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

• 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
• 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

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.

项目摘要/摘要 抑制性免疫切除点的封锁已改变了A的患者的癌症治疗 广泛的恶性肿瘤。免疫检查点封锁（ICB）是通过管理抗体来实现的 阻止细胞毒性T淋巴细胞相关蛋白4（CTLA-4）或编程细胞死亡1（PD-1）途径 振兴抗肿瘤T细胞活性。尽管治疗反应是前所未有且耐用的 大多数患者没有从治疗中获得临床益处，一些响应者接力并获得 反抗。此外，用ICB处理的肿瘤的反应模式是非常规的，可以是 通过射线照相成像被误解为疾病进展。为了最大程度地提高ICB的精度和好处 治疗，鉴定预测性和药效生物标志物，以客观地评估免疫反应 已迅速成为临床优先事项。该提案旨在利用蛋白酶活动作为预测 用于监测ICB响应和抗性的生物标志物。蛋白酶在潜在的生物学中起着核心作用 免疫学，肿瘤学和抗肿瘤反应。 “热”肿瘤的标记由有效的免疫签名 细胞毒性T细胞的浸润，这些细胞通过经典的穿孔蛋白和颗粒状介导的途径 - 后者包括一个潜在的序列蛋白酶家族。蛋白酶的肿瘤表达，包括 炎症性和基质降解蛋白酶是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细胞活性 和肿瘤蛋白酶早期治疗将允许鉴定活动生物标志物，以预测治疗 功效并表明对ICB治疗的抵抗力。为了实现这些目标，该建议旨在开发新的 检查点封闭抗体具有抑制免疫切口点的双重能力的抗体 并在治疗反应期间感知蛋白酶活动。这些活动感应ICB诊断或IDB-DX， 包括-PD-1或-ctla-4抗体，该抗体特异性地与质量模型的库一起官能化 肽底物。在对ICB的反应过程中，这些宠物被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的指纹，然后将其过滤到收件人中 通过质谱法定量的尿液。通过应用机器学习算法，这些签名 蛋白酶活性经过训练和验证，作为区分“热”和“冷”肿瘤的预测分类器， 来自非反应者的响应者和对治疗的抵抗力。