2024
Peripheral blood immune parameters, response, and adverse events after neoadjuvant chemotherapy plus durvalumab in early-stage triple-negative breast cancer
Foldi J, Blenman K, Marczyk M, Gunasekharan V, Polanska A, Gee R, Davis M, Kahn A, Silber A, Pusztai L. Peripheral blood immune parameters, response, and adverse events after neoadjuvant chemotherapy plus durvalumab in early-stage triple-negative breast cancer. Breast Cancer Research And Treatment 2024, 1-9. PMID: 39002068, DOI: 10.1007/s10549-024-07426-3.Peer-Reviewed Original ResearchImmune-related adverse eventsTriple-negative breast cancerAssociated with pathological responsePathological complete responseNeoadjuvant chemotherapyCytokine levelsPathological responseAdverse eventsBreast cancerEarly-stage triple-negative breast cancerPatients treated with immune checkpoint inhibitorsB cell clonal expansionMeasured serum cytokine levelsImmune checkpoint inhibitorsGM-CSF levelsPeripheral blood cytokine levelsBlood cytokine levelsSerum cytokine levelsB cell receptorMagnetic bead panelBenjamini-Hochberg correctionSample of patientsImmunoSEQ platformCheckpoint inhibitorsComplete responsePredicting peripheral neuropathy following neoadjuvant therapy in patients with breast cancer.
Feiger B, Biancalana M, Shelton A, Blenman K, Lustberg M. Predicting peripheral neuropathy following neoadjuvant therapy in patients with breast cancer. Journal Of Clinical Oncology 2024, 42: e12639-e12639. DOI: 10.1200/jco.2024.42.16_suppl.e12639.Peer-Reviewed Original ResearchBreast cancer patientsNeoadjuvant chemotherapyPeripheral neuropathyNeoadjuvant therapyBreast cancerCancer patientsAdministration of neoadjuvant chemotherapyCohort of breast cancer patientsLikelihood of breast-conserving surgeryContrast-enhanced magnetic resonance imagingQuality of lifeDynamic contrast-enhanced magnetic resonance imagingBreast-conserving surgeryReduced tumor burdenTreatment-induced neuropathyOccurrence of neuropathyInduce peripheral neuropathyDensity of blood vesselsPatients' quality of lifeMagnetic resonance imagingAmeliorate neuropathyDCE-MRI dataTumor burdenCumulative toxic effectsIntratumoral vascularityTraining pathologists to assess stromal tumour‐infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research
Ly A, Garcia V, Blenman K, Ehinger A, Elfer K, Hanna M, Li X, Peeters D, Birmingham R, Dudgeon S, Gardecki E, Gupta R, Lennerz J, Pan T, Saltz J, Wharton K, Ehinger D, Acs B, Dequeker E, Salgado R, Gallas B. Training pathologists to assess stromal tumour‐infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research. Histopathology 2024, 84: 915-923. PMID: 38433289, PMCID: PMC10990791, DOI: 10.1111/his.15140.Peer-Reviewed Original ResearchStromal tumor-infiltrating lymphocytesTumor-infiltrating lymphocytesUS Food and Drug AdministrationFood and Drug AdministrationBreast cancerPathologist's visual assessmentDrug AdministrationSignificant interobserver variabilityPredicative biomarkerTILs assessmentTrained pathologistsInterobserver agreementInterobserver variabilityVisual assessmentGold standardReference standardBreastCME coursesClinical practiceClinical careCancerMedical educationPathologistsLymphocytesExpert commentaryReproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models
Elfer K, Gardecki E, Garcia V, Ly A, Hytopoulos E, Wen S, Hanna M, Peeters D, Saltz J, Ehinger A, Dudgeon S, Li X, Blenman K, Chen W, Green U, Birmingham R, Pan T, Lennerz J, Salgado R, Gallas B. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models. Modern Pathology 2024, 37: 100439. PMID: 38286221, DOI: 10.1016/j.modpat.2024.100439.Peer-Reviewed Original ResearchArtificial intelligenceAnnotation of medical imagesMedical imagesTesting artificial intelligenceTest data setsArtificial intelligence modelsAnnotation effortEvaluative annotationsDiagnostic image analysisIntelligence modelsAnnotation workflowData setsDigital pathologyAnnotationQuality frameworkIntelligenceArtificialWorkflowImage analysisPrediction modelMetadataConsolidated Standards of Reporting TrialsFrameworkImagesStandards for Reporting Diagnostic AccuracyImage‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer
Jahangir C, Page D, Broeckx G, Gonzalez C, Burke C, Murphy C, Reis‐Filho J, Ly A, Harms P, Gupta R, Vieth M, Hida A, Kahila M, Kos Z, van Diest P, Verbandt S, Thagaard J, Khiroya R, Abduljabbar K, Haab G, Acs B, Adams S, Almeida J, Alvarado‐Cabrero I, Azmoudeh‐Ardalan F, Badve S, Baharun N, Bellolio E, Bheemaraju V, Blenman K, Fujimoto L, Burgues O, Chardas A, Cheang M, Ciompi F, Cooper L, Coosemans A, Corredor G, Portela F, Deman F, Demaria S, Dudgeon S, Elghazawy M, Fernandez‐Martín C, Fineberg S, Fox S, Giltnane J, Gnjatic S, Gonzalez‐Ericsson P, Grigoriadis A, Halama N, Hanna M, Harbhajanka A, Hart S, Hartman J, Hewitt S, Horlings H, Husain Z, Irshad S, Janssen E, Kataoka T, Kawaguchi K, Khramtsov A, Kiraz U, Kirtani P, Kodach L, Korski K, Akturk G, Scott E, Kovács A, Lænkholm A, Lang‐Schwarz C, Larsimont D, Lennerz J, Lerousseau M, Li X, Madabhushi A, Maley S, Narasimhamurthy V, Marks D, McDonald E, Mehrotra R, Michiels S, Kharidehal D, Minhas F, Mittal S, Moore D, Mushtaq S, Nighat H, Papathomas T, Penault‐Llorca F, Perera R, Pinard C, Pinto‐Cardenas J, Pruneri G, Pusztai L, Rajpoot N, Rapoport B, Rau T, Ribeiro J, Rimm D, Vincent‐Salomon A, Saltz J, Sayed S, Hytopoulos E, Mahon S, Siziopikou K, Sotiriou C, Stenzinger A, Sughayer M, Sur D, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson E, Tramm T, Tran W, van der Laak J, Verghese G, Viale G, Wahab N, Walter T, Waumans Y, Wen H, Yang W, Yuan Y, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Stovgaard E, Salgado R, Gallagher W, Rahman A. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer. The Journal Of Pathology 2024, 262: 271-288. PMID: 38230434, PMCID: PMC11288342, DOI: 10.1002/path.6238.Peer-Reviewed Original ResearchConceptsImmune profileInternational Immuno-Oncology Biomarker Working GroupIdentification of clinically relevant biomarkersField of immuno-oncologyBiomarker Working GroupManagement of cancer patientsImmune profiling of tumorsClinical trial perspectiveTranslational implicationsProfiling of tumorsIndividual tumor cellsPredicting disease prognosisClinically relevant biomarkersSubtypes of cancerImmuno-oncologyTumor microenvironmentMultiplex immunohistochemistryTreatment responseTumor cellsBreast cancerTumor samplesCancer patientsTreatment choiceDisease prognosisRelevant biomarkers
2023
Initial interactions with the FDA on developing a validation dataset as a medical device development tool
Hart S, Garcia V, Dudgeon S, Hanna M, Li X, Blenman K, Elfer K, Ly A, Salgado R, Saltz J, Gupta R, Hytopoulos E, Larsimont D, Lennerz J, Gallas B. Initial interactions with the FDA on developing a validation dataset as a medical device development tool. The Journal Of Pathology 2023, 261: 378-384. PMID: 37794720, PMCID: PMC10841854, DOI: 10.1002/path.6208.Peer-Reviewed Original ResearchPitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Thagaard J, Broeckx G, Page D, Jahangir C, Verbandt S, Kos Z, Gupta R, Khiroya R, Abduljabbar K, Haab G, Acs B, Akturk G, Almeida J, Alvarado‐Cabrero I, Amgad M, Azmoudeh‐Ardalan F, Badve S, Baharun N, Balslev E, Bellolio E, Bheemaraju V, Blenman K, Fujimoto L, Bouchmaa N, Burgues O, Chardas A, Cheang M, Ciompi F, Cooper L, Coosemans A, Corredor G, Dahl A, Portela F, Deman F, Demaria S, Hansen J, Dudgeon S, Ebstrup T, Elghazawy M, Fernandez‐Martín C, Fox S, Gallagher W, Giltnane J, Gnjatic S, Gonzalez‐Ericsson P, Grigoriadis A, Halama N, Hanna M, Harbhajanka A, Hart S, Hartman J, Hauberg S, Hewitt S, Hida A, Horlings H, Husain Z, Hytopoulos E, Irshad S, Janssen E, Kahila M, Kataoka T, Kawaguchi K, Kharidehal D, Khramtsov A, Kiraz U, Kirtani P, Kodach L, Korski K, Kovács A, Laenkholm A, Lang‐Schwarz C, Larsimont D, Lennerz J, Lerousseau M, Li X, Ly A, Madabhushi A, Maley S, Narasimhamurthy V, Marks D, McDonald E, Mehrotra R, Michiels S, Minhas F, Mittal S, Moore D, Mushtaq S, Nighat H, Papathomas T, Penault‐Llorca F, Perera R, Pinard C, Pinto‐Cardenas J, Pruneri G, Pusztai L, Rahman A, Rajpoot N, Rapoport B, Rau T, Reis‐Filho J, Ribeiro J, Rimm D, Roslind A, Vincent‐Salomon A, Salto‐Tellez M, Saltz J, Sayed S, Scott E, Siziopikou K, Sotiriou C, Stenzinger A, Sughayer M, Sur D, Fineberg S, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson E, Tramm T, Tran W, van der Laak J, van Diest P, Verghese G, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen H, Yang W, Yuan Y, Zin R, Adams S, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Salgado R, Stovgaard E. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer. The Journal Of Pathology 2023, 260: 498-513. PMID: 37608772, PMCID: PMC10518802, DOI: 10.1002/path.6155.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesTriple-negative breast cancerBreast cancerTIL assessmentHER2-positive breast cancerRoutine clinical managementTIL evaluationTumor-immune interactionsClinical managementDiscordant assessmentsClinical significancePrognostic biomarkerTIL quantificationCancerDaily practicePatientsTrialsTissue patternsAssessmentLymphocytesBiomarkersSpatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Page D, Broeckx G, Jahangir C, Verbandt S, Gupta R, Thagaard J, Khiroya R, Kos Z, Abduljabbar K, Haab G, Acs B, Akturk G, Almeida J, Alvarado‐Cabrero I, Azmoudeh‐Ardalan F, Badve S, Baharun N, Bellolio E, Bheemaraju V, Blenman K, Fujimoto L, Bouchmaa N, Burgues O, Cheang M, Ciompi F, Cooper L, Coosemans A, Corredor G, Portela F, Deman F, Demaria S, Dudgeon S, Elghazawy M, Ely S, Fernandez‐Martín C, Fineberg S, Fox S, Gallagher W, Giltnane J, Gnjatic S, Gonzalez‐Ericsson P, Grigoriadis A, Halama N, Hanna M, Harbhajanka A, Hardas A, Hart S, Hartman J, Hewitt S, Hida A, Horlings H, Husain Z, Hytopoulos E, Irshad S, Janssen E, Kahila M, Kataoka T, Kawaguchi K, Kharidehal D, Khramtsov A, Kiraz U, Kirtani P, Kodach L, Korski K, Kovács A, Laenkholm A, Lang‐Schwarz C, Larsimont D, Lennerz J, Lerousseau M, Li X, Ly A, Madabhushi A, Maley S, Narasimhamurthy V, Marks D, McDonald E, Mehrotra R, Michiels S, Minhas F, Mittal S, Moore D, Mushtaq S, Nighat H, Papathomas T, Penault‐Llorca F, Perera R, Pinard C, Pinto‐Cardenas J, Pruneri G, Pusztai L, Rahman A, Rajpoot N, Rapoport B, Rau T, Reis‐Filho J, Ribeiro J, Rimm D, Vincent‐Salomon A, Salto‐Tellez M, Saltz J, Sayed S, Siziopikou K, Sotiriou C, Stenzinger A, Sughayer M, Sur D, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson E, Tramm T, Tran W, van der Laak J, van Diest P, Verghese G, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen H, Yang W, Yuan Y, Adams S, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Salgado R, Stovgaard E. Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer. The Journal Of Pathology 2023, 260: 514-532. PMID: 37608771, PMCID: PMC11288334, DOI: 10.1002/path.6165.Peer-Reviewed Original ResearchTransformation of the Healthcare Ecosystem in the Era of Society 5.0
Bida M, Mosito S, Miya T, Demetriou D, Blenman K, Dlamini Z. Transformation of the Healthcare Ecosystem in the Era of Society 5.0. 2023, 223-248. DOI: 10.1007/978-3-031-36461-7_10.Peer-Reviewed Original ResearchHealthcare ecosystemArtificial intelligenceCloud-based technologiesPatient health dataHealthcare technologiesReal-time accessCustomer value propositionPatient health recordsReal-time clinical decisionsMedical InternetTerms of robustnessBlockchain technologyPatient privacyHealth recordsHealth dataPrivacyDefinition of normsPartner networksSociety 5.0Value propositionEcosystem designTechnologyInternetIntelligenceModel pillarsSociety 5.0 Healthcare: Ethics, Legal Rights, Human Rights, Safety and Security
Blenman K, Hull R, Maimela C, Molefi T, Khanyile R, Dlamini Z. Society 5.0 Healthcare: Ethics, Legal Rights, Human Rights, Safety and Security. 2023, 267-291. DOI: 10.1007/978-3-031-36461-7_12.Peer-Reviewed Original ResearchArtificial intelligenceInvasion of privacySmart digital technologiesViolation of privacyOutdated datasetsMobile devicesAI workPatient dataPrivacyNew technologiesVariety of solutionsDigital technologiesBiased dataLow resourcesGathering of informationLack of transparencySociety 5.0Intellectual propertyTechnologyDecision-making processMedical devicesPotential solutionsEase of analysisData collectionGatheringHealth Informatics Applications in Healthcare and Society 5.0
Marima R, Mtshali N, Phillips P, Molefi T, Khanyile R, Mbita Z, Mbeje M, Chatziioannou A, Blenman K, Dlamini Z. Health Informatics Applications in Healthcare and Society 5.0. 2023, 31-49. DOI: 10.1007/978-3-031-36461-7_2.Peer-Reviewed Original ResearchHealth informaticsHealth informatics applicationsInformatics applicationsSociety 5.0Patient data sharingPublic health informaticsData managementData sharingPrivacy invasionImage informaticsTechnology solutionsTranslational bioinformaticsInformaticsClinical bioinformaticsBioinformaticsEfficient applicationHealthcare systemHealthcareApplicationsSecuritySharingArtificial Intelligence–Enhanced Drug Discovery and the Achievement of Next-Generation Human-Centered Health System
Mbatha S, Mulaudzi T, Mbita Z, Adeola H, Batra J, Blenman K, Dlamini Z. Artificial Intelligence–Enhanced Drug Discovery and the Achievement of Next-Generation Human-Centered Health System. 2023, 155-177. DOI: 10.1007/978-3-031-36461-7_7.Peer-Reviewed Original ResearchArtificial intelligenceMachine learningUses of AIDeep learning algorithmsArtificial intelligence technologyDiscovery processIncorporation of AIInformation-intensive societyIntelligence technologyLearning algorithmHealth care systemHuman-centered societyVast amountComprehensive solutionDevelopment processDesign processChallenges of availabilityDrug discovery processCare systemClinical dataIntelligenceSociety 5.0Health needsPhysical spaceComputational modelThe Role of Digital Twinning, the Next Generation of EMR/EHR in Healthcare in a Society 5.0: Collecting Patient Data from Birth to the Grave
Hull R, Chauke-Malinga N, Gaudji G, Blenman K, Dlamini Z. The Role of Digital Twinning, the Next Generation of EMR/EHR in Healthcare in a Society 5.0: Collecting Patient Data from Birth to the Grave. 2023, 179-200. DOI: 10.1007/978-3-031-36461-7_8.Peer-Reviewed Original ResearchDigital twinAccurate virtual modelViolation of privacyAccurate digital twinSociety 5.0Digital twinningVirtual copyBiometric dataVirtual modelingVirtual modelMicrobiomic dataVast amountDigital versionEasy wayPatient dataLarge amountHealthcareInformationPrivacyNext generationEHRGoalPersonalized medicineImplementationHealthcare goalsIntegration of Cyber-Physical Systems in the Advancement of Society 5.0 Healthcare Management
Damane B, Kgokolo M, Gaudji G, Blenman K, Dlamini Z. Integration of Cyber-Physical Systems in the Advancement of Society 5.0 Healthcare Management. 2023, 201-221. DOI: 10.1007/978-3-031-36461-7_9.Peer-Reviewed Original ResearchCyber-physical systemsSelf-sovereign identityArtificial intelligence systemsHealthcare management systemLarge data setsOpen AuthorizationAccurate medical diagnosisSecurity layerSociety 5.0AI systemsIntelligence systemsComputational platformWrong handsPrecise tasksManagement systemDigital platformsInput dataReal-time monitoringMedical diagnosisAutomatic actuationData setsPhysical systemsCertain informationHealthcare managementPhysical elementsImage 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 correlationA review of the impact of energy balance on triple-negative breast cancer
Akingbesote N, Owusu D, Liu R, Cartmel B, Ferrucci L, Zupa M, Lustberg M, Sanft T, Blenman K, Irwin M, Perry R. A review of the impact of energy balance on triple-negative breast cancer. JNCI Monographs 2023, 2023: 104-124. PMID: 37139977, DOI: 10.1093/jncimonographs/lgad011.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsTriple-negative breast cancerInterventional studyBreast cancerCancer treatmentClinical interventional studyClinical observationalImmune activationCancer outcomesCancer careClinical studiesOverall healthEnergy intakeNarrative reviewCancer cellsEnergy expenditureCancerTreatmentEnergy balanceOutcomesExerciseReviewDetrimental effectsImmunotherapyStudyIntake
2022
Molecular differences between younger versus older ER-positive and HER2-negative breast cancers
Qing T, Karn T, Rozenblit M, Foldi J, Marczyk M, Shan N, Blenman K, Holtrich U, Kalinsky K, Meric-Bernstam F, Pusztai L. Molecular differences between younger versus older ER-positive and HER2-negative breast cancers. Npj Breast Cancer 2022, 8: 119. PMID: 36344517, PMCID: PMC9640562, DOI: 10.1038/s41523-022-00492-0.Peer-Reviewed Original ResearchBreast cancerYounger patientsHER2-negative breast cancerNode-positive breast cancerNode-negative diseaseSame clinical featuresHigh mutation burdenLower mRNA expressionAdjuvant chemotherapyMicroarray cohortTAILORx trialOvarian suppressionOlder patientsPatient ageClinical featuresProliferation-related gene expressionScore 0Mutation burdenCopy number gainsOlder womenGATA3 mutationsAge groupsGene signatureMRNA expressionChemotherapyLeveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood
Wall I, Boulat V, Shah A, Blenman KRM, Wu Y, Alberts E, Calado DP, Salgado R, Grigoriadis A. Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood. Cancers 2022, 14: 4505. PMID: 36139665, PMCID: PMC9496983, DOI: 10.3390/cancers14184505.Peer-Reviewed Original ResearchBreast cancerPeripheral bloodImmune responseAnti-tumor responseSystemic immune responsesBreast cancer patientsPatient selection parametersEnvironment triadImmune profileLymph nodesImmunological featuresCancer patientsPrimary tumorDisease progressionImmune sitesSurvival rateCancerPatientsTumorsOverall responseBloodFuture studiesTreatmentSignificant overall responseMultiple sitesPD-L1 protein expression in relation to recurrence score values in early-stage ER + breast cancer
Rozenblit M, Blenman K, Harigopal M, Reisenbichler E, Singh K, Qing T, Ibrahim E, Ramkissoon S, Asmelash S, Lin HK, Roberts M, Ross J, Huang RSP, Pusztai L. PD-L1 protein expression in relation to recurrence score values in early-stage ER + breast cancer. Breast Cancer Research And Treatment 2022, 196: 221-227. PMID: 36028784, DOI: 10.1007/s10549-022-06712-2.Peer-Reviewed Original ResearchConceptsPD-L1 positivityPD-L1 protein expressionPD-L1 expressionGrade 3 cancersPD-L1TIL scoreTumor gradeMultivariate analysisHigher PD-L1 positivityTumor-infiltrating lymphocyte countsConclusionPD-L1 expressionProtein expressionPD-L1 immunohistochemistryChi-square testResultsPD-L1T1 cancersLymphocyte countT3 tumorsIndependent predictorsTumor sizeLarge tumorsPositivity rateCell positivityBreast cancerGrade 2Clinical Outcomes and Immune Markers by Race in a Phase I/II Clinical Trial of Durvalumab Concomitant with Neoadjuvant Chemotherapy in Early-Stage TNBC.
Foldi J, Kahn A, Silber A, Qing T, Reisenbichler E, Fischbach N, Persico J, Adelson K, Katoch A, Chagpar A, Park T, Blanchard A, Blenman K, Rimm DL, Pusztai L. Clinical Outcomes and Immune Markers by Race in a Phase I/II Clinical Trial of Durvalumab Concomitant with Neoadjuvant Chemotherapy in Early-Stage TNBC. Clinical Cancer Research 2022, 28: 3720-3728. PMID: 35903931, PMCID: PMC9444984, DOI: 10.1158/1078-0432.ccr-22-0862.Peer-Reviewed Original ResearchConceptsImmune-related adverse eventsTriple-negative breast cancerNon-AA patientsEvent-free survivalPhase I/II clinical trialsClinical trialsNeoadjuvant chemotherapyOverall survivalAA patientsEarly-stage triple-negative breast cancerIncidence of irAEsPathologic complete response rateSignificant associationMultivariate logistic regression analysisTumor-infiltrating lymphocyte countsComplete response ratePrimary efficacy endpointPD-L1 statusProportional hazards modelLogistic regression analysisAfrican American womenEFS ratesNeoadjuvant immunotherapyEfficacy endpointAdverse events