A Data-driven Pan-Cancer Study of Biological Bases of Cancer Health Disparities

癌症健康差异的生物学基础的数据驱动的泛癌症研究

• 批准号: 10322702
• 负责人: Kun Zhang
• 金额: $ 18.36万
• 依托单位: XAVIER UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-24 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322702
• 关键词: Address; Algorithms; Bioinformatics; Biological; Biological Markers; Biometry; Breast; Characteristics; Clinical; Clinical Data; Collection; Colon; Common Neoplasm; Complement; Complex; Computer software; DNA Methylation; DNA Sequence Alteration; Data; Databases; Development; Disease; Drug Targeting; Environmental Risk Factor; Epigenetic Process; Ethnic Origin; Gaussian model; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Goals; Heterogeneity; Human; In Vitro; Individual; Information Retrieval; Intelligence; Internet; Investigation; Level of Evidence; Link; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Messenger RNA; Methodology; MicroRNAs; Modeling; Molecular; Molecular Profiling; Multiomic Data; National Institute on Minority Health and Health Disparities; Nature; Pathway interactions; Patients; Population; Preventive; Prostate; Race; Research; Research Personnel; Research Project Grants; Resources; Role; Sample Size; Sampling; Socioeconomic Factors; Somatic Mutation; Source; Specimen; System; Techniques; The Cancer Genome Atlas; Therapeutic; Tissues; United States National Institutes of Health; Validation; Variant; Work; analytical tool; anticancer research; base; bioinformatics tool; cancer cell; cancer health disparity; cancer prevention; cancer therapy; cancer type; cohesion; database of Genotypes and Phenotypes; deep learning; epigenome; experience; follow-up; gene function; genetic variant; genomic data; health disparity; health inequalities; high throughput technology; in silico; in vivo; innovation; insight; kernel methods; laboratory experiment; neoplastic cell; novel; novel strategies; open source; prognostic; racial disparity; socioeconomics; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumor DNA

A Data-driven Pan-cancer Study of Biological Bases of Cancer Health Disparities Project Abstract To date, significant progress has been made in our understanding of the role of socioeconomic factors in cancer racial disparities. Increasing evidence now suggests that a number of intrinsic molecular factors specific to malignant cells must also partly account for the observed health inequalities. Although research has begun to explore the biological basis of cancer disparities, most existing work is limited to several common cancer types and does not methodically explore whether the observed genetic and molecular differences represent the clinically-meaningful racial disparities in other fatal human cancers. Moreover, massive amounts of multi-faceted omics data generated by high-throughput technologies have not been fully utilized and well integrated with clinical data to search for race-specific molecular characteristics, biomarkers or potential drug targets. The goal of this RCMI research project is therefore to address these significant limitations by performing an in-depth, data-driven, pan-cancer study to investigate the cancer-specific mutome, epigenome, and RNA-Seq transcriptome differences in different racial groups. The proposed study will focus on the eight TCGA cancer types, with pertinent cancer data from other sources (E.g. dbGaP, GEO, ICGC, etc.) being systematically utilized for methodology development and/or empirical validation throughout the entire project. For a specific cancer, in connection with clinical data, we will develop new bioinformatics algorithms and pipelines to analyze these multiple types of omics data individually and collectively. As such, we will establish a pan-cancer, race-relevant assemblage of single- and multi- level coherent genes, modules and biological pathways, some of which will hold significance and promise for clinical use. This will provide large-scale direct molecular-level evidence for the biological mechanism underlying racial disparities in cancer, which is practically impossible using the approaches of in vitro, in vivo and/or population follow-up. Furthermore, we will biologically validate the identified signatures for prostate cancer using clinical samples. A database for all pinpointed signatures will be constructed so that cancer disparity researchers can interrogate how various levels of molecular variations may alter gene functions in different cancers and races. A set of efficient and powerful analytical tools for the proposed data-driven analyses of health disparities in cancer will also be made publicly available as open source software. We anticipate that this project will have a large and sustained impact that will enable us 1) to better understand the mechanisms underlying the most-studied disparities and to predict understudied disparities across races for various cancer types; 2) to search for race-specific sets of biomarkers (working through the causal mechanisms) and potential drug targets; and 3) to ultimately contribute to reducing and eventually eliminating health disparities in personalized cancer prevention and treatment.

癌症健康差异生物学基础的数据驱动泛癌症研究 项目摘要 迄今为止，我们在理解社会经济因素的作用方面取得了重大进展 在癌症种族差异。越来越多的证据表明，一些内在的分子因素 恶性细胞特有的疾病也必须部分解释观察到的健康不平等。虽然研究表明， 开始探索癌症差异的生物学基础，大多数现有的工作仅限于几个常见的 癌症类型，并没有系统地探讨是否观察到的遗传和分子差异， 代表了其他致命人类癌症中具有临床意义的种族差异。此外，大量 由高通量技术产生的多方面组学数据尚未得到充分利用， 与临床数据相结合，以搜索种族特异性分子特征、生物标志物或潜在药物 目标的 因此，RCMI研究项目的目标是通过以下方式解决这些重大限制 进行深入的，数据驱动的，泛癌症研究，以调查癌症特异性mutome，表观基因组， 以及不同种族群体中RNA-Seq转录组的差异。拟议的研究将集中在八个 TCGA癌症类型，以及来自其他来源的相关癌症数据（例如dbGaP，GEO，ICGC等）被 在整个项目中系统地用于方法学开发和/或经验验证。 对于特定的癌症，结合临床数据，我们将开发新的生物信息学算法， 管道来单独地和共同地分析这些多种类型的组学数据。因此，我们将建立一个 泛癌症、种族相关的单水平和多水平相干基因、模块和生物学组合 途径，其中一些将具有临床应用的意义和前景。这将提供大规模的直接 分子水平的证据表明，癌症的种族差异背后的生物学机制， 实际上不可能使用体外、体内和/或群体随访的方法。此外，我们将 使用临床样品生物学验证所鉴定的前列腺癌特征。数据库为所有 将构建精确的签名，以便癌症差异研究人员可以询问各种 分子变异的水平可能会改变不同癌症和种族的基因功能。一套有效的， 还将为拟议的癌症健康差异数据驱动分析提供强大的分析工具 作为开源软件公开提供。 我们预计，该项目将产生巨大而持续的影响，使我们能够1）更好地 了解研究最多的差异背后的机制，并预测研究不足的差异 2）寻找种族特异性生物标志物组（通过 病因机制）和潜在的药物靶点;以及3）最终有助于减少和最终 消除个性化癌症预防和治疗中的健康差异。