包含增强子-基因边缘的调控网络定义细胞状态。多个努力通过整合多组学数据，为参考组织和细胞系揭示了这些网络。然而，由于限定活检材料的染色质免疫沉淀测序（ChIP-seq）的不可行性，所开发的方法无法应用于大量患者队列。我们使用染色质相互作用分析与末端标签测序（ChIA-PET）和高通量染色体构象捕获联合染色质免疫沉淀（HiChIP）数据来训练机器学习模型，该模型可以仅使用可转座子可及染色质使用测序（ATAC-seq）和RNA-seq数据作为输入来预测连接，这些数据可以从活检中生成。我们的方法克服了基于相关性的方法的局限性，这些方法无法区分给定增强子的不同靶基因或不同样本中的活跃vs.准备状态，这是癌变网络重构的一个标志。我们的模型在22种癌症类型的371个样本上应用揭示了602个癌症基因的1780个增强子-基因连接。使用CRISPR干扰（CRISPRi），我们验证了预测可调控ER+乳腺癌中ESR1的增强子和肝肠细胞癌中A1CF的增强子。版权所有©2023作者。Elsevier Inc.发表。保留所有权利。

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.