Integrated pathway analysis of altered driver genes in adenoid cystic carcinoma

项目摘要/摘要唾液腺腺样囊性癌（ACC）是一种临床行为难以预测的罕见恶性肿瘤。虽然可以通过手术和放射治疗获得局部和局部控制，但高达50%的患者可能会发生远处转移到肺或骨。转移性沉积的存在有时预示着预后不良，但这些转移性沉积多年保持相对休眠和无症状的情况并不罕见。因此，确定全身性药物作为辅助治疗将是理想的，因为可能有足够的机会靶向这种致命疾病。目前，尚无公认的用于ACC的化疗或靶向药物。不幸的是，ACC发展的生物学基础尚不清楚。由于ACC与吸烟无关，也没有家族关联或已知的暴露风险，因此相信一定存在共同的、自发的改变来解释它是如何产生的。ACC癌变的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有关的已知肿瘤相关基因。我们的实验室专注于鉴别和筛选ACC的表观遗传变化。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ACC中涉及的新的遗传和表观遗传改变。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)使用癌症异常基因谱集（coGPS）进行综合途径分析；3)验证途径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样本中采用最新的全基因组研究，我们将更好地了解表观遗传变化、突变、拷贝数变异、易位和剪接变异之间的关系。新的coGPS方法还允许通路分析，从而进一步整合数据并突出ACC中涉及的分子变化。最后，我们已经有了工具和生物材料来验证已鉴定的基因，以确认它们在ACC中的作用。在这项工作结束时，我们将生成一个巨大的数据集，可以通过多种不同的方式挖掘，以造福整个研究界。因此，所有数据集将被存入基因表达综合数据库（GEO）供公众使用。我们的特别兴趣将集中在生物信息学方法上，以途径特异性的方式突出驱动基因。最终，正是这些类型的研究使得该领域的快速发展和可药物靶点的识别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。

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. PUBLIC HEALTH RELEVANCE: The treatment of salivary gland adenoid cystic carcinoma has remained unchanged for the past several decades because of a lack of understanding of the basic mechanisms behind its development. By using the most advanced technology and analyses, we will fully characterize the genetic makeup of these tumors in order to determine how we can design better therapies to help patients afflicted with this deadly cancer.