Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

中文翻译：

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泛癌症分析揭示了新颖的正义和反义融合转录本的多样性


可以探索有助于致癌性的基因融合来识别癌症生物标志物和潜在的药物靶点。为了研究癌症中融合转录本的性质和分布，我们使用 ChimeRScope 检查了 TCGA（癌症基因组图谱）中来自 33 种不同癌症的约 9,000 个原发性肿瘤的转录组数据以及来自 CCLE（癌症细胞系百科全书）的细胞系数据，一种新颖的融合检测算法。我们发现了几种在癌症中反复出现的有义（规范，39％）或反义（非规范，61％）转录本的融合。我们的研究中发现的大多数复发性非典型融合都是新颖的、未经探索的，并且在癌症中表现出高度可变的特征，其中乳腺癌和胶质母细胞瘤的发生率分别最高和最低。总体而言，本研究从 TCGA 中鉴定出 4,344 个复发融合，其中 70% 是新颖的。对 20 种癌症的 802 个肿瘤来源细胞系转录组数据的进一步分析揭示了原发性肿瘤和相应细胞系之间的复发融合谱的显着差异。通过检查全基因组测序（WGS）数据中的结构变异证据或通过融合连接点的桑格测序来验证规范和非规范融合的子集。我们的研究中发现的几个经常出现的融合基因显示出在篮子试验中重新利用药物的希望，并为机制研究提供了机会。