Non-destructive optical spectroscopic assay for high-throughput metabolic characterization of in vitro cell models and patient-derived organoids

用于体外细胞模型和患者来源类器官高通量代谢表征的无损光学光谱测定

• 批准号: 10666355
• 负责人: Caigang Zhu
• 金额: $ 18.74万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666355
• 关键词: 4T1; Algorithms; Biological Assay; Biological Markers; Biomedical Research; Breast Cancer Model; Breast Cancer Patient; Cancer Model; Cancer Patient; Cell Line; Cell model; Cells; Clinic; Clinical; Control Groups; Convulsions; Decision Making; Evaluation; Fatty Acids; Fiber; Fluorescence; Future; Genus Hippocampus; Goals; In Vitro; Incubated; Light; Machine Learning; Matrix Metalloproteinases; Measurement; Measures; Membrane Potentials; Metabolic; Metabolism; Modeling; Nature; Optics; Organoids; Outcome; Patients; Performance; Preparation; Radiation; Radiation Tolerance; Radiation therapy; Reader; Regimen; Role; Sampling; Siblings; Source; Specimen; Spectrum Analysis; Standardization; Stress; Survival Rate; System; Techniques; Technology; Testing; Therapeutic; Therapeutic Studies; Time; Tissues; Update; Work; anticancer research; cancer cell; cancer radiation therapy; cancer therapy; experience; experimental group; fluorophore; glucose uptake; high throughput screening; improved; in vivo; indexing; innovation; machine learning algorithm; malignant breast neoplasm; metabolomics; mitochondrial membrane; new technology; novel; novel strategies; pilot test; pre-clinical; preclinical study; predictive modeling; programs; radiation response; radioresistant; tool; tumor; tumor growth; tumor metabolism; uptake

Abstract To maximize cancer patients’ survival rate post-therapy, in vitro immortal cancer cell models and newly developed patient-derived organoids are widely used to study the role of tumor metabolism reprogramming in tumor growth and survival under therapeutics stresses. Although conducting longitudinal metabolic measurements on the same tumor sample during a course of therapy is critical for therapeutic studies, there are surprisingly few techniques that can provide a systems-level view of tumor metabolism on in vitro cancer models or organoids non-destructively. Several metabolic tools, such as Seahorse Assay and Metabolomics, provide standardized metabolic measurements but often require destructive sample preparation. Relying on the non-invasive nature of optical technique, this proposal seeks to fill the critical technical gap by developing an optical spectroscopic assay that will enable non-destructive high-throughput metabolism measurement on in vitro cancer models and organoids for cancer research. Specifically, we will develop a novel multi-channel fluorescence spectroscopic assay and a machine learning de-convolution algorithm to quantify the key metabolic parameters of in vitro cancer models (Aim 1). As there is a significant unmet clinical need for breast cancer (BC) radiotherapy (RT) sensitivity evaluation prior to treatment, we will demonstrate our non-destructive assay for early prediction of BC radiation responses within the decision-making window via longitudinal metabolic characterization of patient-derived organoids under radiation stresses (Aim 2). Our technology fills an important gap that exists between Seahorse Assay (in vitro cells) and Metabolomics (in vitro cells and ex vivo tissue) by providing a novel approach for non-destructive metabolism measurement on in vitro cancer models and patient-derived organoids. Our innovative RT sensitivity prediction model will directly impact BC patients by providing a novel paradigm for patients’ RT sensitivity prediction during the decision-making window. Once we demonstrate the proof-of-concept of our optical technique and the RT sensitivity prediction model, we will move our study to a large-scale trail in clinics with a goal of providing individualized RT for BC patients in our future R01 plan.

摘要 为了最大限度地提高癌症患者治疗后的生存率，体外永生化癌细胞模型和新的 开发的患者来源的类器官被广泛用于研究肿瘤代谢重编程在肿瘤发生中的作用。 治疗应激下的肿瘤生长和存活。虽然进行纵向代谢 在治疗过程中对同一肿瘤样本进行测量对于治疗研究至关重要， 令人惊讶的是，很少有技术可以提供体外癌症肿瘤代谢的系统水平视图 模型或类器官。几种代谢工具，如海马测定和代谢组学， 提供标准化的代谢测量，但通常需要破坏性的样品制备。依托 由于光学技术的非侵入性，该提案旨在通过开发 一种光学光谱分析，这将使非破坏性的高通量代谢测量，在 用于癌症研究的体外癌症模型和类器官。具体来说，我们将开发一种新颖的多渠道 荧光光谱分析和机器学习去卷积算法来量化关键 体外癌症模型的代谢参数（目的1）。由于乳腺癌的临床需求明显未得到满足， 癌症（BC）放疗（RT）敏感性评估治疗前，我们将证明我们的非破坏性 在决策窗口内通过纵向测量对BC辐射响应进行早期预测的试验 在辐射应激下患者来源的类器官的代谢表征（目的2）。我们的技术 海马测定（体外细胞）和代谢组学（体外细胞和前体细胞）之间存在一个重要的差距， 体内组织），为体外癌症的非破坏性代谢测量提供了一种新的方法 模型和患者衍生的类器官。我们创新的RT敏感性预测模型将直接影响BC 通过为患者在决策过程中的RT敏感性预测提供一种新的范例， 窗口一旦我们证明了我们的光学技术和RT灵敏度预测的概念验证， 模型，我们将把我们的研究转移到一个大规模的临床试验，目标是为BC提供个性化的RT 我们未来的R01计划中的患者。