Recent efforts have shown that structural variations (SVs) can disrupt three-dimensional genome organization and induce enhancer hijacking, yet no computational tools exist to identify such events from chromatin interaction data. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, including interchromosomal translocations, large deletions and inversions. Our framework can automatically resolve complex SVs, reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects and predict chromatin loops induced by SVs. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer hijacking. To experimentally validate NeoLoopFinder, we deleted the hijacked enhancers in prostate adenocarcinoma cells using CRISPR–Cas9, which significantly reduced expression of the target oncogene. In summary, NeoLoopFinder enables identification of critical oncogenic regulatory elements that can potentially reveal therapeutic targets.

最近的努力表明，结构变异 (SV) 可以破坏三维基因组组织并诱导增强子劫持，但不存在可从染色质相互作用数据中识别此类事件的计算工具。在这里，我们开发了 NeoLoopF​​inder，这是一种计算框架，用于识别 SV 诱导的染色质相互作用，包括染色体间易位、大缺失和倒位。我们的框架可以自动解析复杂的 SV，重建断点周围的局部 Hi-C 图，标准化拷贝数变异和等位基因效应，并预测由 SV 诱导的染色质环。我们在来自 50 个癌细胞系和原发性肿瘤的 Hi-C 数据中应用了 NeoLoopF​​inder，并确定了数十个与增强子劫持相关的复发基因。为了通过实验验证 NeoLoopF​​inder，我们使用 CRISPR-Cas9 删除了前列腺癌细胞中被劫持的增强子，这显着降低了靶癌基因的表达。总之，NeoLoopF​​inder 能够识别可能揭示治疗目标的关键致癌调节元件。