The Biology of Aggressive Breast Cancer: Mining the Triple Negative Transcriptome

描述(由申请人提供)：我们提出了一种系统生物学的方法来理解通过对人类肿瘤标本的转录本进行测序来识别的突变套件是如何协作重新连接信号通路从而促进恶性表型的。最近公布的几个癌症测序项目表明，每个肿瘤都携带一组独特的低频突变。这些数据使得很难确定哪些突变是导致恶性表型的(驱动因素)，哪些仅仅是在不稳定的基因组中产生的(乘客)。此外，这种异质性可能掩盖了更高层次的相似性，这些相似性可以被识别并用于癌症治疗。解决这些问题对于开发通过测序产生的信息进入新的癌症疗法是至关重要的。我们认为，大自然给了我们一些线索来揭示这种复杂性中的共性：具有相似形态和生物学行为的肿瘤亚型将包含一组类似的突变。然而，这种相似性可能不是在单个突变的水平上；可能有几种方法可以达到相同的目的。为了发展下一代测序数据成为新的治疗靶点，我们提出了以下框架：目的1：对24例三阴性乳腺癌和24例ER阳性乳腺癌的转录本进行测序，并进行生物信息学分析，以确定突变和基因融合。我们已经建立的生物信息学管道将识别突变和新的融合基因。目的2.三阴性乳腺癌的信号网络分析。我们将使用Solexa测序来识别三阴性和ER阳性癌细胞株的突变。我们将使用一种新的高通量Western印迹方法来测量这些细胞系中数百种磷酸蛋白对生长因子的反应水平。反应的不同可能与突变模式的不同有关。目的3.分析乳腺癌细胞系模型中的突变和基因融合。我们将通过一系列与我们的肿瘤相似的突变来鉴定细胞系。使用敲除/过表达形式，我们将确定这些突变对癌症表型的重要性。这项研究将通过确定三阴性乳腺癌治疗干预的新靶点，对乳腺癌治疗的未来产生重大影响。更广泛地说，我们将建立一个框架，将下一代测序信息发展成临床相关的信息。

DESCRIPTION (provided by applicant): We propose a systems biology approach to understand how suites of mutations, identified by sequencing the transcriptomes of human tumor specimens, collaborate to re-wire signaling pathways and thus contribute to the malignant phenotype. Several recently published cancer sequencing projects suggest that each tumor carries a unique set of low frequency mutations. These data make it difficult to determine which mutations are responsible for the malignant phenotype (drivers) and which are merely generated in an unstable genome (passengers). Additionally, this heterogeneity may mask higher order similarities that can be identified and exploited for cancer therapy. Resolving these issues is essential to develop information generated by sequencing into novel cancer therapeutics. We believe that nature has given us some clues to commonalities in this complexity: tumor subtypes that have similar morphology and biological behavior will harbor a similar set of mutations. However, it may be that the similarities are not at the level of individual mutations; there may be several means to the same end. To develop a framework for developing next generation sequencing data into novel therapeutic targets, we propose the following: Aim 1: Sequencing transcriptomes from 24 triple negative breast carcinomas and 24 ER positive breast carcinomas and bioinformatic analysis to identify mutations and gene fusions. A bioinformatic pipeline we have built will identify mutations and novel fusion genes. Aim 2. Network Analysis of Signaling in Triple Negative Breast Carcinomas. We will use Solexa sequencing to identify mutations in triple negative and ER positive cancer cell lines. We will use a novel high throughput Western blot approach to measure levels hundreds of phophsoproteins in these cell lines in response to growth factors. Differences in responses can be associated with differences mutational patterns. Aim 3. Analysis of mutations and gene fusions in breast cancer cell line models. We will identify cell lines with a suite of mutations similar to that seen in our tumors. Using a knockdown/overexpression format, we will determine the importance of these mutations to the cancer phenotype. This research will significantly impact the future of breast cancer treatment by identifying novel targets for therapeutic intervention in triple negative breast carcinomas. More generally, we will establish a framework for developing next generation sequencing information into clinically relevant information.