Monitoring tumor subclonal heterogeneity over time and space

项目摘要 DNA测序和新的计算方法已经产生了详细的克隆变异图， 人类癌症虽然随着时间的推移和在处理的选择压力下克隆结构的变化 虽然在血液恶性肿瘤中进行了广泛的研究，但由于实体癌的存在， 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变异和个体肿瘤沉积物内的多克隆异质性，然而我们的 了解乳腺癌和卵巢癌中克隆变化的动态及其在治疗中的作用 反应和耐药性的出现还处于初期阶段。 通过将突变检测和基因组分析方面的专业知识与获得独特的患者信息相结合， 该提案将开发急需的方法来识别肿瘤中的所有基因组变化， 以解决肿瘤的克隆亚结构，因为它随着时间或空间的演变，以应对治疗。我们将 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 在初始化疗之前、期间和完成之后对患者进行活检;以及2）来自 转移性乳腺癌和卵巢癌患者在接受多药治疗期间的多个时间点 化疗疗程（乳腺和卵巢）和尸检时（卵巢）。 具体目标是：（1）开发和应用综合突变检测技术， 在化疗过程中，或在化疗期间，随着时间的推移在患者肿瘤中发展的遗传病变， 多处明显的转移病灶使用这些工具，我们将测量突变的细胞流行率， 在来自乳腺和卵巢患者队列的多个活组织检查中。(2)全面优先 根据它们驱动肿瘤演变的可能性来确定突变。我们将使用这些方法来确定优先顺序 结果突变，并深入了解潜在的机制克隆进化。（三） 描述肿瘤亚克隆结构及其在纵向肿瘤活检和多个 转移性病变通过估计每次活检中所有形式突变的细胞患病率， 创新将使我们能够更好地了解肿瘤亚克隆群体如何随时间和空间而演变 逃避化疗的反应(4)创建一个基于Web的交互式软件平台， 肿瘤亚克隆结构的分析探索。总之，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 穿越时空

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.