Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA; Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
Cell Rep Methods. 2023 Sep 25;3(9):100578. doi: 10.1016/j.crmeth.2023.100578. Epub 2023 Sep 5.
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.
调控网络包含增强子-基因边缘，定义了细胞状态。通过整合多组学数据，多项研究已经揭示了这些参考组织和细胞系的网络。然而，由于有限的活检材料进行染色质免疫沉淀测序（ChIP-seq）不可行，因此开发的方法无法应用于大型患者队列。我们使用带有配对末端标签测序（ChIA-PET）的染色质相互作用分析和与染色质免疫沉淀相结合的高通量染色体构象捕获（HiChIP）数据，使用仅使用转座酶可及染色质测序（ATAC-seq）和 RNA-seq 数据作为输入的测定来训练机器学习模型，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方法的局限性，该方法无法区分给定增强子的不同靶基因，也无法区分不同样本中活跃状态与静止状态之间的区别，这是癌症中网络重排的标志。在 22 种癌症类型的 371 个样本上应用我们的模型，揭示了 602 个癌症基因的 1,780 个增强子-基因连接。使用 CRISPR 干扰（CRISPRi），我们验证了预测调节雌激素受体（ER）+乳腺癌中的 ESR1 和肝肝细胞癌中的 A1CF 的增强子。
