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: 9767107
• 负责人: Melissa Caroline Skala
• 金额: $ 30.53万
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767107
• 关键词: 3-Dimensional; 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; Endothelium; 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; drug response prediction; 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; optimal treatments; pre-clinical; preclinical evaluation; preclinical study; predictive test; public health relevance; response; 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（> 100，000例）对初始治疗性治疗策略表现出新发耐药性。不幸的是，目前指导乳腺癌患者初始治疗选择的技术对于识别从头耐药是不准确的。这强调了临床上迫切需要快速、准确和具有成本效益的方法来为乳腺癌患者选择最有效的初始治疗。早期识别那些对治疗有反应的肿瘤与那些有抗性的肿瘤将（1）加快关于治疗过程的临床决策，（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）使预先识别的无反应患者免于毒性反应。 与无效治疗有关。该提案的中心目标是开发一种基于光学代谢成像技术的创新平台，以直接测量原发性肿瘤的从头抗性并预测治疗反应。光学代谢成像（OMI）包括我们实验室开发的独特的变量组合，以准确测量细胞分辨率的早期药物反应。OMI利用代谢辅酶还原型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为识别异质细胞药物反应提供了前所未有的灵敏度，这对于确保没有恶性细胞逃脱治疗至关重要。我们已经表明，OMI可以准确地测量乳腺肿瘤在体内的治疗反应在一个更早的时间点比目前使用的临床技术。此外，本发明还 我们开发了在三维胶原基质（类器官）中培养原发性人乳腺肿瘤的新方法，并应用OMI准确、快速和可重复地预测这些类器官中对多种治疗方案的体内肿瘤反应。类器官测试和成像的新颖且强大的预测价值可用于在使用离体活检样品治疗之前优化临床治疗策略。因此，原发性肿瘤类器官的OMI的意义在于其测量个体患者肿瘤内对多种治疗策略的动态细胞反应的潜力。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最佳的个性化治疗策略。重要性：来源于原发性人类肿瘤的类器官的OMI可以作为多种类型癌症的从头耐药性的准确预测测试，从而改变患者护理，以在治疗开始前确定最佳治疗策略。这项工作也有可能通过开发敏感的体内治疗反应措施来显着加速临床前药物发现，以及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。

项目成果

Development of a Microfluidic Array to Study Drug Response in Breast Cancer.

开发微流体阵列来研究乳腺癌的药物反应。

• DOI: 10.3390/molecules24234385
• 发表时间: 2019
• 期刊: Molecules (Basel, Switzerland)
• 作者: Virumbrales-Muñoz,María;Livingston,MeganK;Farooqui,Mehtab;Skala,MelissaC;Beebe,DavidJ;Ayuso,JoseM
• 通讯作者: Ayuso,JoseM

Temporal binning of time-correlated single photon counting data improves exponential decay fits and imaging speed.

• DOI: 10.1364/boe.7.001385
• 发表时间: 2016-04
• 期刊: Biomedical optics express
• 影响因子: 3.4
• 作者: Alex J. Walsh;Joe T. Sharick;M. Skala;H. Beier

Time-domain single photon-excited autofluorescence lifetime for label-free detection of T cell activation.

• DOI: 10.1364/ol.422445
• 发表时间: 2021-05-01
• 期刊: Optics letters
• 影响因子: 3.6
• 作者: Samimi K;Contreras Guzman E;Trier SM;Pham DL;Qian T;Skala MC
• 通讯作者: Skala MC

Microfluidic Tumor-on-a-Chip Model to Study Tumor Metabolic Vulnerability.

• DOI: 10.3390/ijms21239075
• 发表时间: 2020-11-28
• 期刊: International journal of molecular sciences
• 影响因子: 5.6
• 作者: Ayuso JM;Rehman S;Farooqui M;Virumbrales-Muñoz M;Setaluri V;Skala MC;Beebe DJ
• 通讯作者: Beebe DJ

Metabolic Imaging of Head and Neck Cancer Organoids.

头颈癌类器官的代谢成像。

• DOI: 10.1371/journal.pone.0170415
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Shah AT;Heaster TM;Skala MC
• 通讯作者: Skala MC