大多数癌症类型缺乏靶向治疗选择，当一线靶向治疗可用时，治疗耐药性是一个巨大的挑战。最近的技术进步使得能够以高通量方式对患者组织进行转座酶可及染色质测序 (ATAC-seq) 和 RNA 测序 (RNA-seq) 分析。在这里，我们提出了一种计算方法，利用这些数据集根据肿瘤谱系识别药物靶点。我们使用机器学习方法为 22 种癌症类型的 371 名患者构建了基因调控网络，该方法使用三维基因组数据进行训练，以实现增强子与启动子的接触。接下来，我们确定了这些网络中的关键转录因子 (TF)，这些转录因子用于通过直接靶向 TF 或与其相互作用的蛋白质来发现治疗漏洞。我们验证了四种针对神经内分泌癌、肝癌和肾癌的候选药物，这些癌症在当前的治疗方案下预后不佳。版权所有 © 2024 Elsevier Inc. 保留所有权利。

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.Copyright © 2024 Elsevier Inc. All rights reserved.