Cellular level optical metabolic imaging to predict drug response in cancer

细胞水平光学代谢成像预测癌症药物反应

• 批准号: 9138626
• 负责人: Melissa Caroline Skala
• 金额: $ 31.47万
• 依托单位: MORGRIDGE INSTITUTE FOR RESEARCH, INC.
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-16 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9138626
• 关键词: Aftercare; Animals; Antineoplastic Agents; Apoptosis; Biopsy; Biopsy Specimen; Breast Cancer Cell; Breast Cancer Model; Breast Cancer Patient; Breast Cancer Treatment; Cancer Patient; Cell Proliferation; Cells; Clinical; Clinical Treatment; Collagen; Complement; Cytotoxic agent; Dimensions; Drug resistance; Early identification; Engineering; Ensure; Enzymes; Exhibits; Fibroblasts; Flavin-Adenine Dinucleotide; Fluorescence; Goals; Gold; Hematopoietic; Hour; Human; Image; Imaging technology; Immune; Immunocompetent; Immunohistochemistry; Leukocytes; Longitudinal Studies; Malignant Neoplasms; Mammary Neoplasms; Measures; Metabolic; Metabolism; Methods; Molecular Target; Mus; Neoadjuvant Therapy; Nicotinamide adenine dinucleotide; Operative Surgical Procedures; Optics; Organoids; Outcome; Paclitaxel; Patient Care; Patients; Pharmaceutical Preparations; Prediction of Response to Therapy; Predictive Value; Primary Neoplasm; Regimen; Resistance; Resolution; Scheme; Stromal Cells; Surgical Pathology; Tamoxifen; Techniques; Technology; Testing; Therapeutic; Time; Toxic effect; Translating; Trastuzumab; Tumor Markers; Tumor Volume; Work; Xenograft procedure; base; cancer cell; cancer therapy; cancer type; clinically relevant; cohort; cost effective; drug development; drug discovery; imaging approach; improved; in vivo; indexing; individual patient; individualized medicine; ineffective therapies; inhibitor/antagonist; innovation; lapatinib; malignant breast neoplasm; metabolic imaging; mouse model; neoplastic cell; new technology; novel; pre-clinical; preclinical study; public health relevance; research clinical testing; response; response biomarker; treatment choice; treatment planning; treatment response; treatment strategy; tumor; validation studies

DESCRIPTION (provided by applicant): Each year, 33-43% of breast cancers (>100,000 cases) exhibit de novo resistance to initial therapeutic treatment strategies. Unfortunately, current technologies that guide initial treatment choices for breast cancer patients are inaccurate for identifying de novo resistance. This underscores the critical clinical need for earl, accurate, and cost-effective methods of selecting the most effective initial treatment for breast cancer patients. Early identification of those tumors that will respond to therapy versus those that are resistant will (1) expedite clinical decisions regarding the course of treatment, (2) improve the clinical outcomes of breast cancer patients by identifying those patients who are in need of alternate therapies, and (3) spare pre-identified unresponsive patients from the toxicities associated with ineffective treatment. The central goal of this proposal is to develop an innovative platform based on optical metabolic imaging technologies to directly measure de novo resistance of primary tumors and predict therapy response. Optical metabolic imaging (OMI) includes a unique combination of variables, developed by our lab, to accurately measure early drug response with cellular resolution. OMI exploits the intrinsic fluorescence intensities and lifetimes of the metabolic co-enzymes reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) to probe cellular metabolism. Our innovative combination endpoint, the "OMI index," and unique cellular-level analysis provide unprecedented sensitivity to identify heterogeneous cellular drug response, which is critical to ensure that no malignant cells escape treatment. We have shown that OMI can accurately measure therapeutic response in breast tumors in vivo at an earlier time-point than currently used clinical techniques. Further, we have developed novel methods to culture primary human breast tumors in a three-dimensional collagen matrix (organoids), and have applied OMI to accurately, rapidly, and reproducibly predict in vivo tumor responses to multiple treatment schemes in these organoids. The novel and powerful predictive value of organoid testing and imaging could be used to optimize clinical treatment strategies prior to treatment using ex vivo biopsy samples. Therefore, the significance of OMI of primary tumor organoids lies in its potential to measure the dynamic cellular response to multiple treatment strategies within individual patient tumors. This approach could predict an optimal, individualized treatment strategy within hours, before the patient is actually treated. Significance: OMI of organoids derived from primary human tumors could serve as an accurate predictive test of de novo drug resistance across multiple types of cancers, thus transforming patient care to identify optimal treatment strategies before treatment is initiated. This work also has the potential to significantly accelerate pre-clinical drug discovery by developing sensitive in vivo measures of treatment response, and a high-throughput platform to test tumor response to multiple treatment schemes while reducing animal burden and read-out time.

描述(由申请人提供)：每年，33-43%的乳腺癌(&gt；100,000例)对最初的治疗策略表现出从头开始的抵抗。不幸的是，目前指导乳腺癌患者初始治疗选择的技术在识别从头耐药方面并不准确。这突显了临床上迫切需要早期、准确和成本效益高的方法为乳腺癌患者选择最有效的初始治疗。及早识别那些对治疗有反应的肿瘤与那些耐药的肿瘤将(1)加快关于疗程的临床决策，(2)通过识别那些需要替代治疗的患者来改善乳腺癌患者的临床结果，以及(3)使预先确定的无反应的患者免于毒副作用 与无效的治疗有关。这项提议的中心目标是开发一个基于光学代谢成像技术的创新平台，以直接测量原发肿瘤的从头阻力并预测治疗反应。光学代谢成像(OMI)包括由我们实验室开发的独特的变量组合，以细胞分辨率准确测量早期药物反应。OMI利用代谢辅酶还原烟酰胺腺嘌呤二核苷酸(NADH)和黄素腺嘌呤二核苷酸(FAD)的固有荧光强度和寿命来探测细胞代谢。我们的创新组合终点“OMI指数”和独特的细胞水平分析提供了前所未有的灵敏度来识别不同种类的细胞药物反应，这对于确保没有恶性细胞逃脱治疗至关重要。我们已经证明，与目前使用的临床技术相比，OMI可以在更早的时间点准确地测量体内乳腺肿瘤的治疗反应。此外， 我们开发了在三维胶原基质(有机化合物)中培养原代人类乳腺肿瘤的新方法，并应用OMI准确、快速和重复性地预测了这些有机化合物中的多种治疗方案对体内肿瘤的反应。有机物检测和成像的新的和强大的预测价值可以用来在使用体外活组织检查样本进行治疗之前优化临床治疗策略。因此，原发肿瘤有机物的OMI的意义在于它有可能测量单个患者肿瘤内对多种治疗策略的动态细胞反应。这种方法可以在患者实际接受治疗之前的几个小时内预测出最佳的、个性化的治疗策略。意义：从人类原发肿瘤中提取的有机化合物的OMI可以作为多种类型癌症的从头耐药性的准确预测测试，从而在治疗开始之前改变患者护理以确定最佳治疗策略。这项工作还有可能通过开发灵敏的体内治疗反应指标和一个高通量平台来测试肿瘤对多种治疗方案的反应，同时减少动物负担和读出时间，从而显著加快临床前药物发现。