Integrating Next-Generation Sequencing in the Clinical Pharmacogenomics Workflow

Frequencies of Combined Dysfunction of Cytochromes P450 2C9, 2C19, and 2D6 in an Italian Cohort: Suggestions for a More Appropriate Medication Prescribing Process

Improper drug prescription is a main cause of both drug-related harms (inefficacy and toxicity) and ineffective spending and waste of the healthcare system's resources. Nowadays, strategies to support an improved, informed prescription process may benefit from the adequate use of pharmacogenomic testing. Using next-generation sequencing, we analyzed the genomic profile for three major cytochromes P450 (CYP2C9, CYP2C19, CYP2D6) and studied the frequencies of dysfunctional isozymes (e.g., poor, intermediate, or rapid/ultra-rapid metabolizers) in a cohort of 298 Italian subjects. We found just 14.8% of subjects with a fully normal set of cytochromes, whereas 26.5% of subjects had combined cytochrome dysfunction (more than one isozyme involved). As improper drug prescription is more frequent, and more burdening, in polytreated patients, since drug-drug interactions also cause patient harm, we discuss the potential benefits of a more comprehensive PGX testing approach to support informed drug selection in such patients.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics

BACKGROUND

This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.

METHODS

Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging.

RESULTS

Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99).

CONCLUSION

While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

VIVID: a web application for variant interpretation and visualisation in multidimensional analyses

Large-scale comparative genomics- and population genetic studies generate enormous amounts of polymorphism data in the form of DNA variants. Ultimately, the goal of many of these studies is to associate genetic variants to phenotypes or fitness. We introduce VIVID, an interactive, user-friendly web application that integrates a wide range of approaches for encoding genotypic to phenotypic information in any organism or disease, from an individual or population, in three-dimensional (3D) space. It allows mutation mapping and annotation, calculation of interactions and conservation scores, prediction of harmful effects, analysis of diversity and selection, and 3D visualization of genotypic information encoded in Variant Call Format on AlphaFold2 protein models. VIVID enables the rapid assessment of genes of interest in the study of adaptive evolution and the genetic load, and it helps prioritizing targets for experimental validation. We demonstrate the utility of VIVID by exploring the evolutionary genetics of the parasitic protist Plasmodium falciparum, revealing geographic variation in the signature of balancing selection in potential targets of functional antibodies.

Advancing Pharmacogenomics from Single-Gene to Preemptive Testing

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

miRNAs as potential diagnostic biomarkers and pharmacogenomic indicators in psychiatric disorders

The heterogeneity of psychiatric disorders and the lack of reliable biomarkers for prediction and treatments follow-up pose difficulties towards recognition and understanding of the molecular basis of psychiatric diseases. However, several studies based on NGS approaches have shown that miRNAs could regulate gene expression during onset and disease progression and could serve as potential diagnostic and pharmacogenomics biomarkers during treatment. We provide herein a detailed overview of circulating miRNAs and their expression profiles as biomarkers in schizophrenia, bipolar disorder and major depressive disorder and their role in response to specific treatments. Bioinformatics analysis of miR-34a, miR-106, miR-134 and miR-132, which are common among SZ, BD and MDD patients, showed brain enrichment and involvement in the modulation of critical signaling pathways, which are often deregulated in psychiatric disorders. We propose that specific miRNAs support accurate diagnosis and effective precision treatment of psychiatric disorders.

A systematic review on the cost effectiveness of pharmacogenomics in developing countries: implementation challenges

The major challenges that delay the implementation of pharmacogenomics based clinical practice in the developing countries, primarily the low- and middle-income countries need to be recognized. This review was conducted to systematically review evidence of the cost-effectiveness for the conduct of pharmacogenomics testing in the developing countries. Studies that evaluated the cost-effectiveness of pharmacogenomics testing in the developing countries as defined by the United Nations were included in this study. Twenty-seven articles met the criteria. Pharmacogenomics effectiveness were evaluated for drugs used in the treatment of cancers, cardiovascular diseases and severe cutaneous adverse reactions in gout and epilepsy. Most studies had reported pharmacogenomics testing to be cost-effective (cancers, cardiovascular diseases, and tuberculosis) and economic models were evaluated from multiple perspectives, different cost categories and time horizons. Additionally, most studies used a single gene, rather than a gene panel for the pharmacogenomics testing. Genotyping cost and frequency of risk alleles in the populations influence the cost-effectiveness outcome. Further studies are warranted to examine the clinical and economic validity of pharmacogenomics testing in the developing countries.

Identification and functional validation of novel pharmacogenomic variants using a next-generation sequencing-based approach for clinical pharmacogenomics

Inter-individual variability in pharmacokinetics and drug response is heavily influenced by single-nucleotide variants (SNVs) and copy-number variations (CNVs) in genes with importance for drug disposition. Nowadays, a plethora of studies implement next generation sequencing to capture rare and novel pharmacogenomic (PGx) variants that influence drug response. To address these issues, we present a comprehensive end-to-end analysis workflow, beginning from targeted PGx panel re-sequencing to in silico analysis pipelines and in vitro validation assays. Specifically, we show that novel pharmacogenetic missense variants that are predicted or putatively predicted to be functionally deleterious, significantly alter protein activity levels of CYP2D6 and CYP2C19 proteins. We further demonstrate that variant priorization pipelines tailored with functional in vitro validation assays provide supporting evidence for the deleterious effect of novel PGx variants. The proposed workflow could provide the basis for integrating next-generation sequencing for PGx testing into routine clinical practice.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

The Landscape of Clinical Implementation of Pharmacogenetic Testing in Central China: A Single-Center Study

Purpose

Pharmacogenetic testing is recognized as the major method for the individualized pharmacotherapy in clinical pharmacy practice, but information about the clinical implementation of pharmacogenetic testing in China is limited. The present study aimed to determine the situation of clinical implementation for pharmacogenetic testing in central China.

Methods

The study is conducted in the department of clinical pharmacy in The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. We collected and analyzed pharmacogenetic testing results from November 1, 2013 to November 2, 2018 in our hospital, which were checked in the electronic medical record system. The main outcome measures were the number and type of pharmacogenetic testing across five years.

Results

A total of 47,265 (56.9% male, mean age = 51.5 years) pharmacogenetic testing results were obtained with an average annual rate of growth of 63.0% across five years. A 50.2% (23,748/47,265) of all the pharmacogenetic testing results were for the determination of cytochrome P450 2C19 (CYP2C19) *2, *3 genotypes, and 41.7% were for the methylene tetrahydrofolate reductase (MTHFR) C677T genotype. The number of departments performing the pharmacogenetic testing was 35, 63, 55, 52, 52 and 39 for 2013–2018, respectively, and the main top five departments were cardiology, psychiatry, ICU, cardiac surgery and intervention.

Conclusion

Clinical implementation of pharmacogenetic testing in China is growing rapidly, but the types and implementing departments of pharmacogenetic testing were limited. Our present study reported the real-world implementation modality of pharmacogenomic tests in China. It will help us to understand the testing of pharmacogenetics in China in order to promote the rational development of pharmacogenetics.

Scalable Nucleic Acid Storage and Retrieval Using Barcoded Microcapsules

Rapid advances in nucleic acid sequencing and synthesis technologies have spurred a major need to collect, store, and sequence the DNA and RNA from viral, bacterial, and mammalian sources and organisms. However, current approaches to storing nucleic acids rely on a low-temperature environment and require robotics for access, posing challenges for scalable and low-cost nucleic acid storage. Here, we present an alternative method for storing nucleic acids, termed Preservation and Access of Nucleic aciDs using barcOded micRocApsules (PANDORA). Nucleic acids spanning kilobases to gigabases and from different sources, including animals, bacteria, and viruses, are encapsulated into silica microcapsules to protect them from environmental denaturants at room temperature. Molecular barcodes attached to each microcapsule enable sample pooling and subsequent identification and retrieval using fluorescence-activated sorting. We demonstrate quantitative storage and rapid access to targeted nucleic acids from a pool emulating standard retrieval operations implemented in conventional storage systems, including recovery of 100,000-200,000 samples and Boolean logic selection using four unique barcodes. Quantitative polymerase chain reaction and short-read sequencing of the retrieved samples validated the sorting experiments and the integrity of the released nucleic acids. Our proposed approach offers a scalable long-term, room-temperature storage and retrieval of nucleic acids with high sample fidelity.

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

BACKGROUND

The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility.

METHODS

This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation.

RESULTS

All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function."

CONCLUSION

Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

Genomic Risk Factors Driving Immune-Mediated Delayed Drug Hypersensitivity Reactions

Adverse drug reactions (ADRs) remain associated with significant mortality. Delayed hypersensitivity reactions (DHRs) that occur greater than 6 h following drug administration are T-cell mediated with many severe DHRs now associated with human leukocyte antigen (HLA) risk alleles, opening pathways for clinical prediction and prevention. However, incomplete negative predictive value (NPV), low positive predictive value (PPV), and a large number needed to test (NNT) to prevent one case have practically prevented large-scale and cost-effective screening implementation. Additional factors outside of HLA contributing to risk of severe T-cell-mediated DHRs include variation in drug metabolism, T-cell receptor (TCR) specificity, and, most recently, HLA-presented immunopeptidome-processing efficiencies via endoplasmic reticulum aminopeptidase (ERAP). Active research continues toward identification of other highly polymorphic factors likely to impose risk. These include those previously associated with T-cell-mediated HLA-associated infectious or auto-immune disease such as Killer cell immunoglobulin-like receptors (KIR), epistatically linked with HLA class I to regulate NK- and T-cell-mediated cytotoxic degranulation, and co-inhibitory signaling pathways for which therapeutic blockade in cancer immunotherapy is now associated with an increased incidence of DHRs. As such, the field now recognizes that susceptibility is not simply a static product of genetics but that individuals may experience dynamic risk, skewed toward immune activation through therapeutic interventions and epigenetic modifications driven by ecological exposures. This review provides an updated overview of current and proposed genetic factors thought to predispose risk for severe T-cell-mediated DHRs.

Cases in Precision Medicine: Genetic Testing to Predict Future Risk for Disease in a Healthy Patient

Genetic testing is performed more routinely in clinical practice, and direct-to-consumer tests are widely available. It has obvious appeal as a preventive health measure. Clinicians and their healthy patients increasingly inquire about genetic testing as a tool for predicting diseases, such as cancer, heart disease, or dementia. Despite demonstrated utility for diagnosis in the setting of many diseases, genetic testing still has many limitations as a predictive tool for healthy persons. This article uses a hypothetical case to review key considerations for predictive genetic testing.

Applying genomic and transcriptomic advances to mitochondrial medicine

Next-generation sequencing (NGS) has increased our understanding of the molecular basis of many primary mitochondrial diseases (PMDs). Despite this progress, many patients with suspected PMD remain without a genetic diagnosis, which restricts their access to in-depth genetic counselling, reproductive options and clinical trials, in addition to hampering efforts to understand the underlying disease mechanisms. Although they represent a considerable improvement over their predecessors, current methods for sequencing the mitochondrial and nuclear genomes have important limitations, and molecular diagnostic techniques are often manual and time consuming. However, recent advances in genomics and transcriptomics offer realistic solutions to these challenges. In this Review, we discuss the current genetic testing approach for PMDs and the opportunities that exist for increased use of whole-genome NGS of nuclear and mitochondrial DNA (mtDNA) in the clinical environment. We consider the possible role for long-read approaches in sequencing of mtDNA and in the identification of novel nuclear genomic causes of PMDs. We examine the expanding applications of RNA sequencing, including the detection of cryptic variants that affect splicing and gene expression and the interpretation of rare and novel mitochondrial transfer RNA variants.

PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.

Discrepancies and similarities in the genome-informed guidance for psychiatric disorders amongst different regulatory bodies and research consortia using next generation sequencing-based clinical pharmacogenomics data

Undoubtedly, pharmacogenomics (PGx) aims in optimizing drug treatment responses whilst also improving the patients' quality of life, either via a reduction of adverse drug reactions and/or an enhancement of drug treatment efficacy. To achieve this, PGx guidance is provided by the two major regulatory bodies in a worldwide level, specifically the U.S. Food and Drug Administration (FDA) and the European Medicine Agency (EMA), and occasionally some research consortia, such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) or the Dutch Pharmacogenomics Working Group (DPWG). However, so far, there is a limited number of studies focusing on the delineation of the similarities and more importantly, the discrepancies in the PGx guidance by the different regulatory bodies and consortia. Herein, we use real-life clinical PGx data to highlight such discrepancies and similarities for genome-guided interventions in psychiatric disorders, thus demonstrating the need for harmonization of the guidelines and recommendations. More precisely, we used the PharmCAT genome-informed drug treatment reports from 304 Greek individuals with psychiatric disorders in order to emphasize on the discrepancies in the PGx guidance/guidelines between FDA vs EMA and CPIC vs DPWG, respectively. For example, CYP2D6-pimozide pair is characterized as 'Testing Required' according to FDA and is accompanied by a DPWG PGx guideline, whilst no EMA or CPIC PGx guidance is found for this drug-gene pair. Moreover, discrepancies are observed regarding the type of PGx guidance for CYP2C19-doxepin pair, with 89 individuals from our study cohort requiring a dose prescribing change based on FDA, whilst only 5 individuals have to receive genome-guided treatment adjustment according to CPIC. To our knowledge, this is the first study, in which discrepancies regarding the type of PGx guidance and the number of actionable drug-gene pairs amongst FDA and EMA, as well as CPIC and DPWG, are brought to light with an emphasis on psychiatric disorders.

Pharmacogenetics in developing countries and low resource environments

While significant advances have been made in pharmacogenetics (PGx), especially in countries with developed economies, this field remains at its infancy in developing countries and low resource environments. Herein, we provide insights into the gap and challenges of PGx at the research and clinical fronts, and some perspectives to bridge the gap and move forward with PGx in the developing world. We show that developing countries fall behind in PGx research, evidenced by a lower number of researchers, citations, and research output. In addition, the implementation of PGx in the clinic has been progressing at a much slower pace than research, and more so in developing countries. To bridge this gap, we recommend fostering regional and multinational collaborations to secure funds for high-throughput genotyping and local capacity building while preserving individual countries' identity, implementing next-generation sequencing, and organizing specialized training and exchange programs to move PGx research and clinical applications forward in developing countries.

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

The role of pharmacogenomics in the personalization of Parkinson's disease treatment

Parkinson's disease (PD)-related phenotypes can vary among patients substantially, including response to dopaminergic treatment in terms of efficacy and occurrence of adverse events. Many pharmacogenetic studies have already been conducted to find genetic markers of response to dopaminergic treatment. Integration of genetic and clinical data has already resulted in construction of clinical pharmacogenetic models for prediction of adverse events. However, the results of pharmacogenetic studies are inconsistent. More comprehensive genome-wide approaches are needed to find genetic biomarkers of PD-related phenotypes to better explain the variability in response to treatment. These genetic markers should be integrated with clinical, environmental, imaging, and other omics data to build clinically useful algorithms for personalization of PD management.

PARC report: health outcomes and value of personalized medicine interventions: impact on patient care

The incorporation of personalized medicine interventions into routine healthcare constitutes an opportunity to improve patients' quality of life, as it empowers implementation of innovative, individualized clinical interventions that maximize efficacy and/or minimize the risk of adverse drug reactions. In order to ensure equal access to genomic testing for all patients, the costs associated with these interventions must be reimbursed by payers and insurance bodies. As such, it is of utmost importance to thoroughly evaluate these interventions both in terms of their clinical effectiveness and their economic cost. This article discusses the impact of personalized medicine interventions in terms of both health outcomes and value, which directly impacts on their pricing and reimbursement by the various national healthcare systems.

A Novel Approach for the Identification of Pharmacogenetic Variants in MT-RNR1 through Next-Generation Sequencing Off-Target Data

Specific genetic variants in the mitochondrially encoded 12S ribosomal RNA gene (MT-RNR1) cause aminoglycoside-induced irreversible hearing loss. Mitochondrial DNA is usually not included in targeted sequencing experiments; however, off-target data may deliver this information. Here, we extract MT-RNR1 genetic variation, including the most relevant ototoxicity variant m.1555A>G, using the off-target reads of 473 research samples, sequenced through a capture-based, custom-targeted panel and whole exome sequencing (WES), and of 1245 diagnostic samples with clinical WES. Sanger sequencing and fluorescence-based genotyping were used for genotype validation. There was a correlation between off-target reads and mitochondrial coverage (r_customPanel = 0.39, p = 2 × 10⁻¹³ and rWES = 0.67, p = 7 × 10⁻²¹). The median read depth of MT-RNR1 m.1555 was similar to the average mitochondrial genome coverage, with saliva and blood samples giving comparable results. The genotypes from 415 samples, including three m.1555G carriers, were concordant with fluorescence-based genotyping data. In clinical WES, median MT-RNR1 coverage was 56×, with 90% of samples having ≥20 reads at m.1555 position, and one m.1494T and three m.1555G carriers were identified with no evidence for heteroplasmy. Altogether, this study shows that obtaining MT-RNR1 genotypes through off-target reads is an efficient strategy that can impulse preemptive pharmacogenetic screening of this mitochondrial gene.

Exome-Wide Analysis of the DiscovEHR Cohort Reveals Novel Candidate Pharmacogenomic Variants for Clinical Pharmacogenomics

Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from 50,726 participants, as derived from the DiscovEHR cohort, to identify pharmacogenomic variants of potential clinical relevance, according to their occurrence within the PharmGKB database. We further assessed the distribution of the identified rare and common pharmacogenomics variants amongst different GnomAD subpopulations. Overall, our findings show that the use of publicly available sequence data, such as the DiscovEHR dataset and GnomAD, provides an opportunity for a deeper understanding of genetic variation in pharmacogenes with direct implications in clinical pharmacogenomics.

Roles and mechanisms of alternative splicing in cancer - implications for care

Removal of introns from messenger RNA precursors (pre-mRNA splicing) is an essential step for the expression of most eukaryotic genes. Alternative splicing enables the regulated generation of multiple mRNA and protein products from a single gene. Cancer cells have general as well as cancer type-specific and subtype-specific alterations in the splicing process that can have prognostic value and contribute to every hallmark of cancer progression, including cancer immune responses. These splicing alterations are often linked to the occurrence of cancer driver mutations in genes encoding either core components or regulators of the splicing machinery. Of therapeutic relevance, the transcriptomic landscape of cancer cells makes them particularly vulnerable to pharmacological inhibition of splicing. Small-molecule splicing modulators are currently in clinical trials and, in addition to splice site-switching antisense oligonucleotides, offer the promise of novel and personalized approaches to cancer treatment.

Temporal Changes of Spinal Transcriptomic Profiles in Mice With Spinal Nerve Ligation

Neuropathic pain (NP) is an intractable disease accompanying with allodynia, hyperalgesia and spontaneous pain. Accumulating evidence suggested that large volume of neurotransmitters, genes, and signaling pathways were implicated with the initiation and development of NP, while the underlying mechanism still remained poorly understood. Therefore, it was extremely important to further elucidate the potential regulatory networks for developing appropriate treatment options. Here, the RNA-Seq high-throughput sequencing was employed to determine the genes expression change in mice undergoing spinal nerve ligation (SNL). Meanwhile, the differentially expressed genes (DEGs) were analyzed by using integrated Differential Expression and Pathway analysis (iDEP) tools and String database. Then, quantitative real-time PCR (qRT-PCR) was employed to detect the expression of hub gens. The results showed that the DEGs mainly comprised 1712 upregulated and 1515 downregulated genes at 7 days, and consisted of 243 upregulated and 357 downregulated genes at 28 days after surgery, respectively. Additionally, 133 genes and two pathways including retrograde endocannabinoid signaling and cardiac muscle contraction collectively participated in biological reactions of 7th and 28th day after operation. Moreover, the results showed that the mRNA and protein expression of Ccl5, Cacna2d1, Cacna2d2, Cacnb2, Gabrb3, GluA1, and GluA2 were significantly upregulated in SNL-7/28d group than that of in Sham-7/28d group (SNL-7d vs. Sham-7d; SNL-28d vs. Sham-28d; P < 0.05). And the level of Glra2, Glra4, Glra3, Grik1, Grik2, NR1, NR2A, and NR2B was obviously increased in SNL-7d group compared to Sham-7d group (P < 0.05), but which was no statistical difference between SNL-28d group and Sham-28d group. Therefore, these results provided new perspectives and strategies for deeply illuminating the underlying mechanism, and identifying the key elements for treating NP.