Epigenetic aberrations are widespread in cancer, yet the underlying mechanisms and causality remain poorly understood [1][2][3] . A subset of gastrointestinal stromal tumors (GISTs) lack canonical kinase mutations but instead have succinate dehydrogenase (SDH)-deficiency and global DNA hyper-methylation 4,5 . Here we associate this hyper-methylation with changes in genome topology that activate oncogenic programs. To investigate epigenetic alterations systematically, we mapped DNA methylation, CTCF insulators, enhancers, and chromosome topology in KIT-mutant, PDGFRA-mutant, and SDH-deficient GISTs. Although these respective subtypes shared similar enhancer landscapes, we identified hundreds of putative insulators where DNA methylation replaced CTCF binding in SDH-deficient GISTs. We focused on a disrupted insulator that normally partitions a core GIST super-enhancer from the FGF4 oncogene. Recurrent loss of this insulator alters locus topology in SDH-deficient GISTs, allowing aberrant physical interaction between enhancer and oncogene. CRISPR-mediated excision of the corresponding CTCF motifs in an SDH-intact GIST model disrupted the boundary and strongly up-regulated FGF4 expression. We also identified a second recurrent insulator loss event near the KIT oncogene, which is also highly expressed across SDH-deficient GISTs. Finally, we established a patient-derived xenograft (PDX) from an SDH-deficient GIST that faithfully maintains the epigenetics of the parental tumor, including hyper-methylation and insulator defects. This PDX model is highly sensitive to FGF receptor (FGFR) inhibitor, and more so to combined FGFR and KIT inhibition, validating the functional significance of the underlying epigenetic lesions. Our study reveals how epigenetic alterations can drive oncogenic programs in the absence of canonical kinase mutations, with implications for mechanistic targeting of aberrant pathways in cancers.

Multi-tier heterogeneous networks (HetNets) and device-to-device (D2D) communication are vastly considered in 5G networks. The interference mitigation and resource allocation in the D2D enabled multi-tier HetNets is a cumbersome and challenging task that cannot be solved by the conventional centralized resource allocation techniques proposed in the literature. In this paper, we propose a distributed multi-agent learning-based spectrum allocation scheme in which D2D users learn the wireless environment and select spectrum resources autonomously to maximize their throughput and spectral efficiency (SE) while causing minimum interference to the cellular users. We have employed the distributed learning in a stochastic geometry-based realistic multi-tier heterogeneous network to validate the performance of our scheme. The proposed scheme enables the D2D users to achieve higher throughput and SE, higher signal-to-interferenceplus-noise ratio and low outage ratio for cellular users, and better computational time efficiency and performs well in the dense multi-tier HetNets without affecting network coverage compared with the distance based resource criterion and joint-resource allocation and link adaptation schemes.INDEX TERMS D2D communication, multi-agent reinforcement learning, autonomous spectrum allocation, distributed reinforcement learning, heterogeneous networks, interference mitigation in D2D enabled HetNets.