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

摘要/摘要 在过去的十年里，在癌症的分子水平上取得了巨大的进展，这主要是由于 高通量、大规模批量全基因组、外显子组和RNA测序的广泛应用。在……里面 特别是，发现了许多中到高外显性的驱动因素，表征了致病基因 生殖系变异，以及多对多基因和途径关系的揭示，带来了 富勒对癌症的组合复杂性的看法。事实上，更新的技术，如单细胞和空间 基因组学方法，现在正在扩大大量的序列数据，以推动对癌症动力学的更深入研究， 例如异质性、进化以及与微环境的相互作用。目前的观点是这样的 先进的数据，加上改进的生物信息学分析工具和更大的、经过精心挑选的队列，将 使医学能够超越统计描述，向真正的确定性理解 癌症。为了实现这一目标，我们的提案寻求将已建立的生物信息学系统扩展和应用于 集成上述技术并利用我们广泛的能力，支持NCI基因组 特征描述网络(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的体细胞和生殖系改变，(2)表征肿瘤 细胞种群、进化和肿瘤微环境，以及(3)解锁生物学和临床洞察 个人和交叉癌症(泛癌)水平，以识别主要人类的基本主题 癌症。我们的方法涉及计划RFA：DNA中概述的四个核心能力领域的流畅性 突变、长读序列分析、scRNA-Seq分析和空间基因组数据分析(带 连接到数字成像分析)。

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).