2024
Correlation of eTILs with recurrence free survival (RFS) in stage IIB-IIIA melanoma and use as biomarker for stratification for clinical trials.
Aung T, Zhang C, Espinoza G, Leung L, Moon J, Horst B, Ferringer T, Nastiuk K, Rimm D, Saenger Y. Correlation of eTILs with recurrence free survival (RFS) in stage IIB-IIIA melanoma and use as biomarker for stratification for clinical trials. Journal Of Clinical Oncology 2024, 42: 9567-9567. DOI: 10.1200/jco.2024.42.16_suppl.9567.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesRecurrence free survivalAmerican Joint Committee on CancerFree survivalInfiltrating lymphocytesRetrospective cohortClinical trialsQuantify tumor-infiltrating lymphocytesStage II-III melanomaTumor-infiltrating lymphocytes groupDiagnostic slidesIIb-IIIaRoswell Park Comprehensive Cancer CenterEarly-stage melanoma patientsCox modelStage IIB-IIICAdjuvant clinical trialsKaplan-Meier curvesMultivariate Cox modelUnivariate Cox modelCox proportional hazards modelsClinical pathological featuresGeisinger Medical CenterProportional hazards modelClinical trial design
2023
New Therapies in Melanoma: Current Trends, Evolving Paradigms, and Future Perspectives.
Shafi S, Challa B, Parwani A, Aung T. New Therapies in Melanoma: Current Trends, Evolving Paradigms, and Future Perspectives. Cutis 2023, 112: e32-e39. PMID: 38091429, DOI: 10.12788/cutis.0911.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsLymphocyte-activating gene-3Early phase clinical trialsPrimary treatment failureAggressive skin cancerNew therapeutic agentsICI therapyCheckpoint inhibitorsNovel immunotherapiesMelanoma patientsTreatment failureMetastatic melanomaPredictive biomarkersLong-term benefitsClinical trialsClinical careNew therapiesTherapeutic strategiesAlternative treatmentSkin cancerTherapy outcomeTherapeutic agentsNovel targetNovel therapeuticsPatientsIntegrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality
Zhou J, pour A, Deirawan H, Daaboul F, Aung T, Beydoun R, Ahmed F, Chuang J. Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality. EBioMedicine 2023, 94: 104726. PMID: 37499603, PMCID: PMC10388166, DOI: 10.1016/j.ebiom.2023.104726.Peer-Reviewed Original ResearchConceptsModerate-risk patientsClinical variablesPatient stratificationOverall survivalAdenocarcinoma patientsTCGA-COADLow-risk patientsColorectal cancer patientsEnrollment of patientsRectal adenocarcinoma patientsRisk of mortalityColon adenocarcinoma patientsLow immune infiltrationNational Cancer InstituteMutation signaturesNumber of deathsCancer Genome AtlasColorectal cancerPathological featuresCancer patientsImmune infiltrationImproved stratificationClinical trialsPatient riskCancer Institute
2022
Quantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer.
Shafi S, Aung T, Xirou V, Gavrielatou N, Vathiotis I, Fernandez A, Moutafi M, Yaghoobi V, Herbst R, Liu L, Langermann S, Rimm D. Quantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer. Laboratory Investigation 2022, 102: 1143-1149. PMID: 36775354, DOI: 10.1038/s41374-022-00796-6.Peer-Reviewed Original ResearchConceptsSiglec-15 expressionNon-small cell lung cancerNeck squamous cell carcinomaProgression-free survivalSquamous cell carcinomaCancer typesOverall survivalCell carcinomaBladder cancerImmune cellsSiglec-15PD-1/PD-L1 blockadePotential future clinical trialsQuantitative immunofluorescencePD-L1 blockadeStromal immune cellsImmune checkpoint blockadeCell lung cancerFuture clinical trialsNew potential targetsCheckpoint blockadePD-L1Lung cancerClinical trialsIntra-tumoral heterogeneityObjective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms
Aung TN, Shafi S, Wilmott JS, Nourmohammadi S, Vathiotis I, Gavrielatou N, Fernandez A, Yaghoobi V, Sinnberg T, Amaral T, Ikenberg K, Khosrotehrani K, Osman I, Acs B, Bai Y, Martinez-Morilla S, Moutafi M, Thompson JF, Scolyer RA, Rimm DL. Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms. EBioMedicine 2022, 82: 104143. PMID: 35810563, PMCID: PMC9272337, DOI: 10.1016/j.ebiom.2022.104143.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesMelanoma patientsPrognostic valuePrognostic markerPrimary melanoma patientsRobust prognostic markerStage II patientsSpecific molecular subtypesTIL phenotypeAdjuvant therapyOverall survivalSurgical treatmentTIL scoreII patientsSurvival outcomesLung cancerClinical trialsPrimary melanomaClinical impactT cellsMolecular subtypesHigh riskIndependent cohortLower riskEosin stainingQuantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer
Shafi S, Aung TN, Xirou V, Gavrielatou N, Vathiotis IA, Fernandez A, Moutafi M, Yaghoobi V, Herbst RS, Liu LN, Langermann S, Rimm DL. Quantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer. Laboratory Investigation 2022, 102: 1143-1149. PMID: 35581307, PMCID: PMC10211373, DOI: 10.1038/s41374-022-00796-6.Peer-Reviewed Original ResearchConceptsSiglec-15 expressionNon-small cell lung cancerNeck squamous cell carcinomaProgression-free survivalSquamous cell carcinomaCancer typesOverall survivalCell carcinomaBladder cancerImmune cellsSiglec-15PD-1/PD-L1 blockadePotential future clinical trialsQuantitative immunofluorescencePD-L1 blockadeStromal immune cellsImmune checkpoint blockadeCell lung cancerFuture clinical trialsNew potential targetsCheckpoint blockadePD-L1Lung cancerClinical trialsIntra-tumoral heterogeneityDevelopment of an immunohistochemical assay for Siglec-15
Shafi S, Aung TN, Robbins C, Zugazagoitia J, Vathiotis I, Gavrielatou N, Yaghoobi V, Fernandez A, Niu S, Liu LN, Cusumano ZT, Leelatian N, Cole K, Wang H, Homer R, Herbst RS, Langermann S, Rimm DL. Development of an immunohistochemical assay for Siglec-15. Laboratory Investigation 2022, 102: 771-778. PMID: 35459795, PMCID: PMC9253057, DOI: 10.1038/s41374-022-00785-9.Peer-Reviewed Original ResearchConceptsSiglec-15IHC assaysPD-L1PD-1/PD-L1 inhibitionPD-L1 blockadePD-L1 inhibitionHigh expressionFuture clinical trialsImmunoglobulin-type lectinsSiglec-15 expressionCompanion diagnostic assayPromising new targetTumor histologyImmunotherapeutic targetLung cancerImmune cellsClinical trialsNovel recombinant antibodiesCancer histologyImmunohistochemical assaysMyeloid cellsTumor typesScoring systemNew targetsHigh concordance
2021
Alpha-smooth muscle actin expression in the stroma predicts resistance to trastuzumab in patients with early-stage HER2-positive breast cancer
Vathiotis IA, Moutafi MK, Divakar P, Aung TN, Qing T, Fernandez A, Yaghoobi V, El-Abed S, Wang Y, Guillaume S, Nuciforo P, Huober J, Di Cosimo S, Kim SB, Harbeck N, Gomez H, Shafi S, Syrigos KN, Fountzilas G, Sotiriou C, Pusztai L, Warren S, Rimm DL. Alpha-smooth muscle actin expression in the stroma predicts resistance to trastuzumab in patients with early-stage HER2-positive breast cancer. Clinical Cancer Research 2021, 27: 6156-6163. PMID: 34465600, PMCID: PMC8595766, DOI: 10.1158/1078-0432.ccr-21-2103.Peer-Reviewed Original ResearchConceptsDisease-free survivalHER2-positive breast cancerShorter disease-free survivalBreast cancerQuantitative immunofluorescenceEarly-stage HER2-positive breast cancerAlpha-smooth muscle actin expressionAlpha-smooth muscle actinProgesterone receptor statusHigh α-SMA expressionDigital Spatial ProfilerΑ-SMA expressionPromising candidate biomarkerCompanion diagnostic testsMuscle actin expressionDigital spatial profilingCohort validationNeoadjuvant lapatinibAdjuvant trastuzumabReceptor statusClinical trialsUnivariate analysisEstrogen receptorMAIN OUTCOMEΑ-SMA