Optical imaging to predict cell-level genetic heterogeneity and treatment sensitivity in colorectal cancer

项目摘要/摘要 在所有癌症中，肿瘤学的一个重要问题是目前缺乏可靠的手段来预测对 针对个别患者的抗癌治疗。特别是在局部晚期直肠癌(LARC)中，众所周知 患者可以从化疗、放射治疗和手术治疗中受益。然而，并不是所有这些 每个患者都可能需要接受治疗。在标准做法下，患者暴露在 在不知道每种方式的必要性的情况下，所有这些方式的毒性。此外，对于一些患者来说，他们 由于抗药性疾病，需要在这些治疗之外升级治疗。一种预测治疗反应的方法 迫切需要更好地为个别患者量身定制新辅助治疗范例，这是我们的目标 这项提议。这项提议的长期目标是利用患者的光学代谢成像(OMI)- 衍生结直肠癌(CRC)器官培养以预测治疗方案的敏感性。这项建议 开发新的细胞水平成像技术来预测癌症患者的治疗反应 使用光学代谢成像(OMI)从他们自己的肿瘤中培养出有机化合物。因为肿瘤遗传学可以 对细胞新陈代谢有深刻的影响，更好地理解 肿瘤遗传学改变治疗前和治疗后的OMI是必要的。此外，对细胞水平的评估 需要患者样本的异质性才能完全预测治疗反应。审问这些人 机械的调查，我们已经产生了多种工具和研究技术。我们迄今的数据表明 原发癌和其他肿瘤类型的OMI可预测小鼠和患者的体内药物反应。 在这项建议中，我们检验了患者来源的器官培养的OMI将预测治疗的假设。 接受化疗和/或放射治疗的结直肠癌患者的反应。在这个延期方案中，我们建立了 目的是评估OMI用于评估克隆进化并将这些研究与分子相关 这些细胞的进化。此外，我们进一步验证了OMI反应阈值以预测治疗 在我们最初的目标中确定了对另外一组结直肠癌患者的反应。最后，我们将表演一个 结合有机物直径变化评估多个OMI参数的多变量分析， 对临床影像、治疗前临床分期、年龄、性别、种族/民族、临床治疗剂量的反应 强度、肿瘤浸润性淋巴细胞计数和分子特征可作为预测临床结果的指标 这些结直肠癌患者。总体而言，这些额外目标是最初目标的合乎逻辑的延伸，将是关键 进一步支持将患者来源的有机化合物的OMI纳入未来的预期临床试验。

PROJECT SUMMARY/ABSTRACT Across all cancers a significant problem in oncology is the current lack of reliable means to predict response to anti-cancer treatments for individual patients. Specifically in locally advanced rectal cancer (LARC), it is known that patients can benefit from chemotherapy, radiation, and operative management. However, not all of these therapies may be required for each individual patient. With standard practices, patients are exposed to the toxicities of all of these modalities without knowing the necessity of each. In addition, for some patients they require escalation of therapies beyond these due to resistant disease. A method to predict treatment response is urgently needed to better tailor the neoadjuvant treatment paradigm for individual patients and this is the goal of this proposal. The long-term objective of this proposal is to utilize optical metabolic imaging (OMI) of patient- derived colorectal cancer (CRC) organoid cultures to predict sensitivity to therapeutic regimens. This proposal develops novel cellular-level imaging technologies to predict treatment response in individual cancer patients using optical metabolic imaging (OMI) of organoids cultures from their own tumors. Since tumor genetics can have profound impacts on cellular metabolism, a better understanding of the underlying mechanisms by which tumor genetics alter OMI in the pre- and post-treatment settings is needed. In addition, assessment of cell-level heterogeneity within patient samples is required to fully predict the treatment response. To interrogate these mechanistic inquiries, we have generated multiple tools and research techniques. Our data to date indicate that OMI in primary CRCs and other tumor types predicts in vivo drug response in mice and patients. In this proposal, we test the hypothesis that OMI of patient-derived organoid cultures will predict treatment response for patients with CRC undergoing chemotherapy and/or radiation. In this extension proposal, we build on the prior aims to assess the use of OMI to evaluate clonal evolution and correlate these studies with molecular evolution of these cells. Additionally, we further validate the OMI response thresholds to predict treatment response identified in our initial aims with an additional cohort of CRC patients. Finally, we will perform a multivariate analysis to evaluate multiple OMI parameters in combination with change in organoid diameter, response on clinical imaging, pre-treatment clinical stage, age, gender, race/ethnicity, clinical treatment dose intensity, tumor infiltrating lymphocyte counts, and the molecular profile as predictors of clinical outcomes for these CRC patients. Overall, these additional aims represent logical extensions of the initial aims and will be key to further supporting the incorporation of OMI of patient-derived organoids into future prospective clinical trials.