Integrated Informatic and Experimental Evaluations of Cancer Chronotherapy

癌症时间疗法的综合信息学和实验评估

• 批准号: 10379304
• 负责人: Ron C. Anafi
• 金额: $ 60.38万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-03 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10379304
• 关键词: Algorithms; Alleles; Animal Model; Antineoplastic Agents; Apoptotic; Basic Science; Biopsy; Biopsy Specimen; Cancer Biology; Cancer Model; Cell Cycle; Chronotherapy; Circadian Dysregulation; Circadian Rhythms; Clinical; Clinical Oncology; Clinical Trials; Collection; Colon; Coupled; Data; Databases; Disease; Dose; Drug Side Effects; Drug Targeting; Drug Transport; Drug toxicity; ERBB2 gene; Effectiveness; Enzymes; Esophagus; Evaluation; Future; Genes; Genetic Transcription; Genotype; Gold; Heart; Human; Informatics; Intestines; Islet Cell Tumor; Kidney; Knowledge; Liver; Lung; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of lung; Malignant neoplasm of thyroid; Maps; Mediating; Medical; Medicine; Metabolism; Molecular; Mus; Mutation; Neoplasms; New Agents; Normal tissue morphology; Oncology; Patients; Periodicity; Pharmaceutical Preparations; Pharmacogenomics; Phase; Phylogeny; Physicians; Physiology; Primary carcinoma of the liver cells; Recommendation; Renal Cell Carcinoma; Renal carcinoma; Research; Research Personnel; Role; Sampling; Signal Pathway; Site; Stomach; Streptozocin; Structure; System; TP53 gene; Testing; Therapeutic Index; Time; Tissues; Toxic effect; Translations; Tumor Markers; Tumor Tissue; Variant; Work; base; cancer therapy; chemotherapeutic agent; chemotherapy; circadian; circadian pacemaker; circadian transcriptome; clinically significant; dosage; experience; gene product; human data; human disease; human tissue; improved; individual patient; machine learning algorithm; multidimensional data; neoplastic; neoplastic cell; novel; pharmacokinetics and pharmacodynamics; reconstruction; side effect; tumor

ABSTRACT Years of clinical experience and a growing body of basic research suggest that chemotherapeutic activity can change with time-of-day. But when should our patients take their medicines? Must we test each new agent for circadian modulation in both efficacy and toxicity? Which tumors are most sensitive to chemotherapy administration time? Can we tailor our recommendations for individual patients? Temporal variation in the abundance of drug targets, transporters, and metabolizing enzymes, in both tumors and normal tissues, underlies circadian variation in drug activity. Until recently almost all we knew about tissue specific circadian rhythms came from normal mice. Without human data, a mechanistic, hypothesis-driven transition to medical practice has been slow. Recently we developed CYCLOPS (CYCLic Ordering by Periodic Structure) a machine-learning algorithm to uncover human transcriptional oscillations using existing, unordered biopsy samples. We used CYCLOPS to explore circadian rhythms in human lung and liver, identify disrupted rhythms in hepatocellular carcinoma, and predict circadian changes in drug effectiveness. This proposal will greatly expand that work and accelerate its translation to clinical oncology. Using public data, we will describe the molecular rhythms in an array of normal human tissues and thus the times of day when these tissues are least sensitive to specific drug toxicities. We will also describe rhythms in select tumors, identifying circadian times and cell cycle phases when cancers are most distinct from surrounding tissue and thus uniquely sensitive to various treatments. We will explore the influence of specific mutations and tumor markers on the rhythms observed in patients. Mapping these data onto pharmacogenomics databases we can make testable prediction as to the drugs and side effects most influenced by circadian time. Finally using both experimental mouse data and our preliminary human results, we have compiled a list of some of the most promising chronotheraputic candidates. We will expand and refine this list over the course of the study, testing several of these predictions in established animal models, and exploring the promise and practical principles of cancer chronotherapy. Taken together these aims will help catalyze chronotheraputic translation to clinical oncology and help delineate the role of time in precision cancer therapy.

摘要 多年的临床经验和越来越多的基础研究表明，化疗 活动可以随一天中的时间而改变。病人什么时候应该吃药？必须 我们测试每一种新药的昼夜节律调节的功效和毒性？哪些肿瘤 对化疗给药时间最敏感？我们能为您量身定制建议吗？ 个别患者？ 药物靶点、转运蛋白和代谢酶丰度的时间变化， 在肿瘤和正常组织中，是药物活性昼夜变化的基础。直到最近 几乎我们所知道的关于组织特异性昼夜节律的所有信息都来自于正常小鼠。没有 人类数据，一个机械的，假设驱动的过渡到医疗实践一直很缓慢。 最近，我们开发了CYCLOPS（周期性结构的循环排序）机器学习算法， 使用现有的无序活检发现人类转录振荡的算法 样品我们使用CYCLOPS来探索人类肺和肝的昼夜节律， 肝细胞癌的节律紊乱，并预测药物治疗的昼夜变化。 有效性 该提案将大大扩展这项工作，并加速其向临床肿瘤学的转化。 利用公开数据，我们将描述一系列正常人体组织中的分子节律 从而确定一天中这些组织对特定药物毒性最不敏感的时间。我们 还将描述选定肿瘤的节律，识别昼夜节律时间和细胞周期阶段 当癌症与周围组织最不同时， 治疗。 我们将探讨特定突变和肿瘤标记物对所观察到的节律的影响 在病人身上。将这些数据映射到药物基因组学数据库， 预测受昼夜节律时间影响最大的药物和副作用。 最后，使用实验小鼠数据和我们的初步人类结果，我们有 列出了一些最有前途的时间疗法候选者。做大做 在研究过程中完善这份清单，在既定的标准中测试其中的几个预测。 动物模型，探索癌症时间疗法的前景和实用原则。 这些目标将有助于促进时间治疗向临床肿瘤学的转化 并帮助描述时间在精确癌症治疗中的作用。