Multiscale Analysis of CGH Arrays from Breast Cancer Patients Using Computational

使用计算对乳腺癌患者的 CGH 阵列进行多尺度分析

• 批准号: 8104025
• 负责人: Francisco Javier Arsuaga
• 金额: $ 12.32万
• 依托单位: SAN FRANCISCO STATE UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8104025
• 关键词: 11q; African American; Algorithms; American Cancer Society; Biology; California; Cancer Center; Cancer Etiology; Cancer Patient; Cessation of life; Characteristics; Chromosome Arm; Chromosomes; Chromosomes, Human, Pair 11; Chromosomes, Human, Pair 8; Classification; Clinical; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Detection; Development; Diagnosis; Disease; Distal; Genes; Genome; Genomics; Goals; Hybridization Array; Individual; Internet; Knowledge; Lead; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Mentors; Methods; Minority Groups; Noise; Numerical value; Patients; Phenotype; Physiology; Population; Principal Investigator; Publishing; Recommendation; Recurrence; Research; Research Project Grants; Resolution; Sampling; San Francisco; Stratification; Subgroup; Survival Rate; Techniques; Technology; The University of California San Francisco Cancer Center; Underrepresented Minority; Universities; Woman; abstracting; base; cancer diagnosis; cancer recurrence; cohort; comparative genomic hybridization; design; effective therapy; ethnic minority population; improved; malignant breast neoplasm; meetings; novel; novel strategies; outcome forecast; professor; software development; treatment planning; treatment response; tumor; tumor progression

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.

合作研究项目#3 乳腺癌患者CGH序列的多尺度分析 使用抽象代数拓扑 Javier Arsuaga，主要研究者 旧金山弗朗西斯科州立大学 Mariel Vazquez，主要研究者 旧金山弗朗西斯科州立大学 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 乳腺癌是全球女性最常见的癌症， 癌症死亡率仅次于肺癌美国癌症协会估计， 仅在美国，2015年将有近20万例新的乳腺癌病例被诊断出来。 2008.为了提高这种疾病的存活率，需要新的治疗方法。 有助于更有效地将患者分层为不同临床 亚组可能受益于不同的治疗方案。为了满足这一需求，博士。 Arsuaga和Vazquez提出了一种分析DMA拷贝的计算方法 数字变化。这些变化，这是已知的，与进展， 癌症，可以通过阵列比较基因组杂交（CGH）检测; 然而，应用这项技术预测复发仍然是一个问题。 由于所涉及的数据的复杂性，这是一个挑战。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， 分析多个分辨率水平的CGH曲线，以预测乳腺癌的复发 癌PI将首先确定包含基因的染色体区域 与乳腺癌复发相关，然后建立整体的贡献， CGH曲线的特征与复发的关系。这项研究将包括检查 不仅是独立的染色体间区域， 和远端染色体内区域。为该项目开发的软件将由 可通过网络公开获取。私人侦探将通过指导得到支持， 与弗朗西斯科加州大学丹尼尔平克尔教授的关系 （UCSF），CGH阵列开发和分析以及乳腺癌专家。