Thazin Nwe Aung, PhD
Associate Research Scientist in PathologyCards
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Associate Research Scientist in Pathology
Biography
Thazin Nwe Aung obtained her PhD in biomedical science at the University of Adelaide, Australia in 2019. During her PhD, she focused on understanding the mechanisms of cellular signalling, communications and interactions, especially those involving cancer metastases, and immune function by using systems biology approaches. Upon completion of her PhD, she moved to Yale University. Her current work focuses on identifying prognostic/predictive biomarkers associated with response/resistance to treatments in cancer using spatial multi-omics.
Appointments
Pathology
Associate Research ScientistPrimary
Other Departments & Organizations
Education & Training
- PhD
- University of Adelaide, Dept of Molecular and Biomedical Science (2019)
- MS
- Yangon Technological University, Biotechnology/Molecular Genetics (2006)
- BS (Hon)
- Yangon Technological University, Biotechnology/Molecular Genetics (2004)
Research
Overview
Medical Subject Headings (MeSH)
ORCID
0000-0003-4150-0426- View Lab Website
Rimm Lab
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
David Rimm, MD, PhD
Harriet Kluger, MD
Roy S. Herbst, MD, PhD
Barbara Burtness, MD
Jonathan Warrell
Kurt Schalper, MD, PhD
Melanoma
Immunotherapy
Triple Negative Breast Neoplasms
Publications
2024
High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)
Moutafi M, Bates K, Aung T, Milian R, Xirou V, Vathiotis I, Gavrielatou N, Angelakis A, Schalper K, Salichos L, Rimm D. High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC). Journal For ImmunoTherapy Of Cancer 2024, 12: e009039. PMID: 38857914, PMCID: PMC11168162, DOI: 10.1136/jitc-2024-009039.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsNon-small cell lung cancerImmune checkpoint inhibitorsProgrammed cell death protein 1Associated with OSCell lung cancerTissue microarray spotsTissue microarrayValidation cohortLung cancerNon-small cell lung cancer treated with immune checkpoint inhibitorsAssociated with resistance to immunotherapyCell death protein 1Resistance to immunotherapyAssociated with PFSProgression-free survivalSecreted frizzled-related protein 2Cox proportional-hazards model analysisCheckpoint inhibitorsImmunotherapy strategiesTumor compartmentsRetrospective cohortDiscovery cohortLong-term benefitsPatientsCD68Spatially informed gene signatures for response to immunotherapy in melanoma.
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger H, Gerstein M, Rimm D. Spatially informed gene signatures for response to immunotherapy in melanoma. Clinical Cancer Research 2024, of1-of13. PMID: 38837895, DOI: 10.1158/1078-0432.ccr-23-3932.Peer-Reviewed Original ResearchAltmetricConceptsGene signatureResistance to immunotherapyResponse to immunotherapyPrediction of treatment outcomeResistant to treatmentAccurate prediction of treatment outcomePredictive of responseImmunotherapy outcomesMelanoma patientsMelanoma specimensValidation cohortPatient stratificationDiscovery cohortTreatment outcomesImmunotherapyMelanomaTumorPatientsCohortS100BOutcomesGene expression dataGenesCD68+macrophagesExpression dataCorrelation 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 ResearchConceptsTumor-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 designHigh-Plex Assessment of Biomarkers in Tumors
Aung T, Bates K, Rimm D. High-Plex Assessment of Biomarkers in Tumors. Modern Pathology 2024, 37: 100425. PMID: 38219953, DOI: 10.1016/j.modpat.2024.100425.Peer-Reviewed Original ResearchCitationsAltmetric
2023
B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma
Gavrielatou N, Fortis E, Spathis A, Anastasiou M, Economopoulou P, Foukas G, Lelegiannis I, Rusakiewicz S, Vathiotis I, Aung T, Tissot S, Kastrinou A, Kotsantis I, Vagia E, Panayiotides I, Rimm D, Coukos G, Homicsko K, Foukas P, Psyrri A. B-cell infiltration is associated with survival outcomes following programmed cell death protein 1 inhibition in head and neck squamous cell carcinoma. Annals Of Oncology 2023, 35: 340-350. PMID: 38159908, DOI: 10.1016/j.annonc.2023.12.011.Peer-Reviewed Original ResearchCitationsAltmetricConceptsProlonged progression-free survivalTertiary lymphoid structuresPD-L1 expressionB cellsM HNSCCCell death protein 1 inhibitionPD-1-based immunotherapyNeck squamous cell cancerNeck squamous cell carcinomaHigher B cell countsIncreased B cellsB cell infiltrationB-cell countsPD-L1 positivityProgression-free survivalTreatment of recurrentSquamous cell cancerBlood immune cell compositionSquamous cell carcinomaBiomarkers of responseImmune cell compositionB-cell-associated genesProtein 1 inhibitionCell death proteinMetastatic headNew 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 ResearchMeSH Keywords and ConceptsConceptsImmune 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 therapeuticsPatientsAutomated 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 ResearchAltmetricMeSH Keywords and ConceptsPhase II window study of olaparib alone or with cisplatin or durvalumab in operable Head and Neck Cancer
Moutafi M, Koliou G, Papaxoinis G, Economopoulou P, Kotsantis I, Gkotzamanidou M, Anastasiou M, Pectasides D, Kyrodimos E, Delides A, Giotakis E, Papadimitriou N, Panayiotides I, Perisanidis C, Fernandez A, Xirou V, Poulios C, Gagari E, Yaghoobi V, Gavrielatou N, Shafi S, Aung T, Kougioumtzopoulou A, Kouloulias V, Palialexis K, Gkolfinopoulos S, Strati A, Lianidou E, Fountzilas G, Rimm D, Foukas P, Psyrri A. Phase II window study of olaparib alone or with cisplatin or durvalumab in operable Head and Neck Cancer. Cancer Research Communications 2023, 3: 1514-1523. PMID: 37575280, PMCID: PMC10414130, DOI: 10.1158/2767-9764.crc-23-0051.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsObjective response rateTumor microenvironmentPD-L1Operable headResponse rateDeath ligand 1 (PD-L1) levelsPathologic complete response ratePhase II window studyNeck squamous cell carcinomaPD-L1 CPSComplete response rateSerious adverse eventsPercentage of patientsInhibitor-based treatmentSquamous cell carcinomaEffective antitumor responseImmunosuppressive tumor microenvironmentInflammatory tumor microenvironmentTumor cell proliferationColony-stimulating factor 1 receptor (CSF1R) genePrimary endpointSecondary endpointsAdverse eventsOpportunity trialAntitumor responseIntegrative 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsModerate-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 InstituteSubsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma.
Horowitch B, Lee D, Ding M, Martinez-Morilla S, Aung T, Ouerghi F, Wang X, Wei W, Damsky W, Sznol M, Kluger H, Rimm D, Ishizuka J. Subsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma. Clinical Cancer Research 2023, 29: 2908-2918. PMID: 37233452, PMCID: PMC10524955, DOI: 10.1158/1078-0432.ccr-23-0215.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsImmune checkpoint inhibitorsHuman melanoma cell linesMelanoma cell linesPD-L1Validation cohortYale-New Haven HospitalCombination of ipilimumabPD-L1 markersImmune checkpoint blockadePD-L1 biomarkerNew Haven HospitalSTAT1 levelsCell linesWestern blot analysisCheckpoint inhibitorsCheckpoint blockadeClinical responseOverall survivalImproved survivalResistance of cancersMetastatic melanomaMelanoma responsePredict responseTreatment responseDistinct patterns
Academic Achievements and Community Involvement
activity Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models
Oral PresentationSpatial Biology Symposium - InvitedDetails09/09/2024 - PresentGermantown, MD, United StatesSponsored by AstraZeneca, BioTracactivity Developing spatial specific gene signatures for resistance to immunotherapy in Melanoma
Oral PresentationMulti-Omics 2022 Conference - Invited SpeakerDetails12/11/2022 - 12/13/2022Brisbane City, QLD, AustraliaSponsored by Multi-Omics, University of Queensland, Brisbane, AustraliaAbstract/SynopsisPurpose: We aim to improve the prediction of response or resistance to immunotherapies in melanoma patients. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment.Experimental Design: We collected gene expression data spatially from three cellular compartments defined by CD68+macrophages, CD45+leukocytes and S100B+tumor cells in 55-immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas (DSP-WTA). We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. Results: We achieved robust performance of compartment-specific signatures in predicting the outcome to ICI in the discovery cohort. Of the three signatures, S100B signature showed the best performance in the validation cohort (N=45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the 8-gene S100B signature, 5 genes (PSMB8, TAX1BP3, NOTCH3, LCP2, NQO1) with positive coefficients predict the response and 3 genes (KMT2C, OVCA2, MGRN1) with negative coefficients predict the resistance to treatment. Conclusion: We conclude that the spatially defined compartment signatures utilize tumor and TME-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.
activity Board Member
CommitteesYale UniversityEvent Organizer for Career Cafe and Pint of Postdoc seminar series.Details01/27/2020 - 11/30/2022activity Quantitative, Spatially Defined Expression of Leukocyte-associated Immunoglobulin-like Receptor in Non–small Cell Lung Cancer
Oral PresentationLAIR-focused symposium - Invited SpeakerDetails08/31/2022 - 09/01/2022Baltimore, MD, United StatesSponsored by NextCure.IncAbstract/SynopsisTargeting the interaction of leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) and its ligands has been shown to reinstate antitumor immunity. In addition, the introduction of the LAIR-1 decoy protein, LAIR-2, sensitizes previously resistant lung tumors to programmed death-1 (PD-1) blockade, indicating the potential of LAIR-1 as an alternative marker for anti-PD-1 resistance in lung cancer. Here, we assessed LAIR-1 as compared with programmed death-ligand 1 (PD-L1) expression in various tumors, with a focus on non–small cell lung cancer (NSCLC) and its histologic subtypes using multiplexed quantitative immunofluorescence (mQIF) in 287 (discovery cohort) and 144 (validation cohort) patients with NSCLC. In addition, using multispectral imaging technology on mQIF images, we evaluated the localization of LAIR-1 on various cell types. We observed that CD14+, CD68+, and CD163+ monocytes and CK+ tumor cells predominantly expressed LAIR-1 more than other cell types. Furthermore, LAIR-1 expression in the tumor compartment was significantly higher in patients with lung adenocarcinoma (LUAD) than those with lung squamous cell carcinoma subtype (**, P = 0.003). Our results indicated that high tumor LAIR-1 expression in patients with LUAD is negatively associated with OS (overall survival, HR = 2.4; *, P = 0.02) highlighting its prognostic value in LUAD but not in other subtypes. The Pearson correlation between LAIR-1 and PD-L1 is 0.31; however, mutual exclusive staining pattern (i.e., several cases were positive for LAIR-1 and negative for PD-L1) was observed. Altogether, our data suggest that the combination therapy of anti-PD-1/PD-L1 with anti-LAIR-1 or the anti-LAIR-1 monotherapy alone may be promising cancer immunotherapeutic strategies. Significance: The spatial, quantitative assessment of LAIR-1 in NSCLC shows positive association of OS with high LAIR-1+/CD68+ cell densities and negative association of OS with high LAIR-1 expression in LUAD tumor subtype.
activity Committee Member
Professional OrganizationsYale Postdoctoral AssociationDetails03/01/2020 - 07/31/2022
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