Biomarker Benchmark - GSE46691

<b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br>"Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.<br><br>Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.<br><br>Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.<br><br>Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46691<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).【注意：本数据集已停用，请前往以下链接查看新版数据集（及其他附加数据集）：https://osf.io/mhk93 】 研究目的：临床病理特征与生化复发是转移性疾病及致死性前列腺癌的敏感但非特异性预测因子。本研究假设，原发肿瘤中检测到的基因组表达特征可反映侵袭性疾病的真实生物学潜能，并能优化前列腺癌早期转移的预测效能。 研究方法：本研究采用嵌套病例对照设计，从梅奥诊所肿瘤登记库中选取1987年至2001年间接受根治性前列腺切除术的639例患者。针对前列腺切除术后前列腺特异性抗原（Prostate-Specific Antigen, PSA）升高的男性群体（其中213例在生化复发后出现早期临床转移），研究利用140万特征位点的高密度表达芯片对差异RNA表达进行建模，从而构建基因组分类器（Genomic Classifier, GC）。以训练集开发包含22个标志物的随机森林分类器，用于预测PSA升高后出现早期临床转移的病例。在预留的验证集中，将GC的预测性能与格里森评分（Gleason score）等临床预后因子及既往基因表达特征进行对比。 研究结果：本研究从545例独特患者样本中生成了表达谱，中位随访时长为16.9年。在验证集中，GC的受试者工作特征曲线（Receiver Operating Characteristic curve, ROC）下面积达到0.75（95%置信区间：0.67~0.83），性能优于临床变量与基因表达特征。多变量分析显示，GC是唯一具有统计学意义的预后因子。在不同格里森评分亚组中，GC评分较高的患者更早出现前列腺癌相关死亡，总生存期更短。本研究发现，分类器中的标志物与前列腺癌转移性疾病进展中的多种关键生物学过程密切相关。 研究结论：本研究在包含前列腺癌转移患者及术后PSA升高并最终发生转移的患者的大型患者队列中，开发并验证了一款基因组分类器。这款基于原发肿瘤基因组表达特征的早期转移预测模型，可用于识别侵袭性前列腺癌患者。 本数据集登录号为GSE46691，可通过NCBI基因表达综合数据库（GEO）查询：http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE46691 本数据集包含基因表达数据、待预测的结局（分类标签）以及所有临床协变量。若基因表达数据经多批次处理，本数据集一并提供批次信息。每个数据集均整理为独立文件组，包含对应数据集的全部相关文件。基因表达文件已通过Bioconductor的SCAN.UPC软件包，采用SCAN与UPC两种方法进行标准化处理。本研究利用BrainArray资源（http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp）在基因水平对数据进行汇总，并采用Ensembl标识符进行标注。分类标签、临床数据及批次信息均经过人工整理，以确保数据一致性（采用"tidy data"格式）。此外，本数据集文件已优化格式，可便捷导入ML-Flex机器学习包（http://mlflex.sourceforge.net/）。