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.

抽象的 为了最大限度地提高癌症患者的治疗后存活率，体外永生癌细胞模型和新 开发的源自患者的类器官被广泛用于研究肿瘤代谢重编程在 尽管进行纵向代谢，但肿瘤在治疗应激下生长和存活。 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 多种代谢工具，例如海马测定和代谢组学， 提供标准化的代谢测量，但通常需要依赖于破坏性的样品制备。 鉴于光学技术的非侵入性本质，该提案旨在通过开发来填补关键的技术空白 一种光学光谱测定法，可实现非破坏性高通量代谢测量 具体来说，我们将开发一种新型的多通道体外癌症模型和类器官。 荧光光谱测定和机器学习反卷积算法来量化关键 体外癌症模型的代谢参数（目标 1），因为乳腺的临床需求尚未得到满足。 癌症（BC）放疗（RT）治疗前敏感性评估，我们将展示我们的无损 通过纵向分析在决策窗口内早期预测 BC 辐射反应 辐射应激下患者来源的类器官的代谢特征（目标 2）。 海马测定（体外细胞）和代谢组学（体外细胞和体外细胞）之间存在重要差距 体内组织），为体外癌症的非破坏性代谢测量提供了一种新方法 我们创新的 RT 敏感性预测模型将直接影响 BC。 通过在决策过程中为患者的 RT 敏感性预测提供新的范式 一旦我们展示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临床的大规模试验，目标是为 BC 提供个性化的 RT 我们未来的 R01 计划中的患者。