Deep exploration of drivers, evolution, and microenvironment toward discovering principal themes in cancer

深入探索驱动因素、进化和微环境，以发现癌症的主要主题

• 批准号: 10301100
• 负责人: Li Ding
• 金额: $ 41.09万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10301100
• 关键词: ATAC-seq; Advanced Malignant Neoplasm; Area; Award; Back; Bioinformatics; Biological; Cancer Center; Cancer Patient; Cell Lineage; Cells; Cellular Assay; Clinical; Code; Collection; Competence; Complement; DNA Sequence Alteration; Data; Data Analyses; Detection; Diagnosis; Emerging Technologies; Epigenetic Process; Evolution; Genes; Genetic Transcription; Genomics; Germ-Line Mutation; Goals; Heterogeneity; Histology; Human; Individual; Inherited; Letters; Link; Malignant Neoplasms; Medicine; Methods; Methylation; MicroRNAs; Molecular; Mutation; Pathogenicity; Pathway interactions; Pattern; Penetrance; Population; Predisposition; Production; Quality Control; RNA Splicing; Sequence Analysis; Somatic Mutation; System; Technology; Traction; Trees; Twin Multiple Birth; Untranslated RNA; Variant; Work; XCL1 gene; anticancer research; base; bioinformatics tool; cancer genomics; cancer type; cell type; cohort; combinatorial; data analysis pipeline; data exchange; deep learning; digital imaging; disorder subtype; exome sequencing; genome sequencing; genomic data; improved; insight; neoplastic cell; new technology; programs; single-cell RNA sequencing; tool; transcriptome; transcriptome sequencing; transcriptomics; treatment strategy; tumor; tumor microenvironment; whole genome

Summary/Abstract Tremendous progress on cancer has been made at the molecular level over the past decade, largely due to the broad application of high throughput, large-scale bulk whole genome, exome and RNA sequencing. In particular, the discovery of numerous medium to high-penetrance drivers, characterization of pathogenic germline variants, and the revelation of many-to-many relationships of genes and pathways, have brought a fuller view of the combinatorial complexity of cancer. Indeed, newer technologies, like single-cell and spatial genomics methods, are now augmenting bulk sequence data to power deeper studies of cancer dynamics, such as heterogeneity, evolution, and interaction with the microenvironment. The current view is that such advanced data, augmented by improved bioinformatics analysis tools and larger, well-curated cohorts will enable medicine to push beyond statistical descriptions toward a genuine deterministic understanding of cancer. Toward this goal, our proposal seeks to extend and apply established bioinformatics systems to integrate the above technologies and leverage our broad range of capabilities and to support the NCI Genomic Characterization Network (NCI-GCN) and Center for Cancer Genomics (CCG) via three specific aims: (1) annotating and interpreting coding and non-coding somatic and germline alterations, (2) characterizing tumor cell populations, evolution, and the tumor microenvironment, and (3) unlocking biological and clinical insights at both the individual and cross-cancer (Pan-Cancer) levels to discern basic themes across the major human cancers. Our approach involves fluencies in four areas of core competence outlined in the program RFA: DNA mutations, long-read sequence analysis, scRNA-Seq analysis, and spatial genomics data analysis (with connection to digital imaging analysis).

摘要/摘要 在过去的十年里，癌症在分子水平上取得了巨大的进展，这主要是由于 广泛应用于高通量、大规模批量全基因组、外显子组和RNA测序。在 特别是，发现了许多中到高浓度的驱动因子，表征了致病性 生殖系变异，以及基因和途径的多对多关系的揭示，带来了一个新的挑战。 更全面地了解癌症的组合复杂性。事实上，更新的技术，如单细胞和空间 基因组学方法，现在正在增加大量序列数据，以推动癌症动力学的更深入研究， 例如异质性、进化和与微环境的相互作用。目前的观点是， 先进的数据，通过改进的生物信息学分析工具和更大的，精心策划的队列来增强， 使医学能够超越统计描述，走向真正的确定性理解， 癌为了实现这一目标，我们的建议旨在扩展和应用已建立的生物信息学系统， 整合上述技术，利用我们广泛的能力，并支持NCI基因组 表征网络（NCI-GCN）和癌症基因组学中心（CCG）通过三个具体目标：（1） 注释和解释编码和非编码体细胞和种系改变，（2）表征肿瘤 细胞群、进化和肿瘤微环境，以及（3）解锁生物学和临床见解， 个人和跨癌症（泛癌症）水平，以辨别主要人类疾病的基本主题， 癌的我们的方法包括在RFA计划中概述的四个核心竞争力领域的流畅性：DNA 突变，长读序列分析，scRNA-Seq分析和空间基因组学数据分析（与 与数字成像分析的连接）。