Discovering genomic rearrangements under selection in serious ovarian cancer

描述（由申请人提供）：复发性基因融合和内部串联复制是已知的最具肿瘤特异性的分子标记之一，可以提供潜在的治疗靶点。然而，除了一些值得注意的例外，相对常见的复发性基因融合尚未在常见的癌症中发现，这些癌症通常具有多个复杂的染色体重排，难以通过传统的细胞遗传学方法进行分析。复杂的肿瘤核型使得使用细胞遗传学鉴定基因融合变得困难，但表明复发性重排产生融合或内部串联复制（ITDs）可能普遍存在。本研究旨在利用深度测序和新的分析技术来研究浆液性卵巢癌基因组和转录组中由于技术或分析方法的限制而被隐藏的方面，并测试个体内和个体间对肿瘤的选择压力。本提案的内容包括：1)进一步研究卵巢癌中基因重排的程度，重点发现转录成RNA的局部重排；2)确定我最近发现的一组新型环状转录本的组成，这些转录本在正常和致病性人类细胞中表达水平相对较高；3)研究卵巢癌双分钟的特征，结合生物信息学来确定其序列组成的重排，并结合统计分析来确定肿瘤对其组成的进化压力。申请人在使用超高通量测序（ESRRA-C11orf20融合）发现新的基因融合方面取得了成功，并为超高通量数据设计了原始的严格的统计和生物信息学方法。Patrick O. Brown博士是高通量基因组技术和统计分析方法的先驱，在他的指导下，申请人将继续职业发展和培训。本项目的第一个目标将在指导阶段执行，目标2和目标3的实验将进行试点。K99/R00奖将支持申请人发展成为一名独立的研究者，将统计和实验方法结合起来研究癌症遗传学。

DESCRIPTION (provided by applicant): Recurrent gene fusions and internal tandem duplications are among the most tumor-specific molecular markers known and can provide the potential for therapeutic targets. With a few notable exceptions, however, relatively common recurrent gene fusions have not been identified in commonly occurring carcinomas, which often have multiple, complex chromosomal rearrangements that are difficult to analyze by traditional cytogenetic approaches. Complex tumor karyotpes make it difficult to identify gene fusions using cytogenetics, but suggest the possibility that recurrent rearrangements producing fusions or internal tandem duplications (ITDs) may be prevalent. This proposal aims to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods, and to test intra- individual and inter-individual selective pressures on tumors. The aspects of this proposal are as follows 1) to further investigate the extent of gene rearrangements in ovarian cancer, focusing on discovering local rearrangements transcribed into RNA; 2) to determine the composition of a group of novel circular transcripts that I have recently found to be expressed at relatively high levels in normal and pathogenic human cells; 3) to characterize double minutes in ovarian cancer, combining bioinformatics to determine rearrangements in their sequence composition and statistical analysis to determine evolutionary pressures on their composition exerted by the tumors. The applicant has a track-record of success in discovering novel gene fusions with ultra-high throughput sequencing (the ESRRA-C11orf20 fusion), as well as designing original rigorous statistical and bioinformatic methods for ultra high throughput data. Under the mentorship of Dr. Patrick O. Brown, a pioneer in high throughput genomic technologies and statistical methods for analyzing them, the applicant will continue career development and training. The first aim of this project will be performed during the mentoring phase, and experiments for aims 2 and 3 will be piloted. The K99/R00 award will support the applicant in her development into an independent investigator who combines statistical and experimental approaches to study cancer genetics. PUBLIC HEALTH RELEVANCE: Ovarian cancer is estimated to kill more than 140,000 women every year and has a poor prognosis once it presents with clinical symptoms. Discovery of truly tumor- specific molecular markers or identification of early and selected amplifications may be essential for effective early detection of serous tumors, which account for the majority of ovarian cancer deaths. While this proposal is focused on ovarian cancer, the methods are applicable to any cancer, and thus have broad significance. The consolidating theme of this proposal is to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods. The experimental and analytical methods developed will be applicable to all tumor types and hence of broad relevance to the study of cancer.