Whole genome sequencing in pharmacogenomics

High-coverage whole-genome sequencing of a Jakun individual from the "Orang Asli" Proto-Malay subtribe from Peninsular Malaysia

Jakun, a Proto-Malay subtribe from Peninsular Malaysia, is believed to have inhabited the Malay Archipelago during the period of agricultural expansion approximately 4 thousand years ago (kya). However, their genetic structure and population history remain inconclusive. In this study, we report the genome structure of a Jakun female, based on whole-genome sequencing, which yielded an average coverage of 35.97-fold. We identified approximately 3.6 million single-nucleotide variations (SNVs) and 517,784 small insertions/deletions (indels). Of these, 39,916 SNVs were novel (referencing dbSNP151), and 10,167 were nonsynonymous (nsSNVs), spanning 5674 genes. Principal Component Analysis (PCA) revealed that the Jakun genome sequence closely clustered with the genomes of the Cambodians (CAM) and the Metropolitan Malays from Singapore (SG_MAS). The ADMIXTURE analysis further revealed potential admixture from the EA and North Borneo populations, as corroborated by the results from the F3, F4, and TreeMix analyses. Mitochondrial DNA analysis revealed that the Jakun genome carried the N21a haplogroup (estimated to have occurred ~19 kya), which is commonly found among Malays from Malaysia and Indonesia. From the whole-genome sequence data, we identified 825 damaging and deleterious nonsynonymous single-nucleotide polymorphisms (nsSNVs) affecting 720 genes. Some of these variants are associated with age-related macular degeneration, atrial fibrillation, and HDL cholesterol level. Additionally, we located a total of 3310 variants on 32 core adsorption, distribution, metabolism, and elimination (ADME) genes. Of these, 193 variants are listed in PharmGKB, and 21 are nsSNVs. In summary, the genetic structure identified in the Jakun individual could enhance the mapping of genetic variants for disease-based population studies and further our understanding of the human migration history in Southeast Asia.

Utilizing Pharmacogenomic Data for a Safer Use of Statins among the Emirati Population

BACKGROUND

Statins are the most prescribed lipid-lowering drugs worldwide. The associated adverse events, especially muscle symptoms, have been frequently reported despite their perceived safety. Three pharmacogenes, the solute carrier organic anion transporter family member 1B1 (SLCO1B1), ATP-binding cassette subfamily G member 2 (ABCG2), and cytochrome P450 2C9 (CYP2C9) are suggested as safety biomarkers for statins. The Clinical Pharmacogenomic Implementation Consortium (CPIC) issued clinical guidelines for statin use based on these three genes.

OBJECTIVES

The present study aimed to examine variants in these pharmacogenes to predict the safety of statin use among the Emirati population.

METHODS

Analyzing 242 whole exome sequencing data at the three genes enabled the determination of the frequencies of the single nucleotide polymorphisms (SNPs), annotating the haplotypes and the predicted functions of their proteins.

RESULTS

In our cohort, 29.8% and 5.4% had SLCO1B1 decreased and poor function, respectively. The high frequency warns of the possibility of significant side effects of some statins and the importance of pharmacogenomic testing. We found a low frequency (6%) of the ABCG2:rs2231142 variant, which indicates the low probability of Emirati patients being recommended against higher rosuvastatin doses compared with other populations with higher frequencies of this variant. In contrast, we found high frequencies of the functionally impaired CYP2C9 alleles, which makes fluvastatin a less favorable choice.

CONCLUSION

Among the sparse studies available, the present one demonstrates all SLCO1B1 and CYP2C9 function-impairing alleles among Emiratis. We highlighted how population-specific pharmacogenomic data can predict safer choices of statins, especially in understudied populations.

A Comprehensive review on Pharmacokinetic Studies of Vaccines: Impact of delivery route, carrier-and its modulation on immune response

The lack of knowledge about the absorption, distribution, metabolism, and excretion (ADME) of vaccines makes former biopharmaceutical optimization difficult. This was shown during the COVID-19 immunization campaign, where gradual booster doses were introduced.. Thus, understanding vaccine ADME and its effects on immunization effectiveness could result in a more logical vaccine design in terms of formulation, method of administration, and dosing regimens. Herein, we will cover the information available on vaccine pharmacokinetics, impacts of delivery routes and carriers on ADME, utilization and efficiency of nanoparticulate delivery vehicles, impact of dose level and dosing schedule on the therapeutic efficacy of vaccines, intracellular and endosomal trafficking and in vivo fate, perspective on DNA and mRNA vaccines, new generation sequencing and mathematical models to improve cancer vaccination and pharmacology, and the reported toxicological study of COVID-19 vaccines. Altogether, this review will enhance the reader's understanding of the pharmacokinetics of vaccines and methods that can be implied in delivery vehicle design to improve the absorption and distribution of immunizing agents and estimate the appropriate dose to achieve better immunogenic responses and prevent toxicities.

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.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Genetic Epidemiology of Medication Safety and Efficacy Related Variants in the Central Han Chinese Population With Whole Genome Sequencing

Medication safety and efficacy-related pharmacogenomic research play a critical role in precision medicine. This study comprehensively analyzed the pharmacogenomic profiles of the central Han Chinese population in the context of medication safety and efficacy and compared them with other global populations. The ultimate goal is to improve medical treatment guidelines. We performed whole-genome sequencing in 487 Han Chinese individuals and investigated the allele frequencies of pharmacogenetic variants in 1,731 drug response-related genes. We identified 2,139 (81.18%) previously reported variants in our population with annotations in the PharmGKB database. The allele frequencies of these 2,139 clinical-related variants were similar to those in other East Asian populations but different from those in other global populations. We predicted the functional effects of nonsynonymous variants in the 1,731 pharmacogenes and identified 1,281 novel and 4,442 previously reported deleterious variants. Of the 1,281 novel deleterious variants, five are common variants with an allele frequency >5%, and the rest are rare variants with an allele frequency <5%. Of the 4,442 known deleterious variants, the allele frequencies were found to differ from those in other populations, of which 146 are common variants. In addition, we found many variants in non-coding regions, the functions of which require further investigation. This study compiled a large amount of data on pharmacogenomic variants in the central Han Chinese population. At the same time, it provides insight into the role of pharmacogenomic variants in clinical medication safety and efficacy.

Early Cost Effectiveness of Whole-Genome Sequencing as a Clinical Diagnostic Test for Patients with Inoperable Stage IIIB,C/IV Non-squamous Non-small-Cell Lung Cancer

BACKGROUND

Advanced non-small-cell lung cancer (NSCLC) harbours many genetic aberrations that can be targeted with systemic treatments. Whole-genome sequencing (WGS) can simultaneously detect these (and possibly new) molecular targets. However, the exact added clinical value of WGS is unknown.

OBJECTIVE

The objective of this study was to determine the early cost effectiveness of using WGS in diagnostic strategies compared with currently used molecular diagnostics for patients with inoperable stage IIIB,C/IV non-squamous NSCLC from a Dutch healthcare perspective.

METHODS

A decision tree represented the diagnostic pathway, and a cohort state transition model represented disease progression. Three diagnostic strategies were modelled: standard of care (SoC) alone, WGS as a diagnostic test, and SoC followed by WGS. Treatment effectiveness was based on a systematic review. Probabilistic cost-effectiveness analyses were performed, and threshold analyses (using €80,000 per quality-adjusted life-year [QALY]) was used to explore the early cost effectiveness of WGS.

RESULTS

WGS as a diagnostic test resulted in more QALYs (0.002) and costs (€1534 [incremental net monetary benefit -€1349]), and SoC followed by WGS resulted in fewer QALYs (-0.002) and more costs (€1059 [-€1194]) compared with SoC alone. WGS as a diagnostic test was only cost effective if it was priced at €2000 per patient and identified 2.7% more actionable patients than SoC alone. Treating these additional identified patients with new treatments costing >€4069 per month decreased the probability of cost effectiveness.

CONCLUSIONS

Our analysis suggests that providing WGS as a diagnostic test is cost effective compared with SoC followed by WGS and SoC alone if costs for WGS decrease and additional patients with actionable targets are identified. This cost-effectiveness model can be used to incorporate new findings iteratively and to support ongoing decision making regarding the use of WGS in this rapidly evolving field.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system

Background

The development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy.

Methods

The concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up.

Results

The safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%).

Conclusion

PROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient’s acceptance of data sharing can be increased by patient-centric decision-making.

Graphic abstract

Horizon Scanning: Teaching Genomics and Personalized Medicine in the Digital Age

Digital transformation is currently impacting not only health care but also education curricula for medicine and life sciences. The COVID-19 pandemic has accelerated the deployment of digital technologies such as the Internet of Things and artificial intelligence in diverse fields of biomedicine. Genomics and related fields of inquiry such as pharmacogenomics and personalized medicine have been making important progress over the past decades. However, the genomics knowledge of health care professionals and other stakeholders in society is not commensurate with the current state of progress in these scientific fields. The rise of digital health offers unprecedented opportunities both for health care professionals and the general public to expand their genomics literacy and education. This expert review offers an analysis of the bottlenecks that affect and issues that need to be addressed to catalyze genomics and personalized medicine education in the digital era. In addition, we summarize and critically discuss the various educational and awareness opportunities that presently exist to catalyze the delivery of genomics knowledge in ways closely attuned to the emerging field of digital health.

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.

Early technology assessment of using whole genome sequencing in personalized oncology

Introduction: Personalized medicine-based treatments in advanced cancer hold the promise to offer substantial health benefits to genetic subgroups, but require efficient biomarker-based patient stratification to match the right treatment and may be expensive. Standard molecular diagnostics are currently very heterogeneous, and tests are often performed sequentially. The alternative to whole genome sequencing (WGS) i.e. simultaneously testing for all relevant DNA-based biomarkers thereby allowing immediate selection of the most optimal therapy, is more costly than current techniques. In the current implementation stage, it is important to explore the added value and cost-effectiveness of using WGS on a patient level and to assess optimal introduction of WGS on the level of the healthcare system.Areas covered: First, an overview of current worldwide initiatives concerning the use of WGS in clinical practice for cancer diagnostics is given. Second, a comprehensive, early health technology assessment (HTA) approach of evaluating WGS in the Netherlands is described, relating to the following aspects: diagnostic value, WGS-based treatment decisions, assessment of long-term health benefits and harms, early cost-effectiveness modeling, nation-wide organization, and Ethical, Legal and Societal Implications.Expert opinion: This study provides evidence to guide further development and implementation of WGS in clinical practice and the healthcare system.

Pharmacogenomics research and its clinical implementation in Thailand: Lessons learned from the resource-limited settings

Several barriers present challenges to implementing pharmacogenomics into practice. This review will provide an overview of the current pharmacogenomics practices and research in Thailand, address the challenges and lessons learned from delivering clinical pharmacogenomic services in Thailand, emphasize the pharmacogenomics implementation issues that must be overcome, and identify current pharmacogenomic initiatives and plans to facilitate clinical implementation of pharmacogenomics in Thailand. Ever since the pharmacogenomics research began in 2004 in Thailand, a multitude of pharmacogenomics variants associated with drug responses have been identified in the Thai population, such as HLA-B∗15:02 for carbamazepine and oxcarbazepine, HLA-B∗58:01 for allopurinol, HLA-B∗13:01 for dapsone and cotrimoxazole, CYP2B6 variants for efavirenz, CYP2C9∗3 for phenytoin and warfarin, CYP3A5∗3 for tacrolimus, and UGT1A1∗6 and UGT1A1∗28 for irinotecan, etc. The future of pharmacogenomics guided therapy in clinical settings across Thailand appears promising because of the availability of evidence of clinical validity of the pharmacogenomics testing and support for reimbursement of pharmacogenomics testing.

Scaling Genetic Counseling in the Genomics Era

The development of massively parallel sequencing-based genomic sequencing tests has increased genetic test availability and access. The field and practice of genetic counseling have adapted in response to this paradigm-shifting technology and the subsequent transition to practicing genomic medicine. While the key elements defining genetic counseling remain relevant, genetic counseling service delivery models and practice settings have evolved. Genetic counselors are addressing the challenges of direct-to-consumer and consumer-driven genetic testing, and genetic counseling training programs are responding to the ongoing increased demand for genetic counseling services across a broadening range of contexts. The need to diversify both the patient and participant groups with access to genetic information, as well as the field of genetic counseling, is at the forefront of research and training program initiatives. Genetic counselors are key stakeholders in the genomics era, and their contributions are essential to effectively and equitably deliver precision medicine.

Strategies to Improve the Clinical Outcomes for Direct-to-Consumer Pharmacogenomic Tests

Direct-to-consumer genetic tests (DTC-GT) have become a bridge between marketing and traditional healthcare services. After earning FDA endorsement for such facilities, several fast-developing companies started to compete in the related area. Pharmacogenomic (PGx) tests have been introduced as potentially one of the main medical services of such companies. Most of the individuals will be interested in finding out about the phenotypic consequences of their genetic variants and molecular risk factors against diverse medicines they take or will take later. Direct-to-consumer pharmacogenomic tests (DTC-PT) is still in its young age, however it is expected to expand rapidly through the industry in the future. The result of PGx tests could be considered as the main road toward the implementation of personalized and precision medicine in the clinic. This narrative critical review study provides a descriptive overview on DTC-GT, then focuses on DTC-PT, and also introduces and suggests the potential approaches for improving the clinical related outcomes of such tests on healthcare systems.

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.

Variation in 100 relevant pharmacogenes among emiratis with insights from understudied populations

Genetic variations have an established impact on the pharmacological response. Investigating this variation resulted in a compilation of variants in "pharmacogenes". The emergence of next-generation sequencing facilitated large-scale pharmacogenomic studies and exhibited the extensive variability of pharmacogenes. Some rare and population-specific variants proved to be actionable, suggesting the significance of population pharmacogenomic research. A profound gap exists in the knowledge of pharmacogenomic variants enriched in some populations, including the United Arab Emirates (UAE). The current study aims to explore the landscape of variations in relevant pharmacogenes among healthy Emiratis. Through the resequencing of 100 pharmacogenes for 100 healthy Emiratis, we identified 1243 variants, of which 63% are rare (minor allele frequency ≤ 0.01), and 30% were unique. Filtering the variants according to Pharmacogenomics Knowledge Base (PharmGKB) annotations identified 27 diplotypes and 26 variants with an evident clinical relevance. Comparison with global data illustrated a significant deviation of allele frequencies in the UAE population. Understudied populations display a distinct allelic architecture and various rare and unique variants. We underscored pharmacogenes with the highest variation frequencies and provided investigators with a list of candidate genes for future studies. Population pharmacogenomic studies are imperative during the pursuit of global pharmacogenomics implementation.

Pharmaco-Geno-Proteo-Metabolomics and Translational Research in Cancer

The diagnosis, prognosis and treatment of cancer has had a great improvement due to the "omics" technologies such as genomics, proteomics, epigenomics, pharmacogenomics, and metabolomics. The technological progress of these technologies has allowed precision medicine to become a clinical reality. The study of different biomolecules such as DNA, RNA and proteins has helped to detect alterations in genes, changes in gene expression profiles and loss or gain of protein function, which allows us to make associations and better understand the cancer biology. Data obtained from different "omics" technologies gives a complementary spectrum of information that helps us to understand and unveil new information for a better diagnosis, prognosis, prediction of new molecular targets of anticancer therapies, etc. This chapter presents a general landscape of the interaction between the Pharmaco-Geno-Proteo-Metabolomic and translational medicine research in cancer.

Variation in Actionable Pharmacogenetic Markers in Natives and Mestizos From Mexico

The identification and characterization of pharmacogenetic variants in Latin American populations is still an ongoing endeavor. Here, we investigated SNVs on genes listed by the Pharmacogenomics Knowledge Base in 1284 Mestizos and 94 Natives from Mexico. Five institutional cohorts with NGS data were retrieved from different research projects at INMEGEN, sequencing files were filtered for 55 pharmacogenes present in all cohorts to identify novel and known variation. Bioinformatic tools VEP, PROVEAN, and FATHMM were used to assess, in silico, the functional impact of this variation. Next, we focused on 17 genes with actionable variants that have been clinically implemented. Allele frequencies were compared with major continental groups and differences discussed in the scope of a pharmacogenomic impact. We observed a wide genetic variability for known and novel SNVs, the largest variation was on UGT1A > ACE > COMT > ABCB1 and the lowest on APOE and NAT2. Although with allele frequencies around 1%, novel variation was observed in 16 of 17 PGKB genes. In Natives we identified 59 variants and 58 in Mestizos. Several genes did not show novel variation, on CYP2B6, CYP2D6, and CYP3A4 in Natives; and APOE, UGT1A, and VKORC1 in Mestizos. Similarities in allele frequency, comparing major continental groups for VIP pharmacogenes, hint towards a comparable PGx for drugs metabolized by UGT1A1, DPYD, ABCB1, CBR3, COMT, and TPMT; in contrast to variants on CYP3A5 and CYP2B6 for which significant MAF differences were identified. Our observations offer some discernment into the extent of pharmacogenetic variation registered up-to-date in Mexicans and contribute to quantitatively dissect actionable pharmacogenetic variants in Natives and Mestizos.

Laying Anchor: Inserting Precision Health into a Public Health Genetics Policy Course

The United States Precision Medicine Initiative (PMI) was announced by then President Barack Obama in January 2015. It is a national effort designed to take into account genetic, environmental, and lifestyle differences in the development of individually tailored forms of treatment and prevention. This goal was implemented in March 2015 with the formation of an advisory committee working group to provide a framework for the proposed national research cohort of one million or more participants. The working group further held a public workshop on participant engagement and health equity, focusing on the design of an inclusive cohort, building public trust, and identifying active participant engagement features for the national cohort. Precision techniques offer medical and public health practitioners the opportunity to personally tailor preventive and therapeutic regimens based on informatics applied to large volume genotypic and phenotypic data. The PMI’s (All of Us Research Program’s) medical and public health promise, its balanced attention to technical and ethical issues, and its nuanced advisory structure made it a natural choice for inclusion in the University of Michigan course “Issues in Public Health Genetics” (HMP 517), offered each fall by the University’s School of Public Health. In 2015, the instructors included the PMI as the recurrent case study introduced at the beginning and referred to throughout the course, and as a class exercise allowing students to translate issues into policy. In 2016, an entire class session was devoted to precision medicine and precision public health. In this article, we examine the dialogues that transpired in these three course components, evaluate session impact on student ability to formulate PMI policy, and share our vision for next-generation courses dealing with precision health. Methodology: Class materials (class notes, oral exercise transcripts, class exercise written hand-ins) from the three course components were inspected and analyzed for issues and policy content. The purpose of the analysis was to assess the extent to which course components have enabled our students to formulate policy in the precision public health area. Analysis of student comments responding to questions posed during the initial case study comprised the initial or “pre-” categories. Analysis of student responses to the class exercise assignment, which included the same set of questions, formed the “post-” categories. Categories were validated by cross-comparison among the three authors, and inspected for frequency with which they appeared in student responses. Frequencies steered the selection of illustrative quotations, revealing the extent to which students were able to convert issue areas into actual policies. Lecture content and student comments in the precision health didactic session were inspected for degree to which they reinforced and extended the derived categories. Results: The case study inspection yielded four overarching categories: (1) assurance (access, equity, disparities); (2) participation (involvement, representativeness); (3) ethics (consent, privacy, benefit sharing); and (4) treatment of people (stigmatization, discrimination). Class exercise inspection and analysis yielded three additional categories: (5) financial; (6) educational; and (7) trust-building. The first three categories exceeded the others in terms of number of student mentions (8–14 vs. 4–6 mentions). Three other categories were considered and excluded because of infrequent mention. Students suggested several means of trust-building, including PMI personnel working with community leaders, stakeholder consultation, networking, and use of social media. Student representatives prioritized participant and research institution access to PMI information over commercial access. Multiple schemes were proposed for participant consent and return of results. Both pricing policy and Medicaid coverage were touched on. During the didactic session, students commented on the importance of provider training in precision health. Course evaluation highlighted the need for clarity on the organizations involved in the PMI, and leaving time for student-student interaction. Conclusions: While some student responses during the exercise were terse, an evolution was detectable over the three course components in student ability to suggest tangible policies and steps for implementation. Students also gained surety in presenting policy positions to a peer audience. Students came up with some very creative suggestions, such as use of an electronic platform to assure participant involvement in the disposition of their biological sample and personal health information, and alternate examples of ways to manage large volumes of data. An examination of socio-ethical issues and policies can strengthen student understanding of the directions the Precision Medicine Initiative is taking, and aid in training for the application of more varied precision medicine and public health techniques, such as tier 1 genetic testing and whole genome and exome sequencing. Future course development may reflect additional features of the ongoing All of Us Research Program, and further articulate precision public health approaches applying to populations as opposed to single individuals.

The New Age of -omics in Urothelial Cancer - Re-wording Its Diagnosis and Treatment

Unmet needs in urothelial cancer management represent an important challenge in our effort to improve long-term overall and disease-free survival rates with no significant compromise in quality of life. Radical cystectomy with pelvic lymph node dissection is the standard for the management of muscle-invasive, non-metastatic cancers. In spite of a 90% local disease control, up to 50% of patients ultimately die of distant metastasis. Bladder preservation using chemo-radiation is an acceptable alternative, but optimal patient selection remains elusive. Recent research is focused on the employment of tailored-made strategies in urothelial cancer exploiting the potential of theranostics in patient selection for specific therapies. Herein, we review the current knowledge on molecular theranostics in urothelial cancer and we suggest that this is the time to move toward imaging theranostics, if tailored-made disease management and patient stratification is envisaged.

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Urothelial cancer management represents an important challenge.

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Optimum patient stratification and tailored-made theranostics remain elusive.

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Imaging theranostics is envisaged as a cancer roadmap.

The global spectrum of protein-coding pharmacogenomic diversity

Differences in response to medications have a strong genetic component. By leveraging publically available data, the spectrum of such genomic variation can be investigated extensively. Pharmacogenomic variation was extracted from the 1000 Genomes Project Phase 3 data (2504 individuals, 26 global populations). A total of 12 084 genetic variants were found in 120 pharmacogenes, with the majority (90.0%) classified as rare variants (global minor allele frequency <0.5%), with 52.9% being singletons. Common variation clustered individuals into continental super-populations and 23 pharmacogenes contained highly differentiated variants (FST>0.5) for one or more super-population comparison. A median of three clinical variants (PharmGKB level 1A/B) was found per individual, and 55.4% of individuals carried loss-of-function variants, varying by super-population (East Asian 60.9%>African 60.1%>South Asian 60.3%>European 49.3%>Admixed 39.2%). Genome sequencing can therefore identify clinical pharmacogenomic variation, and future studies need to consider rare variation to understand the spectrum of genetic diversity contributing to drug response.

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

Background

A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses.

Results

VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources.

Conclusions

MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.

Exploring public genomics data for population pharmacogenomics

Racial and ethnic differences in drug responses are now well studied and documented. Pharmacogenomics research seeks to unravel the genetic underpinnings of inter-individual variability with the aim of tailored-made theranostics and therapeutics. Taking into account the differential expression of pharmacogenes coding for key metabolic enzymes and transporters that affect drug pharmacokinetics and pharmacodynamics, we advise that data interpretation and analysis need to occur in light of geographical ancestry, if implications for drug development and global health are to be considered. Herein, we exploit ePGA, a web-based electronic Pharmacogenomics Assistant and publicly available genetic data from the 1000 Genomes Project to explore genotype to phenotype associations among the 1000 Genomes Project populations.

The role of hospital pharmacists in the adoption and use of pharmacogenomics and precision medicine

Aim: Our aim was to assess the knowledge and attitudes of US hospital pharmacists about the implementation of clinical pharmacogenomics, and examine liability risks of adopting pharmacogenomics by pharmacists. Methods: We surveyed hospital pharmacists. Linear regression models of predictor variables for pharmacist adoption and use of pharmacogenomics were analyzed. Results: The survey was administered to 660 hospital pharmacists (23% response rate; n = 149). The majority of respondents (72%) favor implementing pharmacogenomics into pharmacy practice. However, only 25% are confident in their abilities to interpret pharmacogenomic test results. Conclusion: Pharmacists lack confidence in their abilities to interpret and use pharmacogenomic information in clinical care. These results raise potential liability risks that are pertinent to pharmacists.

ePGA: A Web-Based Information System for Translational Pharmacogenomics

One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project.

Cracking the Code of Human Diseases Using Next-Generation Sequencing: Applications, Challenges, and Perspectives

Next-generation sequencing (NGS) technologies have greatly impacted on every field of molecular research mainly because they reduce costs and increase throughput of DNA sequencing. These features, together with the technology's flexibility, have opened the way to a variety of applications including the study of the molecular basis of human diseases. Several analytical approaches have been developed to selectively enrich regions of interest from the whole genome in order to identify germinal and/or somatic sequence variants and to study DNA methylation. These approaches are now widely used in research, and they are already being used in routine molecular diagnostics. However, some issues are still controversial, namely, standardization of methods, data analysis and storage, and ethical aspects. Besides providing an overview of the NGS-based approaches most frequently used to study the molecular basis of human diseases at DNA level, we discuss the principal challenges and applications of NGS in the field of human genomics.

Pharmacogenetics and anaesthetic drugs: Implications for perioperative practice

Pharmacogenetics seeks to elucidate the variations in individual's genetic sequences in order to better understand the differences seen in pharmacokinetics, drug metabolism, and efficacy between patients. This area of research is rapidly accelerating, aided by the use of novel and more economical molecular technologies. A substantial evidence base is being generated with the hopes that in the future it may be used to generate personalised treatment regimens in order to improve patient comfort and safety and reduce incidences of morbidity and mortality. Anaesthetics is an area of particular interest in this field, with previous research leading to better informed practice, specifically with regards to pseudocholinesterase deficiency and malignant hyperthermia. In this review, recent pharmacogenetic data pertaining to anaesthetic drugs will be presented and possible future applications and implications for practice will be discussed.

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Pharmacogenetic variations in anaesthetic drugs affect enzymes, transport proteins and drug receptors.

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Genotyping may provide more clues as to aetiology of conditions related to usage of anaesthetic drugs e.g. propofol infusion syndrome.

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Improved and more economical molecular technology will lead to increase in quantity of pharmacogenetic data.