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：
HEATHER LEIGH HERD GUSTAFSON
金额：
$ 84.58万
依托单位：
SEATTLE CHILDREN'S HOSPITAL
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：
https://reporter.nih.gov/project-details/10584972
关键词：
Adopted Antigens Antitumor Response Automobile Driving Biological Markers Biology Blood Body Weight decreased CD14 gene CD19 gene CD22 gene Cell physiology Cell-Mediated Cytolysis Cells Cellular immunotherapy Cessation of life Characteristics Child Childhood Childhood Hematopoietic Neoplasm Childhood Leukemia Clinical Clinical Trials Collaborations Coupled Cytokine Signaling Dangerousness Data Development Disease Dose Engineering Engraftment Etiology Excision Functional disorder Genetic Predisposition to Disease Genetic Transcription Grant HIF1A gene Hematopoietic Neoplasms Hemophagocytic Lymphohistiocytoses Histology Human IL18 gene Immune Immune system Immunity Immunotherapy In Vitro Individual Inflammatory Interferon Type II Interferons Interleukin-10 Interleukin-12 Investments KLRB1 gene Kinetics Life Machine Learning Measures Mediating Medical Myelogenous Myeloproliferative disease Nervous System Trauma Neuroblastoma Outcome Patients Pediatric Neoplasm Peripheral Blood Mononuclear Cell Phenotype Play Population Production Recording of previous events Recurrence Relapse Research Resources Risk Role Safety Sampling Series Serum Signal Transduction Solid Neoplasm T-Lymphocyte The Jackson Laboratory Therapeutic Time Toxic effect Treatment-related toxicity Tumor Biology Work biomarker validation blood treatment blood-brain barrier crossing chimeric antigen receptor chimeric antigen receptor T cells clinical biomarkers clinical predictors cohort cytokine cytokine release syndrome experience experimental study familial hemophagocytic lymphohistiocytosis humanized mouse improved improved outcome insight interleukin-18 receptor leukemia machine learning algorithm monocyte mouse model neurotoxicity novel patient population patient response patient subsets pediatric patients predicting response predictive marker programs response side effect success survival prediction trafficking trend trial design tumor

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.

抽象的 CAR T 细胞彻底改变了儿童白血病的治疗方法，但约 50% 的患者有反应。最终复发，并且在约 50-70% 的患者中，这种疗法会引起毁灭性的副作用（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 SCRI 已经提出了很大的需求，但还没有临床生物标志物可以预测生存或毒性。投资 CAR T 疗法，是儿科 CAR T 细胞临床试验的世界领先者。将在此计划的基础上帮助预测和减轻所造成的毁灭性的、危及生命的副作用通过这种革命性的疗法，可能有助于预测我们试点的无反应病例。利用 SCRI CAR-T 试验患者样本进行的实验表明，前单核细胞激活患者单核细胞部分的状态和细胞因子谱可以预测两种血液中的 CAR T 毒性和实体瘤，特别是我们看到 IL-18 轴的趋势，我们计划对这一令人鼓舞的试点采取行动。该提案中的数据旨在开发一系列经过验证的单核细胞生物标志物（细胞因子、流和转录）和机器学习算法，可以在患者进行试验之前使用，以节省时间和为治疗失败的患者提供资源，让医疗团队做好更充分的准备来缓解该提案进一步扩展到人源化小鼠的开发。可以采用健康捐赠者和儿科患者免疫系统的模型，重新捕捉自然变异人体免疫研究 IL-18-IFN𝛾𝛾 轴在驱动两种治疗毒性方面的因果关系。将现有但未使用的大量 SCRI CAR T 患者样本/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。通过使用新颖的方法机械地理解为什么某些患者的细胞对他们不利人源化小鼠模型（AIM2）是建立在开发治疗方法或策略以提高安全性和安全性的基础上的。重要的是，我们的临床 CAR T 专家团队（Rebecca Gardner 博士、Navin 博士） Pinto）、机器学习（Bobbie-Jo Webb-Robertson 博士）、人源化小鼠（James Keck 博士）和骨髓细胞肿瘤生物学（Heather Gustafson 博士）提供这笔资助以获得最佳成功。