The IL-18-IFNγ axis predicts response to immunotherapy
IL-18-IFNγ轴预测对免疫治疗的反应
基本信息
- 批准号:10584972
- 负责人:
- 金额:$ 84.58万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-02-01 至 2028-01-31
- 项目状态:未结题
- 来源:
- 关键词:AdoptedAntigensAntitumor ResponseAutomobile DrivingBiological MarkersBiologyBloodBody Weight decreasedCD14 geneCD19 geneCD22 geneCell physiologyCell-Mediated CytolysisCellsCellular immunotherapyCessation of lifeCharacteristicsChildChildhoodChildhood Hematopoietic NeoplasmChildhood LeukemiaClinicalClinical TrialsCollaborationsCoupledCytokine SignalingDangerousnessDataDevelopmentDiseaseDoseEngineeringEngraftmentEtiologyExcisionFunctional disorderGenetic Predisposition to DiseaseGenetic TranscriptionGrantHIF1A geneHematopoietic NeoplasmsHemophagocytic LymphohistiocytosesHistologyHumanIL18 geneImmuneImmune systemImmunityImmunotherapyIn VitroIndividualInflammatoryInterferon Type IIInterferonsInterleukin-10Interleukin-12InvestmentsKLRB1 geneKineticsLifeMachine LearningMeasuresMediatingMedicalMyelogenousMyeloproliferative diseaseNervous System TraumaNeuroblastomaOutcomePatientsPediatric NeoplasmPeripheral Blood Mononuclear CellPhenotypePlayPopulationProductionRecording of previous eventsRecurrenceRelapseResearchResourcesRiskRoleSafetySamplingSeriesSerumSignal TransductionSolid NeoplasmT-LymphocyteThe Jackson LaboratoryTherapeuticTimeToxic effectTreatment-related toxicityTumor BiologyWorkbiomarker validationblood treatmentblood-brain barrier crossingchimeric antigen receptorchimeric antigen receptor T cellsclinical biomarkersclinical predictorscohortcytokinecytokine release syndromeexperienceexperimental studyfamilial hemophagocytic lymphohistiocytosishumanized mouseimprovedimproved outcomeinsightinterleukin-18 receptorleukemiamachine learning algorithmmonocytemouse modelneurotoxicitynovelpatient populationpatient responsepatient subsetspediatric patientspredicting responsepredictive markerprogramsresponseside effectsuccesssurvival predictiontraffickingtrendtrial designtumor
项目摘要
ABSTRACT
CAR T-cells have revolutionized the treatment of pediatric leukemia. However, ~50% of responsive patients
eventually relapse and in ~50-70% of patients, this therapy induces devastating side effects (i.e. neurotoxicity
(NTX) and cytokine release syndrome (CRS)), potentially leading to long term neurological damage and death.
There are no clinical biomarkers to predict survival or toxicity despite a crucial need. SCRI has made a substantial
investment in CAR T therapy and is a world leader in pediatric CAR T cell clinical trials. This proposed project
will build upon this program by helping to predict and mitigate the devastating, life-threatening side effects caused
by this revolutionary therapy and may aide in the prediction of non-response cases. Data from our pilot
experiments utilizing samples from patients on CAR-T trials at SCRI suggests that pre-monocyte activation
status and cytokine profiles from the monocyte fraction of a patient can predict CAR T toxicity across both blood
and solid tumors, in particular we see trends in the IL-18- IFN𝛾𝛾 axis. We plan to act on this encouraging pilot
data in this proposal to develop a series of validated monocytic biomarkers (cytokine, flow and transcriptional)
and machine learning algorithms which can be utilized prior to patients going on trial in order to save time and
resources for patients who are set to fail treatment and allow medical teams to be more prepared to mitigate
toxicity in those who are more prone to it. The proposal further expands to the development a humanized mouse
model that can adopt the healthy donor and pediatric patient immune systems recapturing natural variability in
human immunity to investigate causality of the IL-18- IFN𝛾𝛾 axis in driving both therapeutic toxicity. The project
combines existing but unused large bank of SCRI CAR T patient samples/data with monocytic omics profiling to
develop predictive biomarkers for therapeutic toxicity (AIM 1). We aim to further extend the impact of this work
to mechanistically understand why certain patients’ cells are working against them through the use of novel
humanized mouse models (AIM2). This is built upon to develop therapeutics or strategies to improve safety and
efficacy of CAR-T treatments. Importantly, our team of experts in clinical CAR T (Dr. Rebecca Gardner, Dr. Navin
Pinto), machine learning (Dr. Bobbie-Jo Webb-Robertson), humanized mice (Dr. James Keck), and myeloid
tumor biology (Dr. Heather Gustafson) pose this grant for optimal success.
摘要
CAR T细胞彻底改变了儿童白血病的治疗。然而,约50%的反应患者
最终复发,并且在约50-70%的患者中,这种疗法引起破坏性的副作用(即神经毒性
(NTX)和细胞因子释放综合征(CRS)),可能导致长期神经损伤和死亡。
尽管有迫切的需求,但没有临床生物标志物来预测生存或毒性。SCRI已经做出了实质性的
在CAR T疗法方面的投资,是儿科CAR T细胞临床试验的世界领导者。本拟议项目
将在此项目的基础上,帮助预测和减轻由癌症引起的破坏性的、危及生命的副作用。
通过这种革命性的治疗,并可能有助于预测无反应的情况。我们的飞行员数据
利用来自SCRI CAR-T试验患者的样本进行的实验表明,前单核细胞活化
来自患者单核细胞部分的状态和细胞因子谱可以预测CAR T在血液和外周血中的毒性。
和实体瘤,特别是我们看到IL-18- IFNγ轴的趋势。我们计划就这项令人鼓舞的试验计划采取行动
本提案中的数据用于开发一系列经验证的单核细胞生物标志物(细胞因子、流量和转录)
和机器学习算法,可以在患者进行试验之前使用,以节省时间,
为治疗失败的患者提供资源,并使医疗团队能够更好地准备缓解
该提案进一步扩展到开发一种人源化小鼠,
该模型可以采用健康供体和儿科患者的免疫系统重新捕获自然变异,
研究IL-18- IFNγ轴在驱动两种治疗毒性中的因果关系。项目
将现有但未使用的大型SCRI CAR T患者样本库/数据与单核细胞组学分析相结合,
开发治疗毒性的预测生物标志物(AIM 1)。我们的目标是进一步扩大这项工作的影响
为了机械地理解为什么某些患者的细胞通过使用新的
人源化小鼠模型(AIM 2)。这是建立在开发治疗或策略,以提高安全性和
CAR-T治疗的效果。重要的是,我们的临床CAR T专家团队(Rebecca Gardner博士,Navin博士
平托)、机器学习(Bobbie-Jo Webb-Robertson博士)、人源化小鼠(James Keck博士)和骨髓
肿瘤生物学(石楠古斯塔夫森博士)提出了最佳的成功,这一赠款。
项目成果
期刊论文数量(0)
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HEATHER LEIGH HERD GUSTAFSON的其他文献
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