TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome.

LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution.

Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the 'One Cell, One World' theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

High-resolution structural genomics reveals new therapeutic vulnerabilities in glioblastoma.

We investigated the role of 3D genome architecture in instructing functional properties of glioblastoma stem cells (GSCs) by generating sub-5-kb resolution 3D genome maps by in situ Hi-C. Contact maps at sub-5-kb resolution allow identification of individual DNA loops, domain organization, and large-scale genome compartmentalization. We observed differences in looping architectures among GSCs from different patients, suggesting that 3D genome architecture is a further layer of inter-patient heterogeneity for glioblastoma. Integration of DNA contact maps with chromatin and transcriptional profiles identified specific mechanisms of gene regulation, including the convergence of multiple super enhancers to individual stemness genes within individual cells. We show that the number of loops contacting a gene correlates with elevated transcription. These results indicate that stemness genes are hubs of interaction between multiple regulatory regions, likely to ensure their sustained expression. Regions of open chromatin common among the GSCs tested were poised for expression of immune-related genes, including CD276 We demonstrate that this gene is co-expressed with stemness genes in GSCs and that CD276 can be targeted with an antibody-drug conjugate to eliminate self-renewing cells. Our results demonstrate that integrated structural genomics data sets can be employed to rationally identify therapeutic vulnerabilities in self-renewing cells.

What we can learn from embryos to understand the mesenchymal-to-epithelial transition in tumor progression.

Epithelial plasticity involved the terminal and transitional stages that occur during epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-epithelial transition (MET), both are essential at different stages of early embryonic development that have been co-opted by cancer cells to undergo tumor metastasis. These processes are regulated at multiple instances, whereas the post-transcriptional regulation of key genes mediated by microRNAs is gaining major attention as a common and conserved pathway. In this review, we focus on discussing the latest findings of the cellular and molecular basis of the less characterized process of MET during embryonic development, with special attention to the role of microRNAs. Although we take in consideration the necessity of being cautious when extrapolating the obtained evidence, we propose some commonalities between early embryonic development and cancer progression that can shed light into our current understanding of this complex event and might aid in the design of specific therapeutic approaches.

Identification of circulating biomarkers for differentiating patients with papillary thyroid cancers from benign thyroid tumors.

BACKGROUND: This study aimed to identify the potential circulating biomarkers of protein, mRNAs, and long non-coding RNAs (lncRNAs) to differentiate the papillary thyroid cancers from benign thyroid tumors. METHODS: The study population of 100 patients was classified into identification (10 patients with papillary thyroid cancers and 10 patients with benign thyroid tumors) and validation groups (45 patients with papillary thyroid cancers and 35 patients with benign thyroid tumors). The Sengenics Immunome Protein Array-combined data mining approach using the Open Targets Platform was used to identify the putative protein biomarkers, and their expression validated using the enzyme-linked immunosorbent assay. Next-generation sequencing by Illumina HiSeq was used for the detection of dysregulated mRNAs and lncRNAs. The website Timer v2.0 helped identify the putative mRNA biomarkers, which were significantly over-expressed in papillary thyroid cancers than in adjacent normal thyroid tissue. The mRNA and lncRNA biomarker expression was validated by a real-time polymerase chain reaction. RESULTS: Although putative protein and mRNA biomarkers have been identified, their serum expression could not be confirmed in the validation cohorts. In addition, seven lncRNAs (TCONS_00516490, TCONS_00336559, TCONS_00311568, TCONS_00321917, TCONS_00336522, TCONS_00282483, and TCONS_00494326) were identified and validated as significantly downregulated in patients with papillary thyroid cancers compared to those with benign thyroid tumors. These seven lncRNAs showed moderate accuracy based on the area under the curve (AUC = 0.736) of receiver operating characteristic in predicting the occurrence of papillary thyroid cancers. CONCLUSIONS: We identified seven downregulated circulating lncRNAs with the potential for predicting the occurrence of papillary thyroid cancers.

Identification of prognostic lipid droplet-associated genes in pancreatic cancer patients via bioinformatics analysis.

BACKGROUND: Pancreatic cancer is the fourth leading cause of cancer deaths in the United States both in females and in males, and is projected to become the second deadliest cancer by 2030. The overall 5-year survival rate remains at around 10%. Cancer metabolism and specifically lipid metabolism plays an important role in pancreatic cancer progression and metastasis. Lipid droplets can not only store and transfer lipids, but also act as molecular messengers, and signaling factors. As lipid droplets are implicated in reprogramming tumor cell metabolism and in invasion and migration of pancreatic cancer cells, we aimed to identify lipid droplet-associated genes as prognostic markers in pancreatic cancer. METHODS: We performed a literature search on review articles related to lipid droplet-associated proteins. To select relevant lipid droplet-associated factors, bioinformatics analysis on the GEPIA platform (data are publicly available) was carried out for selected genes to identify differential expression in pancreatic cancer versus healthy pancreatic tissues. Differentially expressed genes were further analyzed regarding overall survival of pancreatic cancer patients. RESULTS: 65 factors were identified as lipid droplet-associated factors. Bioinformatics analysis of 179 pancreatic cancer samples and 171 normal pancreatic tissue samples on the GEPIA platform identified 39 deferentially expressed genes in pancreatic cancer with 36 up-regulated genes (ACSL3, ACSL4, AGPAT2, BSCL2, CAV1, CAV2, CAVIN1, CES1, CIDEC, DGAT1, DGAT2, FAF2, G0S2, HILPDA, HSD17B11, ICE2, LDAH, LIPE, LPCAT1, LPCAT2, LPIN1, MGLL, NAPA, NCEH1, PCYT1A, PLIN2, PLIN3, RAB5A, RAB7A, RAB8A, RAB18, SNAP23, SQLE, VAPA, VCP, VMP1) and 3 down-regulated genes (FITM1, PLIN4, PLIN5). Among 39 differentially expressed factors, seven up-regulated genes (CAV2, CIDEC, HILPDA, HSD17B11, NCEH1, RAB5A, and SQLE) and two down-regulation genes (BSCL2 and FITM1) were significantly associated with overall survival of pancreatic cancer patients. Multivariate Cox regression analysis identified CAV2 as the only independent prognostic factor. CONCLUSIONS: Through bioinformatics analysis, we identified nine prognostic relevant differentially expressed genes highlighting the role of lipid droplet-associated factors in pancreatic cancer.

WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts.

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

Bioassay-Guided Identification of the Antiproliferative Compounds of Cissus trifoliata and the Transcriptomic Effect of Resveratrol in Prostate Cancer Pc3 Cells.

The bioassay-guided fractionation of a CHCl3-MeOH extract from the stems of Cissus trifoliata identified an active fraction against PC3 prostate cancer cells. The treatment for 24 h showed an 80% reduction in cell viability (p ≤ 0.05) by a WST-1 assay at a concentration of 100 µg/mL. The HPLC-QTOF-MS analysis of the fraction showed the presence of coumaric and isoferulic acids, apigenin, kaempferol, chrysoeriol, naringenin, ursolic and betulinic acids, hexadecadienoic and octadecadienoic fatty acids, and the stilbene resveratrol. The exposure of PC3 cells to resveratrol (IC25 = 23 µg/mL) for 24 h induced significant changes in 847 genes (Z-score ≥ ±2). The functional classification tool of the DAVID v6.8 platform indicates that the underlying molecular mechanisms against the proliferation of PC3 cells were associated (p ≤ 0.05) with the process of differentiation and metabolism. These findings provide experimental evidence suggesting the potential of C. trifoliata as a promising natural source of anticancer compounds.

Integrative analysis of DNA methylation-driven genes for the prognosis of lung squamous cell carcinoma using MethylMix.

Background: DNA methylation acts as a key component in epigenetic modifications of genomic function and functions as disease-specific prognostic biomarkers for lung squamous cell carcinoma (LUSC). This present study aimed to identify methylation-driven genes as prognostic biomarkers for LUSC using bioinformatics analysis. Materials and Methods: Differentially expressed RNAs were obtained using the edge R package from 502 LUSC tissues and 49 adjacent non-LUSC tissues. Differentially methylated genes were obtained using the limma R package from 504 LUSC tissues and 69 adjacent non-LUSC tissues. The methylation-driven genes were obtained using the MethylMix R package from 500 LUSC tissues with matched DNA methylation data and gene expression data and 69 non-LUSC tissues with DNA methylation data. Gene ontology and ConsensusPathDB pathway analysis were performed to analyze the functional enrichment of methylation-driven genes. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of differentially methylated genes for predicting the prognosis of LUSC. Results: A total of 44 methylation-driven genes were obtained. Univariate and multivariate Cox regression analyses showed that twelve aberrant methylated genes (ATP6V0CP3, AGGF1P3, RP11-264L1.4, HIST1H4K, LINC01158, CH17-140K24.1, CTC-523E23.14, ADCYAP1, COX11P1, TRIM58, FOXD4L6, CBLN1) were entered into a Cox predictive model associated with overall survival in LUSC patients. Methylation and gene expression combined survival analysis showed that the survival rate of hypermethylation and low-expression of DQX1 and WDR61 were low. The expression of DQX1 had a significantly negatively correlated with the methylation site cg02034222. Conclusion: Methylation-driven genes DQX1 and WDR61 might be potential biomarkers for predicting the prognosis of LUSC.

Identification of Core Genes Involved in the Progression of Cervical Cancer Using an Integrative mRNA Analysis.

In spite of being a preventable disease, cervical cancer (CC) remains at high incidence, and it has a significant mortality rate. Although hijacking of the host cellular pathway is fundamental for developing a better understanding of the human papillomavirus (HPV) pathogenesis, a major obstacle is identifying the central molecular targets involved in HPV-driven CC. The aim of this study is to investigate transcriptomic patterns of HPV-infected and normal tissues to identify novel prognostic markers. Analyses of functional enrichment and interaction networks reveal that altered genes are mainly involved in cell cycle, DNA damage, and regulated cell-to-cell signaling. Analysis of The Cancer Genome Atlas (TCGA) data has suggested that patients with unfavorable prognostics are more likely to have DNA repair defects attributed, in most cases, to the presence of HPV. However, further studies are needed to fully unravel the molecular mechanisms of such genes involved in CC.

iTRAQ-Based Quantitative Proteomic Analysis Strengthens Transcriptomic Subtyping of Triple-Negative Breast Cancer Tumors.

Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis.

Integration of Metabolomics and Transcriptomics To Reveal Metabolic Characteristics and Key Targets Associated with Cisplatin Resistance in Nonsmall Cell Lung Cancer.

Continuous exposure to cisplatin can induce drug resistance to limit efficacy; however, the underlying mechanisms correlated with cisplatin resistance are still unclear. Drug-sensitive A549 cells and cisplatin-resistant A549/DDP cells were used to explore the potential metabolic pathways and key targets associated with cisplatin resistance by integrating untargeted metabolomics with transcriptomics. Data are available via ProteomeXchange with identifier PXD013265. The results of comprehensive analyses showed that 19 metabolites were significantly changed in A549/DDP versus A549 cells, and some pathways had a close relationship with cisplatin resistance, such as the biosynthesis of aminoacyl-tRNA, glycerophospholipid metabolism, and glutathione metabolism. Moreover, transcriptomics analysis showed that the glutathione metabolism was also obviously affected in A549/DDP, which indicated that the glutathione metabolism played an important role in the process of drug resistance. Meanwhile, transcriptomics analysis suggested the four enzymes related to glutathione metabolism-CD13, GPX4, RRM2B, and OPLAH-as potential targets of cisplatin resistance in nonsmall cell lung cancer. Further studies identified the overexpression of these four enzymes in A549/DDP. The elucidation of mechanism and discovery of new potential targets may help us have a better understanding of cisplatin resistance.

An integrated analysis to predict micro-RNAs targeting both stemness and metastasis in breast cancer stem cells.

Several evidences support the idea that a small population of tumour cells representing self-renewal potential are involved in initiation, maintenance, metastasis, and outcomes of cancer therapy. Elucidation of microRNAs/genes regulatory networks activated in cancer stem cells (CSCs) is necessary for the identification of new targets for cancer therapy. The aim of the present study was to predict the miRNAs pattern, which can target both metastasis and self-renewal pathways using integration of literature and data mining. For this purpose, mammospheres derived from MCF-7, MDA-MB231, and MDA-MB468 were used as breast CSCs model. They had higher migration, invasion, and colony formation potential, with increasing in stemness- and EMT-related genes expression. Our results determined that miR-204, -200c, -34a, and -10b contemporarily could target both self-renewal and EMT pathways. This core regulatory of miRNAs could increase the survival rate of breast invasive carcinoma via up-regulation of OCT4, SOX2, KLF4, c-MYC, NOTCH1, SNAI1, ZEB1, and CDH2 and down-regulation of CDH1. The majority of those target genes were involved in the regulation of pluripotency, MAPK, WNT, Hedgehog, p53, and transforming growth factor ß pathways. Hence, this study provides novel insights for targeting core regulatory of miRNAs in breast CSCs to target both self-renewal and metastasis potential and eradication of breast cancer.

Sarcosine influences apoptosis and growth of prostate cells via cell-type specific regulation of distinct sets of genes.

BACKGROUND: Sarcosine is a widely discussed oncometabolite of prostate cells. Although several reports described connections between sarcosine and various phenotypic changes of prostate cancer (PCa) cells, there is still a lack of insights on the complex phenomena of its effects on gene expression patterns, particularly in non-malignant and non-metastatic cells. METHODS: To shed more light on this phenomenon, we performed parallel microarray profiling of RNA isolated from non-malignant (PNT1A), malignant (22Rv1), and metastatic (PC-3) prostate cell lines treated with sarcosine. Microarray results were experimentally verified using semi-quantitative-RT-PCR, clonogenic assay, through testing of the susceptibility of cells pre-incubated with sarcosine to anticancer agents with different modes of actions (inhibitors of topoisomerase II, DNA cross-linking agent, antimicrotubule agent and inhibitor of histone deacetylases) and by evaluation of activation of executioner caspases 3/7. RESULTS: We identified that irrespective of the cell type, sarcosine stimulates up-regulation of distinct sets of genes involved in cell cycle and mitosis, while down-regulates expression of genes driving apoptosis. Moreover, it was found that in all cell types, sarcosine had pronounced stimulatory effects on clonogenicity. Except of an inhibitor of histone deacetylase valproic acid, efficiency of all agents was significantly (P < 0.05) decreased in sarcosine pre-incubated cells. CONCLUSIONS: Our comparative study brings evidence that sarcosine affects not only metastatic PCa cells, but also their malignant and non-malignant counterparts and induces very similar changes in cells behavior, but via distinct cell-type specific targets.

Genetic testing for hereditary prostate cancer: Current status and limitations.

A significant proportion of prostate cancer diagnoses may be associated with a strong hereditary component. Men who have multiple single-gene polymorphisms and a family history of prostate cancer have a significantly greater risk of developing prostate cancer. Numerous single-gene alterations have been confirmed to increase the risk of prostate cancer. These include breast cancer genes 1 and 2 (BRCA1 and BRCA2, respectively), mutL homolog 1 (MLH1), mutS homologs 2 and 6 (MSH2 and MSH6, respectively), postmeiotic segregation increased 2 (PMS2), homeobox B13 (HOXB13), checkpoint kinase 2 (CHEK2), nibrin (NBN), BRCA1-interacting protein C-terminal helicase 1 (BRIP1), and ataxia telangiectasia mutated (ATM). Currently, there are no uniform guidelines on the definition of hereditary prostate cancer and genetic testing. With the advent of next-generation sequencing, which is capable of testing multiple genes simultaneously, and the approval of olaparib for BRCA1/BRCA2 or ATM-mutated, metastatic, castrate-resistant prostate cancer, it is being recognized that the results of genetic testing have an impact on therapeutic strategies. In this review, the authors examine the role of genetic counseling and testing, the challenges of insurance coverage for testing, the available germline and somatic testing panels, and the complexity of each testing method and its implications. Cancer 2018. © 2018 American Cancer Society.

A review of the new HGNC gene family resource.

The HUGO Gene Nomenclature Committee (HGNC) approves unique gene symbols and names for human loci. As well as naming genomic loci, we manually curate genes into family sets based on shared characteristics such as function, homology or phenotype. Each HGNC gene family has its own dedicated gene family report on our website, www.genenames.org . We have recently redesigned these reports to support the visualisation and browsing of complex relationships between families and to provide extra curated information such as family descriptions, protein domain graphics and gene family aliases. Here, we review how our gene families are curated and explain how to view, search and download the gene family data.

What gastric cancer proteomic studies show about gastric carcinogenesis?

Gastric cancer is a complex, heterogeneous, and multistep disease. Over the past decades, several studies have aimed to determine the molecular factors that lead to gastric cancer development and progression. After completing the human genome sequencing, proteomic technologies have presented rapid progress. Differently from the relative static state of genome, the cell proteome is dynamic and changes in pathologic conditions. Proteomic approaches have been used to determine proteome profiles and identify differentially expressed proteins between groups of samples, such as neoplastic and nonneoplastic samples or between samples of different cancer subtypes or stages. Therefore, proteomic technologies are a useful tool toward improving the knowledge of gastric cancer molecular pathogenesis and the understanding of tumor heterogeneity. This review aimed to summarize the proteins or protein families that are frequently identified by using high-throughput screening methods and which thus may have a key role in gastric carcinogenesis. The increased knowledge of gastric carcinogenesis will clearly help in the development of new anticancer treatments. Although the studies are still in their infancy, the reviewed proteins may be useful for gastric cancer diagnosis, prognosis, and patient management.

Going for broke: targeting the human cancer pseudokinome.

Protein phosphorylation lies at the heart of cell signalling, and somatic mutation(s) in kinases drives and sustains a multitude of human diseases, including cancer. The human protein kinase superfamily (the kinome) encodes approximately 50 'pseudokinases', which were initially predicted to be incapable of dynamic cell signalling when compared with canonical enzymatically active kinases. This assumption was supported by bioinformatics, which showed that amino acid changes at one or more key loci, making up the nucleotide-binding site or phosphotransferase machinery, were conserved in multiple vertebrate and non-vertebrate pseudokinase homologues. Protein kinases are highly attractive targets for drug discovery, as evidenced by the approval of almost 30 kinase inhibitors in oncology, and the successful development of the dual JAK1/2 (Janus kinase 1/2) inhibitor ruxolitinib for inflammatory indications. However, for such a large (>550) protein family, a remarkable number have still not been analysed at the molecular level, and only a surprisingly small percentage of kinases have been successfully targeted clinically. This is despite evidence that many are potential candidates for the development of new therapeutics. Indeed, several recent reports confirm that disease-associated pseudokinases can bind to nucleotide co-factors at concentrations achievable in the cell. Together, these findings suggest that drug targeting using either ATP-site or unbiased ligand-discovery approaches should now be attempted using the validation technology currently employed to evaluate their classic protein kinase counterparts. In the present review, we discuss members of the human pseudokinome repertoire, and catalogue somatic amino acid pseudokinase mutations that are emerging as the depth and clinical coverage of the human cancer pseudokinome expand.

International Union of Basic and Clinical Pharmacology. LXXXV: calcium-activated chloride channels.

Calcium-activated chloride channels (CaCCs) are widely expressed in various tissues and implicated in physiological processes such as sensory transduction, epithelial secretion, and smooth muscle contraction. Transmembrane proteins with unknown function 16 (TMEM16A) has recently been identified as a major component of CaCCs. Detailed molecular analysis of TMEM16A will be needed to understand its structure-function relationships. The role this channel plays in physiological systems remains to be established and is currently a subject of intense investigation.