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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的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”，这些定义为细胞的进化相关亚群，并以不同的方式完成获得的DNA突变和表型。当对患者施用化学治疗剂时，这些亚克隆中的一些可能会获得对治疗的选择性优势和发展性的抗性，从而癌症缓解和进展。因此，必须识别这些亚克隆及其进化在治疗中；并了解这些亚克隆中的基因组畸变如何驱动抗性化学疗法。我们将整合跨临时样本的实验生物学和计算模型患者肿瘤在产生抗性状态时，以更好地理解和打击难治性和晚期癌。为了研究患者肿瘤异质性演变，我们将利用高度独特的从乳腺癌和卵巢癌患者中收集转移性肿瘤细胞治疗通常在化学疗法的多种课程中，以及以前进行的临床试验的肿瘤并在治疗后。我们使用深度测序在每个时间点找到基因组畸变，并且开发系统模型以识别亚克隆并遵循表型变化及其功能影响响应化学疗法的亚克隆进化。我们假设1）基于动态系统模型关于亚克隆结构的演变和治疗过程中致癌表型的获取化学抗性状态发展的关键因素； 2）我们可以延迟化学的发展通过抑制通常在时间上出现的表型的发展，通常在治疗。我们将建模抗性癌细胞群体以及外部和免疫微环境确定获得抵抗的关键特征并将这些模型应用于针对的临床试验的因素阻止过渡到抗性癌状态。尽管这些组件可以通过它们的共同依赖性存在他们也可能基于这些互动特征具有脆弱性，并且如果可以抑制依赖性人群中的关系可能有可能使肿瘤的等效物从化学抗性中移动状态达到敏感状态。我们在本提案中制定的算法和程序将对合理实时患者监测并为难治性患者做出治疗选择的基础。结果这项研究将提供阻止或扭转过渡到高级阶段的抵抗状态的方法乳腺癌和卵巢癌患者。