科学家首次将前列腺癌分为5种不同的类型

  近日，来自英国癌症研究院剑桥研究所的研究者通过研究首次发现前列腺癌有5种不同的类型，同时研究者还阐明了将这5种不同前列腺癌区分开的方法。相关研究具有非常重要的意义，其可以帮助医生有效鉴别出更易于生长和扩散的前列腺癌，并及时采取有效的方法治疗前列腺癌。
  相关研究发表于杂志EbioMedicine上，文章中研究者对超过250名男性的健康和癌性前列腺癌组织进行研究，通过观察异常的染色体变化，同时测定和疾病相关的100个不同的基因的活性，研究人员成功地将前列腺癌分为5种不同的类型，每一种类型都携带有特殊的基因特性。因此研究者就可以有效预测哪些癌症更易于变得恶性，这明显优于常规的PSA测试和格里森评分，但本文研究目前还需要对大量男性进行临床实验才能确定。
  Alastair Lamb教授说道，我们的研究结果表明，前列腺癌可以被分类为5种具有不同遗传特性的癌症，而且还可以帮助医生们针对不同前列腺癌患者的类型来确定最佳有效的疗法。下一步研究者将进行大型研究来证实他们的研究结果，通过进行更多研究来深入剖析5种不同的前列腺癌或可帮助开发有效的方法来治疗前列腺癌病人。
  前列腺癌是一种英国最为常见的癌症，每年有大约4.17万人被确诊为该癌症，而且每年都有1.08万人死于该疾病。治疗前列腺癌面临的挑战是该疾病发展非常缓慢，而且在男性一生中都有可能不会引发任何问题，或者犹如猛虎般地扩散，因此治疗该疾病急切需要新型疗法。而且目前并没有可靠的方法来区分不同的前列腺癌，这就意味着有些患者可能得到了不合适的治疗。
  本文研究为后期帮助指导进行前列腺癌患者的治疗提供了新的研究，同时也可以帮助开发新型有效的疗法来治疗不同类型的前列腺癌患者，研究者希望本文的研究结果可以帮助挽救更多患者的生命，并且改善前列腺癌患者的生活质量。（转化医学网360zhyx.com）
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转化医学网推荐的原文摘要：

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study☆
EbioMedicine    doi:10.1016/j.ebiom.2015.07.017
H. Ross-Adams1email, A.D. Lambcorrespondence1email, M.J. Dunning1email, S. Halimemail, J. Lindbergemail, C.M. Massieemail, L.A. Egevademail, R. Russellemail, A. Ramos-Montoyaemail, S.L. Vowleremail, N.L. Sharmaemail, J. Kayemail, H. Whitakeremail, J. Clarkemail, R. Hurstemail, V.J. Gnanapragasamemail, N.C. Shahemail, A.Y. Warrenemail, C.S. Cooperemail, A.G. Lynchemail, R. Starkemail, I.G. Millsemail, H. Grönberg1email, D.E. Neal1email, on behalf of the CamCaP Study Group
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 behaviour, 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 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.

Interpretation
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.