Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma

描述(由申请人提供)：腺样囊性癌改变驱动基因的综合路径分析项目摘要/摘要涎腺腺样囊性癌(ACC)是一种罕见的恶性肿瘤，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%的患者可能会发生远处转移到肺或骨。转移性沉积物的存在有时预示着预后不佳，但这种情况并不少见，这些转移性沉淀物多年来处于相对休眠和无症状状态。因此，将全身药物确定为辅助治疗将是理想的，因为很可能有足够的机会针对这种致命的疾病。目前，还没有公认的化疗药物或靶向药物用于ACC。不幸的是，对ACC发生的生物学基础知之甚少。由于ACC与吸烟无关，而且没有家族关系或已知的暴露风险概况，因此人们认为必须存在常见的、自发的变化来解释它是如何发生的。很可能，ACC致癌的模型将涉及一种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。我们的实验室一直专注于鉴定和筛选ACC的表观遗传学变化。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，我们可以快速识别涉及ACC的新的遗传和表观遗传学改变。因此，我们的具体目标是：1)进行包括全基因组甲基化分析、RNA测序、外显子组测序和SNP阵列在内的多平台全基因组分析；2)使用癌症异常基因图谱集(CoGPS)进行整合通路分析；3)验证通路分析和相关驱动基因靶点的功能分析。通过在匹配的原发ACC样本中使用最新的全基因组研究，我们将更好地理解表观遗传变化、突变、拷贝数变异、易位和剪接变异之间的关系。新的coGPS方法还允许进行路径分析，从而进一步整合数据并突出显示ACC中涉及的分子变化。最后，我们已经有了工具和生物材料来验证已识别的基因，以确认它们在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，可以通过多种不同的方式进行挖掘，以造福于整个研究界。因此，所有数据集都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是一种生物信息学方法，它以特定于路径的方式突出驱动基因。归根结底，正是这些类型的研究允许该领域的快速发展，并确定有可能对这种未被研究的疾病的患者护理产生积极影响的药物靶点。

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.