Searching Environmental Metagenomes for Novel Infectious Cancer Agents (PQ12)

描述（由申请人提供）：这是一项探索性应用，旨在开发用于识别导致癌症的感染因子的新型计算方法。为了实现这一目标，我们将合并来自两种不同方法的数据：基因表达谱分析和宏基因组学。全世界大约20%的癌症与感染因子有关，包括病毒、细菌和寄生虫。这一数字可能被低估了 还有更多的癌症是由有待发现的物质引起的。发现潜在致癌微生物的一种有效方法是虚拟减法，其中搜索肿瘤基因组序列或基因表达谱中的非人类序列。然后，通过搜索公共核酸和蛋白质数据库并鉴定可与已知病毒、细菌或其他生物体相关联的类似序列，在这些非人类序列中鉴定潜在的致癌剂。这种方法的一个主要限制是在数据库中缺乏来自大多数生物体，特别是微生物的序列的表示。宏基因组学是一种方法，其中对所有微生物的特定生物群系进行采样，然后进行深度测序。然后通过将获得的序列读段与公共数据库中保存的序列进行比较来鉴定各个物种。包括我们自己在内的许多实验室的研究表明，宏基因组调查中获得的大多数序列与现有数据库中的任何序列都不匹配，这表明它们来自以前未表征的试剂。例如，我们的研究表明，目前已知的3,000种左右的病毒只占自然界病毒的不到0.01%。同样，绝大多数细菌物种也有待发现和鉴定。我们建议搜索基因表达谱数据，以确定这些新的宏基因组序列是否在肿瘤中表达。我们还将开发计算工具，使我们识别的未表征的病毒，细菌或其他生物体被分离出来，并研究它们与癌症的关系。新的潜在致瘤感染因子的鉴定将对癌症的诊断和治疗产生直接影响。

DESCRIPTION (provided by applicant): This is an exploratory application aimed at developing novel computational approaches for identifying infectious agents that contribute to cancer. To achieve this we will merge data from two distinct approaches: gene expression profiling and metagenomics. Approximately 20% of all cancers worldwide are associated with infectious agents including viruses, bacteria and parasites. It is likely that this number is an underestimate and that many more cancers are caused by agents that await discovery. One powerful approach to uncovering potential cancer-causing microorganisms is virtual subtraction in which tumor genomic sequences or gene expression profiles are searched for non-human sequences. Potential cancer causing agents are then identified among these nonhuman sequences by searching public nucleic acid and protein databases and identifying similar sequences that can then be associated with a known virus, bacteria or other organism. A major limitation of this approach is the lack of representation of sequences from most organisms, especially microorganisms, in the databases. Metagenomics is an approach in which specific biomes are sampled for all microorganisms followed by deep sequencing. Individual species are then identified by comparing the sequence reads obtained with sequences deposited in public databases. Studies from a number of laboratories including our own have shown that most sequences obtained in metagenomic surveys do not match anything in existing databases suggesting they are derived from previously uncharacterized agents. For example, our studies suggest that the 3,000 or so currently known viruses represent less than 0.01% of viruses in nature. Similarly the vast majority of bacterial species await discovery and characterization. We propose to search gene expression profile data to determine if any of these novel metagenomic sequences are expressed in tumors. We will also develop the computational tools that will allow uncharacterized viruses, bacteria or other organisms that we identify to be isolated and their association with cancer studied. The identification of new potential tumorigenic infectious agents will have a direct impact on the diagnosis and treatment of cancer.