Targeting Fluid Stress-induced Chemoresistance in a 3D Carcinomatosis Perfusion Model Using Mechanism-based Photo-immunoconjugate Nanoparticles

摘要 晚期和复发性卵巢癌患者的预后仍然很差， 几十年反应率低的部分原因是对化疗，特别是铂类药物的耐药性， 紫杉烷类药物卵巢癌通常通过跨腔途径沿着腹水癌细胞的电流转移。 腹膜腔内有液体。我们设计了一个3D粘附灌注模型来模拟卵巢结节 研究腹膜表面并重现抗性疾病，从而提供了一个独特的平台， 靶向治疗。我们的研究表明，生理相关的流体剪切应力（FSS）诱导一个前， 转移表型并赋予对铂剂的抗性。光免疫疗法（PIT）显示 在选择性成像和治疗播散性肿瘤方面有希望，它可以使化疗耐药的癌症重新敏感 细胞转化为铂剂。然而，细胞内光致免疫缀合物需要的高阈值是细胞内光致免疫缀合物的高阈值。 死亡阻碍了PIT在生理学相关模型中的有效性。因此， 这项提议是开发一种多用途的纳米平台，打破选择性摄取的权衡， 光免疫缀合物，并使多层癌症靶向下的腹膜FSS。我们最近 显示光免疫缀合物在纳米颗粒上的成功缀合可以有效地增强 肿瘤内光免疫缀合物递送和改善小鼠中PIT结果。我们假设 纳米级工程实现了光免疫缀合物和化疗的高有效载荷共同递送 以安全有效的方式克服FSS诱导的化学抗性。这种方法将 显著提高铂类药物对卵巢癌患者的治疗指数。在目标1中， 将开发光免疫缀合物纳米结构（PICNC），以在3D成像中靶向由FSS改变的生物标志物。 卵巢癌灌注模型。在目标2中，我们将评估对化疗增敏、T细胞保护、 和3D中FSS下PICNC-PIT后免疫支持肿瘤相关巨噬细胞的破坏 灌注模型在目标3中，为了提高治疗的安全性和一致性，我们将开发图像- 指导策略，以告知小鼠模型中PICNC-PIT的时间和剂量。在目标4中，抗肿瘤 将在基于细胞系同基因（免疫活性）和异种移植小鼠中评价PICNC-PIT的功效 模型，以及PDX模型，用于卵巢癌。PI设想了一个简单可行的修改， 标准治疗框架，其中PICNC将在手术减积后腹腔内输送，以及 被光激活，触发PIT并释放化疗。获得的知识可以发挥 在开发针对肿瘤分子特征的改良治疗方案方面的变革作用 个别患者的肿瘤扩散。为达致这些目标，我们会运用我们的跨专业 纳米粒子工程，3D肿瘤灌注模型，癌症生物学，肿瘤免疫学，生物统计学， 和妇科肿瘤专家来检查我们的技术对卵巢癌治疗的影响。

ABSTRACT The prognosis for patients with advanced stage and recurrent ovarian cancer has remained dismal for decades. The poor response rates result in part from resistance to chemotherapy, particularly platinum- and taxane-based agents. Ovarian cancer often metastasizes via transcoelomic routes along currents of ascitic fluid in the peritoneal cavity. We have engineered a 3D adherent perfusion model to mimic ovarian nodules that stud peritoneal surfaces and recapitulate resistant disease, thereby providing a unique platform to develop targeted therapies. Our studies showed that physiologically relevant fluid shear stress (FSS) induces a pro- metastatic phenotype and confers resistance to platinum agents. Photoimmunotherapy (PIT) has shown promise in selectively imaging and treating disseminated tumors, and it can resensitize chemoresistant cancer cells to platinum agents. However, the high thresholds of intracellular photoimmunoconjugate required for cell death have hindered the effectiveness of PIT in physiologically relevant models. Therefore, the main goal of this proposal is to develop a multi-purpose nanoplatform that breaks the selectivity-uptake trade-off of photoimmunoconjugates and enables multi-tier cancer targeting under peritoneal FSS. We have recently shown that successful conjugation of photoimmunoconjugates onto nanoparticles can effectively enhance intratumoral photoimmunoconjugate delivery and improve PIT outcomes in mice. We hypothesize that nanoscale engineering enables high-payload co-delivery of photoimmunoconjugate and chemotherapy in a manner that is safe and efficacious in overcoming FSS-induced chemoresistance. This approach will significantly enhance the therapeutic index of platinum agents for ovarian cancer patients. In Aim 1, a panel of photoimmunoconjugate-nanoconstructs (PICNC) will be developed to target biomarkers altered by FSS in a 3D perfusion model of ovarian cancer. In Aim 2, we will assess the effects on chemosensitization, T cell sparing, and destruction of immune supporting tumor-associated macrophages following PICNC-PIT under FSS in 3D perfusion models. In Aim 3, to improve the safety and consistency of the treatment, we will develop image- guided strategies to inform the timing and dosing of PICNC-PIT in mouse models. In Aim 4, the anti-tumor efficacy of PICNC-PIT will be evaluated in cell line-based syngeneic (immunocompetent) and xenograft mouse models, as well as PDX models, for ovarian cancer. The PIs envision a simple and feasible modification to the standard treatment framework, where PICNC will be delivered intraperitoneally after surgical debulking, and activated by light, triggering PIT and releasing chemotherapy. The knowledge gained could play a transformative role in the development of improved therapeutic regimens tailored to the molecular profile of disseminated tumors in individual patients. To accomplish these aims, we will deploy our multi-disciplinary team of nanoparticle engineering, 3D tumor perfusion model, cancer biology, tumor immunology, biostatistics, and gynecologic oncology experts to examine the impact of our technology on ovarian cancer treatment.