Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma

腺样囊性癌驱动基因改变的整合通路分析

• 批准号: 9205409
• 负责人: Patrick Kyongmin Ha
• 金额: $ 39.63万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9205409
• 关键词: Integrated; pathway; analysis; altered; driver; Integrated; pathway; analysis; altered; driver

DESCRIPTION (provided by applicant): Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma Project Summary/Abstract Salivary gland adenoid cystic carcinoma (ACC) is an unusual malignancy with an unpredictable clinical behavior. While local and regional control can be obtained with surgery and radiation therapy, up to 50% of patients may develop distant metastasis to the lung or bone. The presence of metastatic deposits can sometimes portend a poor prognosis, but it is not uncommon for these to remain relatively dormant and asymptomatic for years. Thus, the identification of systemic agents as adjunctive treatment would be ideal, as there would likely be ample opportunity to target this deadly disease. Currently, there are no well accepted chemotherapeutic or targeted agents for use in ACC. Unfortunately, the biologic basis for ACC development is poorly understood. Because ACC is not smoking- related, and there is no familial association or known exposure risk profile, it is believed that there must be common, spontaneous alterations that exist to explain how it arises. It likely that the model for ACC carcinogenesis will involve a unique pattern or set of genes, and, therefore, we cannot rely on simply evaluating known tumor-related genes involved in other cancers. Our lab has focused on the identification of and screening for epigenetic changes in ACC. We believe that with the newer generation of whole genome sequencing and with more robust bioinformatic approaches, we can rapidly identify novel genetic and epigenetic alterations involved in ACC. Accordingly, our specific aims are: 1) To perform multiplatform whole-genome analysis including whole genome methylation profiling, RNA sequencing, exome sequencing, and SNP array, 2) Integrative pathway analysis using cancer outlier Gene Profile Sets (coGPS), 3) Validation of pathway analysis and functional analysis of relevant driver gene targets. By employing the newest whole genome studies in matched primary ACC samples, we will gain a better understanding of the relationship between epigenetic changes, mutations, copy number variations, translocations, and splice variants. The novel coGPS methodology also allows for pathway analysis, thereby further integrating the data and highlighting the molecular changes that are involved in ACC. Lastly, we already have the tools and biologic material for the validation of identified genes to confirm their role in ACC. At the conclusion of this work, we wil have generated a tremendous data set that can be mined in a number of different ways for the benefit of the entire research community. As such, all data sets will be deposited into the Gene Expression Omnibus (GEO) Database for public use. Our particular interest will focus on a bioinformatic approach that highlights the driver genes in a pathway-specific manner. Ultimately, it is these types of studies that allow for rapid advancement within the field and identification o drug-able targets that have the potential to positively affect patient care in this understudied disease.

描述（由申请人提供）：腺样囊性癌中改变驱动基因的综合途径分析项目摘要/摘要唾液腺腺苷酸腺样性囊性癌（ACC）是一种异常的恶性肿瘤，具有不可预测的临床行为。虽然可以通过手术和放射治疗获得局部和区域控制，但多达50％的患者可能会对肺或骨骼发展遥远的转移。转移沉积物的存在有时会预后不良，但是这些数年来保持相对休眠和无症状并不少见。因此，将系统性药物鉴定为辅助治疗是理想的选择，因为可能有足够的机会靶向这种致命疾病。当前，没有可供ACC使用的化学治疗剂或靶向剂。不幸的是，ACC开发的生物学基础知之甚少。由于ACC与吸烟无关，并且没有家族关联或已知的暴露风险概况，因此人们认为必须有共同的自发改变来解释其出现的方式。 ACC癌变模型可能涉及独特的模式或一组基因，因此，我们不能简单地评估与其他癌症有关的已知肿瘤相关基因。我们的实验室专注于对ACC表观遗传变化的识别和筛查。我们认为，随着整个基因组测序的新产生以及更强大的生物信息学方法，我们可以迅速识别出涉及ACC的新型遗传和表观遗传变化。因此，我们的具体目的是：1）进行乘以全基因组甲基化分析，包括整个基因组甲基化分析，RNA测序，外显子组测序和SNP阵列，2）使用癌症途径基因概况集（COGPS），3）途径分析的途径分析和相关驱动程序基因靶标的功能分析的综合途径分析。通过在匹配的主要ACC样品中采用最新的整个基因组研究，我们将更好地理解表观遗传变化，突变，拷贝数变化，易位和剪接变体之间的关系。新型的COGPS方法还可以进行途径分析，从而进一步整合数据并突出ACC中涉及的分子变化。最后，我们已经拥有验证已识别基因的工具和生物学材料，以确认其在ACC中的作用。在这项工作结束时，我们将产生一个巨大的数据集，可以通过多种方式开采，以使整个研究界受益。因此，所有数据集将存放到基因表达综合（GEO）数据库中，以供公众使用。我们的特别兴趣将集中于一种生物信息学方法，该方法以特定方式突出驱动器基因。最终，正是这些类型的研究允许在现场进行快速发展，并鉴定出可吸毒的靶标，这些靶标有可能对这种研究研究的疾病产生积极影响患者护理。