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.

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Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here we combine deep learning, epigenetic perturbations and base editing to dissect regulatory sequences within the exemplar immune locus encoding CD69. Focusing on a differentially accessible and acetylated upstream enhancer, we find that the complementary strategies converge on a ~150 base interval as critical for CD69 induction in stimulated Jurkat T cells. We pinpoint individual cytosine to thymine base edits that markedly reduce element accessibility and acetylation, with corresponding reduction of CD69 expression. The most potent base edits may be explained by their effect on binding competition between the transcriptional activator GATA3 and the repressor BHLHE40. Systematic analysis of GATA and bHLH/Ebox motifs suggests that interplay between these factors plays a general role in rapid T cell transcriptional responses. Our study provides a framework for parsing gene regulatory elements in their endogenous chromatin contexts and identifying operative engineered variants.

The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino-acid sequence for a given IMP can dramatically affect expression levels; yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve, that, using only sequence information, increases the likelihood of selecting an IMP that expresses in E. coli. The IMProve model, trained on experimental data, combines a set of sequencederived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent datasets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization. Most notably, IMProve demonstrates for the first time that IMP expression levels can be predicted directly from sequence.

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.

5th generation networks are envisioned to provide seamless and ubiquitous connection to 1000-fold more devices and is believed to provide ultra-low latency and higher data rates up to tens of Gbps. Different technologies enabling these requirements are being developed including mmWave communications, Massive MIMO and beamforming, Device to Device (D2D) communications and Heterogeneous Networks. D2D communication is a promising technology to enable applications requiring high bandwidth such as online streaming and online gaming etc. It can also provide ultra-low latencies required for applications like vehicle to vehicle communication for autonomous driving. D2D communication can provide higher data rates with high energy efficiency and spectral efficiency compared to conventional communication. The performance benefits of D2D communication can be best achieved when D2D users reuses the spectrum being utilized by the conventional cellular users. This spectrum sharing in a multi-tier heterogeneous network will introduce complex interference among D2D users and cellular users which needs to be resolved. Motivated by limited number of surveys for interference mitigation and resource allocation in D2D enabled heterogeneous networks, we have surveyed different conventional and artificial intelligence based interference mitigation and resource allocation schemes developed in recent years. Our contribution lies in the analysis of conventional interference mitigation techniques and their shortcomings. Finally, the strengths of AI based techniques are determined and open research challenges deduced from the recent research are presented.