2023
Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer
Barrera C, Corredor G, Viswanathan V, Ding R, Toro P, Fu P, Buzzy C, Lu C, Velu P, Zens P, Berezowska S, Belete M, Balli D, Chang H, Baxi V, Syrigos K, Rimm D, Velcheti V, Schalper K, Romero E, Madabhushi A. Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer. Npj Precision Oncology 2023, 7: 52. PMID: 37264091, PMCID: PMC10235089, DOI: 10.1038/s41698-023-00403-x.Peer-Reviewed Original ResearchNon-small cell lung cancerTumor-infiltrating lymphocytesLung cancerEffective adaptive immune responseImmune checkpoint blockersCell lung cancerLung cancer patientsTumor immune microenvironmentAdaptive immune responsesImmune-related biomarkersTreatment-specific outcomesCheckMate 057Histology variantsImmunotherapy resistanceCheckpoint blockersRegulatory cellsTumor rejectionTumor-immune interactionsClinical outcomesImmunosuppressive signalsClinical benefitInfluence prognosisImmune microenvironmentCancer patientsPatient outcomes
2019
Functional profile and clinical significance of major tumor infiltrating lymphocyte subsets in lung cancer-associated brain metastases.
Lu B, Gupta R, Stewart T, Kluger H, Jilaveanu L, Schalper K, Goldberg S. Functional profile and clinical significance of major tumor infiltrating lymphocyte subsets in lung cancer-associated brain metastases. Journal Of Clinical Oncology 2019, 37: 2066-2066. DOI: 10.1200/jco.2019.37.15_suppl.2066.Peer-Reviewed Original ResearchPrimary lung tumorsMajor T cell subsetsMultiplexed quantitative immunofluorescenceT cell subsetsExtracranial metastasesT cellsLung tumorsBrain metastasesQuantitative immunofluorescenceAdaptive anti-tumor responsesLow T cell infiltrationMajor clinicopathologic variablesYale Cancer CenterAdaptive immune cellsRegulatory T cellsT cell infiltrationAnti-tumor responseLonger overall survivalOptimal treatment strategyLung cancer patientsKi-67 levelsLung cancer histologyLymphocyte subsetsOverall survivalPrimary malignancyPhenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer
Barrera C, Corredor G, Wang X, Schalper K, Rimm D, Velcheti V, Madabhushi A, Castro E. Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer. Progress In Biomedical Optics And Imaging 2019, 10956: 1095607-1095607-8. DOI: 10.1117/12.2513048.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesTIL densityLung cancerEarly stage non-small cell lung cancer patientsNon-small cell lung cancer patientsCell lung cancer patientsEarly-stage lung cancerKaplan-Meier analysisLung cancer patientsStage lung cancerLikelihood of recurrenceBetter prognosisLate recurrenceCancer patientsDifferent cancer typesDisease outcomeRecurrenceDifferent subtypesCancer typesLymphocytesCancerPatientsPrognosisIndependent validation
2016
Quantitative and pathologist-read comparison of the heterogeneity of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer
Rehman JA, Han G, Carvajal-Hausdorf DE, Wasserman BE, Pelekanou V, Mani NL, McLaughlin J, Schalper KA, Rimm DL. Quantitative and pathologist-read comparison of the heterogeneity of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer. Modern Pathology 2016, 30: 340-349. PMID: 27834350, PMCID: PMC5334264, DOI: 10.1038/modpathol.2016.186.Peer-Reviewed Original ResearchConceptsPD-L1 expressionPD-L1Immune cellsImmune cell PD-L1 expressionNon-small cell lung cancerNon-small cell lung cancer (NSCLC) casesCell lung cancer casesTumor cellsPD-L1 assessmentStromal immune cellsPD-L1 positivityCell lung cancerLung cancer patientsLung cancer casesRepresentative tumor areasPathologist scoresLikelihood of responseConcordance correlation coefficientRabbit monoclonal antibodyIntraclass correlation coefficientCancer patientsLung cancerImmunohistochemistry slidesCancer casesTumor tissue