Prostate cancer stratification using molecular profiles [CamCap genotype first set]. Homo sapiens

Background 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. Methods 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 behavior, and compared with either CNA or transcriptomics alone. Findings 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 99 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) in prostate cancer, 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. Interpretation For the first time this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts. Overall design: A total of 482 samples from 289 men with prostate cancer from two cohorts were included in this study. The discovery cohort comprised 125 tumour samples from radical prostatectomy (RP) with 118 matched benign samples, and 85 matched blood samples. An additional 4 benign samples from men undergoing Holmium laser enucleation of the prostate (HoLEP) and 16 radical prostatectomy samples from men with castrate-resistant prostate cancer, with 13 matched blood samples were also included. These were assayed on several platforms, including Illumina HT12v4 gene expression arrays, Illumina OMNI2.5M genotyping arrays and Affymetrix SNP6 genotyping arrays. The validation cohort comprised 103 tumour tissue samples from men with prostate cancer, with 99 matched benign tissue samples and 103 matched blood samples. This datasheet describes samples in the DISCOVERY COHORT only, with complete, QCd Illumina HT12v4 data for 13 CRPC samples, 113 tumour samples and 73 matched benign samples.

背景 解析前列腺癌的异质性基因型与表型，是优化该疾病诊疗方案的核心基础。截至目前，尚无基于整合基因组学、且与临床转归相关联的经验证的前列腺癌亚群描述。
方法 本研究纳入259例原发性前列腺癌男性患者的482份肿瘤、良性组织及生殖系样本，采用整合拷贝数变异（copy number alterations, CNA）与转录组芯片的分析策略，通过表达数量性状位点（expression quantitative trait loci, eQTL）分析方法，筛选影响mRNA表达水平的基因组位点，进而将患者划分为不同亚群；随后将这些亚群与后续临床转归相关联，并与单独使用CNA或转录组学的分析结果进行对比。
结果 本研究在包含125例和99例男性患者的独立发现集与验证集中，基于100个判别基因，鉴定出5个具有独特基因组变异与表达谱的患者亚群。这些亚群可稳定预测生化复发（分别为p=0.0017与p=0.016），并在第三个具有长期随访数据的队列中得到进一步验证（p=0.027）。本研究阐明了基因表达与拷贝数数据对表型的相对贡献，并证实整合分析可提升分析效能。我们验证了6个此前已被报道与前列腺癌相关的基因（MAP3K7、MELK、RCBTB2、ELAC2、TPD52、ZBTB4）在前列腺癌中的变异情况，同时鉴定出94个此前未被报道与前列腺癌进展相关的基因——这些基因无法通过单独的转录组或拷贝数数据分析被检测到。我们验证了多项已发表的与高危前列腺癌相关的分子改变，包括MYC扩增、NKX3-1、RB1与PTEN缺失，以及肿瘤组织中PCA3、AMACR的过表达与MSMB的低表达。相较于已确立的不良预后临床预测指标（前列腺特异性抗原（Prostate-Specific Antigen, PSA）、格里森评分（Gleason score））及此前发表的基因特征，这100个基因中的一个子集表现更优（p=0.0001）。此外，我们展示了本研究的分子特征如何应用于临床场景中侵袭性病例的早期检测，并为治疗决策提供参考。
解读 本研究首次证实，整合良性与肿瘤组织数据的基因组分析，在鉴定可在独立患者队列中预测临床结局的稳健基因集方面具有重要价值。
研究设计 本研究共纳入来自两个队列的289例前列腺癌男性患者的482份样本。发现队列包含125份来自根治性前列腺切除术（radical prostatectomy, RP）患者的肿瘤样本、118份匹配的良性组织样本及85份匹配的血液样本；此外还纳入了4份接受钬激光前列腺剜除术（Holmium laser enucleation of the prostate, HoLEP）患者的良性组织样本，以及16份来自去势抵抗性前列腺癌（castrate-resistant prostate cancer, CRPC）患者的根治性前列腺切除术样本与13份匹配的血液样本。上述样本采用多种平台进行检测，包括Illumina HT12v4基因表达芯片、Illumina OMNI2.5M基因分型芯片与Affymetrix SNP6基因分型芯片。验证队列包含103份前列腺癌患者的肿瘤组织样本、99份匹配的良性组织样本及103份匹配的血液样本。本数据表仅描述发现队列的样本，其中包含13份CRPC样本、113份肿瘤样本与73份匹配良性样本的经质量控制（QC）的完整Illumina HT12v4数据。