Abstract: BACKGROUND: To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. METHODS: We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. RESULTS: Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. CONCLUSIONS: We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.

Abstract: The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.

Abstract: Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) is used for detailed characterization of pathology of lesions sites, such as brain tumors, by quantitative analysis of tracer's data through the use of pharmacokinetic (PK) models. A key component for PK models in DCE-MRI is the estimation of the concentration-time profile of the tracer in a nearby vessel, referred as Arterial Input Function (AIF). The aim of this work was to assess through full body physiologically-based pharmacokinetic (PBPK) model approaches the PK profile of gadoteric acid (Gd-DOTA) and explore potential application for parameter estimation in DCE-MRI based on PBPK-derived AIFs. The PBPK simulations were generated through Simcyp((R)) platform and the predicted PK parameters for Gd-DOTA were compared with available clinical data regarding healthy volunteers and renal impairment patients. The assessment of DCE-MRI parameters was implemented by utilizing similar virtual profiles based on gender, age and weight to clinical profiles of patients diagnosed with glioblastoma multiforme. The PBPK-derived AIFs were then used to compute DCE-MRI parameters through the Extended Tofts Model and compared with the corresponding ones derived from image-based AIF computation. The comparison involved: (i) image measured AIF of patients vs AIF of in silico profile, and, (ii) population average AIF vs in silico mean AIFs. The results indicate that PBPK-derived AIFs allowed the estimation of comparable imaging biomarkers with those calculated from typical DCE-MRI image analysis. The incorporation of PBPK models and potential utilization of in silico profiles to real patient data, can provide new perspectives in DCE-MRI parameter estimation and data analysis.

Abstract: Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with pre-defined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1-4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1-4, respectively. miR147b, miR770-5p, miR220a and miR1247 were the particular miRNAs in subgroups 1-4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required.

Abstract: Loperamide is a mu-opioid receptor agonist commonly used to treat diarrhea and often available as an over-the-counter medication. Recently, numerous reports of QRS widening accompanied by dramatic QT interval prolongation, torsades de pointe arrhythmia, and death have been reported in opioid abusers consuming large amounts of the drug to produce euphoria or prevent opiate withdrawal. The present study was undertaken to determine the mechanisms of this cardiotoxicity. Using whole-cell patch clamp electrophysiology, we tested loperamide on the cloned human cardiac sodium channel (Nav1.5) and the two main repolarizing cardiac K(+) channels cloned from the human heart: KvLQT1/minK and the human ether-a-go-go-related gene (hERG) channel. Loperamide inhibited Nav1.5 with IC50 values of 297 and 239 nM at holding potentials of -90 and -70 mV, respectively. Loperamide was weakly active on KvLQT1/minK producing 17 and 65 % inhibition at concentrations of 1 and 10 muM, respectively. Conversely, loperamide was found to be a very high affinity inhibitor of the hERG channel with an IC50 value of 89 nM at room temperature and 33 nM when measured at physiological temperature. The QRS and QT interval prolongation and the attending arrhythmias, produced by loperamide, derive from high affinity inhibition of Nav1.5 and especially hERG. Since the drug has been widely available and safely used as directed for many years, we believe that the potent inhibition loperamide possesses for cardiac ion channels has only been uncovered because of the excessive misuse of the drug as a consequence of the recent opioid abuse epidemic.

Abstract: We study the binding of plant hormone IAA on its receptor TIR1, introducing a novel computational method that we call tomographic docking and that accounts for interactions occurring along the depth of the binding pocket. Our results suggest that selectivity is related to constraints that potential ligands encounter on their way from the surface of the protein to their final position at the pocket bottom. Tomographic docking helps develop specific hypotheses about ligand binding, distinguishing binders from non-binders, and suggests that binding is a three-step mechanism, consisting of engagement with a niche in the back wall of the pocket, interaction with a molecular filter which allows or precludes further descent of ligands, and binding on the pocket base. Only molecules that are able to descend the pocket and bind at its base allow the co-receptor IAA7 to bind on the complex, thus behaving as active auxins. Analysing the interactions at different depths, our new method helps in identifying critical residues that constitute preferred future study targets and in the quest for safe and effective herbicides. Also, it has the potential to extend the utility of docking from ligand searches to the study of processes contributing to selectivity.

Abstract: Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Abstract: Cancer is a heterogeneous disease afflicting millions of people of all ages and their families worldwide. Tremendous resources have been and continue to be devoted to the development of cancer treatments that target the unique mutation profiles of patients, namely targeted cancer therapy. However, the sheer volume of drugs coupled with cancer heterogeneity becomes a challenge for physicians to prescribe effective therapies targeting patients' unique genetic mutations. Developing a web service that allows clinicians as well as patients to identify effective drug therapies, both approved and experimental, would be helpful for both parties. We have developed an innovative web service, IDICAP, which stands for Integrated Drug Intervention for CAncer Panel. It uses genes that have been linked to a cancer type to search for drug and clinical trial information from ClinicalTrials.gov and DrugBank. IDICAP selects and integrates information pertaining to clinical trials, disease conditions, drugs under trial, locations of trials, drugs that are known to target the queried gene, and any known single nucleotide polymorphism (SNP) effects. We tested IDICAP by gene panels that contribute to breast cancer, ovarian cancer, and cancer in general. Clinical trials and drugs listed by our tool showed improved precision compared to the results from ClinicalTrials.gov and Drug Gene Interaction Database (DGIdb). Furthermore, IDICAP provides patients and doctors with a list of clinical facilities in their proximity, a characteristic that lends credence to the Precision Medicine Initiative launched by the White House in the United States in 2015.

Abstract: Chemical patents contain detailed information on novel chemical compounds that is valuable to the chemical and pharmaceutical industries. In this paper, we introduce a system, NERChem that can recognize chemical named entity mentions in chemical patents. NERChem is based on the conditional random fields model (CRF). Our approach incorporates ( 1 ) class composition, which is used for combining chemical classes whose naming conventions are similar; ( 2 ) BioNE features, which are used for distinguishing chemical mentions from other biomedical NE mentions in the patents; and ( 3 ) full-token word features, which are used to resolve the tokenization granularity problem. We evaluated our approach on the BioCreative V CHEMDNER-patent corpus, and achieved an F-score of 87.17% in the Chemical Entity Mention in Patents (CEMP) task and a sensitivity of 98.58% in the Chemical Passage Detection (CPD) task, ranking alongside the top systems. Database URL: Our NERChem web-based system is publicly available at iisrserv.csie.n cu.edu.tw/nerchem.

Abstract: Given the capacity of self-renewal and multilineage differentiation, stem cells are promising sources for use in regenerative medicines as well as in the clinical treatment of certain hematological malignancies and degenerative diseases. Complex networks of cellular signaling pathways largely determine stem cell fate and function. Small molecules that modulate these pathways can provide important biological and pharmacological insights. However, it is still challenging to identify the specific protein targets of these compounds, to explore the changes in stem cell phenotypes induced by compound treatment and to ascertain compound mechanisms of action. To facilitate stem cell related small molecule study and provide a better understanding of the associated signaling pathways, we have constructed a comprehensive domain-specific chemogenomics resource, called StemCellCKB ( http://www.cbligand.org/StemCellCKB/ ). This new cloud-computing platform describes the chemical molecules, genes, proteins, and signaling pathways implicated in stem cell regulation. StemCellCKB is also implemented with web applications designed specifically to aid in the identification of stem cell relevant protein targets, including TargetHunter, a machine-learning algorithm for predicting small molecule targets based on molecular fingerprints, and HTDocking, a high-throughput docking module for target prediction and systems-pharmacology analyses. We have systematically tested StemCellCKB to verify data integrity. Target-prediction accuracy has also been validated against the reported known target/compound associations. This proof-of-concept example demonstrates that StemCellCKB can (1) accurately predict the macromolecular targets of existing stem cell modulators and (2) identify novel small molecules capable of probing stem cell signaling mechanisms, for use in systems-pharmacology studies. StemCellCKB facilitates the exploration and exchange of stem cell chemogenomics data among members of the broader research community.