW, Wei W, Wong PF, Smithy JW, Weidler J, Rhees B, Bates M, Kluger HM, Rimm DL. Closed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma. Journal For ImmunoTherapy Of Cancer 2019, 7: 254. PMID: 31533832, PMCID: PMC6751819, DOI: 10.1186/s40425-019-0731-9.Peer-Reviewed Original ResearchMeSH KeywordsAgedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorCD8 AntigensFemaleFollow-Up StudiesGene Expression ProfilingHumansInterferon Regulatory Factor-1MaleMelanomaMiddle AgedMonitoring, ImmunologicPrognosisProgrammed Cell Death 1 Ligand 2 ProteinProgression-Free SurvivalReal-Time Polymerase Chain ReactionRetrospective StudiesReverse Transcriptase Polymerase Chain ReactionRNA, MessengerSkin NeoplasmsConceptsCompanion diagnostic testsImmunotherapy outcomesMelanoma patientsClinical benefitAnti-PD-1 therapyImmune checkpoint inhibitor therapyMRNA expressionQuantitative immunofluorescenceDiagnostic testsCheckpoint inhibitor therapyReal-time quantitative reverse transcription polymerase chain reactionMetastatic melanoma patientsQuantitative reverse transcription polymerase chain reactionReverse transcription-polymerase chain reactionTranscription-polymerase chain reactionYale Pathology archivesParaffin-embedded tissue sectionsAdjuvant settingICI therapyOS associationInhibitor therapyBaseline variablesMetastatic melanomaPredictive biomarkersPolymerase chain reaction
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
Characterization of PD-L1 Expression and Associated T-cell Infiltrates in Metastatic Melanoma Samples from Variable Anatomic Sites
Kluger HM, Zito CR, Barr ML, Baine MK, Chiang VL, Sznol M, Rimm DL, Chen L, Jilaveanu LB. Characterization of PD-L1 Expression and Associated T-cell Infiltrates in Metastatic Melanoma Samples from Variable Anatomic Sites. Clinical Cancer Research 2015, 21: 3052-3060. PMID: 25788491, PMCID: PMC4490112, DOI: 10.1158/1078-0432.ccr-14-3073.Peer-Reviewed Original ResearchConceptsPD-L1 expressionT-cell contentPD-1/PD-L1 inhibitorsHigher T-cell contentT-cell infiltratesPD-L1 inhibitorsAnatomic sitesBrain metastasesMetastatic melanomaTissue microarrayHigh PD-L1 expressionLess PD-L1 expressionLow PD-L1 expressionTumor PD-L1 expressionHigher TIL contentImproved overall survivalT cell infiltrationLess T cellsMetastatic melanoma samplesExtracerebral metastasesCerebral metastasesOverall survivalDermal metastasesImproved survivalPD-L1
2014
Correlation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib
Wilson MA, Zhao F, Letrero R, D'Andrea K, Rimm DL, Kirkwood JM, Kluger HM, Lee SJ, Schuchter LM, Flaherty KT, Nathanson KL. Correlation of Somatic Mutations and Clinical Outcome in Melanoma Patients Treated with Carboplatin, Paclitaxel, and Sorafenib. Clinical Cancer Research 2014, 20: 3328-3337. PMID: 24714776, PMCID: PMC4058354, DOI: 10.1158/1078-0432.ccr-14-0093.Peer-Reviewed Original ResearchMeSH KeywordsAdultAntineoplastic Combined Chemotherapy ProtocolsBiomarkers, TumorCarboplatinDouble-Blind MethodFemaleFollow-Up StudiesGenotypeGTP PhosphohydrolasesHumansMaleMelanomaMembrane ProteinsMiddle AgedMutationNeoplasm StagingNiacinamidePaclitaxelPhenylurea CompoundsPrognosisProto-Oncogene Proteins B-rafSkin NeoplasmsSorafenibSurvival RateConceptsProgression-free survivalNRAS-mutant melanomaPlatelet-derived growth factor receptorPerformance statusClinical outcomesNRAS mutationsCox proportional hazards modelVEGF receptorsSomatic mutationsWorse performance statusGood performance statusImproved clinical responseKaplan-Meier methodClinical trial populationsPretreatment tumor samplesSite of diseaseProportional hazards modelEffect of sorafenibBRAF-mutant melanomaFisher's exact testGrowth factor receptorClinical responseOverall survivalClinicopathologic featuresMelanoma patients
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 cohortMarginal and Joint Distributions of S100, HMB-45, and Melan-A Across a Large Series of Cutaneous Melanomas
Viray H, Bradley WR, Schalper KA, Rimm DL, Rothberg BE. Marginal and Joint Distributions of S100, HMB-45, and Melan-A Across a Large Series of Cutaneous Melanomas. Archives Of Pathology & Laboratory Medicine 2013, 137: 1063-73. PMID: 23899062, PMCID: PMC3963468, DOI: 10.5858/arpa.2012-0284-oa.Peer-Reviewed Original ResearchConceptsHMB-45Primary tumorCutaneous melanomaLarge seriesMelanoma-specific survivalMelanoma primary tumorsGroup of antigensLarge tissue microarrayClinicopathologic covariatesClinicopathologic criteriaPrognostic relevanceHistopathologic profileClinicopathologic correlatesAntigen expressionClinicopathologic parametersMelanoma markersTissue microarrayPositive expressionSurvival analysisMelanomaMelanS100Melanoma cellsBivariate associationsSignificant differences
2012
In situ measurement of miR-205 in malignant melanoma tissue supports its role as a tumor suppressor microRNA
Hanna JA, Hahn L, Agarwal S, Rimm DL. In situ measurement of miR-205 in malignant melanoma tissue supports its role as a tumor suppressor microRNA. Laboratory Investigation 2012, 92: 1390-1397. PMID: 22890556, PMCID: PMC3460033, DOI: 10.1038/labinvest.2012.119.Peer-Reviewed Original ResearchMeSH KeywordsAgedAnalysis of VarianceBiomarkers, TumorCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGp100 Melanoma AntigenHumansIn Situ HybridizationMaleMelanomaMicroRNAsMiddle AgedPrognosisRetrospective StudiesReverse Transcriptase Polymerase Chain ReactionRNA, NeoplasmS100 ProteinsSkin NeoplasmsTissue Array AnalysisConceptsMiR-205 levelsMiR-205 expressionMiR-205Shorter melanoma-specific survivalMelanoma-specific survivalMalignant melanoma tissuesPrimary melanoma specimensTypes of cancerImmunofluorescent assessmentBreslow depthAggressive tumorsWorse outcomesPrimary melanomaTumor suppressor miRNADiscovery cohortMelanoma specimensMultivariate analysisMelanoma tissuesQuantitative immunofluorescenceTumorsLow expressionHuman tumorsUse of miRNAsSuppressor miRNAAQUA method
2011
β-Catenin Signaling Controls Metastasis in Braf-Activated Pten-Deficient Melanomas
Damsky WE, Curley DP, Santhanakrishnan M, Rosenbaum LE, Platt JT, Rothberg BE, Taketo MM, Dankort D, Rimm DL, McMahon M, Bosenberg M. β-Catenin Signaling Controls Metastasis in Braf-Activated Pten-Deficient Melanomas. Cancer Cell 2011, 20: 741-754. PMID: 22172720, PMCID: PMC3241928, DOI: 10.1016/j.ccr.2011.10.030.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntigens, DifferentiationBenzamidesBeta CateninCell Transformation, NeoplasticColorectal NeoplasmsEnzyme ActivationGene Knockdown TechniquesHumansImatinib MesylateKaplan-Meier EstimateLung NeoplasmsLymphatic MetastasisMelanocytesMelanoma, ExperimentalMiceMice, 129 StrainMice, Inbred C57BLMice, TransgenicPhosphorylationPiperazinesProtein StabilityProto-Oncogene Proteins B-rafProto-Oncogene Proteins c-aktPTEN PhosphohydrolasePyrimidinesSignal TransductionSkin NeoplasmsSplenic NeoplasmsTranscription, GeneticTumor Cells, CulturedConceptsΒ-catenin levelsPI3K/AktLymph nodesMetastatic tumorsFrequent metastasisTumor differentiationMalignant melanomaMAPK/ERKMelanoma metastasesMouse modelControl metastasisHuman melanomaMelanomaMetastasisΒ-catenin stabilizationPTEN lossCentral mediatorMetastasis regulatorsΒ-cateninSpecific changesFunctional implicationsWntLungProinvasion Metastasis Drivers in Early-Stage Melanoma Are Oncogenes
Scott KL, Nogueira C, Heffernan TP, van Doorn R, Dhakal S, Hanna JA, Min C, Jaskelioff M, Xiao Y, Wu CJ, Cameron LA, Perry SR, Zeid R, Feinberg T, Kim M, Woude G, Granter SR, Bosenberg M, Chu GC, DePinho RA, Rimm DL, Chin L. Proinvasion Metastasis Drivers in Early-Stage Melanoma Are Oncogenes. Cancer Cell 2011, 20: 92-103. PMID: 21741599, PMCID: PMC3176328, DOI: 10.1016/j.ccr.2011.05.025.Peer-Reviewed Original ResearchMeSH KeywordsAcid PhosphataseAnimalsCell LineageConserved SequenceEvolution, MolecularGene Expression ProfilingGene Expression Regulation, NeoplasticGenomeHumansIsoenzymesKaplan-Meier EstimateMelanomaMiceNeoplasm InvasivenessNeoplasm MetastasisNeoplasm StagingOncogenesPhosphorylationReproducibility of ResultsSkin NeoplasmsTartrate-Resistant Acid PhosphataseTissue Array AnalysisConceptsFunctional genetic screensGenetic screenGlobal transcriptomeMetastatic potentialSuch genesGenomic evidenceExpression selectionTranscriptomic profilesHuman melanoma tissuesMetastasis driverCell invasionKey pathwaysOncogenic capabilitiesMelanoma tissuesGenesHuman melanomaHuman primary melanomasTranscriptomeMouse modelSpontaneous metastasisOncogeneEnhancerACP5PathwayInvasionIn vitro studies of dasatinib, its targets and predictors of sensitivity
Jilaveanu LB, Zito CR, Aziz SA, Chakraborty A, Davies MA, Camp RL, Rimm DL, Dudek A, Sznol M, Kluger HM. In vitro studies of dasatinib, its targets and predictors of sensitivity. Pigment Cell & Melanoma Research 2011, 24: 386-389. PMID: 21320292, PMCID: PMC4431976, DOI: 10.1111/j.1755-148x.2011.00835.x.Peer-Reviewed Original Research
2010
Vertical Targeting of the Phosphatidylinositol-3 Kinase Pathway as a Strategy for Treating Melanoma
Aziz SA, Jilaveanu LB, Zito C, Camp RL, Rimm DL, Conrad P, Kluger HM. Vertical Targeting of the Phosphatidylinositol-3 Kinase Pathway as a Strategy for Treating Melanoma. Clinical Cancer Research 2010, 16: 6029-6039. PMID: 21169255, PMCID: PMC3058635, DOI: 10.1158/1078-0432.ccr-10-1490.Peer-Reviewed Original ResearchBiomarkers: The Useful and the Not So Useful—An Assessment of Molecular Prognostic Markers for Cutaneous Melanoma
Rothberg BE, Rimm DL. Biomarkers: The Useful and the Not So Useful—An Assessment of Molecular Prognostic Markers for Cutaneous Melanoma. Journal Of Investigative Dermatology 2010, 130: 1971-1987. PMID: 20555347, PMCID: PMC3180927, DOI: 10.1038/jid.2010.149.Peer-Reviewed Original Research
2009
Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays
Mehnert JM, McCarthy MM, Jilaveanu L, Flaherty KT, Aziz S, Camp RL, Rimm DL, Kluger HM. Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays. Human Pathology 2009, 41: 375-384. PMID: 20004943, PMCID: PMC2824079, DOI: 10.1016/j.humpath.2009.08.016.Peer-Reviewed Original ResearchBlotting, WesternCell LineDisease ProgressionHumansImage Processing, Computer-AssistedImmunohistochemistryMelanomaNevusProportional Hazards ModelsRegression AnalysisSeverity of Illness IndexSkin NeoplasmsStatistics, NonparametricTissue Array AnalysisVascular Endothelial Growth Factor AVascular Endothelial Growth Factor Receptor-1Vascular Endothelial Growth Factor Receptor-2Vascular Endothelial Growth Factor Receptor-3Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms
Rothberg BE, Berger AJ, Molinaro AM, Subtil A, Krauthammer MO, Camp RL, Bradley WR, Ariyan S, Kluger HM, Rimm DL. Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms. Journal Of Clinical Oncology 2009, 27: 5772-5780. PMID: 19884546, PMCID: PMC2792999, DOI: 10.1200/jco.2009.22.8239.Peer-Reviewed Original ResearchConceptsHigh-risk group