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 发现预测性生物标志物，更好地识别哪些患者将对癌症免疫治疗作出反应， 肿瘤学的一个主要未满足的临床需求。抗原状态的免疫组化充满了假阳性 并且对于药物靶标如PD-1和PD-L1为阴性，并且在此基础上，用 定量成像可以提供所有癌症病变中药物靶点表达的更全面的评估 在病人体内。这些数据反过来可以使更复杂和强大的算法用于识别 潜在的响应者考虑到这些因素，该项目将开发一种高灵敏度成像工具 以PD-L1为目标，响应人工翻译的特殊需求。例如，我们将开发一个 基于针对PD-L1的人重组Fab的放射性示踪剂，这两者都将排除对昂贵的放射性示踪剂的需要。 人源化过程和最小化对患者正常组织的吸收剂量。而且还要 使用我们最近开发并发表的新的化学酶技术放射性氟化Fab。 放射性标记技术可以促进更快速的翻译，因为它是位点特异性的，并且它导致更高的转化率。 与该领域目前的金标准相比，N-琥珀酰亚胺基-[18F]- 4-氟苯甲酸酯。在三个具体的目标中，Fab将被放射性标记并在体外表征，证明其在体外的活性。 将进行概念成像研究，以显示已知对以下反应的癌症模型中的特异性结合： 抗PD-1/PD-L1治疗，并将进行纵向成像研究，以确定Fab是否可以 检测由于化疗或放疗引起的PD-L1表达变化，这可以增强抗PD-1抗体的影响。 1/PD-L1免疫疗法。总之，本项目的数据可以为社区做出重大贡献 广泛努力为重要的癌症免疫疗法开发更好的翻译预测生物标志物。