Multiscale Analysis of CGH Arrays from Breast Cancer Patients Using Computational

合作研究项目#3 乳腺癌患者CGH阵列的多尺度分析 使用计算代数拓扑 哈维尔·阿苏阿加，首席调查员 旧金山州立大学 Mariel Vazquez，首席调查员 旧金山州立大学 丹尼尔·平克尔，导师/合作者 加州大学旧金山分校癌症中心 摘要 乳腺癌是全世界女性中最常见的癌症，是导致癌症的主要原因之一。 在癌症死亡人数中，仅次于肺癌。美国癌症协会估计 仅在美国就将有近20万新的乳腺癌病例被诊断出来 2008年。为了提高这种疾病的存活率，需要新的 促进更有效地将患者分层到不同临床的方法 可能从不同的治疗方案中受益的亚群。为了满足这一需求，戴维斯博士。 Arsuaga和Vazquez建议开发一种分析DMA拷贝的计算方法 号码会变。这些已知的变化与 癌症，可通过阵列比较基因组杂交(CGH)检测； 然而，这项技术在预测复发方面的应用仍然是一个 由于所涉及数据的复杂性，这是一项挑战。拟议项目的目标是 发展一种新的计算方法，基于计算代数同调，以 分析不同分辨率的计算全息图以预测乳房复发 癌症。PIS将首先确定包含基因的染色体区域 然后建立与乳腺癌复发相关的总体贡献 计算全息曲线的特征对复发的影响。这项研究将包括考试 不仅是独立的染色体间区域的关系，而且还包括近端染色体之间的关系 和远端染色体内区域。为该项目开发的软件将 通过网络向公众开放。PIS将通过指导得到支持 与加州大学旧金山分校丹尼尔·平克尔教授的关系 (加州大学旧金山分校)，CGH阵列开发和分析以及乳腺癌方面的专家。

COLLABORATIVE RESEARCH PROJECT #3 MULTISCALE ANALYSIS OF CGH ARRAYS FROM BREAST CANCER PATIENTS USING COMPUTATIONAL ALGEBRAIC TOPOLOGY Javier Arsuaga, Principal Investigator San Francisco State University Mariel Vazquez, Principal Investigator San Francisco State University Daniel Pinkel, Mentor/Collaborator UCSF Cancer Center Abstract Breast cancer, the most common cancer in women worldwide, is one of the leading causes of cancer deaths, second only to lung cancer. The American Cancer Society has estimated that in the US alone almost 200,000 new cases of breast cancer will be diagnosed during 2008. In order to improve the survival rates from this disease, there is a need for new approaches to facilitate a more effective stratification of patients into distinct clinical subgroups likely to benefit from different treatment regimes. To meet this need, Drs. Arsuaga and Vazquez propose to develop a computational method for analyzing DMA copy number changes. These changes, which are known to be associated with the progression of cancer, can be detected through array Comparative Genomic Hybridization (CGH); however, the application of this technology for the prediction of recurrence remains a challenge due to the complexity of the data involved. The goal of the proposed project is to develop a novel computational method, based on computational algebraic homology, to analyze CGH profiles at multiple levels of resolution in order to predict recurrence of breast cancer. The Pis will first determine the chromosome regions which contain genes associated with breast cancer recurrence, and then establish the contribution of the overall characteristics of the CGH profile to the recurrence. The study will involve the examination not just of independent interchromosomal regions but also of relationships between proximal and distal intrachromosomal regions. Software developed for the project will be made publicly available through the web. The Pis will be supported through a mentoring relationship with Professor Daniel Pinkel from the University of California, San Francisco (UCSF), an expert on CGH array development and analysis as well as on breast cancer.