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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.

项目总结/文摘