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.

项目总结/摘要 抑制性免疫检查点的阻断已经改变了癌症患者的治疗， 广泛的恶性肿瘤免疫检查点阻断（ICB）通过施用抗体来实现， 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 以恢复抗肿瘤T细胞的活性。尽管治疗反应是前所未有的和持久的， 大多数患者没有从治疗中获得临床益处，一些应答者复发并获得 阻力此外，用ICB治疗的肿瘤的反应模式是非常规的，并且可以是非常规的。 被放射成像误认为疾病进展。最大限度地提高ICB的精度和效益 治疗，鉴定预测性和药效学生物标志物，以客观评估免疫应答 已经迅速成为临床优先考虑的问题。该提案旨在利用蛋白酶活性作为预测 用于监测ICB反应和抗性的生物标志物。蛋白酶在基础生物学中起着核心作用 免疫、肿瘤学和抗肿瘤反应。“热”肿瘤的标志是有效的免疫系统 细胞毒性T细胞浸润，通过经典穿孔素和颗粒酶介导的途径裂解癌细胞- 后者包括一个有效的丝氨酸蛋白酶家族。蛋白酶的肿瘤表达，包括 炎症和基质降解蛋白酶，是公认的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 并且肿瘤蛋白酶在治疗早期将允许鉴定预测治疗的活性生物标志物 有效性和对ICB治疗的耐药性。为了实现这些目标，本提案旨在制定一项新的 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。这些活动感测ICB诊断，或IDB-Dx， 包括用质量条形码化的文库位点特异性官能化的PD-I或PD-CTLA-4抗体， 肽底物。在对ICB的应答期间，这些肽被T细胞和肿瘤蛋白酶切割， 在“热”肿瘤中升高，释放出一种独特的质量条形码指纹，然后过滤到接受者的 尿液用于质谱定量。通过应用机器学习算法， 蛋白酶活性被训练和验证为区分“热”和“冷”肿瘤的预测分类器， 应答者和非应答者以及对治疗的抵抗。