Harnessing protease activity for predictive monitoring of cancer immunotherapy

利用蛋白酶活性进行癌症免疫治疗的预测监测

基本信息

批准号：
10348165
负责人：
Gabriel A Kwong
金额：
$ 33.18万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-20 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10348165
关键词：
Antibodies Antitumor Response Bar Codes Binding Biological Assay Biological Products Biology Blocking Antibodies CTLA4 gene Cancer Patient Caspase Cell Death Cells Clinical Cytotoxic T-Lymphocytes Data Set Decision Making Diagnostic Disease Progression Family Fingerprint Future Goals Granzyme Growth Human Image Immune response Immunity Immunologic Monitoring Inflammatory Libraries Malignant Neoplasms Mass Spectrum Analysis Mediating Methods Modeling Molecular Monitor Mus Neoplasm Metastasis Oncology Pathway interactions Patients Pattern Peptide Hydrolases Peptides Peptidyltransferase Pharmaceutical Preparations Pharmacologic Substance Physicians Play Proteins RNA Recombinants Relapse Resistance Role Sampling Scheme Sensitivity and Specificity Serine Protease Signal Transduction Site T-Lymphocyte Testing Traction Training Training Activity Transcript Treatment Efficacy Tumor-Associated Process Tumor-infiltrating immune cells United States National Institutes of Health Urinalysis Urine Validation angiogenesis base cancer cell cancer immunotherapy cancer therapy checkpoint inhibition cohort design experience humanized mouse immune checkpoint immune checkpoint blockade immune resistance improved machine learning algorithm mouse model perforin personalized immunotherapy pharmacodynamic biomarker pre-clinical predictive marker programmed cell death protein 1 radiological imaging resistance mechanism responders and non-responders response sortase success therapy resistant treatment response tumor

项目摘要

Project Summary/Abstract The blockade of inhibitory immune checkpoints has transformed the treatment of cancer for patients across a broad range of malignancies. Immune checkpoint blockade (ICB) is achieved by administering antibodies that block the cytotoxic T lymphocyte-associated protein 4 (CTLA-4) or the programmed cell death 1 (PD-1) pathway to reinvigorate antitumor T cell activity. Despite treatment responses that are unprecedented and durable, the majority of patients do not experience a clinical benefit from treatment, and some responders relapse and acquire resistance. Moreover, response patterns of tumors treated with ICB are unconventional, and can be misinterpreted as disease progression by radiographic imaging. To maximize the precision and benefit of ICB therapy, identification of predictive and pharmacodynamic biomarkers to objectively assess immune responses has rapidly emerged as a clinical priority. The proposal aims to leverage protease activity as predictive biomarkers for monitoring ICB response and resistance. Proteases play a central role in the underlying biology of immunity, oncology, and anti-tumor responses. The mark of a “hot” tumor is signified by an effective immune infiltrate of cytotoxic T cells that lyse cancer cells via the classical perforin- and granzyme-mediated pathway – the latter of which comprise a family of potent serine proteases. Tumor expression of proteases, including inflammatory and matrix degrading proteases, is well-established as a hallmark of fundamental tumor processes including angiogenesis, growth, and metastasis. The central hypothesis is that quantifying the activity of T cell and tumor proteases early-on-treatment will allow identification of activity biomarkers that predict treatment efficacy and indicate resistance to ICB therapy. To achieve these goals, this proposal aims to develop a new class of checkpoint blockade antibodies that are endowed with the dual capacity to inhibit immune checkpoints and sense protease activity during treatment responses. These activity sensing ICB diagnostics, or IDB-Dx, comprise -PD-1 or -CTLA-4 antibodies that are site-specifically functionalized with a library of mass-barcoded peptide substrates. During responses to ICB, these peptides are cleaved by T cell and tumor proteases that are elevated in “hot” tumors, liberating a unique fingerprint of mass barcodes that are then filtered into the recipient’s urine for quantification by mass spectrometry. By applying machine learning algorithms, these signatures of protease activity are trained and validated as predictive classifiers to discriminate “hot” and “cold” tumors, responders from non-responders, and resistance to therapy.

项目概要/摘要抑制性免疫检查点的阻断改变了癌症患者的治疗方法免疫检查点阻断（ICB）是通过施用抗体来实现的。阻断细胞毒性 T 淋巴细胞相关蛋白 4 (CTLA-4) 或程序性细胞死亡 1 (PD-1) 途径尽管治疗反应前所未有且持久，但大多数患者没有从治疗中获得临床益处，并且一些反应者复发并获得此外，ICB治疗肿瘤的反应模式是非常规的，并且可以是。通过放射成像将其误解为疾病进展为了最大限度地提高 ICB 的精度和益处。治疗、识别预测性和药效生物标志物以客观评估免疫反应该提案旨在利用蛋白酶活性作为预测。用于监测 ICB 反应和耐药性的生物标志物蛋白酶在基础生物学中发挥着核心作用。免疫、肿瘤学和抗肿瘤反应的标志是有效的免疫。细胞毒性 T 细胞浸润，通过经典的穿孔素和颗粒酶介导的途径裂解癌细胞 – 后者包含一个有效的丝氨酸蛋白酶家族，包括蛋白酶的肿瘤表达。炎症和基质降解蛋白酶，已被确定为基本肿瘤过程的标志包括血管生成、生长和转移。中心假设是量化 T 细胞的活性。治疗早期的肿瘤蛋白酶将能够识别预测治疗的活性生物标志物疗效并表明对 ICB 疗法的耐药性为了实现这些目标，本提案旨在开发一种新的药物。一类检查点阻断抗体，具有抑制免疫检查点的双重能力并在治疗反应期间检测蛋白酶活性。这些活性检测 ICB 诊断或 IDB-Dx，包含 α-PD-1 或 α-CTLA-4 抗体，这些抗体通过质量条形码库进行了位点特异性功能化在对 ICB 的反应过程中，这些肽被 T 细胞和肿瘤蛋白酶裂解。在“热”肿瘤中升高，释放出独特的质量条形码指纹，然后过滤到接受者的体内通过应用机器学习算法，对尿液进行定量。蛋白酶活性作为预测分类器进行训练和验证，以区分“热”和“冷”肿瘤，反应者与无反应者的区别，以及对治疗的抵抗力。