EVOLUTIONARY DYNAMICS OF BARRETT'S ESOPHAGUS NEOPLASIA

描述(申请人的描述)：肿瘤的细胞谱系 组织在突变和克隆扩张的双重动力下进化。 这既是它的毒性的基础，也是我们治疗它的困难的基础。 我们假设在进化过程中观察到的突变子集 癌症赋予细胞有益的选择性作用。此外，我们 假设和中这些克隆种群之间的相互作用 肿瘤组织的周围决定了癌症的进展。的目的是 这个项目是为了识别这些选择性突变并推断 巴雷特食道中突变克隆之间的相互作用最终 导致食管腺癌的发生发展。这一分析将是 基于杂合性数据的丢失，启动子甲基化，单核苷酸 多态和同源亚群的基因表达数据 从Barrett‘s食道患者的肿瘤组织中提取的细胞。 组织样本来自西雅图选定的患者的活组织检查。 Barrett‘s食道计划285例。将使用数据挖掘来识别 与克隆性扩张相关的突变以及抑制 相邻的克隆人。遗传事件的顺序在向 食管腺癌将通过系统发育重建确定 每个患者肿瘤组织中的细胞谱系。机器学习 将使用EM算法等技术来推断丢失的数据 以及未抽样突变的影响。将使用计算建模来 生成空假设的比较数据以及生成 根据我们对癌症进展的理解进行的实验预测。 这是我为医学做出贡献的长期职业目标的第一步 通过使用计算和理论方法。在弗雷德酒店工作 哈钦森癌症研究中心将促进从我的 计算机科学和进化论背景，以独立的 基于细胞和分子动力学分析的研究方案 癌症。这个项目的挑战是不同分子的整合 和流行病学数据，以便连贯和详细地了解 在模型系统的肿瘤中进展为癌症。

DESCRIPTION (Applicant's Description): The cell lineages in neoplastic tissues are evolving under the twin dynamics of mutation and clonal expansion. This is the basis for both its virulence and our difficulties in treating it. We hypothesize that a subset of the mutations observed in the progression to cancer confer beneficial selective effects on the cell. Furthermore, we hypothesize that the interactions between these clonal populations in and around the neoplastic tissue determine the progression to cancer. The aim of this project is to identify these selective mutations and to infer the interactions between the mutant clones in Barrett's Esophagus that eventually lead to the development of esophageal adenocarcinoma. This analysis will be based on loss of heterozygosity data, promoter methylation, single nucleotide polymorphisms, and gene expression data for homogeneous subpopulations of cells sampled from neoplastic tissue in patients with Barrett's Esophagus. The tissue samples come from biopsies of selected patients in the Seattle Barrett's Esophagus Project (N=285). Data mining will be used to identify mutations that are associated with clonal expansion as well as inhibition of neighboring clones. The order of genetic events in the progression to esophageal adenocarcinoma will be determined by phylogenetic reconstruction of the cell lineages in the neoplastic tissue of each patient. Machine learning techniques, such as the EM algorithm, will be employed to infer missing data and the effects of unsampled mutations. Computational modeling will be used to generate comparison data for null hypotheses as well as to generate experimental predictions from our understanding of the progression to cancer. This is the first step in my long-term career goal to contribute to medicine through the use of computational and theoretical methods. Working at the Fred Hutchinson Cancer Research Center will facilitate the transition from my background in computer science and evolutionary theory, to an independent research program based on the analysis of cellular and molecular dynamics of cancer. The challenge of this project is the integration of diverse molecular and epidemiological data into a coherent and detailed understanding of the progression to cancer in the neoplasm of a model system.