Fractionated photoimmunotherapy to harness low-dose immunostimulation in ovarian cancer

项目摘要 晚期或复发性上皮性卵巢癌（EOC）的生存率仍然很低，部分原因是 复杂肿瘤免疫微环境（TIME）。肿瘤浸润性T细胞的存在与 改善晚期EOC患者的预后，但检查点抑制剂免疫治疗通常显示 临床试验中对EOC的疗效差。一个主要的挑战是与EOC相比，T细胞数量较少 肿瘤中的细胞建立免疫抑制时间。肿瘤靶向，细胞可激活 光免疫疗法（taPIT，一种近红外光疗）可能提供一种替代治疗方法， 选择性破坏表达细胞表面表皮生长因子受体（EGFR）的EOC细胞， 增强对适应性免疫应答显著的肿瘤浸润免疫细胞的保存（例如， 局部细胞毒性T细胞和树突细胞）。虽然光免疫疗法并不新鲜，但这是首次探索 taPIT剂量分级以选择性根除EOC，同时以较低剂量刺激免疫细胞， 在较高剂量下优先保留免疫细胞。根据我们丰富的初步数据，我们提出， 数学建模告知taPIT作为一个新的范例，为联合细胞毒性治疗， 转移性上皮性卵巢癌的免疫治疗我们的总体目标是一个新的实验，模拟和图像引导 利用taPIT的局部选择性来引发用于免疫检查点抑制的“冷”TIME的方法。这 该提案为癌症治疗提供了一种创新的物理科学方法， 在免疫活性小鼠中用TIME的3D培养模型建模和体内成像实验 EOC模型，包括计算机免疫肿瘤学建模，以优化TIME组合物特异性治疗; 分级的taPIT剂量测定，以降低EOC细胞相对于效应T细胞和树突状细胞的负荷， TIME;以及，体内多路荧光显微内窥镜检查，以询问转移性EOC的TIME 在腹膜腔内。微结节性疾病成像的独特能力将使参数化 具有治疗前条件和对治疗的动态体内反应的数学模型作为治疗的基础， 定制治疗的定量设计。我们的目标是（1）通过关联来训练计算机模型 具有病理性抗PD 1应答的临床治疗前EOC TIME组合物;（2）获得最佳治疗前EOC TIME组合物， 在体外将“冷”转变为抗PD 1敏感的“热”TIME的分次taPIT方案，和（3）在体外启动TIME 用于体内抗PD 1治疗。本文介绍的概念将最终实现taPIT-抗PD 1治疗 协同刺激局部和远端免疫增强效应以影响疾病部位的剂量测定 近红外光错过，结合一线手术肿瘤减积和全身 化疗这里开发的方法可移植到其他肿瘤部位， taPIT，包括头颈癌、皮肤癌和肺癌。

PROJECT SUMMARY The survival of advanced or recurrent epithelial ovarian cancer (EOC) remains dismal due in part to the complex tumor–immune microenvironment (TIME). The presence of tumor-infiltrating T cells correlates with improved patient outcome in advanced EOC, yet checkpoint inhibitor immunotherapy has generally shown poor efficacy against EOC in clinical trials. A major challenge is the low number of T cells compared to EOC cells in the tumor that establish an immune-suppressive TIME. Tumor-targeted, cell-activatable photoimmunotherapy (taPIT, a near infrared phototherapy) may provide an alternative treatment approach that selectively destroys EOC cells expressing cell-surface epidermal growth factor receptor (EGFR) while enhancing the preservation of tumor infiltrating immune cells salient to an adaptive immune response (e.g., local cytotoxic T cells and dendritic cells). While photoimmunotherapy is not new, this is the first exploration of taPIT dose fractionation to selective eradicate EOC while stimulating immune cells at lower doses and preferentially sparing immune cells at higher doses. Based on our rich preliminary data, we propose that mathematical modeling-informed taPIT serves as a new paradigm for combined cytotoxic therapy and immunotherapy in metastatic EOC. Our overall goal is a novel experimental, simulation- and image-guided approach for utilizing the local selectivity of taPIT to prime “cold” TIME’s for immune checkpoint inhibition. This proposal contributes an innovative physical sciences approach to cancer therapy integrating mathematical modeling with a 3D culture model of the TIME and in vivo imaging experiments in immunocompetent mouse models of EOC, including in silico immuno-oncology modeling to optimize TIME composition-specific therapy; fractionated taPIT dosimetry to reduce the EOC cell burden relative to effector T cells and dendritic cells within the TIME; and, in vivo multiplexed fluorescence microendoscopy to interrogate the TIME of metastatic EOC within the peritoneal cavity. The unique ability to image micronodular disease will enable parameterizing a mathematical model with pre-treatment conditions and dynamic in vivo responses to therapy as a basis for the quantitative design of custom-tailored therapies. Our aims are (1) to train an in silico model by correlating clinical pre-treatment EOC TIME compositions with pathological anti-PD1 response; (2) to derive optimal fractionated taPIT protocols that shift “cold” to anti-PD1-sensitive “hot” TIME in vitro, and (3) to prime the TIME for anti-PD1 therapy in vivo. The concepts introduced here will ultimately enable taPIT–anti-PD1 therapy dosimetry that synergistically stimulates both local and distal immune-enhancing effects to impact disease sites missed by near infrared light, in combination with frontline surgical tumor debulking and systemic chemotherapy. The approaches developed here are translatable to other tumor sites that can be treated with taPIT, including head and neck cancers, skin cancers, and lung cancers.