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

光学成像预测结直肠癌的细胞水平遗传异质性和治疗敏感性

• 批准号: 10159077
• 负责人: Dustin A Deming
• 金额: $ 60.64万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-08 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159077
• 关键词: 3-Dimensional; Aftercare; Alternative Therapies; Architecture; Biopsy; Cancer Biology; Cancer Etiology; Cancer Model; Cancer Patient; Cell Communication; Cells; Cessation of life; Chemotherapy and/or radiation; Clinical; Colorectal Cancer; Complex; Data; Development; Disease; Disease Resistance; Enrollment; Enzymes; Exposure to; Fluorescence; Fluorouracil; Generations; Genes; Genetic; Genetic Heterogeneity; Genotype; Goals; Heterogeneity; Human; Image; Imaging technology; Individual; Investigational Therapies; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Metabolic; Metabolism; Methods; Modality; Modeling; Molecular; Molecular Profiling; Mus; Mutation; NADH; Neoadjuvant Therapy; Oncology; Operative Surgical Procedures; Optics; Outcome; Pathologic; Patient imaging; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pilot Projects; Population Density; Prediction of Response to Therapy; Primary Neoplasm; Prior Therapy; Protocols documentation; Radiation; Radiation therapy; Rectal Cancer; Regimen; Registries; Sampling; Secondary to; Staging; Stromal Neoplasm; System; Systemic Therapy; Techniques; Technology; Testing; Therapeutic; Time; Toxic effect; Toxicity due to chemotherapy; Transgenic Organisms; Xenograft procedure; anticancer treatment; base; cancer cell; clinical predictors; colon cancer patients; driver mutation; effective therapy; in vivo; individual patient; individualized medicine; interest; metabolic imaging; metastatic colorectal; mortality; novel; optical imaging; pathology imaging; patient response; personalized medicine; predicting response; prevent; response; screening; side effect; single cell analysis; standard care; standard of care; tool; tumor

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. Beyond that for patients with metastatic colorectal cancer (CRC) multiple standard and experimental therapies exist and a way to predict which patients will respond to which therapies would be a major advance. Sensitivity testing would prevent patients from unnecessarytoxicities and allow escalation of or alternative therapies for those with resistant disease. A method to predict treatment response is urgently needed and this is the goal of this proposal. The long-termobjective of this proposal is to utilize optical metabolic imaging (OMI) of patient-derived CRC spheroid 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 spheroid 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 including transgenic murine CRC models possessing combinations of mutations in commonly altered genes, murine CRC spheroid cultures, isogenic human CRC cells, and patient-derived spheroid cultures and xenografts. Our preliminary data indicate that OMI in primary CRCs and other tumor types predicts in vivo drug response in mice. We have also tested this platform in pilot studies using patient-derived CRC spheroids. In this proposal, we test the hypothesis that OMI of patient-derived spheroid cultures will predict treatment response for patients undergoing chemotherapy and/or radiation for CRC. Specifically, we determine how individual genetic alterations within spheroids impact OMI pre- and post-treatment with chemotherapy and/or radiation. Secondly, we evaluate the heterogeneity within CRC spheroid cultures with complex molecular profiles using OMI pre- and post-treatment with chemotherapy and/or radiation. In addition, we test whether spheroid response using OMI predicts clinical responsein patients undergoing neoadjuvant treatment for locally advanced rectal cancer and for systemic therapies for patients with metastatic CRC. Patients will be enrolled in a registry protocol prior to the initiation of therapy. The OMI predictions will be validated with the clinical outcomes based on pathology and imaging criteria.

项目总结/摘要 在所有癌症中，肿瘤学中的一个重要问题是目前缺乏可靠的方法来预测对化疗的反应。 针对个别患者的抗癌治疗。特别是在局部晚期直肠癌（LARC）中，已知 患者可以从化疗、放疗和手术治疗中获益。然而，并非所有这些 可能需要针对每个个体患者进行治疗。对于转移性结直肠癌患者， (CRC)存在多种标准和实验性疗法，并且有一种方法可以预测哪些患者会对 哪些疗法会是一个重大进步敏感性试验可防止患者出现不必要的毒性反应 并允许对那些患有耐药疾病的人进行升级或替代疗法。预测治疗的方法 迫切需要作出反应，这就是本提案的目标。这项建议的长远目标是 利用患者来源的CRC球状体培养物的光学代谢成像（OMI）来预测对 治疗方案。该提案开发了新的细胞水平成像技术来预测治疗 使用来自他们自己的球状体培养物的光学代谢成像（OMI）， 肿瘤的由于肿瘤遗传学可以对细胞代谢产生深远的影响，因此更好地了解肿瘤遗传学对细胞代谢的影响是必要的。 需要在治疗前和治疗后环境中肿瘤遗传学改变OMI的潜在机制。在 此外，需要评估患者样本中细胞水平的异质性，以充分预测治疗 反应为了探究这些机制性问题，我们已经开发了多种工具，包括转基因技术， 在通常改变的基因中具有突变组合的鼠CRC模型，鼠CRC球状体 培养物、同基因人CRC细胞和患者来源的球状体培养物和异种移植物。我们的初步数据 表明原发性CRC和其它肿瘤类型中OMI预测小鼠体内药物反应。我们还 在试点研究中使用患者来源的CRC球体测试了该平台。 在这个提议中，我们检验了患者来源的球体培养物的OMI将预测治疗的假设 接受CRC化疗和/或放疗的患者的反应。具体来说，我们确定如何 球体内的个体遗传改变影响化疗治疗前后的OMI，和/或 辐射其次，我们评估了具有复杂分子谱的CRC球体培养物内的异质性 在化疗和/或放疗治疗前和治疗后使用OMI。此外，我们还测试了椭球体是否 使用OMI预测接受新辅助治疗的局部晚期乳腺癌患者的临床反应 直肠癌和转移性CRC患者的全身治疗。患者将入组登记研究 在治疗开始前的协议。OMI预测将根据临床结局进行验证 病理学和影像学标准。