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

摘要 为了最大限度地提高癌症患者治疗后的存活率，体外永生癌细胞模型和新的 已开发的患者衍生的有机化合物被广泛用于研究肿瘤代谢重新编程在 治疗压力下的肿瘤生长和存活率。虽然进行纵向代谢 在治疗过程中对同一肿瘤样本的测量对治疗研究至关重要， 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 非破坏性的模型或有机类化合物。几种代谢工具，如海马分析和代谢组学， 提供标准化的新陈代谢测量，但通常需要破坏性的样品准备。依赖于 由于光学技术的非侵入性，这项提议试图通过开发 一种光学光谱分析，将使非破坏性高通量新陈代谢测量成为可能 用于癌症研究的体外癌症模型和有机化合物。具体地说，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量化关键 体外肿瘤模型的代谢参数(目标1)。因为对乳房有很大的未满足的临床需求 癌症(BC)放射治疗(RT)敏感性评估在治疗前，我们将展示我们的无损 在决策窗口内通过纵向进行BC辐射响应早期预测的方法 辐射应激下患者来源的有机化合物的代谢特征(目标2)。我们的技术充满了 海马分析(体外细胞)和代谢组学(体外细胞和EX)之间存在的一个重要差距 活体组织)，为体外肿瘤的非破坏性代谢测量提供了一种新的方法 模型和病人衍生的有机化合物。我们创新的RT敏感度预测模型将直接影响BC 为决策过程中患者的RT敏感性预测提供了一种新的范式 窗户。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上的大规模试验，目标是为BC提供个性化的RT 我们未来R01计划中的患者。

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.