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: 10348268
• 负责人: Caigang Zhu
• 金额: $ 22.58万
• 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/10348268
• 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; 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; base; 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