基于低毒近红外量子点的多模影像探针用于可视化肿瘤免疫分型研究

Each year in the China, millions of people die from cancer, which is one of the most serious diseases threatening human health. As a promising therapy strategy, immunotherapy, especially the programmed cell death-1 antibody, is expected to bring new hope for cancer treatment. However, Only 20-40% of patients respond to PD-1 antibody. It is crucial that screen out those who were likely to be response to PD-1 antibody. Currently, the tumor infiltrating lymphocyte detected by tissue biopsy is used to predict the therapeutic effect of PD-1 antibody in cancer patients. The tumor heterogeneity and dynamic variation of tumor infiltrating lymphocyte have been a great challenge to this strategy. Depending on the research experiences of the applicant in the field of multimode imaging (near-infrared, CT and MR imaging) of quantum dots, the present project is aimed to launch the research on the low-cytotoxic and multimode imaging near-infrared quantum dot based visualization of tumor immunophenotyping. The successful accomplishment of present project will provide more precise method for the early prediction of immune efficacy.

恶性肿瘤是严重威胁人类健康和生命的疾病之一，在中国每年造成数百万人死亡。近年来基于程序性死亡受体-1单克隆抗体的药物治疗为晚期恶性肿瘤患者带来了希望。但仅有30-40%-的肿瘤患者可从PD-1单抗治疗中获益。因此筛选对PD-1治疗敏感的患者至关重要。目前主要通过组织活检法检测肿瘤组织中浸润淋巴细胞数量及位置来预测患者对PD-1单抗的治疗效果。肿瘤组织不同部位之间浸润淋巴细胞水平存在较大差异。更重要的是，肿瘤浸润淋巴细胞数量及位置随着肿瘤进展和治疗干预呈现动态变化，给基于组织活检的预测方法带来了极大的挑战。本项目拟利用申请人在近红外荧光、CT和MR多模成像量子点方面的优势，利用多模成像在特异性、灵敏度、穿透深度、分辨率、组织解剖结构及定位等方面独特优势，探索将基于低毒近红外量子点的多模影像探针用于肿瘤治疗浸润淋巴细胞数量和空间位置动态可视化监测，建立更加准确的免疫疗效早期预测新技术。

基于程序性死亡受体-1（PD-1）单克隆抗体的药物治疗为晚期恶性肿瘤患者带来了希望。但仅有30-40%的肿瘤患者可从PD-1单抗治疗中获益。因此筛选对PD-1治疗敏感的患者至关重要。目前主要通过组织活检法检测肿瘤组织中浸润淋巴细胞数量及位置来预测PD-1单抗的治疗效果。肿瘤组织不同部位之间浸润淋巴细胞数量存在较大差异。更重要的是，肿瘤浸润淋巴细胞数量及位置随着肿瘤进展和治疗干预呈现动态变化，给基于组织活检的预测方法带来了极大的挑战。本项目聚焦近红外二区荧光分子成像技术，开展低毒近红外量子点生物探针的构建、活体成像动态监测肿瘤浸润淋巴细胞数量及分布变化等研究。在低毒近红外二区荧光量子点的制备及性能调控、基于近红外二区量子点的多模影像探针、近红外二区荧光生物探针性能及生物安全性研究、原位无损动态监测肿瘤内部浸润免疫细胞新方法等方面取得了一系列成果。建立了原位无损、高灵敏、多模（多色）动态监测肿瘤组织浸润淋巴细胞数量及位置的活体肿瘤免疫分型及免疫疗效预测新方法，为深入开展相关疾病发生发展过程及疗效研究奠定了基础。已发表论文10篇，申请（获批）发明专利2件，培养博士生1名、硕士生3名。