Project 1: Dynamic Genomic and Microenvironmental Models of Acquired Chemoresistance

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， 它们被定义为在进化上相关的细胞亚群，在身体上具有不同的组成 获得性DNA突变和表型。当给病人使用化疗药物时， 这些亚克隆中的一些可能获得选择优势并对治疗产生抗药性，导致 癌症复发和进展。因此，确定这些亚克隆及其进化是当务之急 跨处理；并了解这些亚克隆内的基因组异常如何驱动对 化疗。我们将跨时间样本整合实验生物学和计算模型 患者肿瘤发展为耐药状态，以便更好地了解和对抗难治性和 晚期癌症。为了能够研究患者的肿瘤异质性进化，我们将使用高度独特的 乳腺癌和卵巢癌患者术前、术中、术后转移瘤细胞的采集 治疗，通常跨越多个疗程的化疗，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每个时间点的基因组异常，并且 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 亚克隆进化对化疗的反应。我们假设1)动力系统模型基于 在治疗过程中亚克隆结构的演变和致癌表型的获得可以识别 化疗耐药状态发展的关键因素；以及2)我们可以推迟化疗药物的发展。 通过抑制通常随着时间推移而出现的表型的发展来抵抗癌症状态 治疗。我们将对耐药癌细胞群体以及外部和免疫微环境进行建模 确定获得性耐药关键特征的因素，并将这些模型应用于旨在 阻碍了向抗药性癌症状态的转变。虽然这些组件可以表现出相互依赖关系，但通过它们的 自然，他们也可以有基于这些交互功能的漏洞，如果一个人可以抑制依赖 人群内的关系可能会改变肿瘤的平衡，而不是耐药 状态转换为敏感状态。我们在这份提案中开发的算法和过程将是一种理性的 为患者的实时监测和难治性患者的治疗选择提供依据。其结果是 这项研究将提供阻止或逆转晚期抵抗状态转变的方法 乳腺癌和卵巢癌患者。

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.