DGIdb 2.0: mining clinically relevant drug-gene interactions

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment

This study highlights the relevance of network-guided controllability analysis as a precision oncology tool. Target controllability through networks is potentially relevant to cancer research for the identification of therapeutic targets. With reference to a recent study on multiple phenotypes from 22 osteosarcoma (OS) cell lines characterized both in vitro and in vivo, we found that a variety of critical proteins in OS regulation circuits were in part phenotype specific and in part shared. To generalize our inference approach and match cancer phenotypic heterogeneity, we employed multitype networks and identified targets in correspondence with protein sub-complexes. Therefore, we established the relevance for diagnostic and therapeutic purposes of inspecting interactive targets, namely those enriched by significant connectivity patterns in protein sub-complexes. Emerging targets appeared with reference to the OS microenvironment, and relatively to small leucine-rich proteoglycan members and D-type cyclins, among other collagen, laminin, and keratin proteins. These described were evidences shared across all phenotypes; instead, specific evidences were provided by critical proteins including IGFBP7 and PDGFRA in the invasive phenotype, and FGFR3 and THBS1 in the colony forming phenotype.

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification

There remains a large discrepancy between the known genetic contributions to cancer and that which can be explained by genomic variants, both inherited and somatic. Recently, understudied repetitive DNA regions called microsatellites have been identified as genetic risk markers for a number of diseases including various cancers (breast, ovarian and brain). In this study, we demonstrate an integrated process for identifying and further evaluating microsatellite-based risk markers for lung cancer using data from the cancer genome atlas and the 1000 genomes project. Comparing whole-exome germline sequencing data from 488 TCGA lung cancer samples to germline exome data from 390 control samples from the 1000 genomes project, we identified 119 potentially informative microsatellite loci. These loci were found to be able to distinguish between cancer and control samples with sensitivity and specificity ratios over 0.8. Then these loci, supplemented with additional loci from other cancers and controls, were evaluated using a target enrichment kit and sample-multiplexed nextgen sequencing. Thirteen of the 119 risk markers were found to be informative in a well powered study (>0.99 for a 0.95 confidence interval) using high-depth (579x±315) nextgen sequencing of 30 lung cancer and 89 control samples, resulting in sensitivity and specificity ratios of 0.90 and 0.94, respectively. When 8 loci harvested from the bioinformatic analysis of other cancers are added to the classifier, then the sensitivity and specificity rise to 0.93 and 0.97, respectively. Analysis of the genes harboring these loci revealed two genes (ARID1B and REL) and two significantly enriched pathways (chromatin organization and cellular stress response) suggesting that the process of lung carcinogenesis is linked to chromatin remodeling, inflammation, and tumor microenvironment restructuring. We illustrate that high-depth sequencing enables a high-precision microsatellite-based risk classifier analysis approach. This microsatellite-based platform confirms the potential to create clinically actionable diagnostics for lung cancer.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

CancerGD: A Resource for Identifying and Interpreting Genetic Dependencies in Cancer

Genes whose function is selectively essential in the presence of cancer-associated genetic aberrations represent promising targets for the development of precision therapeutics. Here, we present CancerGD, a resource that integrates genotypic profiling with large-scale loss-of-function genetic screens in tumor cell lines to identify such genetic dependencies. CancerGD provides tools for searching, visualizing, and interpreting these genetic dependencies through the integration of functional interaction networks. CancerGD includes different screen types (siRNA, shRNA, CRISPR), and we describe a simple format for submitting new datasets.

Molecular and genetic inflammation networks in major human diseases

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

Machine learning for predicting lifespan-extending chemical compounds

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans' lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound's chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans' lifespan in the DGIdb database, where the effect of the compounds on an organism's lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti-inflammatories, and compounds for gonadotropin-releasing hormone therapies.

Molecular Characterization of Somatic Alterations in Dukes' B and C Colorectal Cancers by Targeted Sequencing

Despite global progress in research, improved screening and refined treatment strategies, colorectal cancer (CRC) remains as the third most common malignancy. As each type of cancer is different and exhibits unique alteration patterns, identifying and characterizing gene alterations in CRC that may serve as biomarkers might help to improve diagnosis, prognosis and predict potential response to therapy. With the emergence of next generation sequencing technologies (NGS), it is now possible to extensively and rapidly identify the gene profile of individual tumors. In this study, we aimed to identify actionable somatic alterations in Dukes’ B and C in CRC via NGS. Targeted sequencing of 409 cancer-related genes using the Ion Ampliseq^TM Comprehensive Cancer Panel was performed on genomic DNA obtained from paired fresh frozen tissues, cancer and normal, of Dukes’ B (n = 10) and Dukes’ C (n = 9) CRC. The sequencing results were analyzed using Torrent Suite, annotated using ANNOVAR and validated using Sanger sequencing. A total of 141 somatic non-synonymous sequence variations were identified in 86 genes. Among these, 64 variants (45%) were predicted to be deleterious, 38 variants (27%) possibly deleterious while the other 39 variants (28%) have low or neutral protein impact. Seventeen genes have alterations with frequencies of ≥10% in the patient cohort and with 14 overlapped genes in both Dukes’ B and C. The adenomatous polyposis coli gene (APC) was the most frequently altered gene in both groups (n = 6 in Dukes’ B and C). In addition, TP53 was more frequently altered in Dukes’ C (n = 7) compared to Dukes’ B (n = 4). Ten variants in APC, namely p.R283^∗, p.N778fs, p.R805^∗, p.Y935fs, p.E941fs, p.E1057^∗, p.I1401fs, p.Q1378^∗, p.E1379^∗, and p.A1485fs were predicted to be driver variants. APC remains as the most frequently altered gene in the intermediate stages of CRC. Wnt signaling pathway is the major affected pathway followed by P53, RAS, TGF-β, and PI3K signaling. We reported the alteration profiles in each of the patient which has the potential to affect the clinical decision. We believe that this study will add further to the understanding of CRC molecular landscape.

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines

Background

Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

Methods

We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines.

Results

We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes.

Conclusions

We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0454-9) contains supplementary material, which is available to authorized users.

Identifying prognostic signature in ovarian cancer using DirGenerank

Identifying the prognostic genes in cancer is essential not only for the treatment of cancer patients, but also for drug discovery. However, it's still a big challenge to select the prognostic genes that can distinguish the risk of cancer patients across various data sets because of tumor heterogeneity. In this situation, the selected genes whose expression levels are statistically related to prognostic risks may be passengers. In this paper, based on gene expression data and prognostic data of ovarian cancer patients, we used conditional mutual information to construct gene dependency network in which the nodes (genes) with more out-degrees have more chances to be the modulators of cancer prognosis. After that, we proposed DirGenerank (Generank in direct netowrk) algorithm, which concerns both the gene dependency network and genes' correlations to prognostic risks, to identify the gene signature that can predict the prognostic risks of ovarian cancer patients. Using ovarian cancer data set from TCGA (The Cancer Genome Atlas) as training data set, 40 genes with the highest importance were selected as prognostic signature. Survival analysis of these patients divided by the prognostic signature in testing data set and four independent data sets showed the signature can distinguish the prognostic risks of cancer patients significantly. Enrichment analysis of the signature with curated cancer genes and the drugs selected by CMAP showed the genes in the signature may be drug targets for therapy. In summary, we have proposed a useful pipeline to identify prognostic genes of cancer patients.

Genomic alterations in advanced gastric cancer endoscopic biopsy samples using targeted next-generation sequencing

Gastric cancer (GC) remains the second tumor caused death threat worldwide, and personalized medicine for GC is far from expectation. Finding novel, recurrently mutated genes through next-generation sequencing (NGS) is a powerful and productive approach. However, previous genomic data for GC are based on surgical resected samples while a large proportion of advanced gastric cancer (AGC) patients have already missed the chance for operation. The aim of this study is to assess frequent genomic alteration in AGC via biopsy samples. Here we performed targeted genomic sequencing of 78 AGC patients' tumor biopsies along with matched lymphocyte samples based on a 118 cancer related gene panel. In total, we observed 301 somatic nonsynonymous genomic alterations in 92 different genes, as well as 37 copy number gain events among 15 different genes (fold change 2-12), and validated the fold changes of ERBB2 copy number gains with IHC and FISH test showed an accuracy of 81.8%. Previously reported driver genes for gastric cancer (TP53, KMT2D, KMT2B, EGFR, PIK3CA, GNAQ, and ARID1A), and several unreported mutations (TGFBR2, RNF213, NF1, NSD1, and LRP2) showed high non-silent mutation prevalence (7.7%-34.6%). When comparing intestinal-type gastric cancer (IGC) with diffuse-type gastric cancer (DGC), TP53 and GNAQ appear to be more frequently mutated in IGC (P=0.028 and P=0.023, respectively), whereas LRP2, BRCA2 and FGFR3 mutations are not observed in IGC, but have 12.8%, 7.7% and 7.7% mutation rates, respectively, in DGC patients. Patients with one or more mutations in adherens junction pathway (CREBBP, EP300, CDH1, CTNNB1, EGFR, MET, TGFBR2 and ERBB2) or TGF-β signaling pathway (CREBBP, EP300, MYST4, KRAS and TGFBR2) showed significantly better overall survival (P=0.007 and P=0.014, respectively), consistent with The Cancer Genome Atlas (TCGA) cohort data. Importantly, 57 (73.1%) patients harbored at least one genomic alteration with potential treatments, making NGS-based drug target screening a viable option for AGC patients. Our study established a comprehensive genomic portrait of AGC, and identified several mutation signatures highly associated with clinical features, survival outcomes, which may be used to design future personalized treatments.

Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.

Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER

Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility. In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER. We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework. Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors. In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex. These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling.

Precision medicine for hepatocelluar carcinoma using molecular pattern diagnostics: results from a preclinical pilot study

The aim of this study was to design a road map for personalizing cancer therapy in hepatocellular carcinoma (HCC) by using molecular pattern diagnostics. As an exploratory study, we investigated molecular patterns of tissues of two tumors from individual HCC patients, which in previous experiments had shown contrasting reactions to the phase 2 transforming growth factor beta receptor 1 inhibitor galunisertib. Cancer-driving molecular patterns encompass – inter alias – altered transcription profiles and somatic mutations in coding regions differentiating tumors from their respective peritumoral tissues and from each other. Massive analysis of cDNA ends and all-exome sequencing demonstrate a highly divergent transcriptional and mutational landscape, respectively, for the two tumors, that offers potential explanations for the tumors contrasting responses to galunisertib. Molecular pattern diagnostics (MPDs) suggest alternative, individual-tumor-specific therapies, which in both cases deviate from the standard sorafenib treatment and from each other. Suggested personalized therapies use kinase inhibitors and immune-focused drugs as well as low-toxicity natural compounds identified using an advanced bioinformatics routine included in the MPD protocol. The MPD pipeline we describe here for the prediction of suitable drugs for treatment of two contrasting HCCs may serve as a blueprint for the design of therapies for various types of cancer.

The DrugAge database of aging-related drugs

Aging is a major worldwide medical challenge. Not surprisingly, identifying drugs and compounds that extend lifespan in model organisms is a growing research area. Here, we present DrugAge (http://genomics.senescence.info/drugs/), a curated database of lifespan‐extending drugs and compounds. At the time of writing, DrugAge contains 1316 entries featuring 418 different compounds from studies across 27 model organisms, including worms, flies, yeast and mice. Data were manually curated from 324 publications. Using drug–gene interaction data, we also performed a functional enrichment analysis of targets of lifespan‐extending drugs. Enriched terms include various functional categories related to glutathione and antioxidant activity, ion transport and metabolic processes. In addition, we found a modest but significant overlap between targets of lifespan‐extending drugs and known aging‐related genes, suggesting that some but not most aging‐related pathways have been targeted pharmacologically in longevity studies. DrugAge is freely available online for the scientific community and will be an important resource for biogerontologists.

Intrinsic Subtype Switching and Acquired ERBB2/HER2 Amplifications and Mutations in Breast Cancer Brain Metastases

IMPORTANCE

Patients with breast cancer (BrCa) brain metastases (BrM) have limited therapeutic options. A better understanding of molecular alterations acquired in BrM could identify clinically actionable metastatic dependencies.

OBJECTIVE

To determine whether there are intrinsic subtype differences between primary tumors and matched BrM and to uncover BrM-acquired alterations that are clinically actionable.

DESIGN, SETTING, AND PARTICIPANTS

In total, 20 cases of primary breast cancer tissue and resected BrM (10 estrogen receptor [ER]-negative and 10 ER-positive) from 2 academic institutions were included. Eligible cases in the discovery cohort harbored patient-matched primary breast cancer tissue and resected BrM. Given the rarity of patient-matched samples, no exclusion criteria were enacted. Two validation sequencing cohorts were used-a published data set of 17 patient-matched cases of BrM and a cohort of 7884 BrCa tumors enriched for metastatic samples.

MAIN OUTCOMES AND MEASURES

Brain metastases expression changes in 127 genes within BrCa signatures, PAM50 assignments, and ERBB2/HER2 DNA-level gains.

RESULTS

Overall, 17 of 20 BrM retained the PAM50 subtype of the primary BrCa. Despite this concordance, 17 of 20 BrM harbored expression changes (<2-fold or >2-fold) in clinically actionable genes including gains of FGFR4 (n = 6 [30%]), FLT1 (n = 4 [20%]), AURKA (n = 2 [10%]) and loss of ESR1 expression (n = 9 [45%]). The most recurrent expression gain was ERBB2/HER2, which showed a greater than 2-fold expression increase in 7 of 20 BrM (35%). Three of these 7 cases were ERBB2/HER2-negative out of 13 ERBB2/HER2-negative in the primary BrCa cohort and became immunohistochemical positive (3+) in the paired BrM with metastasis-specific amplification of the ERBB2/HER2 locus. In an independent data set, 2 of 9 (22.2%) ERBB2/HER2-negative BrCa switched to ERBB2/HER2-positive with 1 BrM acquiring ERBB2/HER2 amplification and the other showing metastatic enrichment of the activating V777L ERBB2/HER2 mutation. An expanded cohort revealed that ERBB2/HER2 amplification and/or mutation frequency was unchanged between local disease and metastases across all sites; however, a significant enrichment was appreciated for BrM (13% local vs 24% BrM; P < .001).

CONCLUSIONS AND RELEVANCE

Breast cancer BrM commonly acquire alterations in clinically actionable genes, with metastasis-acquired ERBB2/HER2 alterations in approximately 20% of ERBB2/HER2-negative cases. These observations have immediate clinical implications for patients with ERBB2/HER2-negative breast cancer and support comprehensive profiling of metastases to inform clinical care.

Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach

Neurons are often classified by their morphological and molecular properties. The online knowledge base Hippocampome.org primarily defines neuron types from the rodent hippocampal formation based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites. For each neuron type, this open-access resource reports any and all published information regarding the presence or absence of known molecular markers, including calcium-binding proteins, neuropeptides, receptors, channels, transcription factors, and other molecules of biomedical relevance. The resulting chemical profile is relatively sparse: even for the best studied neuron types, the expression or lack thereof of fewer than 70 molecules has been firmly established to date. The mouse genome-wide in situ hybridization mapping of the Allen Brain Atlas provides a wealth of data that, when appropriately analyzed, can substantially augment the molecular marker knowledge in Hippocampome.org. Here we focus on the principal cell layers of dentate gyrus (DG), CA3, CA2, and CA1, which together contain approximately 90% of hippocampal neurons. These four anatomical parcels are densely packed with somata of mostly excitatory projection neurons. Thus, gene expression data for those layers can be justifiably linked to the respective principal neuron types: granule cells in DG and pyramidal cells in CA3, CA2, and CA1. In order to enable consistent interpretation across genes and regions, we screened the whole-genome dataset against known molecular markers of those neuron types. The resulting threshold values allow over 6000 very-high confidence (>99.5%) expressed/not-expressed assignments, expanding the biochemical information content of Hippocampome.org more than five-fold. Many of these newly identified molecular markers are potential pharmacological targets for major neurological and psychiatric conditions. Furthermore, our approach yields reasonable expression/non-expression estimates for every single gene in each of these four neuron types with >90% average confidence, providing a considerably complete genetic characterization of hippocampal principal neurons.

Learning from biomedical linked data to suggest valid pharmacogenes

Background

A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available.

Method

We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene–drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods –random forest and graph kernel–, which results are compared in this article.

Results

We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-017-0125-1) contains supplementary material, which is available to authorized users.

The druggable genome and support for target identification and validation in drug development

Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.

Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

BACKGROUND

De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.

METHODS

In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships. The algorithm, CorEx, efficiently optimizes over multivariate mutual information and can be iteratively applied to generate a hierarchy of relatively independent latent factors. The learned latent factors are used to stratify patients for survival analysis with respect to both single factors and combinations. These analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies.

RESULTS

Analysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a microRNA that modulates epigenetics. Further, combinations of factors lead to a synergistic survival advantage in some cases.

CONCLUSIONS

In contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are found to both have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366 possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder

We are performing whole-genome sequencing of families with autism spectrum disorder (ASD) to build a resource (MSSNG) for subcategorizing the phenotypes and underlying genetic factors involved. Here we report sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal. We found an average of 73.8 de novo single nucleotide variants and 12.6 de novo insertions and deletions or copy number variations per ASD subject. We identified 18 new candidate ASD-risk genes and found that participants bearing mutations in susceptibility genes had significantly lower adaptive ability (P = 6 × 10^-4). In 294 of 2,620 (11.2%) of ASD cases, a molecular basis could be determined and 7.2% of these carried copy number variations and/or chromosomal abnormalities, emphasizing the importance of detecting all forms of genetic variation as diagnostic and therapeutic targets in ASD.

Pharos: Collating protein information to shed light on the druggable genome

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Neurochemical Markers in the Mammalian Brain: Structure, Roles in Synaptic Communication, and Pharmacological Relevance

BACKGROUND

Knowledge of molecular marker (typically protein or mRNA) expression in neural systems can provide insight to the chemical blueprint of signal processing and transmission, assist in tracking developmental or pathological progressions, and yield key information regarding potential medicinal targets. These markers are particularly relevant in the mammalian brain in the light of its unsurpassed cellular diversity. Accordingly, molecular expression profiling is rapidly becoming a major approach to classify neuron types. Despite a profusion of research, however, the biological functions of molecular markers commonly used to distinguish neuron types remain incompletely understood. Furthermore, most molecular markers of mammalian neuron types are also present in other organs, therefore complicating considerations of their potential pharmacological interactions.

OBJECTIVE

Here, we survey 15 prominent neurochemical markers from five categories, namely membrane transporters, calcium-binding proteins, neuropeptides, receptors, and extracellular matrix proteins, explaining their relation and relevance to synaptic communication.

METHOD

For each marker, we summarize fundamental structural features, cellular functionality, distributions within and outside the brain, as well as known drug effectors and mechanisms of action.

CONCLUSION

This essential primer thus links together the cellular complexity of the brain, the chemical properties of key molecular players in neurotransmission, and possible biomedical opportunities.

Construction of antimicrobial peptide-drug combination networks from scientific literature based on a semi-automated curation workflow

Considerable research efforts are being invested in the development of novel antimicrobial therapies effective against the growing number of multi-drug resistant pathogens. Notably, the combination of different agents is increasingly explored as means to exploit and improve individual agent actions while minimizing microorganism resistance. Although there are several databases on antimicrobial agents, scientific literature is the primary source of information on experimental antimicrobial combination testing. This work presents a semi-automated database curation workflow that supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Currently, the database contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms. Likewise, researchers may look into indirect network associations and identify new potential combinations to be tested. The database is available without charges.

Database URL:http://sing.ei.uvigo.es/antimicrobialCombination/

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

Background

Bladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported.

Results

In the first step of our bioinformatics pipeline, we estimated the pairwise synergistic effects of SNPs on bladder cancer risk in both GWAS data sets using Multifactor Dimensionality Reduction (MDR) machine learning method that is designed specifically for this purpose. Statistical significance was assessed using a 1000-fold permutation test. Each single SNP was assigned a p-value based on its strongest pairwise association. Each SNP was then mapped to one or more genes using a window of 500 kb upstream and downstream from each gene boundary. This window was chosen to capture as many regulatory variants as possible. Using Exploratory Visual Analysis (EVA), we then carried out a gene set enrichment analysis at the gene level to identify those genes with an overabundance of significant SNPs relative to the size of their mapped regions. Each gene was assigned to a biological functional group defined by Gene Ontology (GO). We next used EVA to evaluate the overabundance of significant genes in biological functional groups. Our study yielded one GO category, carboxy-lysase activity (GO:0016831), that was significant in analyses from both GWAS data sets. Interestingly, only the gamma-glutamyl carboxylase (GGCX) gene from this GO group was significant in both the detection and replication data, highlighting the complexity of the pathway-level effects on risk. The GGCX gene is expressed in the bladder, but has not been previously associated with bladder cancer in univariate GWAS. However, there is some experimental evidence that carboxy-lysase activity might play a role in cancer and that genes in this pathway should be explored as drug targets. This study provides a genetic basis for that observation.

Conclusions

Our machine learning analysis of genetic associations in two GWAS for bladder cancer identified numerous associations with pairs of SNPs. Gene set enrichment analysis found aggregation of risk-associated SNPs in genes and significant genes in GO functional groups. This study supports a role for decarboxylase protein complexes in bladder cancer susceptibility. Previous research has implicated decarboxylases in bladder cancer etiology; however, the genes that we found to be significant in the detection and replication data are not known to have direct influence on bladder cancer, suggesting some novel hypotheses. This study highlights the need for a complex systems approach to the genetic and genomic analysis of common diseases such as cancer.

pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis

Molecular pathway data are essential in current computational and systems biology research. While there are many primary and integrated pathway databases, several challenges remain, including low proteome coverage (57%), low overlap across different databases, unavailability of direct information about underlying physical connectivity of pathway members, and high fraction of protein-coding genes without any pathway annotations, i.e. ‘pathway orphans’. In order to address all these challenges, we developed pathDIP, which integrates data from 20 source pathway databases, ‘core pathways’, with physical protein–protein interactions to predict biologically relevant protein–pathway associations, referred to as ‘extended pathways’. Cross-validation determined 71% recovery rate of our predictions. Data integration and predictions increase coverage of pathway annotations for protein-coding genes to 86%, and provide novel annotations for 5732 pathway orphans. PathDIP (http://ophid.utoronto.ca/pathdip) annotates 17 070 protein-coding genes with 4678 pathways, and provides multiple query, analysis and output options.

Understanding Molecular Landscape of Endometrial Cancer through Next Generation Sequencing: What We Have Learned so Far?

Endometrial cancer (EC) is among the most common gynecological cancers affecting women worldwide. Despite the early detection and rather high overall survival rate, around 20% of the cases recur with poor prognosis. The Next Generation Sequencing (NGS) technology, also known as massively parallel sequencing, symbolizes a high-throughput, fast, sensitive and accurate way to study the molecular landscape of a cancer and this has indeed revolutionized endometrial cancer research. Understanding the potential, advantages, and limitations of NGS will be crucial for the healthcare providers and scientists in providing the genome-driven care in this era of precision medicine and pharmacogenomics. This mini review aimed to compile and critically summarize the recent findings contributed by NGS technology pertaining to EC. Importantly, we also discussed the potential of this technology for fundamental discovery research, individualized therapy, screening of at-risk individual and early diagnosis.

IDICAP: A Novel Tool for Integrating Drug Intervention Based on Cancer Panel

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.

URL:http://idicap.lafayette.edu:8000

POSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins

We present POSTAR (http://POSTAR.ncrnalab.org), a resource of POST-trAnscriptional Regulation coordinated by RNA-binding proteins (RBPs). Precise characterization of post-transcriptional regulatory maps has accelerated dramatically in the past few years. Based on new studies and resources, POSTAR supplies the largest collection of experimentally probed (∼23 million) and computationally predicted (approximately 117 million) RBP binding sites in the human and mouse transcriptomes. POSTAR annotates every transcript and its RBP binding sites using extensive information regarding various molecular regulatory events (e.g., splicing, editing, and modification), RNA secondary structures, disease-associated variants, and gene expression and function. Moreover, POSTAR provides a friendly, multi-mode, integrated search interface, which helps users to connect multiple RBP binding sites with post-transcriptional regulatory events, phenotypes, and diseases. Based on our platform, we were able to obtain novel insights into post-transcriptional regulation, such as the putative association between CPSF6 binding, RNA structural domains, and Li-Fraumeni syndrome SNPs. In summary, POSTAR represents an early effort to systematically annotate post-transcriptional regulatory maps and explore the putative roles of RBPs in human diseases.

Genomic variant annotation workflow for clinical applications

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

Genomic variant annotation workflow for clinical applications

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different source databases and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, DGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

Comprehensive genomic analysis reveals FLT3 activation and a therapeutic strategy for a patient with relapsed adult B-lymphoblastic leukemia

The genomic events responsible for the pathogenesis of relapsed adult B-lymphoblastic leukemia (B-ALL) are not yet clear. We performed integrative analysis of whole-genome, whole-exome, custom capture, whole-transcriptome (RNA-seq), and locus-specific genomic assays across nine time points from a patient with primary de novo B-ALL. Comprehensive genome and transcriptome characterization revealed a dramatic tumor evolution during progression, yielding a tumor with complex clonal architecture at second relapse. We observed and validated point mutations in EP300 and NF1, a highly expressed EP300-ZNF384 gene fusion, a microdeletion in IKZF1, a focal deletion affecting SETD2, and large deletions affecting RB1, PAX5, NF1, and ETV6. Although the genome analysis revealed events of potential biological relevance, no clinically actionable treatment options were evident at the time of the second relapse. However, transcriptome analysis identified aberrant overexpression of the targetable protein kinase encoded by the FLT3 gene. Although the patient had refractory disease after salvage therapy for the second relapse, treatment with the FLT3 inhibitor sunitinib rapidly induced a near complete molecular response, permitting the patient to proceed to a matched-unrelated donor stem cell transplantation. The patient remains in complete remission more than 4 years later. Analysis of this patient's relapse genome revealed an unexpected, actionable therapeutic target that led to a specific therapy associated with a rapid clinical response. For some patients with relapsed or refractory cancers, this approach may indicate a novel therapeutic intervention that could alter outcome.

GenVisR: Genomic Visualizations in R

Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor.

AVAILABILITY AND IMPLEMENTATION

GenVisR is an R package available via Bioconductor (https://bioconductor.org/packages/GenVisR) under GPLv3. Support is available via GitHub (https://github.com/griffithlab/GenVisR/issues) and the Bioconductor support website.

CONTACTS

obigriffith@wustl.edu or mgriffit@wustl.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alleviation of pain in painful diabetic neuropathy

INTRODUCTION

Painful diabetic neuropathy (PDN) is a disabling pain condition. Its pathomechanism remains unknown, but a sensitization and neuronal hyperexcitabilty have been suggested. Only symptomatic pharmacological pain management treatment is currently available.

AREAS COVERED

The origin of PDN is enigmatic, and the evidence-based therapeutic guidelines therefore consist only of antidepressants and antiepileptics as first-line recommended drugs. This article relates to a MEDLINE/PubMed systematic search (2005-2015).

EXPERT OPINION

The results of the meta-analysis from the aspect of the efficacy of amitriptyline, duloxetine, venlafaxine, gabapentin and pregabalin are favorable, but the placebo response rate is relatively high in patients with neuropathic pain. For personalization of the medication of PDN patients, the optimum dosing, the genotyping of the metabolizing enzymes and optimum biomarkers are needed. As concerns the future perspectives, specific sodium channel subtype inhibitors acting on peripheral nociceptive neurons or modified T-type voltage-gated calcium channel blockers may be promising targets for pharmaceutical innovations. Another attractive strategy for the treatment is based on the effects of monoclonal antibodies against nerve growth factor, sodium channels, specific receptor and cytokines. Botulinum toxin A, capsaicin patch and spinal cord stimulation therapies are the nearest future therapeutic options for the treatment of PDN patients.

A genomic case study of mixed fibrolamellar hepatocellular carcinoma

We report the first comprehensive genomic analysis of a case of mixed conventional and fibrolamellar HCC (mFL-HCC). This study confirms the expression of DNAJB1:PRKACA, a fusion previously associated with pure FL-HCC but not conventional HCC, in mFL-HCC. These results indicate the DNAJB1:PRKACA fusion has diagnostic utility for both pure and mixed FL-HCC.

Background

Mixed fibrolamellar hepatocellular carcinoma (mFL-HCC) is a rare liver tumor defined by the presence of both pure FL-HCC and conventional HCC components, represents up to 25% of cases of FL-HCC, and has been associated with worse prognosis. Recent genomic characterization of pure FL-HCC identified a highly recurrent transcript fusion (DNAJB1:PRKACA) not found in conventional HCC.

Patients and Methods

We performed exome and transcriptome sequencing of a case of mFL-HCC. A novel BAC-capture approach was developed to identify a 400 kb deletion as the underlying genomic mechanism for a DNAJB1:PRKACA fusion in this case. A sensitive Nanostring Elements assay was used to screen for this transcript fusion in a second case of mFL-HCC, 112 additional HCC samples and 44 adjacent non-tumor liver samples.

Results

We report the first comprehensive genomic analysis of a case of mFL-HCC. No common HCC-associated mutations were identified. The very low mutation rate of this case, large number of mostly single-copy, long-range copy number variants, and high expression of ERBB2 were more consistent with previous reports of pure FL-HCC than conventional HCC. In particular, the DNAJB1:PRKACA fusion transcript specifically associated with pure FL-HCC was detected at very high expression levels. Subsequent analysis revealed the presence of this fusion in all primary and metastatic samples, including those with mixed or conventional HCC pathology. A second case of mFL-HCC confirmed our finding that the fusion was detectable in conventional components. An expanded screen identified a third case of fusion-positive HCC, which upon review, also had both conventional and fibrolamellar features. This screen confirmed the absence of the fusion in all conventional HCC and adjacent non-tumor liver samples.

Conclusion

These results indicate that mFL-HCC is similar to pure FL-HCC at the genomic level and the DNAJB1:PRKACA fusion can be used as a diagnostic tool for both pure and mFL-HCC.

The Genomic Landscape of Male Breast Cancers

PURPOSE

Male breast cancer is rare, and its genomic landscape has yet to be fully characterized. Lacking studies in men, treatment of males with breast cancer is extrapolated from results in females with breast cancer. We sought to define whether male breast cancers harbor somatic genetic alterations in genes frequently altered in female breast cancers.

EXPERIMENTAL DESIGN

All male breast cancers were estrogen receptor-positive, and all but two were HER2-negative. Fifty-nine male breast cancers were subtyped by immunohistochemistry, and tumor-normal pairs were microdissected and subjected to massively parallel sequencing targeting all exons of 241 genes frequently mutated in female breast cancers or DNA-repair related. The repertoires of somatic mutations and copy number alterations of male breast cancers were compared with that of subtype-matched female breast cancers.

RESULTS

Twenty-nine percent and 71% of male breast cancers were immunohistochemically classified as luminal A-like or luminal B-like, respectively. Male breast cancers displayed a heterogeneous repertoire of somatic genetic alterations that to some extent recapitulated that of estrogen receptor (ER)-positive/HER2-negative female breast cancers, including recurrent mutations affecting PIK3CA (20%) and GATA3 (15%). ER-positive/HER2-negative male breast cancers, however, less frequently harbored 16q losses, and PIK3CA and TP53 mutations than ER-positive/HER2-negative female breast cancers. In addition, male breast cancers were found to be significantly enriched for mutations affecting DNA repair-related genes.

CONCLUSIONS

Male breast cancers less frequently harbor somatic genetic alterations typical of ER-positive/HER2-negative female breast cancers, such as PIK3CA and TP53 mutations and losses of 16q, suggesting that at least a subset of male breast cancers are driven by a distinct repertoire of somatic changes. Given the genomic differences, caution may be needed in the application of biologic and therapeutic findings from studies of female breast cancers to male breast cancers. Clin Cancer Res; 22(16); 4045-56. ©2016 AACR.

Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data

Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.