School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk, UK; Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK; The Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK.
Eur Urol Focus. 2018 Dec;4(6):842-850. doi: 10.1016/j.euf.2017.01.016. Epub 2017 Mar 6.
A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes.
To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches.
DESIGN, SETTING, AND PARTICIPANTS: Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants.
Biochemical failure was assessed using log-rank analysis and Cox regression analysis.
Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p=2.65×10, p=4.28×10, and p=2.98×10). When these three datasets were combined the independent predictive value of DESNT membership was p=1.61×10 compared with p=1.00×10 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined.
Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease.
Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT.
在前列腺癌的临床管理中，一个关键问题是其具有高度异质性。因此，在诊断时无法准确预测个体癌症的行为，导致过度治疗的情况大量存在。与乳腺癌形成鲜明对比的是，目前全球表达谱的无监督分析尚未定义具有明显临床结局的稳健前列腺癌类别，这仍然是一个谜。
基于无监督数学方法为人类前列腺癌设计一种新的分类框架。
设计、设置和参与者：我们的分析基于这样的假设，即以前尝试对前列腺癌进行分类之所以不成功，是因为前列腺癌的个别样本通常具有异质组成。为了解决这个问题，我们应用了一种称为潜在过程分解的无监督贝叶斯程序，该程序应用于四个独立的前列腺癌转录组数据集，这些数据集使用前列腺切除术患者的样本获得，包含 78 至 182 名参与者。
使用对数秩分析和 Cox 回归分析评估生化失败。
潜在过程分解的应用在所有四个独立的检查数据集上都确定了一个共同的过程。被分配给该过程的癌症（指定为 DESNT 癌症）的特征是一组核心 45 个基因的低表达，其中许多基因编码参与细胞骨架机械、离子转运和细胞粘附的蛋白质。对于三个具有前列腺特异性抗原失败后前列腺切除术数据链接的数据集，DESNT 癌症患者的预后相对较差（p=2.65×10，p=4.28×10，p=2.98×10）。当将这三个数据集合并时，DESNT 成员的独立预测值为 p=1.61×10，而 Gleason 总和为 p=1.00×10。研究的一个局限性是仅检查了前列腺特异性抗原失败的预测。
我们的结果表明存在一种新的人类前列腺癌预后不良类别，这将有助于靶向治疗，帮助避免患有惰性疾病的男性因治疗相关的发病率而遭受痛苦。
与乳腺癌不同，前列腺癌没有稳健的分类框架。我们提出，这种失败的发生是因为用于分析的前列腺癌样本通常具有杂合组成（单个样本由许多具有不同特征的不同部分组成）。应用一种可以克服这个问题的数学方法，我们确定了一种新的称为 DESNT 的人类前列腺癌预后不良类别。
