2000个乳腺瘤的基因组和转录组结构揭示了新的子组
Christina Curtis1, Sohrab P Shah, Suet-Feung Chin
1Department of Oncology, University of Cambridge, Hills Road, Cambridge CB2 2XZ, UK.
Nature
|April 24, 2012
概括
这项研究整合了近2000个乳腺瘤的基因组和转录组数据,以确定新的分子子组. 身体拷贝数异常显著影响基因表达,揭示了新的癌症驱动因素和不同的患者结果.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 文字转录学 (Transcriptomics) 是一个学科.
背景情况:
- 了解乳腺癌异质性对于向治疗至关重要.
- 综合基因组和转录基因组分析为癌症生物学提供了更深入的见解.
研究的目的:
- 通过使用集成的DNA和RNA分析,识别新的乳腺癌亚组和分子驱动因素.
- 将副本数异常与基因表达和临床结果联系起来.
主要方法:
- 从1,992个初级乳腺瘤 (发现和验证集) 中复制数和基因表达数据的综合分析.
- 鉴定cis-和trans-acting拷贝数异常 (CNAs) 以及它们对基因表达的影响.
- 对配对的DNARNA资料进行无监督分析,以揭示新的子组.
主要成果:
- 阴性CNAs在约40%的基因中影响基因表达,由cis-和trans-acting CNAs主导.
- 通过CNA驱动的表达异常值确定了新的癌症基因 (例如PPP2R2A,MTAP,MAP2K4).
- 发现了具有明显临床结果的新型乳腺癌亚组,包括高风险的雌激素受体阳性亚组和具有良好的预后的CNA-devoid亚组.
- 具有特征的跨作用异常热点调节子组特定网络,如免疫反应和线性网络.
结论:
- 综合基因组和转录基因组分析揭示了乳腺癌的新型分子分层.
- 身体CNA是基因表达异质性的关键驱动因素,并定义了不同的临床子组.
- 这种方法可以确定新的治疗点,并为个性化乳腺癌治疗提供框架.
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