Project 1: Dynamic Genomic and Microenvironmental Models of Acquired Chemoresistance

项目1：获得性化疗耐药的动态基因组和微环境模型

• 批准号: 10207529
• 负责人: ANDREA Hope BILD
• 金额: $ 56.52万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207529
• 关键词: Affect; Aftercare; Algorithms; Biological Models; Biology; Breast Cancer Cell; Cancer Patient; Cancer Relapse; Cell Communication; Cells; Chemoresistance; Clinical; Clinical Research; Clinical Trials; Collection; Complement; Computer Models; Cues; DNA Sequence Alteration; DNA sequencing; Data; Dependence; Development; Disease; Drug Combinations; Drug resistance; Environmental Risk Factor; Equilibrium; Evolution; Exhibits; Genomics; Genotype; Glucose; Heterogeneity; Immune; Individual; KDM1A gene; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Modeling; Nature; Neoplasm Metastasis; Normal Cell; Oncogenic; Outcome; Outcomes Research; Pathway interactions; Patient Monitoring; Patients; Pharmaceutical Preparations; Phenotype; Population; Population Dynamics; Procedures; Refractory; Research; Resistance; Resistance development; Sampling; Signal Transduction; Structure; Testing; Time; advanced breast cancer; base; cancer cell; chemotherapeutic agent; chemotherapy; combat; deep sequencing; dynamic system; genomic aberrations; in vivo; inhibitor/antagonist; malignant breast neoplasm; neoplastic cell; novel; predictive modeling; pressure; refractory cancer; response; single-cell RNA sequencing; standard of care; tool; transcriptome sequencing; treatment choice; tumor; tumor heterogeneity; tumor progression

ABSTRACT Breast and ovarian cancers are heterogeneous diseases, as a typical tumor contains multiple “subclones”, which are defined as evolutionarily related subpopulations of cells with a different complement of somatically acquired DNA mutations and phenotypes. When chemotherapeutic agents are administered to the patient, some of these subclones may gain a selective advantage and develop resistance to the treatment, resulting in cancer relapse and progression. For this reason, it is imperative to identify these subclones and their evolution across treatment; and to understand how the genomic aberrations within these subclones drive resistance to chemotherapy. We will integrate experimental biology and computational models across temporal samples of patient tumors as they develop a resistant state in order to better understand and combat refractory and terminal cancer. To enable the study of tumor heterogeneity evolution in patients, we will utilize a highly unique collection of metastatic tumor cells from breast and ovarian cancer patients before, during, and after treatments, often across multiple courses of chemotherapy, as well as tumors from a clinical trial taken before and after therapy. We use deep sequencing to find genomic aberrations at each of these time points, and develop systems models to identify the subclones and follow phenotypic changes and their functional impacts of subclone evolution in response to chemotherapy. We hypothesize that 1) Dynamical systems models based on the evolution of subclone structure and acquisition of oncogenic phenotypes during treatment can identify key factors in the development of a chemo-resistant state; and 2) We can delay development of a chemo- resistant cancer state by inhibiting development of phenotypes that emerge over time commonly during treatment. We will model resistant cancer cell populations and both extrinsic and immune microenvironmental factors to identify critical features of acquired resistance and apply these models to a clinical trial aimed at blocking transition to a resistant cancer state. While these components can exhibit co-dependencies, by their nature they can also have vulnerabilities based on these interactive features, and if one can inhibit dependent relationships within a population it may be possible to shift the equilibrium of a tumor from a chemoresistant state to a sensitive state. The algorithms and procedures we are developing in this proposal will for a rational basis for real-time patient monitoring and making treatment choices for refractory patients. The outcomes of this research will deliver approaches to block or reverse the transition to a resistant state for advanced stage breast and ovarian cancer patients.

摘要 乳腺癌和卵巢癌是异质性疾病，因为典型的肿瘤包含多个“亚克隆”， 其被定义为具有不同的体细胞互补性的进化相关的细胞亚群， 获得性DNA突变和表型。当对患者施用化疗剂时， 其中一些亚克隆可能获得选择优势并对治疗产生抗性，导致 癌症复发和进展。因此，必须确定这些亚克隆及其进化 并了解这些亚克隆中的基因组畸变如何驱动对 化疗我们将整合实验生物学和计算模型，跨越时间样本， 患者肿瘤，因为他们发展的耐药状态，以更好地了解和打击难治性， 晚期癌症为了能够研究患者的肿瘤异质性演变，我们将利用高度独特的 收集乳腺癌和卵巢癌患者的转移性肿瘤细胞， 治疗，通常跨越多个化疗疗程，以及之前进行的临床试验中的肿瘤 在治疗之后。我们使用深度测序来发现这些时间点的基因组畸变， 开发系统模型，以识别亚克隆，并跟踪表型变化及其功能影响 对化疗反应的亚克隆进化。我们假设：1）动态系统模型基于 亚克隆结构的进化和治疗过程中致癌表型的获得可以识别 关键因素在发展的化学耐药状态;和2）我们可以延迟发展的化学- 通过抑制表型的发展来抵抗癌症状态，这些表型通常在 治疗我们将建立耐药癌细胞群的模型， 确定获得性耐药的关键特征，并将这些模型应用于临床试验， 阻止向耐药癌症状态的转变。虽然这些组件可以表现出相互依赖性，但它们的 自然，它们也可以具有基于这些交互特征的脆弱性，并且如果能够抑制依赖性， 通过改变群体内的关系，可能将肿瘤的平衡从化疗耐药的肿瘤转移到化疗耐药的肿瘤。 一个敏感的国家。我们在本提案中开发的算法和程序将用于合理的 实时患者监测和为难治性患者做出治疗选择的基础。的成果 这项研究将提供方法来阻止或逆转晚期患者向耐药状态的转变， 乳腺癌和卵巢癌患者。