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。前列腺癌的预后各不相同--许多男性的病程迟缓，而 其他人则患有侵袭性疾病，进展为转移和死亡。肿瘤的分类依据 公认的前列腺癌分子亚型并不一定具有预后信息。进展 在区分潜在致命性疾病和惰性疾病和识别前列腺癌分子亚型方面 潜在的治疗反应预测将通过可访问和可靠的 来自前列腺肿瘤和邻近正常组织的高通量分子数据的精选数据库 以及相关的临床注释。我们建议开发最大的协调、多研究数据集 前列腺癌专门为系统性开发和广泛的多项研究验证而设计 翻译相关的多组生物标志物和分子定义的亚型。我们将开发和应用一种 标准化的数据处理流程，一致地捕获所有已报告的患者临床特征 通过45个公共数据集收集。为确保临床特征的数据完整性，我们将手动整理 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 评估肿瘤纯度、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获得分子亚型和 应该控制这些因素，以便有力地确定前列腺癌分子亚型 与临床疗效相关。在此项目中编译的数据集将公开并 可通过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.