Summary Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions . The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro . Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

The interaction of long noncoding RNAs (lncRNAs) with one or more RNA-binding proteins (RBPs) is important to a plethora of cellular and physiological processes. The lncRNA SNHG1 was reported to be aberrantly expressed and associated with poor patient prognosis in several cancers including neuroblastoma. However, the interacting RBPs and biological functions associated with SNHG1 in neuroblastoma remain unknown. In this study, we identified 283, 31, and 164 SNHG1-interacting proteins in SK-N-BE(2)C, SK-N-DZ, and SK-N-AS neuroblastoma cells, respectively, using a RNA-protein pull-down assay coupled with liquid chromatography−tandem mass spectrometry (LC−MS/MS). Twenty-four SNHG1-interacting RBPs were identified in common from these three neuroblastoma cell lines. RBPs MATR3, YBX1, and HNRNPL have the binding sites for SNHG1 predicted by DeepBind motif analysis. Furthermore, the direct binding of MATR3 with SNHG1 was validated by Western blot and confirmed by RNA immunoprecipitation assay (RIP). Coexpression analysis revealed that the expression of SNHG1 is positively correlated with MATR3 (P = 3.402 × 10 −13 ). The high expression of MATR3 is associated with poor event-free survival (P = 0.00711) and overall survival (P = 0.00064). Biological functions such as ribonucleoprotein complex biogenesis, RNA processing, and RNA splicing are significantly enriched and in common between SNHG1 and MATR3. In conclusion, we identified MATR3 as binding to SNHG1 and the interaction might be involved in splicing events that enhance neuroblastoma progression.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causative agent for the outbreak of coronavirus disease 2019 (COVID-19). This global pandemic is now calling for efforts to develop more effective COVID-19 therapies. Here we use a host-directed approach, which focuses on cellular responses to diverse small-molecule treatments, to identify potentially effective drugs for COVID-19. This framework looks at the ability of compounds to elicit a similar transcriptional response to IFN-β, a type I interferon that fails to be induced at notable levels in response to SARS-CoV-2 infection. By correlating the perturbation profiles of ~3,000 small molecules with a high-quality signature of IFN-β-responsive genes in primary normal human bronchial epithelial cells, our analysis revealed four candidate COVID-19 compounds, namely homoharringtonine, narciclasine, anisomycin, and emetine. We experimentally confirmed that the predicted compounds significantly inhibited SARS-CoV-2 replication in Vero E6 cells at nanomolar, relatively non-toxic concentrations, with half-maximal inhibitory concentrations of 165.7 nM, 16.5 nM, and 31.4 nM for homoharringtonine, narciclasine, and anisomycin, respectively. Together, our results corroborate a host-centric strategy to inform protective antiviral therapies for COVID-19.

The Java bytecode language is emerging as a soft.-ware distribution standard. W i t h major vendors conimatted t o porting the Java run-time environment to their platforms, Java bytecode programs are expected to run without modification on multiple platforms. TFiese first generation run-time environmen,ts rely o n an, interpreter t o bridge the gap between the bytecode instructions and the native hardware. However, Java interpreters cause performance problems with, niicroarch,itectural features such as the caches and the Brunch Turget Buffer. S o m e of these problems can, be solved b y translating Java bytecode t o native code. I n this paper we compare the performance of code run through the SUN Java interpreter t o code compiled through Caffeine, a bytecode t o native code translator, as well as t o compaled C/C++ versions of the code, using large applications and common bench"&.W e discuss th,e reasons f o r several performance problems incurred b y both, approaches t o running Java code, and ezamine possible solutions.

Neuroblastoma is a neural crest-derived embryonal tumor and accounts for about 15% of all cancer deaths in children. MYCN amplification is associated with aggressive and advanced stage of high-risk neuroblastoma, which remains difficult to treat and exhibits poor survival under current multimodality treatment. Here, we analyzed the transcriptomic profiles of neuroblastoma patients and showed that aurora kinases lead to poor survival and had positive correlation with MYCN amplification and high-risk disease. Further, pan-aurora kinase inhibitor (tozasertib) treatment not only induces cell-cycle arrest and suppresses cell proliferation, migration, and invasion ability in MYCN-amplified (MNA) neuroblastoma cell lines, but also inhibits tumor growth and prolongs animal survival in Th-MYCN transgenic mice. Moreover, we performed quantitative proteomics and identified 150 differentially expressed proteins after tozasertib treatment in the Th-MYCN mouse model. The functional and network-based enrichment revealed that tozasertib alters metabolic processes and identified a mitochondrial flavoenzyme in fatty acid β-oxidation, ACADM, which is correlated with aurora kinases and neuroblastoma patient survival. Our findings indicate that the aurora kinase inhibitor could cause metabolic imbalance, possibly by disturbing carbohydrate and fatty acid metabolic pathways, and ACADM may be a potential target in MNA neuroblastoma.

We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning a regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.

Purpose: Neuroblastoma is a pediatric malignancy of the sympathetic nervous system with diverse clinical behaviors. Genomic amplification of MYCN oncogene has been shown to drive neuroblastoma pathogenesis and correlate with aggressive disease, but the survival rates for those high-risk tumors carrying no MYCN amplification remain equally dismal. The paucity of mutations and molecular heterogeneity has hindered the development of targeted therapies for most advanced neuroblastomas. We use an alternative method to identify potential drugs that target nononcogene dependencies in high-risk neuroblastoma.Experimental Design: By using a gene expression-based integrative approach, we identified prognostic signatures and potentially effective single agents and drug combinations for high-risk neuroblastoma.Results: Among these predictions, we validated in vitro efficacies of some investigational and marketed drugs, of which niclosamide, an anthelmintic drug approved by the FDA, was further investigated in vivo. We also quantified the proteomic changes during niclosamide treatment to pinpoint nucleoside diphosphate kinase 3 (NME3) downregulation as a potential mechanism for its antitumor activity.Conclusions: Our results establish a gene expression-based strategy to interrogate cancer biology and inform drug discovery and repositioning for high-risk neuroblastoma.

SummaryBiological systems often respond to a specific environmental or genetic perturbation without pervasive gene expression changes. Such robustness to perturbations, however, is not reflected on the current computational strategies that utilize gene expression similarity metrics for drug discovery and repositioning. Here we propose a new expression-intensity-based similarity metric that consistently achieved better performance than other state-of-the-art similarity metrics with respect to the gold-standard clustering of drugs with known mechanisms of action. The new metric directly emphasizes the genes exhibiting the greatest changes in expression in response to a perturbation. Using the new framework to systematically compare 3,332 chemical and 3,934 genetic perturbations across 10 cell types representing diverse cellular signatures, we identified thousands of recurrent and cell type-specific connections. We also experimentally validated two drugs identified by the analysis as potential topoisomerase inhibitors. The new framework is a valuable resource for hypothesis generation, functional testing, and drug repositioning.