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Regulatory networks containing enhancer to gene edges define cellular state and their rewiring is a hallmark of cancer. While efforts, such as ENCODE, have revealed these networks for reference tissues and cell-lines by integrating multi-omics data, the same methods cannot be applied for large patient cohorts due to the constraints on generating ChIP-seq and three-dimensional data from limited material in patient biopsies. Moreover, many cancer types lack effective targeted therapeutic options and in cancers where first-line targeted therapies are available, treatment resistance is a huge challenge, owing to both genetic and epigenetic alterations. Recent technological advances have enabled the use of ATAC-seq and RNA-seq on patient biopsies in a high-throughput manner. To tackle these problems, we trained a supervised machine learning model using genomic 3D signatures of physical enhancer-gene connections that can predict accurate connections using data from ATAC-seq and RNA-seq assays only. Using these data, we achieved an AUROC (area under receiver operating characteristic curve) of 0.91 for the identification of true regulatory element-gene connections and, importantly, can distinguish between active regulatory elements with connections to target genes and poised elements with no connections to target genes. Our predicted regulatory elements are validated by multi-omics data, including histone modification marks from ENCODE, with an average specificity of 0.92. Our model, applied on chromatin accessibility and transcriptomic data from 400 cancer patients across 22 cancer types revealed novel cancer-type and subtype-specific enhancer-gene connections for known cancer genes with experimental support for accurate prediction of subtype-specific enhancer target genes using CRISPRi in MCF7, T47D and MDA-MB-231 cell lines which represent hormone-receptor (HR) positive and HR- subtypes of breast cancer. We leverage these predictions to construct patient-specific gene regulatory networks, identify the key transcription factors (TFs) in these networks, clusters of patients with similar networks across cancer sites of origin and subsequently identify therapeutic vulnerabilities either by direct targeting of TFs or proteins that they co-operate with. We identify commonly used therapeutic agents for specific cancer types such as ESR1-targeting agents in ER+ breast cancer, KRAS and EGFR inhibitors in Lung and Colon cancers in addition to multiple novel therapeutic targets. We validated four novel candidates identified for neuroendocrine, liver and renal cancers, which have a dismal prognosis with current therapeutic options. Here we present a computational approach that combines multi-omics machine learning and network analysis then leverages these datasets to identify novel drug targets based on tumor lineage. Citation Format: Andre Neil Forbes, Duo Xu, Sandra Cohen, Ann Palladino, Priya Pancholi, Ekta Khurana. Discovery of novel therapeutic targets using 3D chromatin conformation and patient-specific gene regulatory networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2034. |
