Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma

描述（由申请人提供）：腺样囊性癌中改变的驱动基因的综合途径分析项目摘要/摘要唾液腺腺样囊性癌（ACC）是一种不寻常的恶性肿瘤，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，但高达50%的患者可能发生肺或骨的远处转移。转移性沉积物的存在有时预示着预后不良，但这些沉积物多年保持相对休眠和无症状并不罕见。因此，将全身性药物确定为连续治疗将是理想的，因为可能有足够的机会靶向这种致命的疾病。目前，还没有公认的用于ACC的化疗或靶向药物。不幸的是，ACC发展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特征，因此认为必须存在常见的自发性改变来解释其如何产生。ACC致癌模型可能涉及一种独特的模式或一组基因，因此，我们不能简单地依赖于评估与其他癌症相关的已知肿瘤相关基因。我们的实验室专注于ACC表观遗传学变化的识别和筛选。我们相信，随着新一代全基因组测序和更强大的生物信息学方法，我们可以快速识别ACC中涉及的新的遗传和表观遗传学变化。因此，我们的具体目标是：1）进行多平台全基因组分析，包括全基因组甲基化分析、RNA测序、外显子组测序和SNP阵列，2）使用癌症离群基因谱集（coGPS）的综合途径分析，3）相关驱动基因靶标的途径分析和功能分析的验证。通过采用最新的全基因组研究匹配的初级ACC样本，我们将获得更好的理解表观遗传变化，突变，拷贝数变异，易位和剪接变异之间的关系。新的coGPS方法还允许进行途径分析，从而进一步整合数据并突出ACC中涉及的分子变化。最后，我们已经有了验证已鉴定基因的工具和生物材料，以确认它们在ACC中的作用。我们将产生一个巨大的数据集，可以通过许多不同的方式进行挖掘，从而使整个研究界受益。因此，所有数据集将存入基因表达综合数据库（GEO）供公众使用。我们特别感兴趣的将集中在生物信息学的方法，突出了驱动基因在特定途径的方式。最终，正是这些类型的研究允许在该领域内快速发展，并确定有可能对这种未充分研究的疾病的患者护理产生积极影响的可药物靶点。

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.