PCAT: an integrated portal for genomic and preclinical testing data of pediatric cancer patient-derived xenograft models.

Although cancer is the leading cause of disease-related mortality in children, the relative rarity of pediatric cancers poses a significant challenge for developing novel therapeutics to further improve prognosis. Patient-derived xenograft (PDX) models, which are usually developed from high-risk tumors, are a useful platform to study molecular driver events, identify biomarkers and prioritize therapeutic agents. Here, we develop PDX for Childhood Cancer Therapeutics (PCAT), a new integrated portal for pediatric cancer PDX models. Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining. The current release comprises 324 models and their associated clinical and genomic data, including gene expression, mutation and copy number alteration. Importantly, PCAT curates preclinical testing results for 68 models and 79 therapeutic agents manually collected from individual agent testing studies published since 2008. To facilitate comparisons of patterns between patient tumors and PDX models, PCAT curates clinical and molecular data of patient tumors from the TARGET project. In addition, PCAT provides access to gene fusions identified in nearly 1000 TARGET samples. PCAT was built using R-shiny and MySQL. The portal can be accessed at http://pcat.zhenglab.info or http://www.pedtranscriptome.org.

A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques.

The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device's movement on a flat surface to a cursor's movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device's movement is detected through an accelerometer and is accordingly translated to the cursor's movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition.

Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody-antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody-antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental "epitope binning" of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call "dock binning" evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody-antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking.

We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.

Copper drives remodeling of metabolic state and progression of clear cell renal cell carcinoma.

Copper (Cu) is a cofactor of cytochrome c oxidase (CuCOX), indispensable for aerobic mitochondrial respiration. This study reveals that advanced clear cell renal cell carcinomas (ccRCCs) accumulate Cu, allocating it to CuCOX. Using a range of orthogonal approaches, including metabolomics, lipidomics, isotope-labeled glucose and glutamine flux analysis, and transcriptomics across tumor samples, cell lines, xenografts, and PDX models, combined with genetic and pharmacological interventions, we explored Cu's role in ccRCC. Elevated Cu levels stimulate CuCOX biogenesis, providing bioenergetic and biosynthetic benefits that promote tumor growth. This effect is complemented by glucose-dependent glutathione production, which facilitates detoxification and mitigates Cu-H2O2 toxicity. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics reveal increased oxidative metabolism, altered glutathione and Cu metabolism, and diminished HIF activity during ccRCC progression. Thus, Cu drives an integrated oncogenic remodeling of bioenergetics, biosynthesis, and redox homeostasis, fueling ccRCC growth, which can be targeted for new therapeutic approaches.

Copper drives remodeling of metabolic state and progression of clear cell renal cell carcinoma.

Copper (Cu) is an essential trace element required for mitochondrial respiration. Late-stage clear cell renal cell carcinoma (ccRCC) accumulates Cu and allocates it to mitochondrial cytochrome c oxidase. We show that Cu drives coordinated metabolic remodeling of bioenergy, biosynthesis and redox homeostasis, promoting tumor growth and progression of ccRCC. Specifically, Cu induces TCA cycle-dependent oxidation of glucose and its utilization for glutathione biosynthesis to protect against H 2 O 2 generated during mitochondrial respiration, therefore coordinating bioenergy production with redox protection. scRNA-seq determined that ccRCC progression involves increased expression of subunits of respiratory complexes, genes in glutathione and Cu metabolism, and NRF2 targets, alongside a decrease in HIF activity, a hallmark of ccRCC. Spatial transcriptomics identified that proliferating cancer cells are embedded in clusters of cells with oxidative metabolism supporting effects of metabolic states on ccRCC progression. Our work establishes novel vulnerabilities with potential for therapeutic interventions in ccRCC. Accumulation of copper is associated with progression and relapse of ccRCC and drives tumor growth.Cu accumulation and allocation to cytochrome c oxidase (CuCOX) remodels metabolism coupling energy production and nucleotide biosynthesis with maintenance of redox homeostasis.Cu induces oxidative phosphorylation via alterations in the mitochondrial proteome and lipidome necessary for the formation of the respiratory supercomplexes. Cu stimulates glutathione biosynthesis and glutathione derived specifically from glucose is necessary for survival of Cu Hi cells. Biosynthesis of glucose-derived glutathione requires activity of glutamyl pyruvate transaminase 2, entry of glucose-derived pyruvate to mitochondria via alanine, and the glutamate exporter, SLC25A22. Glutathione derived from glucose maintains redox homeostasis in Cu-treated cells, reducing Cu-H 2 O 2 Fenton-like reaction mediated cell death. Progression of human ccRCC is associated with gene expression signature characterized by induction of ETC/OxPhos/GSH/Cu-related genes and decrease in HIF/glycolytic genes in subpopulations of cancer cells. Enhanced, concordant expression of genes related to ETC/OxPhos, GSH, and Cu characterizes metabolically active subpopulations of ccRCC cells in regions adjacent to proliferative subpopulations of ccRCC cells, implicating oxidative metabolism in supporting tumor growth.

Inhibition of Neuromyelitis Optica Immunoglobulin G Binding to Aquaporin-4 by the Small Molecule Blocker Melanthioidine.

BACKGROUND: Neuromyelitis optica (NMO) is a severe neurological demyelinating autoimmune disease affecting the optic nerves and spinal cord. The binding of neuromyelitis optica immunoglobulin G (NMO- IgG) and aquaporin-4 (AQP4) on the surface of astrocytes in the serum and cerebrospinal fluid is the main pathogenesis of NMO. Currently, therapeutic strategies for NMO include a reduction of the secondary inflammation response and the number of NMO-IgG, which can only alleviate clinical symptoms rather than fundamentally preventing a series of pathological processes caused by NMO-IgG binding to AQP4. OBJECTIVE: The purpose of this study was to investigate the blocking effect of melanthioidine on the binding of NMO-IgG to AQP4 and its potential cytotoxicity. METHODS: The current study developed a cell-based high-throughput screening approach to identify a molecular blocker of NMO-IgG binding to AQP4 using the Chinese hamster lung fibroblast (V79) cells expressing M23- AQP4. By screening ~400 small molecules, we identified melanthioidine with blocking effects without affecting AQP4 expression or its water permeability. RESULTS: Melanthioidine effectively blocked the binding of NMO-IgG to AQP4 in immunofluorescence assays and reduced complement-dependent cytotoxicity against both NMO-IgG/complement-treated Fischer rat thyroid- AQP4 cells and primary astrocytes. The docking computations identified the putative sites of blocker binding at the extracellular surface of AQP4. CONCLUSION: This study serves as proof of a potential NMO therapy by using a small-molecule blocker to target NMO pathogenesis.

Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation.

Active telomerase is essential for stem cells and most cancers to maintain telomeres. The enzymatic activity of telomerase is related but not equivalent to the expression of TERT, the catalytic subunit of the complex. Here we show that telomerase enzymatic activity can be robustly estimated from the expression of a 13-gene signature. We demonstrate the validity of the expression-based approach, named EXTEND, using cell lines, cancer samples, and non-neoplastic samples. When applied to over 9,000 tumors and single cells, we find a strong correlation between telomerase activity and cancer stemness. This correlation is largely driven by a small population of proliferating cancer cells that exhibits both high telomerase activity and cancer stemness. This study establishes a computational framework for quantifying telomerase enzymatic activity and provides new insights into the relationships among telomerase, cancer proliferation, and stemness.

Celastrol suppresses nitric oxide synthases and the angiogenesis pathway in colorectal cancer.

The thunder god vine (Tripterygium wilfordii Hook. F) is traditionally used for inflammation-related diseases in traditional Chinese medicine. In recent years, celastrol (a natural compound from the root of the thunder god vine) has attracted great interest for its potential anticancer activities. The free radical nitric oxide (NO) is known to play a critical role in colorectal cancer growth by promoting tumour angiogenesis. However, how celastrol influences the NO pathway and its mechanism against colorectal cancer is largely unknown. In this study, we investigated the effects and mechanism of celastrol on nitric oxide synthase (NOS) and the angiogenesis pathway in colorectal cancer. Our data show that celastrol inhibited HT-29 and HCT116 cell proliferation, migration, and NOS activity in the cytoplasm. The antiproliferation activity of celastrol was associated with the inhibition of iNOS and eNOS in colorectal cancer cells. Treatment with celastrol inhibited colorectal cancer cell growth and migration, and was associated with suppression of the expression of key genes (TYMP, CDH5, THBS2, LEP, MMP9, and TNF) and proteins (IL-1b, MMP-9, PDGF, Serpin E1, and TIMP-4) involved in the angiogenesis pathway. In addition, combinational use of celastrol with 5-fluorouracil, salinomycin, 1400 W, and L-NIO showed enhanced inhibition of colorectal cancer cell proliferation and migration. In sum, our study suggests that celastrol could suppress colorectal cancer cell growth and migration, likely through suppressing NOS activity and inhibiting the angiogenesis pathway.

Chronic platelet-derived growth factor receptor signaling exerts control over initiation of protein translation in glioma.

Activation of the platelet-derived growth factor receptors (PDGFRs) gives rise to some of the most important signaling pathways that regulate mammalian cellular growth, survival, proliferation, and differentiation and their misregulation is common in a variety of diseases. Herein, we present a comprehensive and detailed map of PDGFR signaling pathways assembled from literature and integrate this map in a bioinformatics protocol designed to extract meaningful information from large-scale quantitative proteomics mass spectrometry data. We demonstrate the usefulness of this approach using a new genetically engineered mouse model of PDGFRα-driven glioma. We discovered that acute PDGFRα stimulation differs considerably from chronic receptor activation in the regulation of protein translation initiation. Transient stimulation activates several key components of the translation initiation machinery, whereas the clinically relevant chronic activity of PDGFRα is associated with a significant shutdown of translational members. Our work defines a step-by-step approach to extract biologically relevant insights from global unbiased phospho-protein datasets to uncover targets for therapeutic assessment.