Copy number variation profiling in pharmacogenes using panel-based exome resequencing and correlation to human liver expression

MTAP as an emerging biomarker in thoracic malignancies

S-methyl-5'-thioadenosine phosphorylase (MTAP) deficiency is an emerging biomarker in non-small cell lung cancer (NSCLC) and beyond. The MTAP gene is located in the chromosomal region 9p21.3, which shows one of the most common homozygous deletions across all human cancers (9p21 loss). Loss of 9p21 is found in the majority of pleural mesotheliomas, where it serves as an established diagnostic marker. Until recently, fluorescence in situ hybridization (FISH) was the gold standard for the detection of 9p21 losses, but loss of MTAP expression by immunohistochemistry (IHC) gains increasing importance as an easy to apply and cost-effective diagnostic surrogate marker. Besides, MTAP loss, which has been reported in 13% of NSCLC, is becoming an emerging predictive biomarker in two different scenarios in NSCLC and other cancer types: 1) MTAP loss seems to negatively predict the response to immune checkpoint inhibitor (ICI) treatment via silencing of the tumor microenvironment, and 2) MTAP loss serves as a predictive biomarker for novel targeted treatment strategies. MTAP deficiency leads to an impaired function of the protein arginine methyltransferase 5 (PRMT5) due to its partial inhibition by MTAP's accumulating substrate methylthioadenosine (MTA). This process leaves MTAP deficient tumor cells heavily dependent on the remaining function of PRMT5, making it a perfect target for synthetic lethality. Indeed, MTA-cooperative PRMT5-inhibitors are now tested in several clinical trials with promising early results in solid malignancies. With its emergence as a predictive biomarker, the implementation of MTAP IHC into diagnostic routine for NSCLC and other tumors is likely to take place soon. In this review article, we summarize the current literature on the role of MTAP in thoracic tumors and evaluate different testing methods, including IHC, FISH and next generation sequencing.

Pharmacogenetic analysis of structural variation in the 1000 genomes project using whole genome sequences

While significant strides have been made in understanding pharmacogenetics (PGx) and gene-drug interactions, there remains limited characterization of population-level PGx variation. This study aims to comprehensively profile global star alleles (haplotype patterns) and phenotype frequencies in 58 pharmacogenes associated with drug absorption, distribution, metabolism, and excretion. PyPGx, a star-allele calling tool, was employed to identify star alleles within high-coverage whole genome sequencing (WGS) data from the 1000 Genomes Project (N = 2504; 26 global populations). This process involved detecting structural variants (SVs), such as gene deletions, duplications, hybrids, as well as single nucleotide variants and insertion-deletion variants. The majority of our PyPGx calls for star alleles and phenotype frequencies aligned with the Pharmacogenomics Knowledge Base, although notable population-specific frequencies differed at least twofold. Validation efforts confirmed known SVs while uncovering several novel SVs currently undefined as star alleles. Additionally, we identified 210 small nucleotide variants associated with severe functional consequences that are not defined as star alleles. The study serves as a valuable resource, providing updated population-level star allele and phenotype frequencies while incorporating SVs. It also highlights the burgeoning potential of cost-effective WGS for PGx genotyping, offering invaluable insights to improve tailored drug therapies across diverse populations.

Structural variation of the coding and non-coding human pharmacogenome

Genetic variants in drug targets and genes encoding factors involved in drug absorption, distribution, metabolism and excretion (ADME) can have pronounced impacts on drug pharmacokinetics, response, and toxicity. While the landscape of genetic variability at the level of single nucleotide variants (SNVs) has been extensively studied in these pharmacogenetic loci, their structural variation is only poorly understood. Thus, we systematically analyzed the genetic structural variability across 908 pharmacogenes (344 ADME genes and 564 drug targets) based on publicly available whole genome sequencing data from 10,847 unrelated individuals. Overall, we extracted 14,984 distinct structural variants (SVs) ranging in size from 50 bp to 106 Mb. Each individual harbored on average 10.3 and 1.5 SVs with putative functional effects that affected the coding regions of ADME genes and drug targets, respectively. In addition, by cross-referencing pharmacogenomic SVs with experimentally determined binding data of 224 transcription factors across 130 cell types, we identified 1276 non-coding SVs that overlapped with gene regulatory elements. Based on these data, we estimate that non-coding structural variants account for 22% of the genetically encoded pharmacogenomic variability. Combined, these analyses provide the first comprehensive map of structural variability across pharmacogenes, derive estimates for the functional impact of non-coding SVs and incentivize the incorporation of structural genomic data into personalized drug response predictions.

Copy number variations of cytochrome P450 genes in Kinh Vietnamese

Background

The cytochrome P450 (CYP450) family is well known as a major group of drug metabolizing enzymes. The polymorphism of CYP450 genes is the main factor having an impact on the interindividual difference in drug response, including drug efficacy and drug safety. The single nucleotide polymorphism (SNPs) of Vietnamese Kinh has been widely studied, but information about the copy number variations (CNVs) of other CYP450 genes is still unknown.

Objective

To identify the CNV variability of CYP450 in 154 healthy unrelated Kinh Vietnamese, except eCYP2D6, which was previously reported.

Methods

Multiplex Ligation-Dependent Probe Amplification (MLPA) was applied for determination of copy number of 10 CYP450 genes. Later, PCR or quantitative PCR (qPCR) was used to confirm the detected CNVs in randomly chosen subjects.

Results

Of the 154 subjects, along with CYP2D6, 4 other CYP450 genes showed CNVs including duplications (CYP1B1), deletions (CYP2A6 and CYP2C9), and both duplications and deletions (CYP2E1). Among these, CYP2A6 exhibited the greatest frequency of CNVs compared with other CYP450, in which CYP2A6Del accounted for 11%. Meanwhile, allele CYP2E1Del showed the lowest frequency with only 0.3%.

Conclusions

The present study provides new insight into CYP450 CNVs in the Kinh Vietnamese cohort. Our data have contributed to genetic profiling of CYP450 CNVs in Vietnam, which would be helpful for facilitating implementation of pharmacogenetics in drug dosing adjustment in Vietnam.

Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data

Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.

Technologies for Pharmacogenomics: A Review

The continuous development of new genotyping technologies requires awareness of their potential advantages and limitations concerning utility for pharmacogenomics (PGx). In this review, we provide an overview of technologies that can be applied in PGx research and clinical practice. Most commonly used are single nucleotide variant (SNV) panels which contain a pre-selected panel of genetic variants. SNV panels offer a short turnaround time and straightforward interpretation, making them suitable for clinical practice. However, they are limited in their ability to assess rare and structural variants. Next-generation sequencing (NGS) and long-read sequencing are promising technologies for the field of PGx research. Both NGS and long-read sequencing often provide more data and more options with regard to deciphering structural and rare variants compared to SNV panels-in particular, in regard to the number of variants that can be identified, as well as the option for haplotype phasing. Nonetheless, while useful for research, not all sequencing data can be applied to clinical practice yet. Ultimately, selecting the right technology is not a matter of fact but a matter of choosing the right technique for the right problem.

A membrane transporter determines the spectrum of activity of a potent platinum-acridine hybrid anticancer agent

Cytotoxic drugs that are mechanistically distinct from current chemotherapies are attractive components of personalized combination regimens for combatting aggressive forms of cancer. To gain insight into the cellular mechanism of a potent platinum-acridine anticancer agent (compound 1), a correlation analysis of NCI-60 compound screening results and gene expression profiles was performed. A plasma membrane transporter, the solute carrier (SLC) human multidrug and toxin extrusion protein 1 (hMATE1, SLC47A1), emerged as the dominant predictor of cancer cell chemosensitivity to the hybrid agent (Pearson correlation analysis, p < 10-5) across a wide range of tissues of origin. The crucial role of hMATE1 was validated in lung adenocarcinoma cells (A549), which expresses high levels of the membrane transporter, using transporter inhibition assays and transient knockdown of the SLC47A1 gene, in conjunction with quantification of intracellular accumulation of compound 1 and cell viability screening. Preliminary data also show that HCT-116 colon cancer cells, in which hMATE1 is epigenetically repressed, can be sensitized to compound 1 by priming the cells with the drugs EPZ-6438 (tazemetostat) and EED226. Collectively, these results suggest that hMATE1 may have applications as a pan-cancer molecular marker to identify and target tumors that are likely to respond to platinum-acridines.

CDK4/6 Inhibitors in Breast Cancer Treatment: Potential Interactions with Drug, Gene, and Pathophysiological Conditions

Palbociclib, ribociclib, and abemaciclib belong to the third generation of cyclin-dependent kinases inhibitors (CDKis), an established therapeutic class for advanced and metastatic breast cancer. Interindividual variability in the therapeutic response of CDKis has been reported and some individuals may experience increased and unexpected toxicity. This narrative review aims at identifying the factors potentially concurring at this variability for driving the most appropriate and tailored use of CDKis in the clinic. Specifically, concomitant medications, pharmacogenetic profile, and pathophysiological conditions could influence absorption, distribution, metabolism, and elimination pharmacokinetics. A personalized therapeutic approach taking into consideration all factors potentially contributing to an altered pharmacokinetic/pharmacodynamic profile could better drive safe and effective clinical use.

Variant discovery using next-generation sequencing and its future role in pharmacogenetics

Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.

Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

Although several pharmacogenetic (PGx) predispositions affecting drug efficacy and safety are well established, drug selection and dosing as well as clinical trials are often performed in a non-pharmacogenetically-stratified manner, ultimately burdening healthcare systems. Pre-emptive PGx testing offers a solution which is often performed using microarrays or targeted gene panels, testing for common/known PGx variants. However, as an added value, whole-genome sequencing (WGS) could detect not only disease-causing but also pharmacogenetically-relevant variants in a single assay. Here, we present our WGS-based pipeline that extends the genetic testing of Mendelian diseases with PGx profiling, enabling the detection of rare/novel PGx variants as well. From our in-house WGS (PCR-free 60× PE150) data of 547 individuals we extracted PGx variants with drug-dosing recommendations of the Dutch Pharmacogenetics Working Group (DPWG). Furthermore, we explored the landscape of DPWG pharmacogenes in gnomAD and our in-house cohort as well as compared bioinformatic tools for WGS-based structural variant detection in CYP2D6. We show that although common/known PGx variants comprise the vast majority of detected DPWG pharmacogene alleles, for better precision medicine, PGx testing should move towards WGS-based approaches. Indeed, WGS-based PGx profiling is not only feasible and future-oriented but also the most comprehensive all-in-one approach without generating significant additional costs.