Curated prostate cancer data for novel and reproducible prognostic modeling

项目摘要 前列腺癌是美国最常见的癌症，也是癌症死亡的第二大原因 男人前列腺癌的预后具有异质性-许多男性具有惰性病程， 其他人患有进展为转移和死亡的侵袭性疾病。肿瘤的分类 公认的前列腺癌分子亚型不一定携带预后信息。进展 在区分潜在致命性疾病和惰性疾病以及识别前列腺癌的分子亚型方面， 通过一种可获得的、可靠的方法， 来自前列腺肿瘤和邻近正常组织的高通量分子数据的策划数据库 以及相关的临床注释。我们建议开发最大的协调的多研究数据集， 前列腺癌专门设计用于系统开发和广泛的多研究验证， 诊断相关的多组学生物标志物和分子定义的亚型。我们将开发和应用一个 标准化的数据处理管道，并一致地捕获所有报告的患者临床特征 收集了超过45个公共数据集。为了确保临床特征的数据完整性，我们将手动策划 这些数据。除了这些标本目前可用的临床注释外，我们还将通过计算 估计肿瘤纯度、免疫浸润和周围间质的贡献。我们将测试 假设估计的微环境因素影响我们推导分子亚型的能力， 这些因素应该得到控制，以便有力地定义前列腺癌分子亚型， 与具有临床影响力的结果相关。本项目中编制的数据集将公开， 可以通过curatedProstateData包和GitHub访问。

Project Summary Prostate cancer is the most commonly diagnosed cancer and the second-leading cause of cancer death in US men. Prostate cancer has a heterogeneous prognosis - many men have an indolent disease course while others have aggressive disease that progresses to metastases and death. Classification of tumors by recognized molecular subtypes of prostate cancer does not necessarily carry prognostic information. Progress in distinguishing potentially lethal from indolent disease and identifying molecular subtypes of prostate cancer potentially predictive of therapeutic response would be greatly accelerated through an accessible and reliably curated database of high-throughput molecular data from prostate tumors and adjacent normal tissue alongside relevant clinical annotations. We propose to develop the largest harmonized, multi-study dataset for prostate cancer specifically designed for systematic development and extensive multi-study validation of translationally relevant multi-omic biomarkers and molecularly defined subtypes. We will develop and apply a standardized data processing pipeline and consistently capture all reported clinical features of patients collected across >45 public datasets. To ensure data integrity of the clinical features, we will manually curate these data. In addition to currently available clinical annotations for these specimens we will computationally estimate tumor purity, immune infiltration and the contribution by the surrounding stroma. We will test the hypothesis that the estimated microenvironmental factors impact our ability to derive molecular subtypes and that these factors should be controlled for in order to robustly define prostate cancer molecular subtypes associated with clinically impactful outcomes. The dataset compiled in this project will be made public and accessible through the curatedProstateData package and GitHub.