Next generation sequencing: implications in personalized medicine and pharmacogenomics

Detection accuracy and clinical applications of DP-TOF mass spectrometry

OBJECTIVE

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently used in clinical microbiology laboratories. This study aimed to determine whether dual-polarity time-of-flight mass spectrometry (DP-TOF MS) could be applied to clinical nucleotide detection.

METHODS

This prospective study included 40 healthy individuals and 110 patients diagnosed with cardiovascular diseases. We used DP-TOF MS and Sanger sequencing to evaluate 17 loci across 11 genes associated with cardiovascular drug responses. In addition, we used DP-TOF MS to test 998 retrospectively collected clinical DNA samples with known results.

RESULTS

A, T, and G nucleotide detection by DP-TOF MS and Sanger sequencing revealed 100% concordance, whereas the C nucleotide concordance was 99.86%. Genotyping based on the results of the two methods showed 99.96% concordance. Regarding clinical applications, DP-TOF MS yielded a 99.91% concordance rate for known loci. The minimum detection limit for DNA was 0.4 ng; the inter-assay and intra-assay precision rates were both 100%. Anti-interference analysis showed that aerosol contamination greater than 1013 copies/µL in the laboratory environment could influence the results of DP-TOF MS.

CONCLUSIONS

The DP-TOF MS platform displayed good detection performance, as demonstrated by its 99.96% concordance rate with Sanger sequencing. Thus, it may be applied to clinical nucleotide detection.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Construction of a prognostic prediction model in liver cancer based on genes involved in integrin cell surface interactions pathway by multi-omics screening

Background: Liver cancer is a common malignant tumor with an increasing incidence in recent years. We aimed to develop a model by integrating clinical information and multi-omics profiles of genes to predict survival of patients with liver cancer. Methods: The multi-omics data were integrated to identify liver cancer survival-associated signal pathways. Then, a prognostic risk score model was established based on key genes in a specific pathway, followed by the analysis of the relationship between the risk score and clinical features as well as molecular and immunologic characterization of the key genes included in the prediction model. The function experiments were performed to further elucidate the undergoing molecular mechanism. Results: Totally, 4 pathways associated with liver cancer patients' survival were identified. In the pathway of integrin cell surface interactions, low expression of COMP and SPP1, and low CNVs level of COL4A2 and ITGAV were significantly related to prognosis. Based on above 4 genes, the risk score model for prognosis was established. Risk score, ITGAV and SPP1 were the most significantly positively related to activated dendritic cell. COL4A2 and COMP were the most significantly positively associated with Type 1 T helper cell and regulatory T cell, respectively. The nomogram (involved T stage and risk score) may better predict short-term survival. The cell assay showed that overexpression of ITGAV promoted tumorigenesis. Conclusion: The risk score model constructed with four genes (COMP, SPP1, COL4A2, and ITGAV) may be used to predict survival in liver cancer patients.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

DNA Sequencing Technologies and DNA Barcoding

DNA barcodes are short, standardized DNA segments that geneticists can use to identify all living taxa. On the other hand, DNA barcoding identifies species by analyzing these specific regions against a DNA barcode reference library. In its initial years, DNA barcodes sequenced by Sanger's method were extensively used by taxonomists for the characterization and identification of species. But in recent years, DNA barcoding by next-generation sequencing (NGS) has found broader applications, such as quality control, biomonitoring of protected species, and biodiversity assessment. Technological advancements have also paved the way to metabarcoding, which has enabled massive parallel sequ.encing of complex bulk samples using high-throughput sequencing techniques. In future, DNA barcoding along with high-throughput techniques will show stupendous progress in taxonomic classification with reference to available sequence data.

Applications of Innovation Technologies for Personalized Cancer Medicine: Stem Cells and Gene-Editing Tools

Personalized medicine is a new approach toward safer and even cheaper treatments with minimal side effects and toxicity. Planning a therapy based on individual properties causes an effective result in a patient's treatment, especially in a complex disease such as cancer. The benefits of personalized medicine include not only early diagnosis with high accuracy but also a more appropriate and effective therapeutic approach based on the unique clinical, genetic, and epigenetic features and biomarker profiles of a specific patient's disease. In order to achieve personalized cancer therapy, understanding cancer biology plays an important role. One of the crucial applications of personalized medicine that has gained consideration more recently due to its capability in developing disease therapy is related to the field of stem cells. We review various applications of pluripotent, somatic, and cancer stem cells in personalized medicine, including targeted cancer therapy, cancer modeling, diagnostics, and drug screening. CRISPR-Cas gene-editing technology is then discussed as a state-of-the-art biotechnological advance with substantial impacts on medical and therapeutic applications. As part of this section, the role of CRISPR-Cas genome editing in recent cancer studies is reviewed as a further example of personalized medicine application.

Optimal Protocols and Management of Clinical and Genomic Data Collection to Assist in the Early Diagnosis and Treatment of Multiple Congenital Anomalies

Standardized protocols have been designed and developed specifically for clinical information collection and obtaining trio genomic information from infants affected with congenital anomalies (CA) and their parents, as well as securing human biological resources. The protocols include clinical and genomic information collection on multiple CA that were difficult to diagnose using pre-existing screening methods. We obtained human-derived resources and genomic information from 138 cases, including 45 families of infants with CA and their parent trios. For the clinical information collection protocol, criteria for target patient selection and a consent system for collecting and utilizing research resources are crucial. Whole genome sequencing data were generated for all participants, and standardized protocols were developed for resource collection and manufacturing. We recorded the phenotype information according to the Human Phenotype Ontology term, and epidemiological information was collected through an environmental factor questionnaire. Updating and recording of clinical symptoms and genetic information that have been newly added or changed over time are significant. The protocols enabled long-term tracking by including the growth and development status that reflect the important characteristics of newborns. Using these clinical and genetic information collection protocols for CA, an essential platform for early genetic diagnosis and diagnostic research can be established, and new genetic diagnostic guidelines can be presented in the near future.

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Characterization of ADME Gene Variation in Colombian Population by Exome Sequencing

In genes related to drug pharmacokinetics, molecular variations determine interindividual variability in the therapeutic efficacy and adverse drug reactions. The assessment of single-nucleotide variants (SNVs) is used with growing frequency in pharmacogenetic practice, and recently, high-throughput genomic analyses obtained through next-generation sequencing (NGS) have been recognized as powerful tools to identify common, rare and novel variants. These genetic profiles remain underexplored in Latin-American populations, including Colombia. In this study, we investigated the variability of 35 genes included in the ADME core panel (absorption, distribution, metabolism, and excretion) by whole-exome sequencing (WES) of 509 unrelated Colombian individuals with no previous reports of adverse drug reactions. Rare variants were filtered according to the minor allele frequencies (MAF) <1% and potential deleterious consequences. The functional impact of novel and rare missense variants was assessed using an optimized framework for pharmacogenetic variants. Bioinformatic analyses included the identification of clinically validated variants described in PharmGKB and ClinVar databases. Ancestry from WES data was inferred using the R package EthSEQ v2.1.4. Allelic frequencies were compared to other populations reported in the public gnomAD database. Our analysis revealed that rare missense pharmacogenetic variants were 2.1 times more frequent than common variants with 121 variants predicted as potentially deleterious. Rare loss of function (LoF) variants were identified in 65.7% of evaluated genes. Regarding variants with clinical pharmacogenetic effect, our study revealed 89 sequence variations in 28 genes represented by missense (62%), synonymous (22.5%), splice site (11.2%), and indels (3.4%). In this group, ABCB1, ABCC2, CY2B6, CYP2D6, DPYD, NAT2, SLC22A1, and UGTB2B7, are the most polymorphic genes. NAT2, CYP2B6 and DPYD metabolizer phenotypes demonstrated the highest variability. Ancestry analysis indicated admixture in 73% of the population. Allelic frequencies exhibit significant differences with other Latin-American populations, highlighting the importance of pharmacogenomic studies in populations of different ethnicities. Altogether, our data revealed that rare variants are an important source of variability in pharmacogenes involved in the pharmacokinetics of drugs and likely account for the unexplained interindividual variability in drug response. These findings provide evidence of the utility of WES for pharmacogenomic testing and into clinical practice.

Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows

BACKGROUND

The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations.

METHODS

AMP-ASCO-CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method.

RESULTS

A subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods.

CONCLUSION

CACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at ( https://github.com/tsobahytm/CACSV/tree/main/dataset ).

Machine-learning of complex evolutionary signals improves classification of SNVs

Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant prediction, while others demonstrated species that optimize prediction. These insights led to developing EvoDiagnostics, which uses the conservation against each species as a feature within a random-forest machine-learning classification algorithm. EvoDiagnostics outperformed traditional conservation algorithms, deep-learning based methods and most ensemble tools in every prediction-task, highlighting the strength of optimizing conservation analysis per-species and per-gene. Overall, we suggest a new and a more biologically relevant approach for analyzing conservation, which improves prediction of variant pathogenicity.

Pharmacogenomic landscape of Indian population using whole genomes

Ethnic differences in pharmacogenomic (PGx) variants have been well documented in literature and could significantly impact variability in response and adverse events to therapeutics. India is a large country with diverse ethnic populations of distinct genetic architecture. India's national genome sequencing initiative (IndiGen) provides a unique opportunity to explore the landscape of PGx variants using population-scale whole genome sequences. We have analyzed the IndiGen variation dataset (N = 1029 genomes) along with global population scale databases to map the most prevalent clinically actionable and potentially deleterious PGx variants among Indians. Differential frequencies for the known and novel variants were studied and interaction of the disrupted PGx genes affecting drug responses were analyzed by performing a pathway analysis. We have highlighted significant differences in the allele frequencies of clinically actionable PGx variants in Indians when compared to the global populations. We identified 134 mostly common (allele frequency [AF] > 0.1) potentially deleterious PGx variants that could alter or inhibit the function of 102 pharmacogenes in Indians. We also estimate that on, an average, each Indian individual carried eight PGx variants (single nucleotide variants) that have a direct impact on the choice of treatment or drug dosing. We have also highlighted clinically actionable PGx variants and genes for which preemptive genotyping is most recommended for the Indian population. The study has put forward the most comprehensive PGx landscape of the Indian population from whole genomes that could enable optimized drug selection and genotype-guided prescriptions for improved therapeutic outcomes and minimizing adverse events.

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.

DNA Genotyping Based on Isothermal Amplification and Colorimetric Detection by Consumer Electronics Devices

The point-of-care testing of DNA biomarkers requires compact biosensing systems and consumer electronic technologies provide fascinating opportunities. Their portability, mass-produced components, and high-performance readout capabilities are the main advantages for the development of novel bioanalytical methods.This chapter describes the detection of single nucleotide polymorphisms (SNP) through methods based on user-friendly optical devices (e.g., USB digital microscope, flatbed scanner, smartphone, and DVD drive). Loop mediated isothermal amplification (LAMP) enables the required discrimination of each specific variant prior to the optical reading. In the first method, products are directly hybridized to the allele-specific probes attached to plastic chips in an array format. The second method, allele-specific primers are used, enabling the direct end-point detection based a colorimetric dyer and a microfluidic chamber chip. In both approaches, devices are employed for chip scanning.A representative application to the genotyping of a clinically relevant SNP from human samples is provided, showing the excellent features achieved. Consumer electronic devices are able to register sensitive precise measurements in terms of signal-to-noise ratios, image resolution, and scan-to-scan reproducibility. The integrated DNA-based method lead a low detection limit (100 genomic DNA copies), reproducible (variation <15%), high specificity (genotypes validated by reference method), and cheap assays (<10 €/test). The underlying challenge is the reliable implementation into minimal-specialized clinical laboratories, incorporating additional advantages, such as user-friendly interface, low cost, and connectivity for telemedicine needs.

Identification of Molecular Alterations Challenging Initial Pathologic Classification in Cases of Clinician-Initiated Next-Generation Sequencing Testing

OBJECTIVES

Large gene panel next-generation sequencing (NGS) is a powerful tool capable of generating predictive data on cancer prognosis and response to specific therapeutic interventions. The utility of large panel NGS data on tumor classification, however, may be underappreciated because of a workflow that often circumvents the surgical pathologist. We sought to describe cases in which NGS data lead to an unanticipated change in tumor classification and to discuss current workflow practices of NGS testing that limit its use as a diagnostic adjunct.

METHODS

We performed a retrospective review to identify cases in which NGS testing uncovered data that led to a revision of the initial pathologic diagnosis that an outside or in-house pathologist had made.

RESULTS

Nine cases are presented in which NGS data provided insights that led to a revision of the original pathologic diagnosis. Distinctive molecular signatures, mutational signatures, fusions, or identification of viral sequencing provided the critical evidence on which these tumors were reclassified.

CONCLUSIONS

The current workflow of NGS testing should always include the surgical pathologist as an active partner to ensure that the molecular results are fully reflected in the final diagnosis. In some instances, active participation by the surgical pathologist may require amendment of previously issued pathology reports.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

Transcriptomics and sequencing analysis of gene expression profiling for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common psychiatric disorder characterized by constant sadness and a lack of interest in work and social interactions. Maintaining the transcriptome levels via the controlled regulation of mRNA processing and transport is essential to alleviating MDD. Various molecular phenotypes such as aberrant RNA splicing and stability are identified as critical determinants of MDD.

AIM

This study aims to compare the mRNA expression profiles between major depressive disorder non-suicide (MDD), major depressive disorder suicide (MDD-S), and control groups using RNA-Seq.

MATERIALS AND METHODS

A transcriptomics and sequencing analysis of gene expression profiling was conducted in 9 patients with MDD, 10 patients with MDD-S, and 10 control patients.

RESULTS

A comparison of the sample groups revealed that the PRKACB gene was upregulated in patients with MDD. At the same time, GRM3, DLGAP1, and GRIA2 were downregulated in these patients-these genes are majorly involved in the glutamatergic pathway. Five genes (GRIA1, CAMK2D, PPP3CA, MAPK10, and PPP2R2A) of the dopaminergic pathway were downregulated in patients with the MDD-S condition when compared with the MDD and control groups. Cholinergic synapses were altered in patients with MDD when compared to the control group due to the presence of dysregulated genes (KCNQ5, PLCB4, ADCY9, CAMK2D, PIK3CA, and GNG2).

CONCLUSION

The results provide a new understanding of the etiology of depression in humans and identify probable depression-associated biomarkers.

GRIA2/ENPP3 Regulates the Proliferation and Migration of Vascular Smooth Muscle Cells in the Restenosis Process Post-PTA in Lower Extremity Arteries

Restenosis is the main restriction on the long-term efficacy of percutaneous transluminal angioplasty (PTA) therapy for peripheral artery disease (PAD). Interventions to prevent restenosis are poor, and the exact mechanism is unclear. Here, we aimed to elucidate the role of GRIA2 in the restenosis process post-PTA in lower extremity arteries. We searched the differentially expressed genes (DEGs) between atherosclerotic and restenotic artery plaques in the Gene Expression Omnibus (GEO), and five DEGs were identified. Combined with Gene Ontology (GO) enrichment analysis, GRIA2 was significantly correlated with the restenosis process. Tissue samples were used to examine GRIA2 expression by immunofluorescence staining of atherosclerotic and restenotic artery plaques. The regulation of GRIA2 in vascular smooth muscle cells (VSMCs) was confirmed by lentiviral transfection. Overexpression of GRIA2 promoted the proliferation and migration of VSMCs. Using Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein–protein interaction (PPI) network, a strong connection between ENPP3 and GRIA2 was discovered. In vitro results showed that the high expression of GRIA2 in VSMCs enhanced the expression of ENPP3, while downregulation of GRIA2 downregulated ENPP3. GRIA2 is highly differentially expressed in restenotic arterial plaques, promoting the proliferation and migration of VSMCs through upregulation of ENPP3. These discoveries will help us to obtain a better understanding of restenosis in lower extremity arteries.

Current Insights into Interethnic Variability in Testicular Cancers: Population Pharmacogenetics, Clinical Trials, Genetic Basis of Chemotherapy- Induced Toxicities and Molecular Signal Transduction

Testicular cancer is an aggressive malignancy with a rising incidence rate across the globe. Testicular germ cell tumors are the most commonly diagnosed cancers, and surgical removal of the testes is often a radical necessity along with chemotherapy and radiotherapy. While seminomas are receptive to radiotherapy as well as chemotherapy, non-seminomatous germ cell tumors respond to chemotherapy only. Due to the singular nature of testicular cancers with associated orchiectomy and mortality, it is important to study the molecular basis and genetic underpinnings of this group of cancers across male populations globally. In this review, we shed light on the population pharmacogenetics of testicular cancer, pediatric and adult tumors, current clinical trials, genetic determinants of chemotherapy-induced toxicity in testicular cancer, as well as the molecular signal transduction pathways operating in this malignancy. Taken together, our discussions will help in enhancing our understanding of genetic factors in testicular carcinogenesis and chemotherapy-induced toxicity, augment our knowledge of this aggressive cancer at the cellular and molecular level, as well as improve precision medicine approaches to combat this disease.

DEEPGENTM-A Novel Variant Calling Assay for Low Frequency Variants

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGEN^TM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%-0.5%) to determine DEEPGEN^TM's performance and minimal input requirements. Our findings confirm DEEPGEN^TM's effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

Performance comparison: exome sequencing as a single test replacing Sanger sequencing

Next generation sequencing tests are used routinely as first-choice tests in the clinic. However, systematic performance comparing the results of exome sequencing as a single test replacing Sanger sequencing of targeted gene(s) is still lacking. Performance comparison data are critically important for clinical case management. In this study, we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger sequencing was completed for all exons, including flanking ± 8 bp regions. For the 258 genes, NGS mean coverage was > 20 × for > 98 and > 91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger sequencing and 97-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger sequencing and NGS performances. Both methods demonstrated false-positive and false-negative calls. High clinical suspicion for a specific diagnosis should, therefore, override negative results of either Sanger sequencing or NGS.

Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area

Pharmacogenomics (PGx) is the knowledge of diverse drug responses and effects in people, based on their genomic profiles. Such information is considered as one of the main directions to reach personalized medicine in future clinical practices. Since the start of applying next generation sequencing (NGS) methods in drug related clinical investigations, many common medicines found their genetic data for the related metabolizing/shipping proteins in the human body. Yet, the employing of technology is accompanied by big obtained data, which most of them have no clear guidelines for consideration in routine treatment decisions for patients. This review article talks about different types of NGS derived PGx variants in clinical studies and try to display the current and newly developed approaches to deal with pharmacogenetic data with/without clear guidelines for considering in clinical settings.

Covalent labeling of nucleic acids

Labeling of nucleic acids is required for many studies aiming to elucidate their functions and dynamics in vitro and in cells. Out of the numerous labeling concepts that have been devised, covalent labeling provides the most stable linkage, an unrivaled choice of small and highly fluorescent labels and - thanks to recent advances in click chemistry - an incredible versatility. Depending on the approach, site-, sequence- and cell-specificity can be achieved. DNA and RNA labeling are rapidly developing fields that bring together multiple areas of research: on the one hand, synthetic and biophysical chemists develop new fluorescent labels and isomorphic nucleobases as well as faster and more selective bioorthogonal reactions. On the other hand, the number of enzymes that can be harnessed for post-synthetic and site-specific labeling of nucleic acids has increased significantly. Together with protein engineering and genetic manipulation of cells, intracellular and cell-specific labeling has become possible. In this review, we provide a structured overview of covalent labeling approaches for nucleic acids and highlight notable developments, in particular recent examples. The majority of this review will focus on fluorescent labeling; however, the principles can often be readily applied to other labels. We will start with entirely chemical approaches, followed by chemo-enzymatic strategies and ribozymes, and finish with metabolic labeling of nucleic acids. Each section is subdivided into direct (or one-step) and two-step labeling approaches and will start with DNA before treating RNA.

A novel TJP1-ROS1 fusion in malignant peripheral nerve sheath tumor responding to crizotinib: A case report

RATIONALE

Malignant peripheral nerve sheath tumor (MPNST) is a rare sarcoma. Owing to the lack of specific histological criteria, immunohistochemical, and molecular diagnostic markers, several differential diagnoses must be considered. Advances in molecular testing can provide significant insights for management of rare tumor.

PATIENT CONCERNS

The patient was a 50-year-old man with a history of lumpectomy on the right back 30 years ago. He felt a stabbing pain at the right iliac fossa and went to the local hospital.

DIAGNOSIS

By immunohistochemistry, the tumor cells stained positively for S-100 (focal +), CD34 (strong +++) and Ki-67 (20%), and negatively for smooth muscle actin, pan-cytokeratin, neurofilament, pan-cytokeratin-L, GFAP, CD31, STAT6, ERG, myogenin, and MyoD1. Combined with the histopathology and immunohistochemistry results, our initial diagnosis was solitary fibrous tumor (SFT) or MPNST. The tissue biopsy was sent for next-generation sequencing. neurofibromatosis type 1 Q1395Hfs*22 somatic mutation, neurofibromatosis type 1 D483Tfs*15 germline mutation, and amplifications of BTK, MDM2, ATF1, BMPR1A, EBHA2, GNA13, PTPN11, RAD52, RPTOR, and SOX9, as well as TJP1-ROS1 fusion, CDKN2A-IL1RAPL2 fusion and CDKN2A/UBAP1 rearrangement were identified. Given that NAB2-STAT6 fusion, a specific biomarker of SFT, was not identified in our patient's tumor, the SFT was excluded by through genetic testing results. Therefore, our finally diagnosis was a MPNST by 2 or more pathologists.

INTERVENTIONS AND OUTCOMES

Subsequently, the patient received crizotinib therapy for 2 months and showed stable disease. However, after crizotinib continued treatment for 4 months, the patient's disease progressed. Soon after, the patient stopped crizotinib treatment and died in home.

LESSONS

To our knowledge, this is the first report of the TJP1-ROS1 fusion, which expands the list of gene fusions and highlights new targets for targeted therapy. Also, our case underlines the value of multi-gene panel next-generation sequencing for diagnosis of MPNST.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

Yeast systems biology in understanding principles of physiology underlying complex human diseases

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

Integrated analysis identifies DUSP5 as a novel prognostic indicator for thyroid follicular carcinoma

BACKGROUND

Differentiated thyroid cancer involves thyroid follicular carcinoma (FTC) and papillary thyroid carcinoma (PTC). Patients with FTC have a worse prognosis than those with PTC for early metastasis through blood of FTC. However, the mechanism of poor prognosis of FTC is still unclear. Here, we aim to evaluate the role of DUSP5 in the prognostic evaluation of FTC.

METHOD

We searched the Gene Expression Omnibus (GEO) database for the differentially expressed genes (DEGs) between FTC and PTC, and then combined with survival analysis of cBioPortal database to locate the gene significantly related to prognosis. Tissue microarrays and clinical tissues were used to examine DUSP5 expression by immunohistochemical (IHC) staining between FTC and PTC tissues. In vitro experiment, proliferation, migration and invasion of FTC were observed after regulation of DUSP5 by transfection of siRNA and plasmids, respectively.

RESULTS

After searching the GEO database, 26 DEGs were found. DUSP5 was significantly associated with prognosis of FTC in combination with survival analysis. Data of IHC staining showed lower expression of DUSP5 in FTC compared to PTC tissues. Furthermore, overexpression of DUSP5 inhibited the proliferation, migration and invasion accompanied with low level of MMP9 and Vimentin and high level of E-cadherin. Nevertheless, inhibition of DUSP5 ameliorated above damaging effect on the proliferation, migration and invasion.

CONCLUSION

DUSP5 was differentially expressed in FTC and PTC tissues. Low level of DUSP5 in FTC participates in the high frequency of metastasis, and further contributes to poor prognosis of FTC. DUSP5 could be served as a novel therapeutic target for FTC.

MOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules

Survival analyses of gene expression data has been a useful and widely used approach in clinical applications. But, in complex diseases, such as cancer, the identification of survival-associated cell processes - rather than single genes - provides more informative results because the efficacy of survival prediction increases when multiple prognostic features are combined to enlarge the possibility of having druggable targets. Moreover, genome-wide screening in molecular medicine has rapidly grown, providing not only gene expression but also multi-omic measurements such as DNA mutations, methylation, expression, and copy number data. In cancer, virtually all these aberrations can contribute in synergy to pathological processes, and their measurements can improve a patient’s outcome and help in diagnosis and treatment decisions. Here, we present MOSClip, an R package implementing a new topological pathway analysis tool able to integrate multi-omic data and look for survival-associated gene modules. MOSClip tests the survival association of dimensionality-reduced multi-omic data using multivariate models, providing graphical devices for management, browsing and interpretation of results. Using simulated data we evaluated MOSClip performance in terms of false positives and false negatives in different settings, while the TCGA ovarian cancer dataset is used as a case study to highlight MOSClip’s potential.

Clinical Application of Next-Generation Sequencing as A Liquid Biopsy Technique in Advanced Colorectal Cancer: A Trick or A Treat?

Owing to its advantages over prior relevant technologies, massive parallel or next-generation sequencing (NGS) is rapidly evolving, with growing applications in a wide range of human diseases. The burst in actionable molecular alterations in many cancer types advocates for the practicality of using NGS in the clinical setting, as it permits the parallel characterization of multiple genes in a cost- and time-effective way, starting from low-input DNA. In advanced clinical practice, the oncological management of colorectal cancer requires prior knowledge of KRAS, NRAS, and BRAF status, for the design of appropriate therapeutic strategies, with more gene mutations still surfacing as potential biomarkers. Tumor heterogeneity, as well as the need for serial gene profiling due to tumor evolution and the emergence of novel genetic alterations, have promoted the use of liquid biopsies-especially in the form of circulating tumor DNA (ctDNA)-as a promising alternative to tissue molecular analysis. This review discusses recent studies that have used plasma NGS in advanced colorectal cancer and summarizes the clinical applications, as well as the technical challenges involved in adopting this technique in a clinically beneficial oncological practice.

Implementation and obstacles of pharmacogenetics in clinical practice: An international survey

AIMS

Eight years ago, a paper-based survey was administered during the World Pharma 2010 meeting, asking about the challenges of implementing pharmacogenetics (PGx) in clinical practice. The data collected at the time gave an idea about the progress of this implementation and what still needs to be done. Since then, although there have been major initiatives to push PGx forward, PGx clinical implementation may still be facing different challenges in different parts of the world. Our aim was therefore to distribute a follow-up international survey in electronic format to elucidate an overview on the current stage of implementation, acceptance and challenges of PGx in academic institutions around the world.

METHODS

This is an online anonymous LimeSurvey-based study launched on 11 November 2018. Survey questions were adapted from the initially published manuscript in 2010. An extensive web search for worldwide scientists potentially involved in PGx research resulted in a total of 1973 names. Countries were grouped based on the Human Development Index.

RESULTS

There were 204 respondents from 43 countries. Despite the wide availability of PGx tests, the consistently positive attitude towards their applications and advances in the field, progress of the clinical implementation of PGx still faces many challenges all around the globe.

CONCLUSIONS

Clinical implementation of PGx started over a decade ago but there is a gap in progress around the globe and discrepancies between the challenges reported by different countries, despite some challenges being universal. Further studies on ways to overcome these challenges are warranted.

Next-Generation Sequencing in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is lethal, and the majority of patients present with locally advanced or metastatic disease that is not amenable to cure. Thus, with surgical resection being the only curative modality, it is critical that disease is identified at an earlier stage to allow the appropriate therapy to be applied. Unfortunately, a specific biomarker for early diagnosis has not yet been identified; hence, no screening process exists. Recently, high-throughput screening and next-generation sequencing (NGS) have led to the identification of novel biomarkers for many disease processes, and work has commenced in PDAC. Genomic data generated by NGS not only have the potential to assist clinicians in early diagnosis and screening, especially in high-risk populations, but also may eventually allow the development of personalized treatment programs with targeted therapies, given the large number of gene mutations seen in PDAC. This review introduces the basic concepts of NGS and provides a comprehensive review of the current understanding of genetics in PDAC as related to discoveries made using NGS.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

Pharmacogenetic and pharmacogenomic discovery strategies

Genetic/genomic profiling at a single-patient level is expected to provide critical information for determining inter-individual drug toxicity and potential efficacy in cancer therapy. A better definition of cancer subtypes at a molecular level, may correspondingly complement such pharmacogenetic and pharmacogenomic approaches, for more effective personalized treatments. Current pharmacogenetic/pharmacogenomic strategies are largely based on the identification of known polymorphisms, thus limiting the discovery of novel or rarer genetic variants. Recent improvements in cost and throughput of next generation sequencing (NGS) are now making whole-genome profiling a plausible alternative for clinical procedures. Beyond classical pharmacogenetic/pharmacogenomic traits for drug metabolism, NGS screening programs of cancer genomes may lead to the identification of novel cancer-driving mutations. These may not only constitute novel therapeutic targets, but also effector determinants for metabolic pathways linked to drug metabolism. An additional advantage is that cancer NGS profiling is now leading to discovering targetable mutations, e.g., in glioblastomas and pancreatic cancers, which were originally discovered in other tumor types, thus allowing for effective repurposing of active drugs already on the market.

SDM: a server for predicting effects of mutations on protein stability

Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

RNA‑seq analyses of antibiotic resistance mechanisms in Serratia marcescens

The present study aimed to further clarify the genetic mechanisms responsible for the antimicrobial resistance of Serratia marcescens (S. marcescens) using RNA sequencing. Three drug-susceptible S. marcescens strains (named MYQT1, MYQT2, and MYQT3) and three multidrug-resistant S. marcescens strains (named MYQT4, MYQT5, and MYQT6) were isolated from six different patients and subjected to RNA sequencing. Differentially expressed genes (DEGs) between the multidrug-resistant S. marcescens strains and drug-susceptible strains were screened and compared, followed by functional enrichment analysis. In addition, a protein-protein interaction (PPI) network was constructed, and significant modules were extracted from it. Genes enriched in the significant modules were subjected to further enrichment analysis. MYQT3 had very a different expression pattern from MYQT1 and MYQT2, and thus, MYQT3 was excluded from the following analysis. A total of 225 DEGs were identified, of which SMDB11_RS09300 (GTP cyclohydrolase FolE2) was the most significantly upregulated with a log₂ FC of 6.4; these DEGs were enriched in different GO terms, including hydrogen sulfide biosynthetic process, sulfur compound transmembrane transporter activity, and ABC transporter complex. Additionally, several genes were identified to be important genes in the PPI network, including SMDB11_RS17755 (upregulated; glutamate synthase large subunit), SMDB11_RS00590 (upregulated; sulfite reductase subunit α), and SMDB11_RS04505 (upregulated; cystathionine β-synthase). Thus, SMDB11_RS09300, SMDB11_RS17755, SMDB11_RS00590, and SMDB11_RS04505 may play significant roles in the antimicrobial resistance of S. marcescens by participating in folate metabolism or the integrity of cell membranes. However, further experiments are required to clarify these findings.

Clopidogrel Pharmacogenetics in Iranian Patients Undergoing Percutaneous Coronary Intervention

Clopidogrel is used in patients with coronary syndromes and at risk of thrombotic events or receiving percutaneous coronary intervention (PCI) for reducing heart attack and stroke. Here we present genotype and phenotype study of Iranian patients undergoing PCI treated with clopidogrel during a 6-month period of follow-up; common variants of CYP2C19, CYP3A5, CYP3A4, and ABCB1 genes were determined as well as the patients' cardiovascular outcomes to find out the effect of these variants individually and in combination. 388 individuals receiving PCI were enrolled in this study. Different pretreatment doses of clopidogrel were prescribed under the interventional cardiologists' guidance. The patients were followed for a duration of 1 month, and 6 months. Six SNPs were selected for genotyping including CYP2C19*2 (c.681G > A), CYP2C19*3 (c.636G > A), CYP2C19*17 allele (c.-806C > T), ABCB1 (c.3435C > T), CYP3A5 (c.6986A > G), and CYP3A4 (c.1026 + 12G > A). The mean loading dose was 600 mg/day in 267 (68.8%) individuals, 300 mg/day in 121 (31.2%). 8 patients had cardiovascular events such as thrombosis, unstable angina, and non-STEMI. The studied alleles and genotypes were in Hardy-Weinberg equilibrium. None of the SNPs individually were significantly associated with outcome events. Our results indicate that combinations of different alleles of genes are involved in pharmacokinetic variability and joint factors are important; this means that genotyping and analysis of an individual variant may not be as straightforward in risk assessment and pharmacogenetics. This highlights the importance of personalized medicine in risk assessment and treatment.

Clinical relevance of circulating molecules in cancer: focus on gastrointestinal stromal tumors

In recent years, growing research interest has focused on the so-called liquid biopsy. A simple blood test offers access to a plethora of information, which might be extremely helpful in understanding or characterizing specific diseases. Blood contains different molecules, of which circulating free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and extracellular vesicles (EVs) are the most relevant. Conceivably, these molecules have the potential for tumor diagnosis, monitoring tumor evolution, and evaluating treatment response and pharmacological resistance. This review aims to present a state-of-the-art of recent advances in circulating DNA and circulating RNA in gastrointestinal stromal tumors (GISTs). To date, progress in liquid biopsy has been scarce in GISTs due to several issues correlated with the nature of the pathology. Namely, heterogeneity in primary and secondary mutations in key driver genes has greatly slowed the development and application in GISTs, unlike in other tumor types in which liquid biopsy has already been translated into clinical practice. However, meaningful novel data have shown in recent years a significant clinical potential of ctDNA, CTCs, EVs and circulating RNA in GISTs.

New Challenges in Oncology for the Brazilian Private Health Sector: Specialists' Concerns After the ISPOR International Congress in Boston, Massachusetts, 2017

The congress of the International Society for Pharmacoeconomics and Outcomes Research is one of the main worldwide forums for the dissemination of research and knowledge on healthcare economics. Brazil is the largest country in Latin America, with a per-capita gross domestic product of $15 200 in 2017 and healthcare expenditure of the order of $1 318 per inhabitant. Brazilian specialists participated actively in the society's latest congress, which took place in Boston, Massachusetts, from May 20 to 24, 2017. They met to discuss the main topics dealt with at the congress and their applicability to Brazilian realities. The topics chosen were precision medicine, new challenges for economic modeling within oncology and immuno-oncology, data to aid in managerial decision making (ie, data from real-world studies), and, lastly, strategies for accessing high-cost medications in Brazil. This opinion article sought to report the main conclusions and consensus reached by this group of specialists on the occasion of this discussion.

SMARTcleaner: identify and clean off-target signals in SMART ChIP-seq analysis

Background

Noises and artifacts may arise in several steps of the next-generation sequencing (NGS) process. Recently, an NGS library preparation method called SMART, or Switching Mechanism At the 5′ end of the RNA Transcript, is introduced to prepare ChIP-seq (chromatin immunoprecipitation and deep sequencing) libraries from small amount of DNA material, using the DNA SMART ChIP-seq Kit. The protocol adds Ts to the 3′ end of DNA templates, which is subsequently recognized and used by SMART poly(dA) primers for reverse transcription and then addition of PCR primers and sequencing adapters. The poly(dA) primers, however, can anneal to poly(T) sequences in a genome and amplify DNA fragments that are not enriched in the immunoprecipitated DNA templates. This off-target amplification results in false signals in the ChIP-seq data.

Results

Here, we show that the off-target ChIP-seq reads derived from false amplification of poly(T/A) genomic sequences have unique and strand-specific features. Accordingly, we develop a tool (called “SMARTcleaner”) that can exploit these features to remove SMART ChIP-seq artifacts. Application of SMARTcleaner to several SMART ChIP-seq datasets demonstrates that it can remove reads from off-target amplification effectively, leading to significantly improved ChIP-seq peaks and results.

Conclusions

SMARTcleaner could identify and clean the false signals in SMART-based ChIP-seq libraries, leading to improvement in peak calling, and downstream data analysis and interpretation.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2577-4) contains supplementary material, which is available to authorized users.

Genomic Interventions in Medicine

Lately, the term "genomics" has become ubiquitous in many scientific articles. It is a rapidly growing aspect of the biomedical sciences that studies the genome. The human genome contains a torrent of information that gives clues about human origin, evolution, biological function, and diseases. In a bid to demystify the workings of the genome, the Human Genome Project (HGP) was initiated in 1990, with the chief goal of sequencing the approximately 3 billion nucleotide base pairs of the human DNA. Since its completion in 2003, the HGP has opened new avenues for the application of genomics in clinical practice. This review attempts to overview some milestone discoveries that paved way for the initiation of the HGP, remarkable revelations from the HGP, and how genomics is influencing a paradigm shift in routine clinical practice. It further highlights the challenges facing the implementation of genomic medicine, particularly in Africa. Possible solutions are also discussed.

Highlights on the Application of Genomics and Bioinformatics in the Fight Against Infectious Diseases: Challenges and Opportunities in Africa

Genomics and bioinformatics are increasingly contributing to our understanding of infectious diseases caused by bacterial pathogens such as Mycobacterium tuberculosis and parasites such as Plasmodium falciparum. This ranges from investigations of disease outbreaks and pathogenesis, host and pathogen genomic variation, and host immune evasion mechanisms to identification of potential diagnostic markers and vaccine targets. High throughput genomics data generated from pathogens and animal models can be combined with host genomics and patients’ health records to give advice on treatment options as well as potential drug and vaccine interactions. However, despite accounting for the highest burden of infectious diseases, Africa has the lowest research output on infectious disease genomics. Here we review the contributions of genomics and bioinformatics to the management of infectious diseases of serious public health concern in Africa including tuberculosis (TB), dengue fever, malaria and filariasis. Furthermore, we discuss how genomics and bioinformatics can be applied to identify drug and vaccine targets. We conclude by identifying challenges to genomics research in Africa and highlighting how these can be overcome where possible.

Sudden Arrhythmic Death During Exercise: A Post-Mortem Genetic Analysis

BACKGROUND

Sudden cardiac death is a natural and unexpected death that occurs within 1 h of the first symptom. Most sudden cardiac deaths occur during exercise, mostly as a result of myocardial infarction. After autopsy, some cases, especially in the young, are diagnosed as cardiomyopathies or remain without a conclusive cause of death. In both situations, genetic alterations may explain the arrhythmia.

OBJECTIVE

Our aim was to identify a genetic predisposition to sudden cardiac death in a cohort of post-mortem cases of individuals who died during exercise, with a structurally normal heart, and were classified as arrhythmogenic death.

METHODS

We analyzed a cohort of 52 post-mortem samples from individuals <50 years old who had a negative autopsy. Next-generation sequencing technology was used to screen genes associated with sudden cardiac death.

RESULTS

Our cohort showed a male prevalence (12:1). Half of the deaths occurred in individuals 41-50 years of age. Running was the most common exercise activity during the fatal event, accounting for 46.15% of cases. Genetic analysis identified 83 rare variants in 37 samples (71.15% of all samples). Of all rare variants, 36.14% were classified as deleterious, being present in 53.84% of all cases.

CONCLUSIONS

A comprehensive analysis of sudden cardiac death-related genes in individuals who died suddenly while exercising enabled the identification of potentially causative variants. However, many genetic variants remain of indeterminate significance, thus further work is needed before clinical translation. Nonetheless, comprehensive genetic analysis of individuals who died during exercise enables the detection of potentially causative variants and helps to identify at-risk relatives.

Biomarkers: paving stones on the road towards the personalized precision medicine for oral squamous cell carcinoma

Traditional therapeutics have encountered a bottleneck caused by diagnosis delay and subjective and unreliable assessment. Biomarkers can overcome this bottleneck and guide us toward personalized precision medicine for oral squamous cell carcinoma. To achieve this, it is important to efficiently and accurately screen out specific biomarkers from among the huge number of molecules. Progress in omics-based high-throughput technology has laid a solid foundation for biomarker discovery. With credible and systemic biomarker models, more precise and personalized diagnosis and assessment would be achieved and patients would be more likely to be cured and have a higher quality of life. However, this is not straightforward owing to the complexity of molecules involved in tumorigenesis. In this context, there is a need to focus on tumor heterogeneity and homogeneity, which are discussed in detail. In this review, we aim to provide an understanding of biomarker discovery and application for precision medicine of oral squamous cell carcinoma, and have a strong belief that biomarker will pave the road toward future precision medicine.

Simultaneous sequencing of coding and noncoding RNA reveals a human transcriptome dominated by a small number of highly expressed noncoding genes

Comparing the abundance of one RNA molecule to another is crucial for understanding cellular functions but most sequencing techniques can target only specific subsets of RNA. In this study, we used a new fragmented ribodepleted TGIRT sequencing method that uses a thermostable group II intron reverse transcriptase (TGIRT) to generate a portrait of the human transcriptome depicting the quantitative relationship of all classes of nonribosomal RNA longer than 60 nt. Comparison between different sequencing methods indicated that FRT is more accurate in ranking both mRNA and noncoding RNA than viral reverse transcriptase-based sequencing methods, even those that specifically target these species. Measurements of RNA abundance in different cell lines using this method correlate with biochemical estimates, confirming tRNA as the most abundant nonribosomal RNA biotype. However, the single most abundant transcript is 7SL RNA, a component of the signal recognition particle. Structured noncoding RNAs (sncRNAs) associated with the same biological process are expressed at similar levels, with the exception of RNAs with multiple functions like U1 snRNA. In general, sncRNAs forming RNPs are hundreds to thousands of times more abundant than their mRNA counterparts. Surprisingly, only 50 sncRNA genes produce half of the non-rRNA transcripts detected in two different cell lines. Together the results indicate that the human transcriptome is dominated by a small number of highly expressed sncRNAs specializing in functions related to translation and splicing.