Optimizing molecular signatures for prostate cancer recurrence. Homo sapiens

The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue-derived microarray data is warranted before clinical application of molecular signatures is considered. Overall design: mRNA profiling was performed using 79 cases of prostate cancer of known disease recurrence status

可指示疾病状态、预测后续病程转归的分子特征（molecular signatures）的推导，能够助力前列腺癌患者优化治疗方案的选择。本研究针对79例已知疾病复发状态的前列腺癌患者的基因表达谱数据开展计算分析，其中39例被归类为疾病复发病例，旨在探究先进的计算算法能否为前列腺癌推导得到更为精准的预后特征。在90%灵敏度的阈值下，新推导得到的预后基因特征实现了85%的特异性。这是首个被报道的、性能优于临床使用的术后列线图（postoperative nomogram）的基因特征。此外，将列线图与基因表达数据相结合所推导得到的混合预后特征，其性能显著优于单一的基因特征与临床特征，特异性可达95%。本研究证实，借助基因表达信息可实现超越当前临床体系的高精度前列腺癌预后预测，同时表明在考虑将分子特征应用于临床之前，有必要针对组织来源的微阵列（microarray）数据开展更为先进的计算建模。研究整体设计：针对79例已知疾病复发状态的前列腺癌患者开展mRNA表达谱分析。