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Cytotoxic lymphocyte function PET imaging to predict cancer immunotherapy response

Cytotoxic lymphocyte function PET imaging to predict cancer immunotherapy response
细胞毒性淋巴细胞功能 PET 成像预测癌症免疫治疗反应
批准号:
10219982
负责人:
Umar Mahmood
金额:
$38.28万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-01 至 2023-07-31
关键词:
AddressAffinityAnatomyAntibodiesAvidityBindingBiological MarkersBiopsyBiopsy SpecimenCD8B1 geneCTLA4 geneCell DeathCellsClinicClinicalClinical ResearchClinical TrialsCytotoxic T-LymphocytesDevelopmentDisease remissionDoseDrug KineticsEvaluationFailureGenerationsGoalsGranzymeHumanImageImmuneImmune ToleranceImmune checkpoint inhibitorImmune responseImmune systemImmunomodulatorsImmunotherapyIncidenceLifeLymphocyteLymphocyte FunctionMalignant NeoplasmsMeasurementMeasuresMediatingMetabolicMethodsModelingModificationMonitorOutcomePD-1 inhibitorsPatient CarePatientsPeptidesPharmaceutical PreparationsPlayPositron-Emission TomographyProgression-Free SurvivalsRegimenResearchRoleSamplingSavingsSerine ProteaseSignal TransductionStructureTimeTreatment EfficacyTumor BurdenTumor-infiltrating immune cellsVaccinesWorkanaloganatomic imaginganti-CTLA-4 therapyanti-PD-1antibody inhibitorbasebiomarker developmentbiomarker discoverycancer cellcancer immunotherapycheckpoint therapyclinical developmentclinical imagingclinical translationcytotoxicdesigneffective therapyengineered T cellsexhaustionfluorodeoxyglucosefluorodeoxyglucose positron emission tomographyhumanized mouseimaging agentimaging approachimaging biomarkerimaging modalityimaging probeimmune checkpointimmune-related adverse eventsimprovedinhibitor/antagonistinterestmelanomamolecular imagingmouse modelneoplasm immunotherapyneoplastic cellnovelnovel therapeutic interventionpatient populationpatient responsepre-clinicalpreclinical developmentpredicting responsepredictive markerprogrammed cell death ligand 1programmed cell death protein 1protein aminoacid sequenceresearch clinical testingresponseresponse biomarkersmall moleculesuccesstargeted treatmenttooltreatment responsetumortumor heterogeneity

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Abstract There have been recent significant advances in understanding the role that immune-checkpoints play in down regulating the immune response to cancer. These discoveries have in turn led to the development of immune- checkpoint inhibitors that activate cytotoxic T-cells, and have demonstrated strikingly positive clinical outcomes across multiple tumor types. However, despite durable remissions in many patients, the overall response rate remains low. Immune checkpoint inhibitors are also associated with a high percentage of potentially lethal immune-related adverse events. Further, assessing therapeutic response is challenging, as tumors that may ultimately respond can appear to increase in size on anatomic imaging due to an influx of immune cells. This same immune infiltrate obscures FDG-PET analysis, as the immune cells are highly FDG avid. The lack of a useful response assessment has significantly complicated patient care and clinical development. Patients are frequently kept on therapies longer than necessary, as it cannot be ascertained whether they are responding. In order to address the difficulty with response assessment, there has been significant effort investigating predictive biomarkers, including novel imaging methods. The imaging biomarkers analyzed thus far have focused on identifying the presence of tumoral immune infiltrate and have not proven strongly predictive of response. Their lack of utility is likely because they cannot distinguish between active and inactive immune infiltrate, the latter of which is hypothesized to be a common cause of immunotherapy failure. To monitor cytotoxic T lymphocyte (CTL) activity, we have developed a first-in-class peptide-based PET imaging agent that binds to granzyme B, a serine protease released by CTLs when they are actively attacking tumor cells. We have demonstrated our imaging agent in two different immunotherapy models and shown that it is able to predict response to checkpoint inhibitors. We have also interrogated checkpoint-inhibitor treated human melanoma samples for granzyme B expression. These results corroborate our pre-clinical findings of high granzyme B expression correlating with response to immunotherapy. Finally, we designed a human analogue of our peptide, which specifically bound to granzyme B in human samples. This proposal aims to finalize an optimized human probe and inform the patient population and timing for near-term clinical evaluation. To achieve this goal, we will first develop second-generation peptides that may provide enhanced affinity or improved pharmacokinetics for granzyme B measurement, and assess them in humanized mouse immunotherapy models. In order to better structure clinical trial imaging time-points, we will continue our assessment of granzyme B expression in human checkpoint inhibitor treated melanoma biopsy specimens. Quantification of target expression focused on dosing intervals will help to maximize clinical impact by identifying response prior to administration of subsequent therapy. Together, we hope these aims can rapidly advance granzyme B imaging into the clinic to provide the response biomarker that is so desperately needed.
期刊论文(3)
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会议论文
Molecular Imaging, Oncology, and the Arc toward Our Precision Future.
分子成像、肿瘤学和通往精准未来的弧线。
DOI: 10.1148/radiol.231930
发表时间: 2023
期刊: Radiology
影响因子: 19.7
作者: [Mahmood,Umar]
通讯作者: Mahmood,Umar
Granzyme B PET imaging as a marker of inflammatory bowel disease activity
  • 批准号:
    10404617
  • 项目类别:
  • 资助金额:
    $57.43万
  • 财政年份:
    2020
  • 负责人:
    Umar Mahmood
  • 依托单位:
Granzyme B PET imaging as a marker of inflammatory bowel disease activity
  • 批准号:
    10641731
  • 项目类别:
  • 资助金额:
    $57.43万
  • 财政年份:
    2020
  • 负责人:
    Umar Mahmood
  • 依托单位:
Granzyme B PET imaging as a marker of inflammatory bowel disease activity
  • 批准号:
    10254339
  • 项目类别:
  • 资助金额:
    $57.43万
  • 财政年份:
    2020
  • 负责人:
    Umar Mahmood
  • 依托单位:
Optical and electromagnetic tracking guidance for hepatic interventions
  • 批准号:
    9260276
  • 项目类别:
  • 资助金额:
    $42.2万
  • 财政年份:
    2017
  • 负责人:
    Umar Mahmood
  • 依托单位:
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