Data Science Core

项目摘要/摘要。 BAATAAR-UP NCI ARTNet U54 应用程序的目标是表征和 治疗上抵消针对突变体的分子靶向治疗的获得性耐药机制 通过描绘肿瘤-肿瘤微环境来了解非小细胞肺癌 (NSCLC) 中的 EGFR 和 KRAS （TME）生态系统及其在治疗过程中的可塑性。为了实现这一目标，来自注释的多组学数据 将生成临床标本和几个免费模型系统。生物信息学、计算 生物学和生物统计学在这个 ARTNet 研究中心发挥着重要作用。数据的主要目标 Science Core是建立和管理集中式多组学数据库并提供全套生物信息学、 计算和统计支持并整合所有 3 个项目。这将包括基础科学和转化科学 在临床活检、PDX、PDO 和细胞系模型等系统中，以及转录组学、空间、 基因组学、蛋白质组学和功能生物学研究。我们将通过提供计算和 统计支持以及应用和开发最佳生物信息学和统计算法、工具和 管道。该核心由来自生物信息学和计算机科学领域的专家教师和计算科学家组成。 MD 安德森大学的计算生物学和生物统计学系以及生物工程和 加州大学旧金山分校治疗科学系。该项目的 PI 和联合研究员之前曾工作过 与数据科学核心的研究人员在其他项目和拨款申请中密切协同。的 数据科学核心将与项目 1-3 和管理核心密切合作来管理和分析 利用MD安德森大学和加州大学旧金山分校现有的、强大的IT结构的数据资源。数据 Science Core 为“组学”和功能生物学数据处理和构建了各种管道和算法 分析。核心将把这些管道和算法应用于生成的所有类型的数据。核心将利用 标准设计原理、生物信息、计算和统计算法，并将开发新的 分析这些项目中收集的所有数据所需的方法，包括空间转录组学、细胞间组学 相互作用分析以及 CRISPR 和蛋白质组分析。将使用参数和非参数方法 用于参数估计和假设检验。线性模型和广义加性模型将用于 找到适合复杂数据结构的最佳模型。核心将通过以下方式促进跨项目的假设检验 使用各种算法（包括基于贝叶斯网络的算法）集成来自多个实验室的数据集 癌症基因组网络模型和模块化分析（MAGNETIC）。所有数据分析将 使用 R 和 Bioconductor 包执行。核心将记录所有分析并生成 HTML 或 PDF 报告（使用 R 包：Sweave、knitR、markdown）用于记录和再现性并促进 内部以及与外部 ARTNet 和科学界共享数据。通过其能力， 数据科学核心充当中央枢纽，确保 BAATAAR-UP 和 ARTNet 的成功和集成。

Project Summary/Abstract. The goal of this BAATAAR-UP NCI ARTNet U54 application is to characterize and therapeutically counteract mechanisms of acquired resistance to molecularly-targeted therapies against mutant EGFR and KRAS in non-small cell lung cancer (NSCLC) by delineating the tumor-tumor microenvironment (TME) ecosystem and its plasticity during treatment. To achieve this goal, multi-omics data from annotated clinical specimens and several complimentary model systems will be generated. Bioinformatics, computational biology and biostatistics play an important role in this ARTNet Research Center. The major objective of the Data Science Core is to build and manage centralized multi-omics database and provide a full set of bioinformatical, computational and statistical support and integrate all 3 Project. This will include basic and translational science in systems such as clinical biopsies, PDX, PDO and cell line models, and integration of transcriptomics, spatial, genomics, proteomics and functional biology studies. We will contribute by providing computational and statistical support and applying and developing optimal bioinformatic, and statistical algorithms, tools and pipelines. The Core is staffed by expert faculty and computational scientists from the Bioinformatics and Computational Biology and Biostatistics Departments at MD Anderson and from the Bioengineering and Therapeutic Sciences Department at UCSF. This program’s PI and Co-investigators have previously worked closely and synergistically with Data Science Core’s investigators in other projects and grant applications. The Data Science Core will work closely with Projects 1–3 and the Administrative Core to manage and analyze the data resources utilizing the existing, robust IT structure in place at MD Anderson and UCSF. The Data Science Core has built various pipelines and algorithms for “-omics” and functional biology data processing and analyses. The Core will apply these pipelines and algorithms to all types of data generated. The Core will utilize standard design principles, bioinformatical, computational and statistical algorithms, and will develop new methods as needed to analyze all data collected in these projects, including spatial transcriptomics, cell-cell interaction analysis, and CRISPR- and proteomic profiling. Parametric and nonparametric methods will be used for parameter estimation and hypothesis testing. Linear models and generalized additive models will be used to find the best models to fit complex data structures. The Core will facilitate hypothesis testing across projects by integrating datasets from multiple laboratories using various algorithms, including Bayesian network-based models and Modular Analysis of Genomic NET works In Cancer (MAGNETIC). All data analyses will be performed using R and Bioconductor packages. The Core will document all analyses and produce HTML or PDF reports (using R packages: Sweave, knitR, markdown) for documentation and reproducibility and to facilitate data sharing internally and with the external ARTNet and scientific communities. Through its capabilities, the Data Science Core serves as a central hub to ensure success and integration across BAATAAR-UP and ARTNet.