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: 9906199
• 负责人: Ron C. Anafi
• 金额: $ 59.08万
• 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/9906199
• 关键词: Adverse drug effect; 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 Targeting; Drug Transport; Drug toxicity; ERBB2 gene; Effectiveness; Enzymes; Esophagus; Estrogen receptor positive; 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; 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; transcriptome; 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 活动可能会随着时间的不同而变化。但是我们的病人应该在什么时候服药呢？必须 我们测试了每一种调节昼夜节律的新药物的有效性和毒性？哪些肿瘤是 对化疗用药时间最敏感？我们是否可以针对以下情况定制我们的建议 个别病人？ 药物靶标、转运体和代谢酶丰度的时间变化， 在肿瘤和正常组织中，药物活性的昼夜变化是其基础。直到最近 我们所知道的几乎所有关于组织特定昼夜节律的信息都来自正常小鼠。如果没有 人类数据，一种机械性的、假设驱动的向医疗实践的过渡一直很缓慢。 最近，我们开发了一种机器学习方法--Cyclops(Cycle Order By Perioic Structure) 利用现有的无序活检发现人类转录振荡的算法 样本。我们使用独眼巨人来探索人类肺和肝脏的昼夜节律，确定 肝细胞癌的节律紊乱，并预测药物的昼夜变化 有效性。 这项提议将极大地扩展这项工作，并加快其向临床肿瘤学的转变。 利用公开数据，我们将描述一组正常人体组织中的分子节律 因此，这些组织对特定药物毒性最不敏感的时间。我们 还将描述特定肿瘤的节律，确定昼夜节律和细胞周期时相 当癌症与周围组织最不同，因此对各种 治疗。 我们将探索特定突变和肿瘤标志物对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中，我们可以使其可测试 对受昼夜节律影响最大的药物和副作用的预测。 最后，使用实验老鼠的数据和我们的初步人类结果，我们得到了 汇编了一些最有希望的时间疗法候选人的名单。我们将扩大和 在研究过程中细化这个列表，测试其中几个预测 动物模型，并探索癌症计时疗法的前景和实用原则。 综合起来，这些目标将有助于催化时间疗法向临床肿瘤学的转化 并帮助勾勒出时间在精确癌症治疗中的作用。