Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), P,O, Box 8905, N-7491 Trondheim, Norway.
BMC Med Genomics. 2014 Aug 12;7:50. doi: 10.1186/1755-8794-7-50.
Good prognostic tools for predicting disease progression in early stage prostate cancer (PCa) are still missing. Detection of molecular subtypes, for instance by using microarray gene technology, can give new prognostic information which can assist personalized treatment planning. The detection of new subtypes with validation across additional and larger patient cohorts is important for bringing a potential prognostic tool into the clinic.
We used fresh frozen prostatectomy tissue of high molecular quality to further explore four molecular subtype signatures of PCa based on Gene Set Enrichment Analysis (GSEA) of 15 selected gene sets published in a previous study. For this analysis we used a statistical test of dependent correlations to compare reference signatures to signatures in new normal and PCa samples, and also explore signatures within and between sample subgroups in the new samples.
An important finding was the consistent signatures observed for samples from the same patient independent of Gleason score. This proves that the signatures are robust and can surpass a normally high tumor heterogeneity within each patient. Our data did not distinguish between four different subtypes of PCa as previously published, but rather highlighted two groups of samples which could be related to good and poor prognosis based on survival data from the previous study.The poor prognosis group highlighted a set of samples characterized by enrichment of ESC, ERG-fusion and MYC + rich signatures in patients diagnosed with low Gleason score,. The other group consisted of PCa samples showing good prognosis as well as normal samples. Accounting for sample composition (the amount of benign structures such as stroma and epithelial cells in addition to the cancer component) was important to improve subtype assignments and should also be considered in future studies.
Our study validates a previous molecular subtyping of PCa in a new patient cohort, and identifies a subgroup of PCa samples highly interesting for detecting high risk PCa at an early stage. The importance of taking sample tissue composition into account when assigning subtype is emphasized.
目前仍缺乏用于预测早期前列腺癌(PCa)疾病进展的良好预后工具。例如,通过使用微阵列基因技术检测分子亚型可以提供新的预后信息,从而辅助个性化治疗计划。在更多更大的患者队列中验证和发现新的亚型对于将潜在的预后工具引入临床非常重要。
我们使用高质量的新鲜冷冻前列腺切除术组织,进一步探索基于先前研究中 15 个选定基因集的基因集富集分析(GSEA)的 PCa 的四个分子亚型特征。对于这项分析,我们使用相关相关性的统计检验来比较参考特征与新的正常和 PCa 样本中的特征,还探索了新样本中样本亚组内和亚组间的特征。
一个重要的发现是,即使在 Gleason 评分相同的情况下,来自同一患者的样本中也观察到一致的特征。这证明了这些特征是稳健的,可以超越每个患者中通常存在的高肿瘤异质性。我们的数据没有像以前发表的那样区分 PCa 的四种不同亚型,而是根据以前研究中的生存数据突出显示了两组可能与预后相关的样本。预后不良组突出了一组特征,其特征是在低 Gleason 评分的患者中富集了 ESC、ERG 融合和 MYC+丰富的特征。另一组由具有良好预后的 PCa 样本和正常样本组成。考虑到样本组成(除了癌症成分之外,良性结构如基质和上皮细胞的数量)对于改善亚型分配很重要,在未来的研究中也应该考虑到这一点。
我们的研究在新的患者队列中验证了之前的 PCa 分子分型,并确定了一个亚组的 PCa 样本,该样本对于在早期检测高危 PCa 非常有趣。强调在分配亚型时考虑样本组织组成的重要性。