CPD-002, a novel VEGFR2 inhibitor, relieves rheumatoid arthritis by reducing angiogenesis through the suppression of the VEGFR2/PI3K/AKT signaling pathway.

Synovial angiogenesis is a key player in the development of rheumatoid arthritis (RA), and anti-angiogenic therapy is considered a promising approach for treating RA. CPD-002 has demonstrated efficacy in suppressing tumor angiogenesis as a VEGFR2 inhibitor, but its specific impacts on RA synovial angiogenesis and possible anti-RA effects need further study. We examined the influences of CPD-002 on the migration and invasion of human umbilical vein endothelial cells (HUVECs) and its impacts on HUVECs' tube formation and vessel sprouting ex vivo. The therapeutic potential of CPD-002 in adjuvant-induced arthritis (AIA) rats and its suppression of synovial angiogenesis were examined. The involvement of the VEGFR2/PI3K/AKT pathway was assessed both in HUVECs and AIA rat synovium. Here, CPD-002 inhibited the migration and invasion of VEGF-stimulated HUVECs, decreased their chemotactic response to RA fibroblast-like synoviocyte-released chemoattractants, and exhibited anti-angiogenic effects in vitro and ex vivo. CPD-002's targeting of VEGFR2 was confirmed with molecular docking and cellular thermal shift assays, supported by the abolishment of CPD-002's effects upon using VEGFR2 siRNA. CPD-002 relieved paw swelling, arthritis index, joint damage, and synovial angiogenesis, indicating its anti-arthritic and anti-angiogenic effects in AIA rats. Moreover, the anti-inflammatory effects in vivo and in vitro of CPD-002 contributed to its anti-angiogenic effects. Mechanistically, CPD-002 hindered the activation of VEGFR2/PI3K/AKT pathway in VEGF-induced HUVECs and AIA rat synovium, as evidenced by reduced p-VEGFR2, p-PI3K, and p-AKT protein levels alongside elevated PTEN protein levels. Totally, CPD-002 showed anti-rheumatoid effects via attenuating angiogenesis through the inhibition of the VEGFR2/PI3K/AKT pathway.

Allosterically inhibited PFKL via prostaglandin E2 withholds glucose metabolism and ovarian cancer invasiveness.

Metastasis is the leading cause of high ovarian-cancer-related mortality worldwide. Three major processes constitute the whole metastatic cascade: invasion, intravasation, and extravasation. Tumor cells often reprogram their metabolism to gain advantages in proliferation and survival. However, whether and how those metabolic alterations contribute to the invasiveness of tumor cells has yet to be fully understood. Here we performed a genome-wide CRISPR-Cas9 screening to identify genes participating in tumor cell dissemination and revealed that PTGES3 acts as an invasion suppressor in ovarian cancer. Mechanistically, PTGES3 binds to phosphofructokinase, liver type (PFKL) and generates a local source of prostaglandin E2 (PGE2) to allosterically inhibit the enzymatic activity of PFKL. Repressed PFKL leads to downgraded glycolysis and the subsequent TCA cycle for glucose metabolism. However, ovarian cancer suppresses the expression of PTGES3 and disrupts the PTGES3-PGE2-PFKL inhibitory axis, leading to hyperactivation of glucose oxidation, eventually facilitating ovarian cancer cell motility and invasiveness.

Neodb: a comprehensive neoantigen database and discovery platform for cancer immunotherapy.

Neoantigens derived from somatic deoxyribonucleic acid alterations are ideal cancer-specific targets. However, integrated platform for neoantigen discovery is urgently needed. Recently, many scattered experimental evidences suggest that some neoantigens are immunogenic, and comprehensive collection of these experimentally validated neoantigens is still lacking. Here, we have integrated the commonly used tools in the current neoantigen discovery process to form a comprehensive web-based analysis platform. To identify experimental evidences supporting the immunogenicity of neoantigens, we performed comprehensive literature search and constructed the database. The collection of public neoantigens was obtained by using comprehensive features to filter the potential neoantigens from recurrent driver mutations. Importantly, we constructed a graph neural network (GNN) model (Immuno-GNN) using an attention mechanism to consider the spatial interactions between human leukocyte antigen and antigenic peptides for neoantigen immunogenicity prediction. The new easy-to-use R/Shiny web-based neoantigen database and discovery platform, Neodb, contains currently the largest number of experimentally validated neoantigens. In addition to validated neoantigen, Neodb also includes three additional modules for facilitating neoantigen prediction and analysis, including 'Tools' module (comprehensive neoantigen prediction tools); 'Driver-Neo' module (collection of public neoantigens derived from recurrent mutations) and 'Immuno-GNN' module (a novel immunogenicity prediction tool based on a GNN). Immuno-GNN shows improved performance compared with known methods and also represents the first application of GNN model in neoantigen immunogenicity prediction. The construction of Neodb will facilitate the study of neoantigen immunogenicity and the clinical application of neoantigen-based cancer immunotherapy. Database URL https://liuxslab.com/Neodb/.

Copy number alteration features in pan-cancer homologous recombination deficiency prediction and biology.

Homologous recombination deficiency (HRD) renders cancer cells vulnerable to unrepaired double-strand breaks and is an important therapeutic target as exemplified by the clinical efficacy of poly ADP-ribose polymerase (PARP) inhibitors as well as the platinum chemotherapy drugs applied to HRD patients. However, it remains a challenge to predict HRD status precisely and economically. Copy number alteration (CNA), as a pervasive trait of human cancers, can be extracted from a variety of data sources, including whole genome sequencing (WGS), SNP array, and panel sequencing, and thus can be easily applied clinically. Here we systematically evaluate the predictive performance of various CNA features and signatures in HRD prediction and build a gradient boosting machine model (HRDCNA) for pan-cancer HRD prediction based on these CNA features. CNA features BP10MB[1] (The number of breakpoints per 10MB of DNA is 1) and SS[ > 7 & <=8] (The log10-based size of segments is greater than 7 and less than or equal to 8) are identified as the most important features in HRD prediction. HRDCNA suggests the biallelic inactivation of BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BARD1 as the major genetic basis for human HRD, and may also be applied to effectively validate the pathogenicity of BRCA1/2 variants of uncertain significance (VUS). Together, this study provides a robust tool for cost-effective HRD prediction and also demonstrates the applicability of CNA features and signatures in cancer precision medicine.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Surgical Treatment of Pineal Region Tumors: An 18 year-Experience at a Single Institution.

BACKGROUND: The pineal tumor was once considered as a restricted area for surgery. Such cases are rare, with many different opinions on surgical treatment. This study aimed to review our experience of tumor treatment in the pineal region and explore the optimal treatment strategy. METHODS: The clinical data of 72 patients with pineal tumors from January 1997 to May 2015 (18 years) were retrospectively analyzed. Preoperative preparation, pathology type, tumor resection rate, surgical approach, and follow-up outcomes were used as the indicators to evaluate the treatment efficacy. RESULTS: The Krause approach was used in 46 cases, the Poppen approach in 10 cases, and the transcallosal-lateral ventricle-choroid fissure approach in 16 cases. The postoperative pathological results were as follows: 24 cases of germinoma, 11 of teratoma, 15 of glioma, 6 of meningioma, 11 of Pineocytoma, 2 of cholesteatoma, 2 of cavernous hemangioma, and 1 of choriocarcinoma. Further, the study included 64 cases of total surgical resections, 8 of subtotal resections, and 2 deaths. The follow-up period was from 7 months to 10 years. Further, 51 (70.8%) patients were followed up. The multivariate regression model showed that the surgical method and the pathological type contributed significantly to predicting outcomes. CONCLUSIONS: The type of pathology, extent of excision, and surgical approach had a significant impact on the prognosis of patients. The transcallosal-lateral ventricle-choroid fissure approach for large and medium-sized pineal tumors near the posterior part of the third ventricle had good efficacy.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis.

Lymph nodes metastases are common in patients with lung cancer. Additionally, those patients are often at a higher risk for death from lung tumor than those with tumor-free lymph nodes. Somatic DNA alterations are key drivers of cancer, and copy number alterations (CNAs) are major types of DNA alteration that promote lung cancer progression. Here, we performed genome-wide DNA copy number analysis, and identified a novel lung-cancer-metastasis-related gene, EFNA4. The EFNA4 genome locus was significantly amplified, and EFNA4 mRNA expression was significantly up-regulated in lung cancer compared with normal lung tissue, and also in lung cancer with lymph node metastases compared with lung cancer without metastasis. EFNA4 encodes Ephrin A4, which is the ligand for Eph receptors. The function of EFNA4 in human lung cancer remains largely unknown. Through cell line experiments we showed that EFNA4 overexpression contributes to lung tumor cells growth, migration and adhesion. Conversely, EFNA4 knockdown or knockout led to the growth suppression of cells and tumor xenografts in mice. Lung cancer patients with EFNA4 overexpression have poor prognosis. Together, by elucidating a new layer of the role of EFNA4 in tumor proliferation and migration, our study demonstrates a better understanding of the function of the significantly amplified and overexpressed gene EFNA4 in lung tumor metastasis, and suggests EFNA4 as a potential target in metastatic lung cancer therapy.

A metal chaperone OsHIPP16 detoxifies cadmium by repressing its accumulation in rice crops.

Cadmium (Cd) is an environmentally polluted toxic heavy metal and seriously risks food safety and human health through food chain. Mining genetic potentials of plants is a crucial step for limiting Cd accumulation in rice crops and improving environmental quality. This study characterized a novel locus in rice genome encoding a Cd-binding protein named OsHIPP16, which resides in the nucleus and near plasma membrane. OsHIPP16 was strongly induced by Cd stress. Histochemical analysis with pHIPP16::GUS reveals that OsHIPP16 is primarily expressed in root and leaf vascular tissues. Expression of OsHIPP16 in the yeast mutant strain ycf1 sensitive to Cd conferred cellular tolerance. Transgenic rice overexpressing OsHIPP16 (OE) improved rice growth with increased plant height, biomass, and chlorophyll content but with a lower degree of oxidative injury and Cd accumulation, whereas knocking out OsHIPP16 by CRISPR-Cas9 compromised the growth and physiological response. A lifelong trial with Cd-polluted soil shows that the OE plants accumulated much less Cd, particularly in brown rice where the Cd concentrations declined by 11.76-34.64%. Conversely, the knockout oshipp16 mutants had higher levels of Cd with the concentration in leaves being increased by 26.36-35.23% over the wild-type. These results suggest that adequate expression of OsHIPP16 would profoundly contribute to Cd detoxification by regulating Cd accumulation in rice, suggesting that both OE and oshipp16 mutant plants have great potentials for restricting Cd acquisition in the rice crop and phytoremediation of Cd-contaminated wetland soils.

Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization.

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service.

OsZIP11 is a trans-Golgi-residing transporter required for rice iron accumulation and development.

Iron (Fe) is a mineral nutrient necessary for plant growth and development. Whether the rice ZRT/IRT-like protein family metal transporter OsZIP11 is involved in Fe transport has not been functionally defined. The objective of the study is to figure out the essential role of the uncharacterized OsZIP11 played in rice growth, development, and iron accumulation, particularly in seeds. Transient subcellular location assays show that OsZIP11 was targeted to the trans-Golgi network. OsZIP11 was preferentially expressed in the rice tissues (or organs) at later flowering and seed development stages. Transcripts of OsZIP11 were significantly induced under Fe but not under zinc (Zn), copper (Cu) or manganese (Mn) deficiency. Yeast (Saccharomyces cerevisiae) transformed with OsZIP11 sequences displayed an active iron input which turned out that excessive iron accumulated in the cells. Knocking out OsZIP11 by CRISPR-Cas9 approach led to the attenuated rice growth and physiological phenotypes, depicting shorter plant height, reduced biomass, chlorosis (a symptom of lower chlorophyll concentration), and over-accumulation of malondialdehyde (complex representing the peroxidation of membrane lipids) in rice plantlets. The field trials demonstrated that OsZIP11 mutation impaired the capacity of seed development, with shortened panicle and seed length, compromised spikelet fertility, and reduced grain per plant or 1000-grain weight. Knocking out OsZIP11 also lowered the accumulation of iron in the brown rice by 48-51% compared to the wild-type. Our work pointed out that OsZIP11 is required for iron acquisition for rice growth and development.

Ascyrones A-E, type B bicyclic ployprenylated acylphloroglucinol derivatives from Hypericum ascyron.

Five new polycyclic polyprenylated acylphloroglucinols (1-5), ascyrones A-E, and four known compounds (6-9) were isolated from the aerial parts of Hypericum ascyron. All of the isolates containing a bicyclo[3.3.1]nonane-2,4,9-trione core and a benzoyl group, belonged to type B bicyclic polyprenylated acylphloroglucinols (BPAPs). Their structures and absolute configurations were established based on spectroscopic analyses and calculated electronic circular dichroism (ECD) data. The anti-inflammatory, neuroprotective and cytotoxicity activities of compounds 1-4 and 6-9 were evaluated. Compound 6 exhibited obvious anti-inflammatory activity in lipopolysaccharide (LPS)-induced RAW264.7 cells. Compounds 1 and 9 exhibited slight cytotoxicity against Hep3B cells. Meanwhile, compound 1 showed mild neuroprotective activity against corticosterone (CORT)-induced PC12 cell damage at 10 µmol·L-1.

Quantification of Neoantigen-Mediated Immunoediting in Cancer Evolution.

Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual patients with cancer. Moreover, this method demonstrated the prevalence of immunoediting in the immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer. SIGNIFICANCE: Quantification of neoantigen-mediated negative selection in cancer progression reveals distinct features of cancer immunoediting and can serve as a potential biomarker to predict immunotherapy response.

Extrachromosomal DNA formation enables tumor immune escape potentially through regulating antigen presentation gene expression.

Extrachromosomal DNA (ecDNA) is a type of circular and tumor specific genetic element. EcDNA has been reported to display open chromatin structure, facilitate oncogene amplification and genetic material unequal segregation, and is associated with poor cancer patients' prognosis. The ability of immune evasion is a typical feature for cancer progression, however the tumor intrinsic factors that determine immune evasion remain poorly understood. Here we show that the presence of ecDNA is associated with markers of tumor immune evasion, and obtaining ecDNA could be one of the mechanisms employed by tumor cells to escape immune surveillance. Tumors with ecDNA usually have comparable TMB and neoantigen load, however they have lower immune cell infiltration and lower cytotoxic T cell activity. The microenvironment of tumors with ecDNA shows increased immune-depleted, decreased immune-enriched fibrotic types. Both MHC class I and class II antigen presentation genes' expression are decreased in tumors with ecDNA, and this could be the underlying mechanism for ecDNA associated immune evasion. This study provides evidence that ecDNA formation is an immune escape mechanism for cancer cells.

Synthesis and biological evaluation of novel hybrids of phenylsulfonyl furoxan and phenstatin derivatives as potent anti-tumor agents.

Hybridization of nitric oxide (NO) donors with known anti-cancer agents have been emerged as a strategy to achieve improved therapeutic effect and to overcome chemo-resistance in cancer therapy. In this study, furoxan moiety as an efficient NO donor was introduced to phenstatin, a microtubule-interfering agent (MIA), leading to the design and synthesis of a series of furoxan-based NO-releasing arylphenones derivatives. In biological evaluation, the synthesized compounds showed moderate to potent anti-tumor activities against several human cancer cell lines. Among them, compound 15h showed the most potent activities against both chemo-sensitive and resistant cancer cell lines with IC50 values ranging from 0.008 to 0.021 µM. Further mechanistic studies revealed that 15h worked as a bifunctional agent exhibiting both tubulin polymerized inhibition and NO-releasing activities, resulting in potent anti-angiogenesis, colony formation inhibition, cell cycle arrest and apoptosis induction effects. In the nude mice xenograft model, 15h significantly inhibited the paclitaxel-resistant tumor growth with low toxicity, demonstrating the promising potential for further preclinical evaluation as a therapeutic agent, particularly for the treatment of chemo-resistant cancers.

UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data.

SUMMARY: UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer. AVAILABILITY AND IMPLEMENTATION: UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ggct (γ-glutamyl cyclotransferase) plays an important role in erythrocyte antioxidant defense and red blood cell survival.

The expression of GGCT (γ-glutamyl cyclotransferase) is upregulated in various human cancers. γ-glutamyl cyclotransferase enzyme activity was originally purified from human red blood cells (RBCs), but the physiological function of GGCT in RBCs is still not clear. Here we reported that Ggct deletion in mice leads to splenomegaly and progressive anaemia phenotypes, due to elevated oxidative damage and the shortened life span of Ggct-/- RBCs. Ggct-/- RBCs have increased reactive oxygen species (ROS), and are more sensitive to H2 O2 -induced damage compared to control RBCs. Glutathione (GSH) and GSH synthesis precursor l-cysteine are decreased in Ggct-/- RBCs. Our study suggests a critical function of Ggct in RBC redox balance and life span maintenance through regulating GSH metabolism.