grant

Proteomic determinants of response to checkpoint blockade in malignant pleural mesothelioma

Organization UNIVERSITY OF CALIFORNIA LOS ANGELESLocation LOS ANGELES, UNITED STATESPosted 1 Jan 2026Deadline 31 Dec 2027
NIHUS FederalResearch GrantFY2026AccelerationAdvanced CancerAdvanced Malignant NeoplasmAntigen TargetingArchitectureAsbestosAtlasesB7-H1BioinformaticsBiological MarkersBiologyBody TissuesCD274CancersCell BodyCell Communication and SignalingCell SignalingCellsCheckpoint inhibitorClinicalClinical DataClinical MarkersClinical ResearchClinical StudyClinical TrialsCytometryDataDiseaseDisorderElementsEngineering / ArchitectureEquilibriumFormalinFrequenciesHLA testingHLA typingHumanImageImmuneImmune checkpoint inhibitorImmune mediated therapyImmunesImmunochemical ImmunologicImmunohistochemistryImmunohistochemistry Cell/TissueImmunohistochemistry Staining MethodImmunologicImmunologicalImmunologicallyImmunologically Directed TherapyImmunologicsImmunooncologyImmunotherapyIndividualIntracellular Communication and SignalingIntratumoral heterogeneityMHC antigenMalignant Mesothelioma of the PleuraMalignant NeoplasmsMalignant Pleural MesotheliomaMalignant Pleural NeoplasmMalignant Pleural TumorMalignant TumorMass Photometry/Spectrum AnalysisMass SpectrometryMass SpectroscopyMass SpectrumMass Spectrum AnalysesMass Spectrum AnalysisMeasuresMesotheliomaMethodsModalityModern ManNivolumabOpdivoOperative ProceduresOperative Surgical ProceduresPD-1 antibodyPD-1 blockadePD-L1PD1 antibodyPD1 blockadePDL-1PLRAParaffin EmbeddingPatient CarePatient Care DeliveryPatientsPeptidesPhenotypePleuraPleura CancerPleural CancerPleural TissueProductionProgrammed Cell Death 1 Ligand 1Programmed Death Ligand 1Prospective cohortProtein AnalysisProteinsProteomicsRNA SeqRNA sequencingRNAseqRadiation therapyRadiotherapeuticsRadiotherapyResearchResearch SpecimenResistanceSamplingSignal TransductionSignal Transduction SystemsSignalingSpecimenStructureSurgicalSurgical InterventionsSurgical ProcedureT cell responseT-CellsT-LymphocyteTestingTissuesToxic effectToxicitiesTranslatingTriageTumor BurdenTumor LoadValidationaPD-1aPD1abstractinganti programmed cell death 1anti-PD-1anti-PD-1 Abanti-PD-1 antibodiesanti-PD-1 blockadeanti-PD-1 monoclonal antibodiesanti-PD1anti-PD1 Abanti-PD1 antibodiesanti-PD1 blockadeanti-PD1 monoclonal antibodiesanti-programmed cell death protein 1anti-programmed cell death protein 1 antibodiesanti-programmed death-1 antibodyantiPD-1antigen based detectionantigen detectionbalancebalance functionbio-informatics pipelinebio-markersbioinformatics pipelinebiologic markerbiological signal transductionbiomarkercare for patientscare of patientscaring for patientscell typecheck point blockadecheck point inhibitioncheckpoint blockadecheckpoint inhibitionchemotherapyclinical biomarkersclinical translationclinically translatableclinically useful biomarkerscohortcytokinedesigndesigningdetect antigeneffective therapyeffective treatmentexome sequencingexome-seqheterogeneity in tumorshigh dimensionalityhuman leukocyte antigen testinghuman leukocyte antigen typingimagingimmune check point blockadeimmune check point inhibitionimmune check point inhibitorimmune checkpoint blockadeimmune checkpoint inhibitionimmune microenvironmentimmune therapeutic approachimmune therapeutic interventionsimmune therapeutic regimensimmune therapeutic strategyimmune therapyimmune-based therapiesimmune-based treatmentsimmune-oncologyimmuno oncologyimmuno therapyimmunology oncologyimmunosuppressive microenvironmentimmunosuppressive tumor microenvironmentimprovedin silicoindexingintra-tumoral heterogeneityintratumor heterogeneitymalignancymalignant pleura neoplasmmalignant pleura tumorneo-antigenneo-epitopesneoantigensneoepitopesneoplasm/cancernew markernovelnovel biomarkernovel markeroncoimmunologyparent awardparent projectpredict responsivenesspredicting responsepredictive biological markerpredictive biomarkerspredictive markerpredictive molecular biomarkerprogrammed cell death ligand 1programmed cell death protein ligand 1prospectiveprotein death-ligand 1radiation treatmentresistantresponders and non-respondersresponders from non-respondersresponders or non-respondersresponders versus non-respondersresponders vs non-respondersresponders/nonrespondersresponseside effectsingle cell analysisstandard of caresurgerythymus derived lymphocytetime usetranscriptome sequencingtranscriptomic sequencingtreatment with radiationtumortumor heterogeneitytumor immune microenvironmenttumor-immune system interactionsvalidationsαPD-1αPD1
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Full Description

PROJECT SUMMARY / ABSTRACT
Malignant pleural mesothelioma (MPM) is a highly aggressive asbestos-related malignancy of the pleura for

which effective therapy is highly limited. Recent clinical trials have shown that immune checkpoint inhibition (ICI)

result in durable clinical benefit in ~50% of patients with MPM. Reciprocally, only ~50% of patients benefit at all

from therapy and objective responses occur in less than 20% of patients. There are currently no reliable

biomarkers (including PD-L1 expression) that identify individuals with MPM who are likely to respond to ICI and

identification of such a pre-treatment biomarker prior could avoid unnecessary toxicity, triage non-responders to

other treatment modalities, and extend long-term survival. We constructed a single cell atlas of immune

organization in human MPM using time-of-flight mass cytometry (CyTOF) and identified two dominant cellular

networks within its tumor immune microenvironment (TiME) that discriminated response and resistance to ICI.

Based on the frequencies of key cell types from these opposing networks, we designed a “real-TiME” score for

predicting the likelihood of response to ICI in MPM. To accelerate clinical translation, we developed and validated

a bioinformatics platform to abstract this score from clinical tissue sections using imaging mass cytometry (IMC)

and show robust prediction of response in a sample cohort of ICI-treated MPM patients. Further, mechanisms of

response to ICI in MPM are unknown and their understanding will advance the care of patients with this disease.

In some tumor types, neoantigen burden is predictive of response to ICI, however these findings are inconsistent

and clinical studies have relied exclusively on in silico prediction methods to derive neoantigen burden. We used

mass spectrometry (MS) to quantify the amounts of neoantigens within MPM tumors and our recent studies were

the first to evaluate the relationship between the actual presence of tumor neoantigens within tumors and

responses to immunotherapy. In Aim 1, we will test our hypothesis that response and resistance to ICI can be

predicted by a novel single cell immunoproteomic score that can be translated to clinical tissue sections, using

prospectively collected pre-treatment tumors from MPM patients treated with ICI. In Aim 2, we will test our

hypothesis that response to ICI is more accurately predicted by neoantigen abundance than computationally-

derived estimates of neoantigen burden, and is dependent on concordant expression of neoantigens and the

MHC proteins specific for those neoantigens. In Aim 3, we will test our hypothesis that that a balance between

the repertoire of HLA-presented peptides of MPM, its immunopeptidome, and its TiME regulate response and

resistance to ICI. Our results will define core elements of the immunoproteomic structure of MPM that may

improve treatment and potentially redirect efforts in the expanding field of immuno-oncology.

Grant Number: 4R37CA248478-06
NIH Institute/Center: NIH

Principal Investigator: Bryan Burt

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