Network mechanisms and bioengineering of precision immunotherapies for glioblastomas

胶质母细胞瘤精准免疫治疗的网络机制和生物工程

基本信息

批准号：
10600870
负责人：
Dileep Daniel Monie
金额：
$ 2.7万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2023-05-19
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10600870
关键词：
Adenoviruses Adjuvant Chemotherapy Animal Model Antigens Astrocytes Basic Science Behavior Big Data Biomedical Engineering Brain Neoplasms Cells Central Nervous System Chemotherapy and/or radiation Classification Clinical Trials Complex Data Data Set Dependence Diagnosis Diagnostic Disease Drug resistance Dryness Elasticity Engineering Excision FDA approved Future Gene Deletion Gene Expression Gene Targeting Genes Genetic Transcription Glioblastoma Herpesvirus 1 Heterogeneity Human Immunotherapy Inflammation Injections Interferon Type I Interferons Life Expectancy Machine Learning Malignant Neoplasms Modeling Monitor Morbidity - disease rate Multiomic Data Neurosphere Oncolytic Oncolytic viruses Operative Surgical Procedures Outcome Pathway Analysis Patients Phenotype Physicians Play RNA Sequences Research Research Training Resources Role Scientist Serotyping Serum Signal Transduction Solid Neoplasm System Systems Biology Testing Time Translational Research Tumor Antigens Tumor Immunity Tumor Subtype Tumor Suppressor Proteins Virus Work biological systems cost design design and construction efficacy testing gene network immune resistance improved in silico in vivo in vivo Model insight knock-down multiple omics neoplastic cell network models neurotoxicity novel oncolysis oncolytic adenovirus patient derived xenograft model personalized immunotherapy preclinical development predicting response protein protein interaction resistance mechanism response retinoblastoma tumor suppressor side effect single-cell RNA sequencing small hairpin RNA standard of care success supervised learning tool transcriptome transcriptomics tumor tumorigenesis viral resistance

项目摘要

Project Summary/Abstract Glioblastoma multiforme (GBM) is the most common and lethal malignancy of the human central nervous system (CNS). This devastating disease has a median overall survival of 3 months from time of diagnosis in untreated patients. Despite the significant cost and morbidity with standard of care surgical resection combined with adjuvant chemotherapy and radiation, life expectancy is only extended by a few months. Oncolytic viruses (OVs) are a class of immunotherapies with an FDA-approved treatment for solid tumors. These work by directly lysing tumor cells, which then release tumor antigens with danger signals that elicit antitumor immunity. Adenovirus is a strong candidate OV immunotherapy for GBM because of its low neurotoxicity when delivered by intratumoral injection and its amenability to bioengineering. Ad5-Δ24, an adenovirus modified to replicate in GBM but not in other CNS cells, is perhaps the most promising OV in clinical trials. Major hurdles remain, however, including innate immunoresistance. This study will overcome these by elucidating complex tumor-OV interaction mechanisms at the systems level, designing and constructing improved OV candidates that exploit these mechanisms, and then validating the mechanisms by experimentally testing the OVs. Preliminary studies for this proposal demonstrate that GBM patient-derived xenograft (PDX) models have distinct transcriptomes in vivo and can be cultured as neurospheres (Sp-GBMs) under serum-free conditions ex vivo. Ad5-Δ24 OV can lyse several different Sp-GBMs ex vivo and the level of this oncolytic activity is PDX specific. Additionally, preliminary in silico models suggest that such OV activity levels are contextually dependent on protein-protein interaction networks in the target GBM cells. This project will test the central hypothesis that dynamic transcriptional states of human GBM play key mechanistic roles in Ad5-Δ24 oncolytic efficacy; therefore, modeling the emergent system behaviors will guide the engineering of precision OV immunotherapies. The first aim will classify human GBM PDXs using transcriptomes to predict Ad5-Δ24 OV responses and identify context-specific network features. The resulting classifier and network models will be validated by assessing their ability to predict the response of Sp-GBM to Ad5-Δ24 OV treatment. The second aim will elucidate Ad5- Δ24 OV gene dependencies and resistance mechanisms in human GBM PDXs. Predictions from both aims will both experimentally and computationally validated. Synthetic Ad5-Δ24 OVs with shRNAs that perturb these gene dependencies and resistance mechanisms will be constructed and tested for efficacy. These precisely targeted alterations result in the quantifiable, predictable phenotypic effects of modulating human GBM gene expression and oncolysis ex vivo and in vivo. Such synthetic OVs will be useful as GBM research tools. As a future direction, the big data generated will be a valuable public resource to further interrogate antitumor immunity and inflammation states. Successful completion of these aims will yield novel GBM diagnostic subtypes with paired engineered OVs ready for preclinical development as precision immunotherapies.

项目概要/摘要多形性胶质母细胞瘤（GBM）是人类中枢神经系统最常见和致命的恶性肿瘤这种毁灭性疾病自诊断之日起的中位总生存期为 3 个月。尽管标准护理手术切除联合治疗的成本和发病率很高。通过辅助化疗和放疗，溶瘤病毒的预期寿命仅延长几个月。 (OV) 是一类经 FDA 批准的实体瘤免疫疗法。直接裂解肿瘤细胞，然后释放带有危险信号的肿瘤抗原，引发抗肿瘤免疫。腺病毒是 GBM 的有力候选 OV 免疫疗法，因为它在递送时神经毒性较低通过瘤内注射及其对生物工程的适应性，Ad5-Δ24是一种经过修饰以在其中复制的腺病毒。 GBM 可能是临床试验中最有希望的 OV，但在其他中枢神经系统细胞中则不然，但主要障碍仍然存在。然而，包括先天免疫抵抗，这项研究将通过阐明复杂的肿瘤-OV 来克服这些问题。系统层面的交互机制，设计和构建改进的OV候选者这些机制，然后通过实验测试 OV 来验证这些机制。该提案表明 GBM 患者来源的异种移植（PDX）模型在 Ad5-Δ24 OV 可在体内无血清条件下培养为神经球 (Sp-GBM)。离体裂解几种不同的 Sp-GBM，并且这种溶瘤活性的水平是 PDX 特异性的。初步的计算机模型表明，这种 OV 活性水平取决于蛋白质-蛋白质该项目将测试动态的中心假设。因此，人类 GBM 的转录状态在 Ad5-Δ24 溶瘤功效中发挥着关键的机制作用；对突发系统行为进行建模将指导精准 OV 免疫疗法的工程设计。 Target 将使用转录组对人类 GBM PDX 进行分类，以预测 Ad5-Δ24 OV 反应并识别特定于上下文的网络特征将通过评估进行验证。他们预测 Sp-GBM 对 Ad5-Δ24 OV 治疗的反应的能力第二个目标将阐明 Ad5-。人类 GBM PDX 中的 Δ24 OV 基因依赖性和耐药机制将来自这两个目标的预测。经过实验和计算验证的合成 Ad5-Δ24 OVs 以及扰乱这些的 shRNA。将构建基因依赖性和耐药机制并测试其功效。有针对性的改变导致调节人类 GBM 基因的可量化、可预测的表型效应此类合成的 OV 将可用作 GBM 研究工具。未来的方向，产生的大数据将成为进一步审问抗肿瘤的宝贵公共资源免疫和炎症状态的成功完成将产生新的 GBM 诊断。亚型与配对的工程化 OV 已准备好作为精准免疫疗法进行临床前开发。