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方法，以了解通过对人类肿瘤标本的转录组进行测序而鉴定的突变套件如何合作重新连接信号通路，从而促成恶性表型。最近发表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恶性表型的原因（驱动者），哪些只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可以被识别并用于癌症治疗的高阶相似性。解决这些问题对于将测序产生的信息开发成新的癌症治疗方法至关重要。我们相信，自然界已经给了我们一些线索，让我们了解这种复杂性的共性：具有相似形态和生物学行为的肿瘤亚型将包含一组相似的突变。然而，可能的是，相似性不是在单个突变的水平上;可能有几种方法达到同一目的。为了开发用于将下一代测序数据开发成新的治疗靶点的框架，我们提出以下内容：目的1：对来自24个三阴性乳腺癌和24个ER阳性乳腺癌的转录组进行测序，并进行生物信息学分析以鉴定突变和基因融合。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。目标二。三阴性乳腺癌信号通路的网络分析。我们将使用Solexa测序来鉴定三阴性和ER阳性癌细胞系中的突变。我们将使用一种新的高通量蛋白质印迹方法来测量这些细胞系中响应生长因子的数百种磷酸蛋白的水平。反应的差异可能与突变模式的差异有关。目标3：乳腺癌细胞系模型中突变和基因融合的分析。我们将鉴定出具有一系列突变的细胞系，这些突变与我们在肿瘤中看到的突变相似。使用敲低/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来显著影响乳腺癌治疗的未来。更广泛地说，我们将建立一个框架，将下一代测序信息发展为临床相关信息。

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.