Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.
Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.
复制标题
DOI：
10.1016/j.ebiom.2015.07.017
复制
发表时间：
2015-09
期刊：
影响因子：
11.1
通讯作者：
CamCaP Study Group
中科院分区：
文献类型：
--
作者：
Ross-Adams H;Lamb AD;Dunning MJ;Halim S;Lindberg J;Massie CM;Egevad LA;Russell R;Ramos-Montoya A;Vowler SL;Sharma NL;Kay J;Whitaker H;Clark J;Hurst R;Gnanapragasam VJ;Shah NC;Warren AY;Cooper CS;Lynch AG;Stark R;Mills IG;Grönberg H;Neal DE;CamCaP Study Group
了解前列腺癌的异质基因型和表型对改善我们治疗这种疾病的方式至关重要。到目前为止，还没有来自与临床结果相关的整合基因组学的前列腺癌亚群的有效描述。在一项来自259名原发性前列腺癌男性的482份肿瘤、良性和种系样本的研究中，我们使用拷贝数改变（CNA）和转录组阵列的综合分析，以表达数量性状位点（eQTL）方法确定影响mRNA表达水平的基因组位点，将患者分层，然后与未来的临床行为相关联，并与单独的CNA或转录组进行比较。我们在125名男性和103名男性的单独发现和验证集中，基于100个鉴别基因，确定了5个不同的患者亚组，它们具有不同的基因组改变和表达谱。这些亚组能够一致地预测生化复发（p = 0.0017和p = 0.016分别），并在长期随访的第三个队列中进一步验证（p = 0.027）。我们展示了基因表达和拷贝数数据对表型的相对贡献，并展示了综合分析获得的改进能力。我们确认了先前与前列腺癌相关的6个基因的改变（MAP3K7， MELK, RCBTB2, ELAC2, TPD52, ZBTB4），并且还鉴定了94个先前与前列腺癌进展无关的基因，这些基因单独使用转录本或拷贝数数据无法检测到。我们证实了许多先前发表的与高风险疾病相关的分子变化，包括MYC扩增、NKX3-1、RB1和PTEN缺失，以及PCA3和AMACR的过表达，以及肿瘤组织中MSMB的缺失。100个基因中的一个子集优于已建立的不良预后临床预测指标（PSA， Gleason评分），以及先前发表的基因特征（p = 0.0001）。我们进一步展示了我们的分子图谱如何用于临床环境中侵袭性病例的早期发现，并为治疗决策提供信息。这项研究首次在前列腺癌中证明了整合良性和肿瘤组织数据的基因组分析在识别分子改变方面的重要性，从而产生强大的基因集，这些基因集可以预测独立患者队列的临床结果。对259名前列腺癌男性患者的综合基因组分析：剑桥发现队列和斯德哥尔摩验证队列的100个特征基因集，可可靠地区分前列腺癌的5个亚组（聚类）。预测无复发生存的预后基因标记在其他癌症的研究中已经显示了综合基因组方法在疾病分层方面的优势（例如乳腺癌），但此类方法尚未在前列腺癌中进行。在这项研究中，我们对剑桥发现和斯德哥尔摩验证队列进行了全面的综合分析，根据100个关键基因的拷贝数和基因表达谱，将男性分为5个不同临床风险的分子群。该研究随后表明，我们可以基于该基因集的一个精细亚组来预测疾病复发，并显示出与其他可用特征相比，该“特征”的优越性。该研究向前列腺癌研究界介绍了259名男性的拷贝数、转录表达、TMPRSS2:ERG基因融合状态和组织微阵列数据。
Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts. Integrated genomic profiling of 259 men with prostate cancer Cambridge discovery cohort and Stockholm validation cohort 100-feature gene set that reliably differentiates five subgroups (iClusters) of prostate cancer Prognostic gene signature that predicts relapse-free survival Studies in other cancers have shown the advantage of integrated genomic approaches in stratifying disease (e.g., breast cancer) but such approaches have not yet been undertaken in prostate cancer. In this study we conducted a comprehensive integrated analysis of a Cambridge discovery and Stockholm validation cohort to stratify men into 5 molecular clusters of varying clinical risk based on copy number and gene expression profiling of 100 key genes. The study then showed that we could predict disease relapse based on a refined subgroup of this gene set and showed the superiority of this ‘signature’ compared to other available signatures. The study introduces to the prostate cancer research community 259 men who have been profiled with copy number, transcript expression, TMPRSS2:ERG gene fusion status and tissue microarray data.