Laajala Teemu D, Sreekanth Varsha, Soupir Alex, Creed Jordan, Calboli Federico Cf, Singaravelu Kalaimathy, Orman Michael, Colin-Leitzinger Christelle, Gerke Travis, Fidley Brooke L, Tyekucheva Svitlana, Costello James C
Department of Mathematics and Statistics, University of Turku, Turku, Finland.
Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
bioRxiv. 2023 Jan 19:2023.01.17.524403. doi: 10.1101/2023.01.17.524403.
Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. is an open and accessible community resource with code made available for reproducibility.
在广泛的前列腺癌（PCa）研究队列中已经生成了基因组和转录组数据。这些数据可用于更好地表征与临床结果相关的分子特征，并在多个独立的患者队列中检验假设。此外，诸如细胞组成估计、风险评分和雄激素受体（AR）评分等衍生特征，可用于利用现有的多组学数据集提出新的假设。由于独立数据集存在于不同的存储库中，使用不同的流程进行处理，并且衍生特征和临床特征通常未提供或未标准化，此类数据的全部潜力尚未实现。在这里，我们展示了R包，这是一个经过协调的数据资源，它代表了超过2900个原发性肿瘤、超过200个正常组织以及超过500个转移性PCa样本，这些样本来自19个使用标准化流程和更新的基因注释处理的数据集。我们表明，跨协调研究的荟萃分析对于获得可靠且具有临床意义的见解具有巨大潜力。 是一个开放且可访问的社区资源，提供了代码以实现可重复性。
