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: 8294466
• 负责人: Francisco Javier Arsuaga
• 金额: $ 12.53万
• 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/8294466
• 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; Underrepresented Minority; Universities; Woman; abstracting; base; cancer diagnosis; cancer recurrence; cohort; comparative genomic hybridization; design; effective therapy; ethnic minority population; health disparity; 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.

协作研究项目＃3 来自乳腺癌患者CGH阵列的多尺度分析 使用计算代数拓扑 首席研究员哈维尔·阿苏加（Javier Arsuaga） 旧金山州立大学 首席研究员Mariel Vazquez 旧金山州立大学 丹尼尔·平克尔（Daniel Pinkel），导师/合作者 UCSF癌症中心 抽象的 乳腺癌是全球女性最常见的癌症，是主要原因之一 癌症死亡，仅次于肺癌。美国癌症协会估计 仅在美国，将在美国将近200,000例新的乳腺癌病例 2008年。为了提高这种疾病的生存率，需要新的 促进患者更有效分层为不同临床的方法 亚组可能会从不同的治疗方案中受益。为了满足这种需求，博士。 Arsuaga和Vazquez提议开发一种用于分析DMA副本的计算方法 数字更改。这些变化，已知与 可以通过阵列比较基因组杂交（CGH）检测癌症； 但是，该技术用于预测复发仍然是一个 由于涉及的数据的复杂性，挑战。拟议项目的目标是 开发一种基于计算代数同源性的新型计算方法 分析以多个分辨率的CGH分布以预测乳房的复发 癌症。 PI将首先确定包含基因的染色体区域 与乳腺癌复发有关，然后确定整体的贡献 CGH轮廓的特征与复发。该研究将涉及检查 不仅是独立的染色体区域，而且是近端之间的关系 和远端内粒体区域。将为该项目开发的软件 通过网络公开获得。 PI将通过指导来支持 与加利福尼亚大学旧金山大学的丹尼尔·平克尔教授的关系 （UCSF），CGH阵列开发和分析以及乳腺癌的专家。