Quantitative Fluorescence Imaging-Guided Detection and Targeted Therapy Monitoring Platform for Ovarian Cancer Micrometastases

卵巢癌微转移定量荧光成像引导检测及靶向治疗监测平台

• 批准号: 10058694
• 负责人: Ulas Sunar
• 金额: $ 34.09万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10058694
• 关键词: Acids; Address; Adjuvant Chemotherapy; Algorithms; Antineoplastic Agents; Biodistribution; Biopsy; Cancer Patient; Cell Culture Techniques; Cell Line; Clinic; Clinical; Combination Drug Therapy; Data; Detection; Diagnosis; Disease; Dose; Doxorubicin; Drug Targeting; Ensure; Epithelial; Epithelial ovarian cancer; Epithelium; Excision; Exposure to; FOLR1 gene; Feedback; Fluorescence; Folic Acid; Goals; Image; Imaging technology; In Vitro; Kinetics; Laparoscopes; Lead; Lesion; Light; Lighting; Lipids; Liposomal Doxorubicin; Liposomes; Malignant Neoplasms; Malignant neoplasm of gastrointestinal tract; Malignant neoplasm of lung; Malignant neoplasm of ovary; Maps; Metastatic Malignant Neoplasm to the Ovary; Micrometastasis; Microscopic; Modality; Monitor; Neoplasm Metastasis; Nodule; Normal tissue morphology; Operative Surgical Procedures; Ovarian; Ovarian Carcinoma; PUVA Photochemotherapy; Patients; Pharmaceutical Preparations; Phospholipids; Photosensitization; Photosensitizing Agents; Phototherapy; Physiological; Platinum; Porphyrins; Protocols documentation; Public Health; Quality of life; Recurrence; Resistance; Resolution; Scheme; Sensitivity and Specificity; Shapes; Signal Transduction; Spatial Frequency Domain Imaging; Structure; Surface; Survival Rate; System; Technology; Therapeutic; Time; Tissues; Toxic effect; Translating; Treatment Efficacy; Visualization; absorption; anti-cancer; attenuation; base; cancer cell; cancer diagnosis; cancer therapy; chemotherapy; contrast enhanced; controlled release; deep learning; deep learning algorithm; fluorescence imaging; image guided; image guided therapy; image-guided drug delivery; imaging approach; imaging modality; imaging platform; improved; in vivo; intraperitoneal; liposome vector; malignant mouth neoplasm; nano; nanocarrier; nanomedicine; novel; optical imaging; phantom model; quantitative imaging; side effect; spatiotemporal; targeted treatment; treatment optimization; tumor; tumor specificity; uptake

PROJECT SUMMARY/ABSTRACT A major challenge in the management of advanced ovarian cancer is the presence of disseminated microscopic tumor nodules within the intraperitoneal cavity. Despite surgery and adjuvant chemotherapy, as many as 50% of patients can show occult disseminated disease, with only a 43% survival rate. Furthermore, systemic chemotherapy can have toxic side effects. Thus, recent efforts have aimed at improving detection and treatment of micromets. Chemophototherapy (CPT), the combination of chemotherapy and photodynamic therapy, is an emerging cancer treatment modality that can provide synergistic efficacy of both therapies. The overall goal is to implement a quantitative laparoscopic imaging and treatment approach for advanced detection of micromets and optimization of CPT for targeted destruction of ovarian micromets and reduced toxic side effects. Quantitative fluorescence laparoscopic imaging will provide high sensitivity and resolution for detecting micromets as well as image guided drug delivery. Folate receptor alpha (FA) will be used as a promising target because it is highly specific of epithelial ovarian cancer. The proposed targeted CPT compound has a ~6-fold tumor-specificity providing enhanced fluorescence contrast. These folate-targeted, porphyrin-phospholipid doped liposomes are triggered directly by near infrared (NIR) light. This activates the anti-cancer photosensitizer outer layer and releases the anti-cancer agent Doxorubicin (Dox). While this nanocarrier is expected to improve detection of micromets, tissue absorption and scattering in living tissue can confound fluorescence contrast. Quantitative imaging based on spatial frequency domain imaging can eliminate these confounding effects and provide quantitative contrasts to enable more sensitive detection compared to raw fluorescence or white light visualization. Furthermore, this quantitative capability can function in near-real-time to provide feedback on drug release, thus allowing image-guided optimization of treatment light to ensure full drug release within each tumor. In Aim 1, a wide-field dual-channel laparoscope, fast quantification algorithms and targeted liposomal nano-construct will be implemented and optimized. In Aim 2, the platform will be validated in vivo for improved detection of micromets vs. raw fluorescence and white light. In Aim 3, the platform’s efficacy will be validated in vivo for destroying micromets in targeted tumors while reducing toxicity to surrounding normal tissues. Successful completion of this approach is expected to result in improved detection and treatment of micromets with reduced side effects. This is ultimately expected to lead to reduced recurrence rates and overall improved survival. Although this imaging approach focuses on epithelial ovarian cancer diagnosis and treatment, it can be applicable to a wide range of epithelial diseases, such as oral, lung, and gastrointestinal cancers.

项目总结/文摘