Biostatistics/Bioinformatics Core

生物统计学和生物信息学核心将支持软组织肉瘤的SPORE研究人员， 他们的研究工作的计算和统计方面，包括临床研究的设计和分析， 试验、实验室实验、分子谱分析和测序，以及综合基因组分析。 在临床试验设计阶段，核心成员将与主要研究者一起进行方案审查。 根据该审查，将提供方案的统计学章节，概述主要科学 研究目的、研究人群、主要和次要终点、实验设计、分析计划， 和一个目标样本量证明在概率方面。在试验结束时，将进行数据分析， 用于评估方案中规定的主要和次要终点的结局。在临床前 研究，核心成员将协助制定实验设计和分析， 在研究结束时对数据进行解释。核心还将协助大规模的分子 人类肉瘤样本的表征，包括分析RNA表达、微小RNA 表达、突变和DNA拷贝数变化，以确定肿瘤特征、药物治疗和预后的预测因子。 repsonse，和生存。核心成员将使用双样本t检验和考克斯比例风险模型， 识别信息特征。将不同类型的分子数据进行整合和途径分析 用于阐明有助于肿瘤发生和肿瘤表型的途径。风险预测 基于这些特征，将使用考克斯比例风险模型开发工具

The Biostatistics & Bioinformatics Core will support investigators of the SPORE in Soft Tissue Sarcoma in the computational and statistical aspects of their research efforts, including the design and analysis of clinical trials, laboratory experiments, molecular profiling, and sequencing, as well as integrated genomic analyses. In the clinical trial design phase, a core member will conduct a protocol review with the principal investigator. Based on this review, a statistical section for the protocol will be provided, outlining major scientific objecfives, population to be studied, primary and secondary endpoints, experimental design, analysis plans, and a targeted sample size justified in probabilisfic terms. At the conclusion of the trial, data analyses will be performed to assess outcomes of the primary and secondary endpoints stated in the protocol. In preclinical studies, core members will assist in the formulation of the experimental design and in the analysis and interpretafion of the data at the conclusion of the study. The core will also assist in the large-scale molecular characterizafion of human sarcoma samples, which involves analyzing RNA expression, microRNA expression, mutafions, and DNA copy number changes to identify predictors of tumor characterisfics, drug repsonse, and survival. Core members will use two-sample t-tests and Cox proportional hazards models to identify informafive features. The different types of molecular data will be integrated and pathway analysis performed to elucidate the pathways that contribute to tumorigenesis and tumor phenotype. Risk predicfion tools will be developed using Cox proportional hazards models based on these features