Development of a translational imaging tool as a predictive biomarker for anti-PD-1/PD-L1 immunotherapies

开发转化成像工具作为抗 PD-1/PD-L1 免疫疗法的预测生物标志物

• 批准号: 9904618
• 负责人: Michael John Evans
• 金额: $ 59.13万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904618
• 关键词: Address; Algorithms; Amides; Animal Model; Antibody Affinity; Antigens; Binding; Biochemical; Biodistribution; Biopsy; Cancer Model; Chemistry; Clinical; Combined Modality Therapy; Communities; Data; Detection; Development; Disease; Dose; Doxorubicin; Drug Targeting; Enzymes; Epitopes; Fluorides; Fluorine; Formulation; Foundations; Goals; Gold; Human; IgG1; Image; Imaging Device; Immunocompetent; Immunocompromised Host; Immunoglobulin G; Immunohistochemistry; Immunotherapy; In Vitro; Investigational Therapies; Ionizing radiation; Label; Lesion; Ligase; Lysine; Malignant Neoplasms; Measures; Mind; Modeling; Molecular Weight; Mus; Neoplasm Metastasis; Non-Small-Cell Lung Carcinoma; Normal tissue morphology; Oncology; Paclitaxel; Patient imaging; Patients; Peptides; Pharmacology; Positron-Emission Tomography; Process; Production; Property; Publishing; Radiation therapy; Radiolabeled; Recombinants; Research; Scanning; Side; Site; Technology; Testing; Thioctic Acid; Time; Translations; amino group; analog; anti-PD-1/PD-L1; anti-PD-L1; anti-PD-L1 antibodies; anti-PD-L1 therapy; base; cancer immunotherapy; chemotherapy; clinical translation; clinically relevant; companion diagnostics; cost; extracellular; frontier; human imaging; imaging probe; imaging study; immunoreactivity; in vivo; innovation; man; melanoma; novel; preclinical study; predictive marker; preference; programmed cell death ligand 1; programmed cell death protein 1; prospective; protein expression; quantitative imaging; radiochemical; radiotracer; response; serial imaging; standard of care; tool; tumor; tumor immunology

Project Abstract Discovering predictive biomarkers that better identify which patients will respond to cancer immunotherapies is a major unmet clinical need for oncology. Immunohistochemistry of antigen status is fraught with false positives and negatives for drug targets like PD-1 and PD-L1, and on this basis, measuring antigen levels with quantitative imaging may provide a more global assessment of drug target expression in all cancer lesions within a patient. These data may in turn empower more sophisticated and robust algorithms for identifying potential responders. With these considerations in mind, this project will develop a high sensitivity imaging tool targeting PD-L1 that is responsive to the special demands of human translation. For instance, we will develop a radiotracer based on a human recombinant Fab against PD-L1, which will both preclude the need for a costly humanization process and minimize the absorbed dose to normal tissues in patients. Moreover, we will radiofluorinate the Fab using a new chemoenzymatic technology that we recently developed and published. The radiolabeling technology may facilitate more rapid translation as it is site specific and it results in higher specific activity and radiochemical yield compared to the current gold standard in the field, N-succinimidyl-[18F]- 4-fluorobenzoate. In three specific aims, the Fab will be radiolabeled and characterized in vitro, proof of concept imaging studies will be conducted to show specific binding in models of cancer known to respond to anti-PD-1/PD-L1 therapies, and longitudinal imaging studies will be conducted to determine if the Fab can detect PD-L1 expression changes due to chemo or radiation therapy that can enhance the impact of anti-PD- 1/PD-L1 immunotherapy. In summary, the data from this project could significantly contribute to the community wide effort to develop better translational predictive biomarkers for important cancer immunotherapies.

项目摘要 发现能够更好地识别哪些患者对癌症免疫疗法有反应的预测性生物标记物 肿瘤学尚未得到满足的主要临床需求。抗原状态的免疫组织化学充满了假阳性 以及PD-1和PD-L1等药物靶点的阴性，在此基础上，用 定量成像可能提供对所有癌症病变中药物靶点表达的更全面的评估 在一个病人体内。这些数据可能反过来支持更复杂和更健壮的算法来识别 潜在的应答者。考虑到这些因素，该项目将开发一种高灵敏度成像工具 以响应人工翻译特殊需求的PD-L1为目标。例如，我们将开发一种 基于针对PD-L1的人重组Fab的放射性示踪剂，这将排除对昂贵的 人性化过程，最大限度地减少对患者正常组织的吸收剂量。此外，我们还将 使用我们最近开发和发表的一种新的化学酶技术对Fab进行放射性氟化。 放射性标记技术可以促进更快速的翻译，因为它是特定于站点的，并且它导致更高的 比活度和放化产额与目前该领域的金标准N-琥珀酰亚胺-[18F]- 4-氟苯甲酸酯。在三个特定的目标中，Fab将被放射性标记并在体外表征，证明 将进行概念成像研究，以显示已知对癌症有效的模型中的特定结合 将进行抗PD-1/PD-L1疗法和纵向成像研究，以确定FAB是否可以 检测化疗或放射治疗后PD-L1表达的变化，以增强抗PD的效果 1/PD-L1免疫治疗。总而言之，这个项目的数据可以为社区做出重大贡献 为重要的癌症免疫疗法开发更好的翻译预测生物标记物的广泛努力。