DGIdb 2.0: mining clinically relevant drug-gene interactions

Deep learning-based transcriptome data classification for drug-target interaction prediction

Background

The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity.

Results

In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions.

Conclusions

Our model’s capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process.

Genomic case report of a low grade bladder tumor metastasis to lung

BACKGROUND

We present a rare case where distant metastasis of a low grade bladder tumor was observed. We carried out detailed genomic analysis and cell based experiments on patient tumor samples to study tumor evolution, possible cause of disease and provide personalized treatment strategies.

CASE PRESENTATION

A man with a smoking history was diagnosed with a low-grade urothelial carcinoma of the bladder and a concurrent high-grade upper urinary tract tumor. Seven years later he had a lung metastasis. We carried out exome sequencing on all the patient's tumors and peripheral blood (germline) to identify somatic variants. We constructed a phylogenetic tree to capture how the tumors are related and to identify somatic changes important for metastasis. Although distant metastasis of low-grade bladder tumor is rare, the somatic variants in the tumors and the phylogenetic tree showed that the metastasized tumor had a mutational profile most similar to the low grade urothelial carcinoma. The primary and the metastatic tumors shared several important mutations, including in the KMT2D and the RXRA genes. The metastatic tumor also had an activating MTOR mutation, which may be important for tumor metastasis. We developed a mutational signature to understand the biologic processes responsible for tumor development. The mutational signature suggests that the tumor mutations are associated with tobacco carcinogen exposure, which is concordant with the patient's smoking history. We cultured cells from the lung metastasis to examine proliferation and signaling mechanisms in response to treatment. The mTOR inhibitor Everolimus inhibited downstream mTOR signaling and induced cytotoxicity in the metastatic tumor cells.

CONCLUSION

We used genomic analysis to examine a rare case of low grade bladder tumor metastasis to distant organ (lung). Our analysis also revealed exposure to carcinogens found is tobacco as a possible cause in tumor development. We further validated that the patient might benefit from mTOR inhibition as a potential salvage therapy in an adjuvant or recurrent disease setting.

Systematic analysis reveals molecular characteristics of ERG-negative prostate cancer

The TMPRSS2:ERG gene fusion is the most prevalent early driver gene activation in prostate cancers of European ancestry, while the fusion frequency is much lower in Africans and Asians. The genomic characteristics and mechanisms for patients lacking ERG fusion are still unclear. In this study, we systematically compared the characteristics of gene fusions, somatic mutations, copy number alterations and gene expression signatures between 201 ERG fusion positive and 296 ERG fusion negative prostate cancer samples. Both common and group-specific genomic alterations were observed, suggesting shared and different mechanisms of carcinogenesis in prostate cancer samples with or without ERG fusion. The genomic alteration patterns detected in ERG-negative group showed similarities with 77.5% of tumor samples of African American patients. These results emphasize that genomic and gene expression features of the ERG-negative group may provide a reference for populations with lower ERG fusion frequency. While the overall expression patterns were comparable between ERG-negative and ERG-positive tumors, we found that genomic alterations could affect the same pathway through distinct genes in the same pathway in both groups of tumor types. Altogether, the genomic and molecular characteristics revealed in our study may provide new opportunities for molecular stratification of ERG-negative prostate cancers.

Pathway-Based Drug Repositioning for Breast Cancer Molecular Subtypes

Breast cancer is a major public health problem which treatment needs new pharmacological options. In the last decades, during the postgenomic era new theoretical and technological tools that give us novel and promising ways to address these problems have emerged. In this work, we integrate several tools that exploit disease-specific experimental transcriptomic results in addition to information from biological and pharmacological data bases obtaining a contextual prioritization of pathways and drugs in breast cancer subtypes. The usefulness of these results should be evaluated in terms of drug repurposing in each breast cancer molecular subtype therapy. In favor of breast cancer patients, this methodology could be further developed to provide personalized treatment schemes. The latter are particularly needed in those breast cancer subtypes with limited therapeutic options or those who have developed resistance to the current pharmacological schemes.

Biobank-driven genomic discovery yields new insight into atrial fibrillation biology

To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of >1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans (GATA4, MYH6, NKX2-5, PITX2, TBX5)¹, or near genes important for striated muscle function and integrity (for example, CFL2, MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and functional enrichment analyses also suggested that many of the putative atrial fibrillation genes act via cardiac structural remodeling, potentially in the form of an 'atrial cardiomyopathy'², either during fetal heart development or as a response to stress in the adult heart.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia

OBJECTIVE

Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product.

METHOD

The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328).

RESULTS

Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms.

CONCLUSIONS

The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.

Unveiling "Musica Universalis" of the Cell: A Brief History of Biological 12-Hour Rhythms

"Musica universalis" is an ancient philosophical concept claiming the movements of celestial bodies follow mathematical equations and resonate to produce an inaudible harmony of music, and the harmonious sounds that humans make were an approximation of this larger harmony of the universe. Besides music, electromagnetic waves such as light and electric signals also are presented as harmonic resonances. Despite the seemingly universal theme of harmonic resonance in various disciplines, it was not until recently that the same harmonic resonance was discovered also to exist in biological systems. Contrary to traditional belief that a biological system is either at stead-state or cycles with a single frequency, it is now appreciated that most biological systems have no homeostatic "set point," but rather oscillate as composite rhythms consisting of superimposed oscillations. These oscillations often cycle at different harmonics of the circadian rhythm, and among these, the ~12-hour oscillation is most prevalent. In this review, we focus on these 12-hour oscillations, with special attention to their evolutionary origin, regulation, and functions in mammals, as well as their relationship to the circadian rhythm. We further discuss the potential roles of the 12-hour clock in regulating hepatic steatosis, aging, and the possibility of 12-hour clock-based chronotherapy. Finally, we posit that biological rhythms are also musica universalis: whereas the circadian rhythm is synchronized to the 24-hour light/dark cycle coinciding with the Earth's rotation, the mammalian 12-hour clock may have evolved from the circatidal clock, which is entrained by the 12-hour tidal cues orchestrated by the moon.

VAReporter: variant reporter for cancer research of massive parallel sequencing

Background

High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes.

Results

We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations.

Conclusions

In summary, VAReporter is the first web server designed to provide a “one-stop” resource for individual’s diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter.

SMART Cancer Navigator: A Framework for Implementing ASCO Workshop Recommendations to Enable Precision Cancer Medicine

Purpose

Data standards and interoperability are critical for improving care for patients with cancer. Recent efforts by ASCO include the Data Standards and Interoperability Summit in 2016, which led to the Omics and Precision Oncology and Advancing Interoperability workshops. To facilitate improved patient care, several recommendations for data sharing and standardization were made to the community.

Methods

To address these recommendations, we developed SMART Cancer Navigator, a Web application that uses application programming interfaces to gather clinical and genomic data from 11 public knowledge bases ranging from basic to clinical content coverage; three (CIViC, ClinVar, and OncoKB) explicitly linked genomic variants to clinical factors such as prognosis and treatment selection. We illustrated the utility of this application by selecting one of the monthly case studies presented by the ASCO University Molecular Oncology Tumor Board: Ovarian Cancer (BRCA Mutation). We also performed analyses on information from the three clinico-genomic knowledge bases to corroborate previous work and illustrate the state of data sharing among publicly available resources.

Results

SMART Cancer Navigator aggregates and contextualizes data from 11 different knowledge bases and stores user queries in a lightweight Web application that can link into Fast Healthcare Interoperability Resources-enabled electronic health records. Potentially relevant clinical trials and/or approved treatments were identified for three mutations found in a hypothetical patient with advanced ovarian cancer. A comparison of the three clinico-genomic knowledge bases indicated substantial differences in coverage at the gene and variant levels.

Conclusion

SMART Cancer Navigator has immediate relevance to practicing oncologists and others. Additional knowledge bases can be added without undue effort. As a first step toward utility, we generalized and disseminated the resulting implementation (https://smart-cancer-navigator.github.io) and data sets.

Construction of a Transcriptome-Driven Network at the Early Stage of Infection with Influenza A H1N1 in Human Lung Alveolar Epithelial Cells

We aimed to understand the molecular changes in host cells that accompany infection by the seasonal influenza A H1N1 virus because the initial response rapidly changes owing to the fact that the virus has a robust initial propagation phase. Human epithelial alveolar A549 cells were infected and total RNA was extracted at 30 min, 1 h, 2 h, 4 h, 8 h, 24 h, and 48 h post infection (h.p.i.). The differentially expressed host genes were clustered into two distinct sets of genes as the infection progressed over time. The patterns of expression were significantly different at the early stages of infection. One of the responses showed roles similar to those associated with the enrichment gene sets to known 'gp120 pathway in HIV.' This gene set contains genes known to play roles in preventing the progress of apoptosis, which infected cells undergo as a response to viral infection. The other gene set showed enrichment of 'Drug Metabolism Enzymes (DMEs).' The identification of two distinct gene sets indicates that the virus regulates the cell's mechanisms to create a favorable environment for its stable replication and protection of gene metabolites within 8 h.

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

IMPACT web portal: oncology database integrating molecular profiles with actionable therapeutics

Background

With the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs.

Results

IMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes.

Conclusion

IMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT.

Genetic heterogeneity and actionable mutations in HER2-positive primary breast cancers and their brain metastases

Brain metastases constitute a challenge in the management of patients with HER2-positive breast cancer treated with anti-HER2 systemic therapies. Here we sought to define the repertoire of mutations private to or enriched for in HER2-positive brain metastases. Massively parallel sequencing targeting all exons of 254 genes frequently mutated in breast cancers and/or related to DNA repair was used to characterize the spatial and temporal heterogeneity of HER2-positive breast cancers and their brain metastases in six patients. Data were analyzed with state-of-the-art bioinformatics algorithms and selected mutations were validated with orthogonal methods. Spatial and temporal inter-lesion genetic heterogeneity was observed in the HER2-positive brain metastases from an index patient subjected to a rapid autopsy. Genetic alterations restricted to the brain metastases included mutations in cancer genes FGFR2, PIK3CA and ATR, homozygous deletion in CDKN2A and amplification in KRAS. Shifts in clonal composition and the acquisition of additional mutations in the progression from primary HER2-positive breast cancer to brain metastases following anti-HER2 therapy were investigated in additional five patients. Likely pathogenic mutations private to or enriched in the brain lesions affected cancer and clinically actionable genes, including ATR, BRAF, FGFR2, MAP2K4, PIK3CA, RAF1 and TP53. Changes in clonal composition and the acquisition of additional mutations in brain metastases may affect potentially actionable genes in HER2-positive breast cancers. Our observations have potential clinical implications, given that treatment decisions for patients with brain metastatic disease are still mainly based on biomarkers assessed in the primary tumor.

Clinical Next-Generation Sequencing for Precision Oncology in Rare Cancers

The European Society for Medical Oncology defines rare cancers as 5 or fewer cases per 100,000 persons per year. For many rare cancers, no standard of care exists, and treatment is often extrapolated. Identifying potentially targetable genomic alterations in rare tumors is a rational approach to improving treatment options. We sought to catalog these mutations in rare tumors and to assess their clinical utility.For this retrospective analysis, we selected rare tumor patients from a dataset of patients who underwent clinical tumor genomic profiling. Sarcomas were excluded. To index potentially actionable alterations, patients' reports were reviewed for mutations in cancer-associated genes and pathways. Respective clinical records were abstracted to appraise the benefit of using a targeted therapy approach. Actionable alterations were defined as targeted by a drug available on-label, off-label, or in clinical trials.The 95 patients analyzed had 40 different tumor subtypes, most common being adenoid cystic (13%), cholangiocarcinoma (7%), and metaplastic breast (6%). At least one genomic alteration was identified in 87 patients (92%). The most common identifiable mutations were in TP53 (23%), KRAS (10%), PIK3CA (9%), CDKN2A/B (8%), BRAF (7%), MLL (7%), and ARID1A (6%). Thirty-six patients (38%) with 21 different tumors had at least one potentially actionable alteration. Thirteen patients received targeted therapy. Of these, 4 had a partial response, 6 had stable disease, and 3 had progressive disease as the best response.The addition of genomic profiling to management of rare cancers adds a potential line of therapy for cancers that have little or no standard of care. In our analysis, tumors with a BRAF alteration responded well to BRAF inhibitors. Mol Cancer Ther; 17(7); 1595-601. ©2018 AACR.

The genetics of smoking in individuals with chronic obstructive pulmonary disease

Background

Smoking is the principal modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD) which affects 300 million people and is the 3rd leading cause of death worldwide. Most of the genetic studies of smoking have relied on self-reported smoking status which is vulnerable to reporting and recall bias. Using data from the Lung Health Study (LHS), we sought to identify genetic variants associated with quantitative smoking and cessation in individuals with mild to moderate COPD.

Methods

The LHS is a longitudinal multicenter study of mild-to-moderate COPD subjects who were all smokers at recruitment. We performed genome-wide association studies (GWASs) for salivary cotinine (n = 4024), exhaled carbon monoxide (eCO) (n = 2854), cigarettes per day (CPD) (n = 2706) and smoking cessation at year 5 follow-up (n = 717 quitters and 2175 smokers). The GWAS analyses were adjusted for age, gender, and genetic principal components.

Results

For cotinine levels, SNPs near UGT2B10 gene achieved genome-wide significance (i.e. P < 5 × 10^− 8) with top SNP rs10023464, P = 1.27 × 10^− 11. For eCO levels, one significant SNP was identified which mapped to the CHRNA3 gene (rs12914385, P = 2.38 × 10^− 8). A borderline region mapping to KCNMA1 gene was associated with smoking cessation (rs207675, P = 5.95 × 10^− 8). Of the identified loci, only the CHRNA3/5 locus showed significant associations with lung function but only in heavy smokers. No regions met genome-wide significance for CPD.

Conclusion

The study demonstrates that using objective measures of smoking such as eCO and/or salivary cotinine can more precisely capture the genetic contribution to multiple aspects of smoking behaviour. The KCNMA1 gene association with smoking cessation may represent a potential therapeutic target and warrants further studies.

Trial registration

The Lung Health Study ClinicalTrials.gov Identifier: NCT00000568. Date of registration: October 28, 1999.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0762-7) contains supplementary material, which is available to authorized users.

Personalized cancer therapy-leveraging a knowledge base for clinical decision-making

Next-generation sequencing (NGS), also known as massively parallel sequencing, is rapidly being incorporated into oncology practice. Interpretation of genomic reports and selecting treatments based on the tumor's genomic analysis becomes more and more complicated for the treating oncologist because of the use of larger panels covering dozens to hundreds of genes and the amount of rapidly emerging clinical/translational data. To help guide personalized treatments in oncology, The Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (IPCT) at MD Anderson Cancer Center has developed a knowledge base, available at https://personalizedcancertherapy.org or https://pct.mdanderson.org (PCT). This knowledge base provides information on the function of common genomic alterations and their therapeutic implications. Here, we describe how such genomic information can be used by health-care providers to identify genomically matched therapies.

Functional transcriptomic annotation and protein-protein interaction network analysis identify NEK2, BIRC5, and TOP2A as potential targets in obese patients with luminal A breast cancer

PURPOSE

Although obesity is a risk factor for breast cancer, little effort has been made in the identification of druggable molecular alterations in obese-breast cancer patients. Tumors are controlled by their surrounding microenvironment, in which the adipose tissue is a main component. In this work, we intended to describe molecular alterations at a transcriptomic and protein-protein interaction (PPI) level between obese and non-obese patients.

METHODS AND RESULTS

Gene expression data of 269 primary breast tumors were compared between normal-weight (BMI < 25, n = 130) and obese (IMC > 30, n = 139) patients. No significant differences were found for the global breast cancer population. However, within the luminal A subtype, upregulation of 81 genes was observed in the obese group (FC ≥ 1.4). Next, we explored the association of these genes with patient outcome, observing that 39 were linked with detrimental outcome. Their PPI map formed highly compact cluster and functional annotation analyses showed that cell cycle, cell proliferation, cell differentiation, and cellular response to extracellular stimuli were the more altered functions. Combined analyses of genes within the described functions are correlated with poor outcome. PPI network analyses for each function were to search for druggable opportunities. We identified 16 potentially druggable candidates. Among them, NEK2, BIRC5, and TOP2A were also found to be amplified in breast cancer, suggesting that they could act as strategic players in the obese-deregulated transcriptome.

CONCLUSION

In summary, our in silico analysis describes molecular alterations of luminal A tumors and proposes a druggable PPI network in obese patients with potential for translation to the clinical practice.

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

Genome-wide CRISPR-Cas9 Screen Identifies Leukemia-Specific Dependence on a Pre-mRNA Metabolic Pathway Regulated by DCPS

To identify novel targets for acute myeloid leukemia (AML) therapy, we performed genome-wide CRISPR-Cas9 screening using AML cell lines, followed by a second screen in vivo. Here, we show that the mRNA decapping enzyme scavenger (DCPS) gene is essential for AML cell survival. The DCPS enzyme interacted with components of pre-mRNA metabolic pathways, including spliceosomes, as revealed by mass spectrometry. RG3039, a DCPS inhibitor originally developed to treat spinal muscular atrophy, exhibited anti-leukemic activity via inducing pre-mRNA mis-splicing. Humans harboring germline biallelic DCPS loss-of-function mutations do not exhibit aberrant hematologic phenotypes, indicating that DCPS is dispensable for human hematopoiesis. Our findings shed light on a pre-mRNA metabolic pathway and identify DCPS as a target for AML therapy.

A proteomic landscape of diffuse-type gastric cancer

The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

Identification of relevant drugable targets in diffuse large B-cell lymphoma using a genome-wide unbiased CD20 guilt-by association approach

Forty percent of patients with diffuse large B-cell lymphoma (DLBCL) show resistant disease to standard chemotherapy (CHOP) in combination with the anti-CD20 monoclonal antibody rituximab (R). Although many new anti-cancer drugs were developed in the last years, it is unclear which of these drugs can be safely combined to improve standard therapy without antagonizing anti-CD20 efficacy. In this study, we aimed to identify rituximab compatible drug-target combinations for DLBCL. For this, we collected gene expression profiles of 1,804 DLBCL patient samples. Subsequently, we performed a guilt-by-association analysis with MS4A1 (CD20) and prioritized the 500 top-ranked CD20-associated gene probes for drug-target interactions. This analysis showed the well-known genes involved in DLBCL pathobiology, but also revealed several genes that are relatively unknown in DLBCL, such as WEE1 and PARP1. To demonstrate potential clinical relevance of these targets, we confirmed high protein expression of WEE1 and PARP1 in patient samples. Using clinically approved WEE1 and PARP1 inhibiting drugs in combination with rituximab, we demonstrated significantly improved DLBCL cell killing, also in rituximab-insensitive cell lines. In conclusion, as exemplified by WEE1 and PARP1, our CD20-based genome-wide analysis can be used as an approach to identify biological relevant drug-targets that are rituximab compatible and may be implemented in phase 1/2 clinical trials to improve DLBCL treatment.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

INTRODUCTION

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy.

OBJECTIVES

Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways.

METHODS

OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM.

RESULTS

Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success.

CONCLUSIONS

Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Investigating the mechanism of hepatocellular carcinoma progression by constructing genetic and epigenetic networks using NGS data identification and big database mining method

The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2, MYC, and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3, PDGFB, and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3, IL1B, and NFKB1 markers influencing HCC progression from stage III to stage IV.

Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks (“modules”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.

Differential gene expression induced by anti-cancer agent plumbagin is mediated by androgen receptor in prostate cancer cells

Treatment of mice harboring PTEN-P2 tumors in the prostate or on prostate tissue in vivo with 5-hydroxy-2-methyl-1,4-naphthoquinone, also known as plumbagin, results in tumor regression in castrated mice, but not in intact mice. This suggested that dihydrotestosterone (DHT) production in the testes may prevent cell death due to plumbagin treatment, but the underlying mechanism is not understood. We performed RNA-seq analysis on cells treated with combinations of plumbagin and DHT, and analyzed differential gene expression, to gain insight into the interactions between androgen and plumbgin. DHT and plumbagin synergize to alter the expression of many genes that are not differentially regulated by either single agent when used alone. These experiments revealed that, for many genes, increases in mRNAs caused by DHT are sharply down-regulated by plumbagin, and that many transcripts change in response to plumbagin in a DHT-dependent manner. This suggests that androgen receptor mediates some of the effects of plumbagin on gene expression.

Differences in somatic mutation landscape of hepatocellular carcinoma in Asian American and European American populations

The incidence rate of hepatocellular carcinoma (HCC) is higher in populations of Asian ancestry than European ancestry (EA). We sought to investigate HCC mutational differences between the two populations, which may reflect differences in the prevalence of etiological factors. We compared HCC somatic mutations in patients of self-reported Asian American and EA from The Cancer Genome Atlas (TCGA), and assessed associations of tumor mutations with established HCC risk factors. Although the average mutation burden was similar, TP53 and RB1 were mutated at a much higher frequency in Asian Americans than in EAs (TP53: 43% vs. 21%; RB1: 19% vs. 2%). Three putative oncogenic genes, including TRPM3, SAGE1, and ADAMTS7, were mutated exclusively in Asians. In addition, VEGF binding pathway, a druggable target by tyrosine kinase inhibitors such as sorafenib, was mutated at a higher frequency among Asians (13% vs. 2%); while the negative regulation of IL17 production, involved in inflammation and autoimmunity, was mutated only in EAs (12% vs. 0). Accounting for HCC risk factors had little impact on any of the mutational differences. In conclusion, we demonstrated here mutational differences in important cancer genes and pathways between Asian and European ancestries. These differences may have implications for the prevention and treatment of HCC.

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database

The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

VarCards: an integrated genetic and clinical database for coding variants in the human genome

A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards (http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant- and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease- and phenotype-related knowledge; (v) general meaningful gene-level information; and (vi) drug-gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.

Sphingolipid Metabolism Is Dysregulated at Transcriptomic and Metabolic Levels in the Spinal Cord of an Animal Model of Amyotrophic Lateral Sclerosis

Lipid metabolism is drastically dysregulated in amyotrophic lateral sclerosis and impacts prognosis of patients. Animal models recapitulate alterations in the energy metabolism, including hypermetabolism and severe loss of adipose tissue. To gain insight into the molecular mechanisms underlying disease progression in amyotrophic lateral sclerosis, we have performed RNA-sequencing and lipidomic profiling in spinal cord of symptomatic SOD1^G86R mice. Spinal transcriptome of SOD1^G86R mice was characterized by differential expression of genes related to immune system, extracellular exosome, and lysosome. Hypothesis-driven identification of metabolites showed that lipids, including sphingomyelin(d18:0/26:1), ceramide(d18:1/22:0), and phosphatidylcholine(o-22:1/20:4) showed profound altered levels. A correlation between disease severity and gene expression or metabolite levels was found for sphingosine, ceramide(d18:1/26:0), Sgpp2, Sphk1, and Ugt8a. Joint-analysis revealed a significant enrichment of glycosphingolipid metabolism in SOD1^G86R mice, due to the down-regulation of ceramide, glucosylceramide, and lactosylceramide and the overexpression of genes involved in their recycling in the lysosome. A drug-gene interaction database was interrogated to identify potential drugs able to modulate the dysregulated genes from the signaling pathway. Our results suggest that complex lipids are pivotally changed during the first phase of motor symptoms in an animal model of amyotrophic lateral sclerosis.

Drug knowledge bases and their applications in biomedical informatics research

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.

Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles

Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.

Extracting Fitness Relationships and Oncogenic Patterns among Driver Genes in Cancer

Driver mutation provides fitness advantage to cancer cells, the accumulation of which increases the fitness of cancer cells and accelerates cancer progression. This work seeks to extract patterns accumulated by driver genes (“fitness relationships”) in tumorigenesis. We introduce a network-based method for extracting the fitness relationships of driver genes by modeling the network properties of the “fitness” of cancer cells. Colon adenocarcinoma (COAD) and skin cutaneous malignant melanoma (SKCM) are employed as case studies. Consistent results derived from different background networks suggest the reliability of the identified fitness relationships. Additionally co-occurrence analysis and pathway analysis reveal the functional significance of the fitness relationships with signaling transduction. In addition, a subset of driver genes called the “fitness core” is recognized for each case. Further analyses indicate the functional importance of the fitness core in carcinogenesis, and provide potential therapeutic opportunities in medicinal intervention. Fitness relationships characterize the functional continuity among driver genes in carcinogenesis, and suggest new insights in understanding the oncogenic mechanisms of cancers, as well as providing guiding information for medicinal intervention.

Systematic-analysis of mRNA expression profiles in skeletal muscle of patients with type II diabetes: The glucocorticoid was central in pathogenesis

Since the past 30 years, the prevalence of diabetes has more than doubled, making it an urgent challenge globally. We carried out systematic analysis with the public data of mRNA expression profiles in skeletal muscle to study the pathogenesis, since insulin resistance in the skeletal muscle is an early feature. We utilized three GEO datasets, containing total 60 cases and 63 normal samples. After the background removal, R package QC was utilized to finish the preprocessing of datasets. We obtained a dataset containing 2481 genes and 123 samples after the preprocessing. Quantitative quality control measures were calculated to represent the quality of these datasets. MetaDE package provides functions for conducting different systematic analysis methods for differential expression analysis. The GO term enrichment was carried out using PANTHER. Protein-protein interactions, drug-gene interactions, and genetic association of the identified differentially expressed genes were analyzed using STRING v10.0 online tool, DGIdb, and the Genetic Association Database, respectively. The datasets had good performances on IQC and EQC, which suggested that the datasets had good internal and external quality. Totally 96 differentially expressed genes were detected using 0.01 as cutoff of AW. The enriched GO terms were mainly associated with the response to glucocorticoid. There were seven genes involving in the gluconeogenesis were differentially expressed, which might be the potential treatment target for this disease. The closely connected networks and potential targets of existed drugs suggested that some of the drugs might be applied to the treatment of diabetes as well.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

Expanded molecular profiling of myxofibrosarcoma reveals potentially actionable targets

Myxofibrosarcomas are morphologically heterogeneous soft tissue sarcomas lacking a specific immunohistochemical expression profile and recurrent genetic changes. The study was designed to gain further insights into the molecular landscape of myxofibrosarcomas by targeted re-sequencing of known cancer driver hotspot mutations and the analysis of genomewide somatic copy number alterations. A well-defined group of myxofibrosarcomas, including myxofibrosarcomas G1 (n=6), myxofibrosarcomas G3 (n=7), myxofibrosarcomas with morphologically heterogeneous and independently selectable G1 and G3 areas within a tumor (n=8), and myxofibrosarcomas G3 with subsequent tumor recurrence (n=1) or metastatic disease (n=3) were evaluated. Mutational analysis demonstrated mutations in TP53, PTEN, FGFR3, CDKN2A, and RB1. TP53 mutations were seen in 11 (44%) of patients and detected in myxofibrosarcomas G1, G3, with heterogeneous morphology and G3 with subsequent metastases in 1 patient (16%), 3 patients (42%), 2 patients (62.5%), and 3 patients (75%), respectively. Additional mutations were detected in 2 patients, intratumoral mutational heterogeneity in 1 patient. We observed a variety of copy number alterations typical for myxofibrosarcomas, with higher numbers in G3 compared with G1 myxofibrosarcomas. Cluster analysis revealed distinctive features especially in metastatic and recurrent disease. Focal alterations affected CDKN2A, CCND1, CCNE1, EGFR, EPHA3, EPHB1, FGFR1, JUN, NF1, RB1, RET, TP53, and additional novel amplifications in CCNE1, KIT, EGFR, RET, BRAF, NTRK2 were seen in G3 compared with the G1 tumor areas. The total number of focal events in G1 versus G3 tumors differed significantly (P=0.0014). TRIO and RICTOR co-amplification was seen in 8 (44%) G3 and 1 (10%) G1 myxofibrosarcomas and RICTOR amplification alone in 4 (40%) G1 myxofibrosarcomas. TRIO amplification was significantly (P=0.0218) higher in G3 myxofibrosarcomas indicating a late genetic event. These findings support the use of expanded molecular profiling in myxofibrosarcomas to detect drug-able targets to allow patients to participate in basket trials.

Co-expression Network Approach Reveals Functional Similarities among Diseases Affecting Human Skeletal Muscle

Diseases affecting skeletal muscle exhibit considerable heterogeneity in intensity, etiology, phenotypic manifestation and gene expression. Systems biology approaches using network theory, allows for a holistic understanding of functional similarities amongst diseases. Here we propose a co-expression based, network theoretic approach to extract functional similarities from 20 heterogeneous diseases comprising of dystrophinopathies, inflammatory myopathies, neuromuscular, and muscle metabolic diseases. Utilizing this framework we identified seven closely associated disease clusters with 20 disease pairs exhibiting significant correlation (p < 0.05). Mapping the diseases onto a human protein-protein interaction network enabled the inference of a common program of regulation underlying more than half the muscle diseases considered here and referred to as the “protein signature.” Enrichment analysis of 17 protein modules identified as part of this signature revealed a statistically non-random dysregulation of muscle bioenergetic pathways and calcium homeostasis. Further, analysis of mechanistic similarities of less explored significant disease associations [such as between amyotrophic lateral sclerosis (ALS) and cerebral palsy (CP)] using a proposed “functional module” framework revealed adaptation of the calcium signaling machinery. Integrating drug-gene information into the quantitative framework highlighted the presence of therapeutic opportunities through drug repurposing for diseases affecting the skeletal muscle.

GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk

Male pattern baldness (MPB) or androgenetic alopecia is one of the most common conditions affecting men, reaching a prevalence of ~50% by the age of 50; however, the known genes explain little of the heritability. Here, we present the results of a genome-wide association study including more than 70,000 men, identifying 71 independently replicated loci, of which 30 are novel. These loci explain 38% of the risk, suggesting that MPB is less genetically complex than other complex traits. We show that many of these loci contain genes that are relevant to the pathology and highlight pathways and functions underlying baldness. Finally, despite only showing genome-wide genetic correlation with height, pathway-specific genetic correlations are significant for traits including lifespan and cancer. Our study not only greatly increases the number of MPB loci, illuminating the genetic architecture, but also provides a new approach to disentangling the shared biological pathways underlying complex diseases.

Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach

Using successful genome-wide association results in psychiatry for drug repurposing is an ongoing challenge. Databases collecting drug targets and gene annotations are growing and can be harnessed to shed a new light on psychiatric disorders. We used genome-wide association study (GWAS) summary statistics from the Psychiatric Genetics Consortium (PGC) Schizophrenia working group to build a drug repositioning model for schizophrenia. As sample size increases, schizophrenia GWAS results show increasing enrichment for known antipsychotic drugs, selective calcium channel blockers, and antiepileptics. Each of these therapeutical classes targets different gene subnetworks. We identify 123 Bonferroni-significant druggable genes outside the MHC, and 128 FDR-significant biological pathways related to neurons, synapses, genic intolerance, membrane transport, epilepsy, and mental disorders. These results suggest that, in schizophrenia, current well-powered GWAS results can reliably detect known schizophrenia drugs and thus may hold considerable potential for the identification of new therapeutic leads. Moreover, antiepileptics and calcium channel blockers may provide repurposing opportunities. This study also reveals significant pathways in schizophrenia that were not identified previously, and provides a workflow for pathway analysis and drug repurposing using GWAS results.

Resources for Interpreting Variants in Precision Genomic Oncology Applications

Precision genomic oncology-applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions-is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an ad hoc approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.

Analysis of the whole transcriptome from gingivo-buccal squamous cell carcinoma reveals deregulated immune landscape and suggests targets for immunotherapy

Background

Gingivo-buccal squamous cell carcinoma (GBSCC) is one of the most common oral cavity cancers in India with less than 50% patients surviving past 5 years. Here, we report a whole transcriptome profile on a batch of GBSCC tumours with diverse tobacco usage habits. The study provides an entire landscape of altered expression with an emphasis on searching for targets with therapeutic potential.

Methods

Whole transcriptomes of 12 GBSCC tumours and adjacent normal tissues were sequenced and analysed to explore differential expression of genes. Expression changes were further compared with those in TCGA head and neck cohort (n = 263) data base and validated in an independent set of 10GBSCC samples.

Results

Differentially expressed genes (n = 2176) were used to cluster the patients based on their tobacco habits, resulting in 3 subgroups. Immune response was observed to be significantly aberrant, along with cell adhesion and lipid metabolism processes. Different modes of immune evasion were seen across 12 tumours with up-regulation or consistent expression of CD47, unlike other immune evasion genes such as PDL1, FUT4, CTLA4 and BTLA which were downregulated in a few samples. Variation in infiltrating immune cell signatures across tumours also indicates heterogeneity in immune evasion strategies. A few actionable genes such as ITGA4, TGFB1 and PTGS1/COX1 were over expressed in most samples.

Conclusion

This study found expression deregulation of key immune evasion genes, such as CD47 and PDL1, and reasserts their potential as effective immunotherapeutic targets for GBSCC, which requires further clinical studies. Present findings reiterate the idea of using transcriptome profiling to guide precision therapeutic strategies.

Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

Bone metastases (BoM) are a significant cause of morbidity in patients with estrogen receptor-positive (ER-positive) breast cancer; yet, characterizations of human specimens are limited. In this study, exome-capture RNA sequencing (ecRNA-seq) on aged (8-12 years), formalin-fixed, paraffin-embedded (FFPE), and decalcified cancer specimens was evaluated. Gene expression values and ecRNA-seq quality metrics from FFPE or decalcified tumor RNA showed minimal differences when compared with matched flash-frozen or nondecalcified tumors. ecRNA-seq was then applied on a longitudinal collection of 11 primary breast cancers and patient-matched synchronous or recurrent BoMs. Overtime, BoMs exhibited gene expression shifts to more Her2 and LumB PAM50 subtype profiles, temporally influenced expression evolution, recurrently dysregulated prognostic gene sets, and longitudinal expression alterations of clinically actionable genes, particularly in the CDK/Rb/E2F and FGFR signaling pathways. Taken together, this study demonstrates the use of ecRNA-seq on decade-old and decalcified specimens and defines recurrent longitudinal transcriptional remodeling events in estrogen-deprived breast cancers.

Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.

Genomic analysis of follicular dendritic cell sarcoma by molecular inversion probe array reveals tumor suppressor-driven biology

Follicular dendritic cell sarcoma is a rare malignant neoplasm of dendritic cell origin that is currently poorly characterized by genetic studies. To investigate whether recurrent genomic alterations may underlie the biology of follicular dendritic cell sarcoma and to identify potential contributory regions and genes, molecular inversion probe array analysis was performed on 14 independent formalin-fixed, paraffin-embedded samples. Abnormal genomic profiles were observed in 11 out of 14 (79%) cases. The majority showed extensive genomic complexity that was predominantly represented by hemizygous losses affecting multiple chromosomes. Alterations of chromosomal regions 1p (55%), 2p (55%), 3p (82%), 3q (45%), 6q (55%), 7q (73%), 8p (45%), 9p (64%), 11q (64%), 13q (91%), 14q (82%), 15q (64%), 17p (55%), 18q (64%), and 22q (55%) were recurrent across the 11 samples showing abnormal genomic profiles. Many recurrent genomic alterations in follicular dendritic cell sarcoma overlap deletions that are frequently observed across human cancers, suggesting selection, or an active role for these alterations in follicular dendritic cell sarcoma pathogenesis. In support of a tumor suppressor-driven biology, homozygous deletions involving tumor suppressor genes CDKN2A, RB1, BIRC3, and CYLD were also observed. Neither recurrent gains nor amplifications were observed. This genomic characterization provides new information regarding follicular dendritic cell sarcoma biology that may improve understanding about the underlying pathophysiology, provide better prognostication, and identify potential therapeutic markers for this rare disease.

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney

Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.