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

侵袭性乳腺癌的生物学：挖掘三阴性转录组

• 批准号: 8220833
• 负责人: Thomas Paul Stricker
• 金额: $ 2.93万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8220833
• 关键词: Anchorage-Independent Growth; Behavior; Bioinformatics; Biological; Biological Assay; Biology; Breast Cancer Cell; Breast Cancer Treatment; Breast Carcinoma; Cancer cell line; Cell Line; Cells; Collection; Data; Development; Disease; Epithelial Cells; Estrogen Receptors; Estrogen receptor positive; Frequencies; Future; Gene Expression Profile; Gene Fusion; Genes; Genome; Growth Factor; Heart; Heterogeneity; Human; Human Genome; In Vitro; Individual; Large-Scale Sequencing; Malignant Neoplasms; Mammary gland; Masks; Measurement; Measures; Messenger RNA; Metabolic Pathway; Mining; Modeling; Morphology; Mutation; Mutation Analysis; Nature; Output; Paper; Pathway Analysis; Patients; Pattern; Phenotype; Phosphoproteins; Progesterone Receptors; Proteins; Publishing; Research; Signal Pathway; Signal Transduction; Specimen; Systems Biology; Tamoxifen; Technology; Testing; Therapeutic; Therapeutic Intervention; Trastuzumab; Tumor Subtype; Tumor-Derived; Western Blotting; base; cancer therapy; clinical care; clinically relevant; fusion gene; gain of function; improved; loss of function; malignant breast neoplasm; malignant phenotype; mutant; new therapeutic target; next generation; novel; overexpression; response; tumor

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.

描述（由申请人提供）：我们提出了一种系统生物学方法，以了解突变套件如何通过对人肿瘤样本的转录组进行测序，合作以重新 - 织机信号通路，从而有助于恶性表型。最近发表的一些癌症测序项目表明，每个肿瘤都带有一组独特的低频突变。这些数据使得难以确定哪些突变是导致恶性表型（驱动因素），而哪些突变仅在不稳定的基因组（乘客）中产生。此外，这种异质性可能掩盖了可以识别和利用用于癌症治疗的高阶相似性。解决这些问题对于开发通过对新型癌症治疗剂进行测序产生的信息至关重要。我们认为，自然已经为我们提供了这种复杂性方面的一些线索：具有相似形态和生物学行为的肿瘤亚型将带有类似的突变。但是，可能的相似之处不是在个体突变的水平上。可能有几种方式达到同一末端。为了开发一个将下一代测序数据开发到新的治疗靶标中的框架，我们提出以下方面：目标1：从24个三重阴性乳腺癌和24个ER阳性乳腺癌和生物信息学分析的测序转录组和生物信息学分析以识别突变和基因融合。我们构建的生物信息学管道将识别突变和新型融合基因。目标2。三重阴性乳腺癌中信号传导的网络分析。我们将使用Solexa测序来鉴定三重阴性和ER阳性癌细胞系中的突变。我们将使用一种新型的高吞吐量蛋白质印迹方法来测量这些细胞系中数百种哲学蛋白的水平，以响应生长因子。反应的差异可能与差异突变模式有关。目标3。乳腺癌细胞系模型中突变和基因融合的分析。我们将鉴定出具有与肿瘤中相似的突变套件的细胞系。使用敲低/过表达格式，我们将确定这些突变对癌症表型的重要性。这项研究将通过鉴定出三重阴性乳腺癌的治疗性干预靶标，从而显着影响乳腺癌治疗的未来。更一般而言，我们将建立一个框架，以将下一代测序信息开发为临床相关信息。