Genetic variability and population diversity of the human SLCO (OATP) transporter family

Genetic ancestry in population pharmacogenomics unravels distinct geographical patterns related to drug toxicity

Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals from publicly available datasets to assess the risk proximity of experiencing drug-related adverse events. Our findings indicate that Admixed Americans and Europeans have demonstrated a higher risk of experiencing drug toxicity, whereas individuals with East Asian ancestry and, to a lesser extent, Oceanians displayed a lower risk proximity. Polygenic risk scores for drug-gene interactions did not necessarily follow similar assumptions, reflecting distinct genetic patterns and population-specific differences that vary depending on the drug class. Overall, our results provide evidence that genetic ancestry is a pivotal factor in population pharmacogenomics and should be further exploited to strengthen even more personalized drug therapy.

Population-scale variability of the human UDP-glycosyltransferase gene family

Human UDP-glycosyltransferases (UGTs) are responsible for the glucuronidation of a wide variety of endogenous substrates and commonly prescribed drugs. Different genetic polymorphisms in UGT genes are implicated in interindividual differences in drug response and cancer risk. However, the genetic complexity beyond these variants has not been comprehensively assessed. We here leveraged whole-exome and whole-genome sequencing data from 141,456 unrelated individuals across 7 major human populations to provide a comprehensive profile of genetic variability across the human UGT gene family. Overall, 9666 exonic variants were observed of which 98.9% were rare. To interpret the functional impact of UGT missense variants, we developed a gene family-specific variant effect predictor. This algorithm identified a total of 1208 deleterious variants, most of which were found in African and South Asian populations. Structural analysis corroborated the predicted effects for multiple variations in substrate binding sites. Combined, our analyses provide a systematic overview of UGT variability, which can yield insights into interindividual differences in phase 2 metabolism and facilitate the translation of sequencing data into personalized predictions of UGT substrate disposition.

Rifampin- and Silymarin-Mediated Pharmacokinetic Interactions of Exogenous and Endogenous Substrates in a Transgenic OATP1B Mouse Model

Organic anion-transporting polypeptides (OATP) 1B1 and OATP1B3, encoded by the SLCO gene family of the solute carrier superfamily, are involved in the disposition of many exogenous and endogenous compounds. Preclinical rodent models help assess risks of pharmacokinetic interactions, but interspecies differences in transporter orthologs and expression limit direct clinical translation. An OATP1B transgenic mouse model comprising a rodent Slco1a/1b gene cluster knockout and human SLCO1B1 and SLCO1B3 gene insertions provides a potential physiologically relevant preclinical tool to predict pharmacokinetic interactions. Pharmacokinetics of exogenous probe substrates, pitavastatin and pravastatin, and endogenous OATP1B biomarkers, coproporphyrin-I and coproporphyrin-III, were determined in the presence and absence of known OATP/Oatp inhibitors, rifampin or silymarin (an extract of milk thistle [Silybum marianum]), in wild-type FVB mice and humanized OATP1B mice. Rifampin increased exposure of pitavastatin (4.6- and 2.8-fold), pravastatin (3.6- and 2.2-fold), and coproporphyrin-III (1.6- and 2.1-fold) in FVB and OATP1B mice, respectively, but increased coproporphyrin-I AUC0-24h only (1.8-fold) in the OATP1B mice. Silymarin did not significantly affect substrate AUC, likely because the silymarin flavonolignan concentrations were at or below their reported IC50 values for the relevant OATPs/Oatps. Silymarin increased the Cmax of pitavastatin 2.7-fold and pravastatin 1.9-fold in the OATP1B mice. The data of the OATP1B mice were similar to those of the pitavastatin and pravastatin clinical data; however, the FVB mice data more closely recapitulated pitavastatin clinical data than the data of the OATP1B mice, suggesting that the OATP1B mice are a reasonable, though costly, preclinical strain for predicting pharmacokinetic interactions when doses are optimized to achieve clinically relevant plasma concentrations.

The macrocycle inhibitor landscape of SLC-transporter

In the past years the interest in Solute Carrier Transporters (SLC) has increased due to their potential as drug targets. At the same time, macrocycles demonstrated promising activities as therapeutic agents. However, the overall macrocycle/SLC-transporter interaction landscape has not been fully revealed yet. In this study, we present a statistical analysis of macrocycles with measured activity against SLC-transporter. Using a data mining pipeline based on KNIME retrieved in total 825 bioactivity data points of macrocycles interacting with SLC-transporter. For further analysis of the SLC inhibitor profiles we developed an interactive KNIME workflow as well as an interactive map of the chemical space coverage utilizing parametric t-SNE models. The parametric t-SNE models provide a good discrimination ability among several corresponding SLC subfamilies' targets. The KNIME workflow, the dataset, and the visualization tool are freely available to the community.

SLCO1B1 Exome Sequencing and Statin Treatment Response in 64,000 UK Biobank Patients

The solute carrier organic anion transporter family member 1B1 (SLCO1B1) encodes the organic anion-transporting polypeptide 1B1 (OATP1B1 protein) that transports statins to liver cells. Common genetic variants in SLCO1B1, such as *5, cause altered systemic exposure to statins and therefore affect statin outcomes, with potential pharmacogenetic applications; yet, evidence is inconclusive. We studied common and rare SLCO1B1 variants in up to 64,000 patients from UK Biobank prescribed simvastatin or atorvastatin, combining whole-exome sequencing data with up to 25-year routine clinical records. We studied 51 predicted gain/loss-of-function variants affecting OATP1B1. Both SLCO1B1*5 alone and the SLCO1B1*15 haplotype increased LDL during treatment (beta*5 = 0.08 mmol/L, p = 6 × 10-8; beta*15 = 0.03 mmol/L, p = 3 × 10-4), as did the likelihood of discontinuing statin prescriptions (hazard ratio*5 = 1.12, p = 0.04; HR*15 = 1.05, p = 0.04). SLCO1B1*15 and SLCO1B1*20 increased the risk of General Practice (GP)-diagnosed muscle symptoms (HR*15 = 1.22, p = 0.003; HR*20 = 1.25, p = 0.01). We estimated that genotype-guided prescribing could potentially prevent 18% and 10% of GP-diagnosed muscle symptoms experienced by statin patients, with *15 and *20, respectively. The remaining common variants were not individually significant. Rare variants in SLCO1B1 increased LDL in statin users by up to 1.05 mmol/L, but replication is needed. We conclude that genotype-guided treatment could reduce GP-diagnosed muscle symptoms in statin patients; incorporating further SLCO1B1 variants into clinical prediction scores could improve LDL control and decrease adverse events, including discontinuation.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

A paradigm shift in pharmacogenomics: From candidate polymorphisms to comprehensive sequencing

Genetic factors have long been recognized as important determinants of interindividual variability in drug efficacy and toxicity. However, despite the increasing number of established gene-drug associations, candidate polymorphisms can only explain a fraction of the genetically encoded functional variability in drug disposition. Advancements in genetic profiling methods now allow to analyse the landscape of human pharmacogenetic variations comprehensively, which opens new opportunities to identify novel factors that could explain the "missing heritability." Here, we provide an updated overview of the landscape of pharmacogenomic variability based on recent analyses of population-scale sequencing projects. We then summarize the current state-of-the-art how the functional consequences of variants with unknown effects can be quantitatively estimated while discussing challenges and peculiarities that are specific to pharmacogenes. In the last sections, we discuss the importance of considering ethnogeographic diversity to provide equitable benefits of pharmacogenomics and summarize current roadblocks for the implementation of sequencing-based guidance of clinical decision-making. Based on the current state of the field, we conclude that testing is likely to gradually shift from the interrogation of selected candidate polymorphisms to comprehensive sequencing, which allows to consider the full spectrum of pharmacogenomic variations for a true personalization of genomic prescribing.

Challenges and opportunities associated with rare-variant pharmacogenomics

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

Congeners-Specific Intestinal Absorption Of Microcystins In An In Vitro 3D Human Intestinal Epithelium: The Role Of Influx/Efflux Transporters

Microcystins constitute a group of over 200 variants and are increasingly considered as emerging toxins in food and feed safety, particularly with regards to sea-food and fish consumption. Toxicity of MCs is congener-specific, being characterised by different acute potencies, likely related to the differential activity of metabolic enzymes and transporters proteins involved in their cellular uptake. However, the active transport of MCs across intestinal membranes has not been fully elucidated. Our results, obtained using a fit for purpose 3D human reconstructed intestinal epithelium, provide new information on the complex mechanisms involved in the absorption of 5 MC variants’: it is indeed characterised by the equilibrium between uptake and extrusion, since the selected congeners are substrates of both influx and efflux proteins. In the range of tested nominal concentrations (10–40 µM) fully representative of relevant exposure scenarios, none of the active tested transporters were saturated. The comparison of permeability (Papp) values of MCs variants highlighted a dose independent relationship for MC-LR, -YR and -RR (Papp x 10^–7 ranged from 2.95 to 3.54 cm/s), whereas -LW and–LF showed a dose dependent increase in permeability reaching Papp values which were similar to the other congeners at 40 µM. MC-RR, -LR, -YR show absorption values around 5% of the administered dose. Due to their lipophilicity, MC-LW and -LF were also detected within the cellular compartment. The intestinal uptake was only partially attributable to OATPs, suggesting the involvement of additional transporters. Regarding the efflux proteins, MCs are not P-gp substrates whereas MRP2 and to a lesser extent Breast cancer resistance protein are active in their extrusion. Despite the presence of GST proteins, as an indication of metabolic competence, in the intestinal tissue, MC-conjugates were never detected in our experimental settings.

The Challenge and Importance of Integrating Drug-Nutrient-Genome Interactions in Personalized Cardiovascular Healthcare

Despite the rich armamentarium of available drugs against different forms of cardiovascular disease (CVD), major challenges persist in their safe and effective use. These include high rates of adverse drug reactions, increased heterogeneity in patient responses, suboptimal drug efficacy, and in some cases limited compliance. Dietary elements (including food, beverages, and supplements) can modulate drug absorption, distribution, metabolism, excretion, and action, with significant implications for drug efficacy and safety. Genetic variation can further modulate the response to diet, to a drug, and to the interaction of the two. These interactions represent a largely unexplored territory that holds considerable promise in the field of personalized medicine in CVD. Herein, we highlight examples of clinically relevant drug–nutrient–genome interactions, map the challenges faced to date, and discuss their future perspectives in personalized cardiovascular healthcare in light of the rapid technological advances.

Race and Drug Toxicity: A Study of Three Cardiovascular Drugs with Strong Pharmacogenetic Recommendations

The Clinical Pharmacogenetics Implementation Consortium (CPIC^®) establishes evidence-based guidelines for utilizing pharmacogenetic information for certain priority drugs. Warfarin, clopidogrel and simvastatin are cardiovascular drugs that carry strong prescribing guidance by CPIC. The respective pharmacogenes for each of these drugs exhibit considerable variability amongst different ethnic/ancestral/racial populations. Race and ethnicity are commonly employed as surrogate biomarkers in clinical practice and can be found in many prescribing guidelines. This is controversial due to the large variability that exists amongst different racial/ethnic groups, lack of detailed ethnic information and the broad geographic categorization of racial groups. Using a retrospective analysis of electronic health records (EHR), we sought to determine the degree to which self-reported race/ethnicity contributed to the probability of adverse drug reactions for these drugs. All models used individuals self-reporting as White as the comparison group. The majority of apparent associations between different racial groups and drug toxicity observed in the "race only" model failed to remain significant when we corrected for covariates. We did observe self-identified Asian race as a significant predictor (p = 0.016) for warfarin hemorrhagic events in all models. In addition, patients identifying as either Black/African-American (p = 0.001) or Other/Multiple race (p = 0.019) had a lower probability of reporting an adverse reaction than White individuals while on simvastatin even after correcting for other covariates. In both instances where race/ethnicity was predictive of drug toxicity (i.e., warfarin, simvastatin), the findings are consistent with the known global variability in the pharmacogenes described in the CPIC guidelines for these medications. These results confirm that the reliability of using self-identified race/ethnic information extracted from EHRs as a predictor of adverse drug reactions is likely limited to situations where the genes influencing drug toxicity display large, distinct ethnogeographic variability.

Organic Anion Transporting Polypeptide 2B1 (OATP2B1) Genetic Variants: In Vitro Functional Characterization and Association With Circulating Concentrations of Endogenous Substrates

Organic anion transporting polypeptide 2B1 (OATP2B1, gene SLCO2B1) is an uptake transporter that is thought to determine drug disposition and in particular, the oral absorption of medications. At present, the clinical relevance of SLCO2B1 genetic variation on pharmacokinetics is poorly understood. We sought to determine the functional activity of 5 of the most common missense OATP2B1 variants (c.76_84del, c.601G>A, c.917G>A, c.935G>A, and c.1457C>T) and a predicted dysfunctional variant (c.332G>A) in vitro. Furthermore, we measured the basal plasma concentrations of endogenous OATP2B1 substrates, namely estrone sulfate, dehydroepiandrosterone sulfate (DHEAS), pregnenolone sulfate, coproporphyrin I (CPI), and CPIII, and assessed their relationships with SLCO2B1 genotypes in 93 healthy participants. Compared to reference OATP2B1, the transport activities of the c.332G>A, c.601G>A and c.1457C>T variants were reduced among the substrates examined (estrone sulfate, DHEAS, CPI, CPIII and rosuvastatin), although there were substrate-dependent effects. Lower transport function of OATP2B1 variants could be explained by diminished cell surface expression. Other OATP2B1 variants (c.76-84del, c.917G>A and c.935G>A) had similar activity to the reference transporter. In the clinical cohort, the SLCO2B1 c.935G>A allele was associated with both higher plasma CPI (42%) and CPIII (31%) concentrations, while SLCO2B1 c.917G>A was linked to lower plasma CPIII by 28% after accounting for the effects of age, sex, and SLCO1B1 genotypes. No association was observed between SLCO2B1 variant alleles and estrone sulfate or DHEAS plasma concentrations, however 45% higher plasma pregnenolone sulfate level was associated with SLCO2B1 c.1457C>T. Taken together, we found that the impacts of OATP2B1 variants on transport activities in vitro were not fully aligned with their associations to plasma concentrations of endogenous substrates in vivo. Additional studies are required to determine whether circulating endogenous substrates reflect OATP2B1 activity.

Genetic landscape of 125 pharmacogenes in Chinese from the Chinese Millionome Database

Inter-individual differences of drug responses could be attributed to genetic variants of pharmacogenes such as cytochrome P450 (CYP), phase 2 enzymes, and transporters. In contrast to extensive studies on the genetic polymorphisms of CYP gene, genetic mutation spectrum of other pharmacogenes was under-representative in the pharmacogenetics investigations. Here we studied the genetic variations of 125 pharmacogenes including drug transporters, non-CYP phase 1 enzymes, phase 2 enzymes, nuclear receptors and others in Chinese from the Chinese Millionome Database (CMDB), of which 38,188 variants were identified. Computational analyses of the 2554 exonic variants found 617 deleterious missense variants, 91.1% of which were rare, and of the 54 loss-of-function (splice acceptor, splice donor, start lost, and stop gained) variants, 53 (98.1%) were rare. These results suggested an enrichment of rare variants in functional ones for pharmacogenes. Certain common functional variants including NUDT15 13:48611934 G/A (rs186364861), UGT1A1 2:234676872 C/T (rs34946978), and ALDH2 12:112241766 G/A (rs671) were population-specific for CMDB Chinese because they were absent (with a zero of variant allele frequency) or very rare in other gnomAD populations. These findings might be useful for the further pharmacogenomics research and clinical application in Chinese.

Pharmacogenomics of statins: lipid response and other outcomes in Brazilian cohorts

Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase, a key enzyme in cholesterol biosynthesis, that are highly effective in reducing plasma low-density lipoprotein (LDL) cholesterol and decreasing the risk of cardiovascular events. In recent years, a multitude of variants in genes involved in pharmacokinetics (PK) and pharmacodynamics (PD) have been suggested to influence the cholesterol-lowering response. However, the vast majority of studies have analyzed the pharmacogenetic associations in populations in Europe and the USA, whereas data in other populations, including Brazil, are mostly lacking. This narrative review provides an update of clinical studies on statin pharmacogenomics in Brazilian cohorts exploring lipid-lowering response, adverse events and pleiotropic effects. We find that variants in drug transporter genes (SLCO1B1 and ABCB1) positively impacted atorvastatin and simvastatin response, whereas variants in genes of drug metabolizing enzymes (CYP3A5) decreased response. Furthermore, multiple associations of variants in PD genes (HMGCR, LDLR and APOB) with statin response were identified. Few studies have explored statin-related adverse events, and only ABCB1 but not SLCO1B1 variants were robustly associated with increased risk in Brazil. Statin-related pleiotropic effects were shown to be influenced by variants in PD (LDLR, NR1H2) and antioxidant enzyme (NOS3, SOD2, MTHFR, SELENOP) genes. The findings of these studies indicate that statin pharmacogenomic associations are distinctly different in Brazil compared to other populations. This review also discusses the clinical implications of pharmacogenetic studies and the rising importance of investigating rare variants to explore their association with statin response.

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.

Pharmacogene Sequencing of a Gabonese Population with Severe Plasmodium falciparum Malaria Reveals Multiple Novel Variants with Putative Relevance for Antimalarial Treatment

Malaria remains one of the deadliest diseases in Africa, particularly for children. While successful in reducing morbidity and mortality, antimalarial treatments are also a major cause of adverse drug reactions (ADRs). Host genetic variation in genes involved in drug disposition or toxicity constitutes an important determinant of ADR risk and can prime for parasite drug resistance. Importantly, however, the genetic diversity in Africa is substantial, and thus, genetic profiles in one population cannot be reliably extrapolated to other ethnogeographic groups. Gabon is considered a high-transmission country, with more than 460,000 malaria cases per year. Yet the pharmacogenetic landscape of the Gabonese population or its neighboring countries has not been analyzed. Using targeted sequencing, here, we profiled 21 pharmacogenes with importance for antimalarial treatment in 48 Gabonese pediatric patients with severe Plasmodium falciparum malaria. Overall, we identified 347 genetic variants, of which 18 were novel, and each individual was found to carry 87.3 ± 9.2 (standard deviation [SD]) variants across all analyzed genes. Importantly, 16.7% of these variants were population specific, highlighting the need for high-resolution pharmacogenomic profiling. Between one in three and one in six individuals harbored reduced-activity alleles of CYP2A6, CYP2B6, CYP2D6, and CYP2C8 with important implications for artemisinin, chloroquine, and amodiaquine therapy. Furthermore, one in three patients harbored at least one G6PD-deficient allele, suggesting a considerably increased risk of hemolytic anemia upon exposure to aminoquinolines. Combined, our results reveal the unique genetic landscape of the Gabonese population and pinpoint the genetic basis for interindividual differences in antimalarial drug responses and toxicity.

Pharmacogenomics in the era of next generation sequencing - from byte to bedside

Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.

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.

Impact of variants in ATP-binding cassette transporters on breast cancer treatment

There has been substantial interest in the impact of ATP-binding cassette (ABC) transporter variability on breast cancer drug resistance. Here, we provide a systematic review of ABC variants in breast cancer therapy. Notably, most studies used small heterogeneous cohorts and their identified associations lack statistical stringency, replication and mechanistic support. We conclude that commonly studied ABC polymorphisms are not suitable to accurately predict therapy response or toxicity in breast cancer patients and cannot guide treatment decisions. However, recent research shows that ABC transporters harbor a plethora of rare variants with individually small effect sizes, and we argue that a shift in strategy from target variant interrogation to comprehensive profiling might hold promise to drastically improve the predictive power of outcome models.

Two common polymorphic variants of OATP4A1 as potential risk factors for colorectal cancer

Genetic variations in the organic-anion-transporting polypeptide (OATP)-encoding solute carrier of organic anions (SLCO) genes can promote cancer development and progression. The overexpression of solute carrier organic anion transporter family member 4A1 (OATP4A1), a transporter for steroid hormones, prostaglandins, and bile acids, has been previously associated with tumor recurrence and progression in colorectal cancer (CRC). Therefore, the present study aimed to investigate the association between 2 frequent single nucleotide polymorphisms (SNPs) in SLCO4A1 (rs34419428, R70Q; rs1047099G, V78I) and CRC predisposition. Following restriction fragment length polymorphism-PCR analysis in 178 patients with CRC [Union for International Cancer Control (UICC) stage I/II] and 65 healthy controls, no significant difference was observed in allele frequency and the number of heterozygous/homozygous individuals between the groups. Notably, the R70Q minor allele was identified to be associated with the V78I minor allele in the genome. Comparing of the individual genotypes of CRC patients to clinical data, including sex, UICC-stage and relapse revealed no increased risk for CRC. In addition, the OATP4A1 immunoreactivity assay in paraffin-embedded CRC and adjacent non-tumorous mucosa sections, examined using quantitative microscopy image analysis, did not reveal any association with these polymorphisms. No significant differences were observed in the expression levels, localization, and sodium fluorescein transport capacity among the OATP4A1 variants, which was studied using functional assays in Sf9-insect and A431 tumor cells overexpressing the 2 single and a double mutant OATP4A1 SNP variants. These results suggested that the 2 most frequent polymorphisms located in the first intracellular loop of OATP4A1 do not associate with CRC predisposition and tumor recurrence. They are unlikely to affect the outcome of CRC in patients.

Germline variant burden in multidrug resistance transporters is a therapy-specific predictor of survival in breast cancer patients

Multidrug resistance due to facilitated drug efflux mediated by ATP-binding cassette (ABC) transporters is a main cause for failure of cancer therapy. Genetic polymorphisms in ABC genes affect the disposition of chemotherapeutics and constitute important biomarkers for therapeutic response and toxicity. Here we correlated germline variability in ABC transporters with disease-specific survival (DSS) in 960 breast cancer (BRCA), 314 clear cell renal cell carcinoma and 325 hepatocellular carcinoma patients. We find that variant burden in ABCC1 is a strong predictor of DSS in BRCA patients, whereas candidate polymorphisms are not associated with DSS. This association is highly drug-specific for subgroups treated with the MRP1 substrates cyclophosphamide (log-rank p = 0.0011) and doxorubicin (log-rank p = 0.0088) independent of age and tumor stage, whereas no association was found in individuals treated with tamoxifen (log-rank p = 0.13). Structural mapping of significant variants revealed multiple variants at residues involved in protein stability, cofactor stabilization or substrate binding. Our results demonstrate that BRCA patients with high variant burden in ABCC1 are less prone to respond appropriately to pharmacological therapy with MRP1 substrates, thus incentivizing the consideration of genomic germline data for precision cancer medicine.

Ethnogeographic and inter-individual variability of human ABC transporters

ATP-binding cassette (ABC) transporters constitute a superfamily of 48 structurally similar membrane transporters that mediate the ATP-dependent cellular export of a plethora of endogenous and xenobiotic substances. Importantly, genetic variants in ABC genes that affect gene function have clinically important effects on drug disposition and can be predictors of the risk of adverse drug reactions and efficacy of chemotherapeutics, calcium channel blockers, and protease inhibitors. Furthermore, loss-of-function of ABC transporters is associated with a variety of congenital disorders. Despite their clinical importance, information about the frequencies and global distribution of functionally relevant ABC variants is limited and little is known about the overall genetic complexity of this important gene family. Here, we systematically mapped the genetic landscape of the entire human ABC superfamily using Next-Generation Sequencing data from 138,632 individuals across seven major populations. Overall, we identified 62,793 exonic variants, 98.5% of which were rare. By integrating five computational prediction algorithms with structural mapping approaches using experimentally determined crystal structures, we found that the functional ABC variability is extensive and highly population-specific. Every individual harbored between 9.3 and 13.9 deleterious ABC variants, 76% of which were found only in a single population. Carrier rates of pathogenic variants in ABC transporter genes associated with autosomal recessive congenital diseases, such as cystic fibrosis or pseudoxanthoma elasticum, closely mirrored the corresponding population-specific disease prevalence, thus providing a novel resource for rare disease epidemiology. Combined, we provide the most comprehensive, systematic, and consolidated overview of ethnogeographic ABC transporter variability with important implications for personalized medicine, clinical genetics, and precision public health.

Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation

The genomic inter-individual heterogeneity remains a significant challenge for both clinical decision-making and the design of clinical trials. Although next-generation sequencing (NGS) is increasingly implemented in drug development and clinical trials, translation of the obtained genomic information into actionable clinical advice lags behind. Major reasons are the paucity of sufficiently powered trials that can quantify the added value of pharmacogenetic testing, and the considerable pharmacogenetic complexity with millions of rare variants with unclear functional consequences. The resulting uncertainty is reflected in inconsistencies of pharmacogenomic drug labels in Europe and the United States. In this review, we discuss how the knowledge gap for bridging pharmacogenomics into the clinics can be reduced. First, emerging methods that allow the high-throughput experimental characterization of pharmacogenomic variants combined with novel computational tools hold promise to improve the accuracy of drug response predictions. Second, tapping of large biobanks of therapeutic drug monitoring data allows to conduct high-powered retrospective studies that can validate the clinical importance of genetic variants, which are currently incompletely characterized. Combined, we are confident that these methods will improve the accuracy of drug response predictions and will narrow the gap between variant identification and its utilization for clinical decision-support.

Pharmacogenomic network analysis of the gene-drug interaction landscape underlying drug disposition

In recent decades the identification of pharmacogenomic gene-drug associations has evolved tremendously. Despite this progress, a major fraction of the heritable inter-individual variability remains elusive. Higher-dimensional phenomena, such as gene-gene-drug interactions, in which variability in multiple genes synergizes to precipitate an observable phenotype have been suggested to account at least for part of this missing heritability. However, the identification of such intricate relationships remains difficult partly because of analytical challenges associated with the complexity explosion of the problem. To facilitate the identification of such combinatorial pharmacogenetic associations, we here propose a network analysis strategy. Specifically, we analyzed the landscape of drug metabolizing enzymes and transporters for 100 top selling drugs as well as all compounds with pharmacogenetic germline labels or dosing guidelines. Based on this data, we calculated the posterior probabilities that gene i is involved in metabolism, transport or toxicity of a given drug under the condition that another gene j is involved for all pharmacogene pairs (i, j). Interestingly, these analyses revealed significant patterns between individual genes and across pharmacogene families that provide insights into metabolic interactions. To visualize the gene-drug interaction landscape, we use multidimensional scaling to collapse this similarity matrix into a two-dimensional network. We suggest that Euclidian distance between nodes can inform about the likelihood of epistatic interactions and thus might provide a useful tool to reduce the search space and facilitate the identification of combinatorial pharmacogenomic associations.

The genetic landscape of the human solute carrier (SLC) transporter superfamily

The human solute carrier (SLC) superfamily of transporters is comprised of over 400 membrane-bound proteins, and plays essential roles in a multitude of physiological and pharmacological processes. In addition, perturbation of SLC transporter function underlies numerous human diseases, which renders SLC transporters attractive drug targets. Common genetic polymorphisms in SLC genes have been associated with inter-individual differences in drug efficacy and toxicity. However, despite their tremendous clinical relevance, epidemiological data of these variants are mostly derived from heterogeneous cohorts of small sample size and the genetic SLC landscape beyond these common variants has not been comprehensively assessed. In this study, we analyzed Next-Generation Sequencing data from 141,456 individuals from seven major human populations to evaluate genetic variability, its functional consequences, and ethnogeographic patterns across the entire SLC superfamily of transporters. Importantly, of the 204,287 exonic single-nucleotide variants (SNVs) which we identified, 99.8% were present in less than 1% of analyzed alleles. Comprehensive computational analyses using 13 partially orthogonal algorithms that predict the functional impact of genetic variations based on sequence information, evolutionary conservation, structural considerations, and functional genomics data revealed that each individual genome harbors 29.7 variants with putative functional effects, of which rare variants account for 18%. Inter-ethnic variability was found to be extensive, and 83% of deleterious SLC variants were only identified in a single population. Interestingly, population-specific carrier frequencies of loss-of-function variants in SLC genes associated with recessive Mendelian disease recapitulated the ethnogeographic variation of the corresponding disorders, including cystinuria in Jewish individuals, type II citrullinemia in East Asians, and lysinuric protein intolerance in Finns, thus providing a powerful resource for clinical geneticists to inform about population-specific prevalence and allelic composition of Mendelian SLC diseases. In summary, we present the most comprehensive data set of SLC variability published to date, which can provide insights into inter-individual differences in SLC transporter function and guide the optimization of population-specific genotyping strategies in the bourgeoning fields of personalized medicine and precision public health.

Beyond drugs: the evolution of genes involved in human response to medications

The genetic variation of our species reflects human demographic history and adaptation to diverse local environments. Part of this genetic variation affects individual responses to exogenous substances, such as food, pollutants and drugs, and plays an important role in drug efficacy and safety. This review provides a synthesis of the evolution of loci implicated in human pharmacological response and metabolism, interpreted within the theoretical framework of population genetics and molecular evolution. In particular, I review and discuss key evolutionary aspects of different pharmacogenes in humans and other species, such as the relationship between the type of substrates and rate of evolution; the selective pressure exerted by landscape variables or dietary habits; expected and observed patterns of rare genetic variation. Finally, I discuss how this knowledge can be translated directly or after the implementation of specific studies, into practical guidelines.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.