To discover driver fusions beyond canonical exon-to-exon chimeric transcripts, we develop CICERO, a local assembly-based algorithm that integrates RNA-seq read support with extensive annotation for candidate ranking. CICERO outperforms commonly used methods, achieving a 95% detection rate for 184 independently validated driver fusions including internal tandem duplications and other non-canonical events in 170 pediatric cancer transcriptomes. Re-analysis of TCGA glioblastoma RNA-seq unveils previously unreported kinase fusions (KLHL7-BRAF) and a 13% prevalence of EGFR C-terminal truncation. Accessible via standard or cloud-based implementation, CICERO enhances driver fusion detection for research and precision oncology. The CICERO source code is available at https://github.com/stjude/Cicero .

中文翻译：

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CICERO：使用癌症 RNA 测序数据检测复杂多样的驱动融合的通用方法


为了发现规范外显子到外显子嵌合转录本之外的驱动融合，我们开发了 CICERO，这是一种基于本地组装的算法，它将 RNA-seq 读取支持与候选排序的广泛注释集成在一起。 CICERO 的性能优于常用方法，对 184 个独立验证的驱动融合（包括 170 个儿科癌症转录组中的内部串联重复和其他非规范事件）实现了 95% 的检测率。 TCGA 胶质母细胞瘤 RNA-seq 的重新分析揭示了先前未报告的激酶融合 (KLHL7-BRAF) 和 13% 的 EGFR C 末端截短发生率。 CICERO 可通过标准或基于云的实施进行访问，增强了研究和精准肿瘤学的驱动器融合检测。 CICERO 源代码可在 https://github.com/stjude/Cicero 获取。