Monitoring tumor subclonal heterogeneity over time and space

项目总结 DNA测序和新的计算方法已经产生了详细的克隆变异图 人类癌症。而克隆结构随着时间的推移和治疗的选择性压力的变化 在血液系统恶性肿瘤中被广泛研究，实体癌的特征较差，因为 相对缺乏合适的肿瘤材料。对乳腺癌和卵巢癌的分析表明 转移部位之间的克隆性变异和单个肿瘤沉积内的多克隆异质性，但我们的 乳腺癌和卵巢癌克隆改变的动态变化及其在治疗中的作用 反应和抵抗的出现还处于初级阶段。 通过将突变检测和基因组分析方面的专业知识与接触独特患者的机会相结合 这项建议将开发出急需的方法来识别肿瘤中的所有基因组变化 当肿瘤因治疗而在时间或空间上进化时，解析其克隆性亚结构。我们会 将我们的工具应用于两个关键的患者队列：1)来自早期新辅助乳腺癌的纵向样本 患者在完成初始化疗之前、期间和之后进行活组织检查；以及2)来自 转移性乳腺癌和卵巢癌患者在多个时间点的应用 化疗疗程(乳房和卵巢)和尸检时(卵巢)。 具体目标是：(1)发展和应用全面的突变检测来鉴定 在化疗过程中或在化疗过程中，随着时间的推移，患者肿瘤中发生的遗传损伤 多个明显的转移灶。使用这些工具，我们将测量突变的细胞流行率 在来自乳房和卵巢患者队列的多个活检中。(二)全面排定优先顺序 突变基于它们驱动肿瘤进化的可能性。我们将使用这些方法来确定优先顺序 由此产生的突变，并深入了解克隆进化的潜在机制。(3) 勾画肿瘤亚克隆结构及其在纵向和多次肿瘤活检中的演变 转移的病变。通过估计每次活检中所有形式突变的细胞流行率，这些 创新将使人们能够更好地了解肿瘤亚克隆群体如何在时间和空间上进化 并逃避化疗的反应。(4)创建基于网络的交互式软件平台 肿瘤亚克隆结构的分析探索。总而言之，拟议的研究将设计和应用 新的算法将提高我们对乳腺癌和卵巢癌演变动态的理解 在时间和空间上。

PROJECT SUMMARY DNA sequencing and new computational approaches have yielded detailed maps of clonal variation in human cancer. While changes in clonal structure over time and under the selective pressure of treatment have been extensively studied in hematologic malignancies, solid cancers are less well characterized owing to the relative lack of suitable tumor material. Analyses of breast and ovarian cancer have demonstrated substantial clonal variation between metastatic sites and polyclonal heterogeneity within individual tumor deposits, yet our understanding of the dynamics of clonal change in breast and ovarian cancer and its role in therapeutic response and the emergence of resistance is in its infancy. By combining expertise in mutation detection and genomic analysis with access to unique patient cohorts, this proposal will develop critically needed methods to identify all genomic changes in tumors in order to resolve a tumor's clonal substructure as it evolves over time or space in response to treatment. We will apply our tools in two key patient cohorts: 1) longitudinal samples from early stage, neoadjuvant breast cancer patients biopsied before, during, and after the completion of initial chemotherapy; and 2) tumor cells from metastatic breast and ovarian cancer patients at multiple time-points during their treatment with multiple courses of chemotherapy (breast and ovarian) and at time of autopsy (ovarian). The Specific Aims are to: (1) Develop and apply comprehensive mutation detection to identify the genetic lesions that develop in patient tumors over time during the course of chemotherapy, or at multiple distinct metastatic lesions. Using these tools, we will measure the cellular prevalence of mutations among multiple biopsies from both breast and ovarian patient cohorts. (2) Comprehensively prioritize mutations based on the likelihood that they drive tumor evolution. We will use these methods to prioritize consequential mutations and to gain insight into the potential mechanisms underlying clonal evolution. (3) Delineate tumor subclone structure and its evolution across longitudinal tumor biopsies and multiple metastatic lesions. By estimating the cellular prevalence of all forms of mutation in each biopsy, these innovations will enable a better understanding of how tumor subclone populations evolve over time and space and evade response to chemotherapy. (4) Create an interactive, web-based software platform for the analysis exploration of tumor subclone structure. In summary, the proposed research will devise and apply new algorithms that will improve our understanding of the dynamics of breast and ovarian cancer evolution over time and space.