What’s Known

Next-generation sequencing enabled new personalized medicine and pharmacogenomics theranostic options. Whole-exome sequencing panels are well-known targeted therapy in the diagnosis of disease pathogenicity and risk alleles in many phenotypes including various cancers. The pharmacogenomics impacts on carcinogenesis and cancer incidences in drug consumption, remarkably on pain pathways, are poorly studied.

What’s New

This study investigated the most challenging category of pharmacogenomics known as pain, anti-inflammatory, and immunomodulating agent pathways. 54 variants and 128 genes were suggested, a Pharmacogenomics-based Multiplex-Amplification Refractory Mutation System by polymerase chain reaction was established, and risks of chemical carcinogenesis resulting from dysregulation of investigated genes were found via in silico analyses.

Introduction

One subset of patients may respond well to a certain medicine and experience major adverse drug reactions (ADR), whereas other patients may not experience any ADR or therapeutic benefit from it. Growing data suggests that an individual’s genetic background plays a remarkable role in observing such different results, accounting for an estimated 20%-95% of diversity in drug administration and consequences. ¹ Earlier reports documented that pharmacogenomics testing before drug prescribing leads to disease improvements for about 80 drugs. ² Pharmacogenomics (PGx) studies the genetic variations in drug-metabolizing enzymes, receptors, transporters, and targets, as well as how these genetic variations link together to create drug-related phenotypes such as drug response or toxicity. Undoubtedly, the advent of next-generation sequencing (NGS) has opened up previously unimaginable capabilities for the analysis of whole genomes and generating a comprehensive portrait of each individual’s variome. ³ The important point is that the proteins involved in Absorption, Distribution, Metabolism, and Excretion (ADME) determine the pharmacokinetic characteristics of drugs and their high variability affects drug safety and tolerance in different individuals and various ethnicities. The main factor determining this heterogeneity is the allelic frequency of ADME-associated variants. ⁴ Previous research has underscored the importance of prescribing the right drug(s) with precise dosage, emphasizing the advantages of personalized medicine and the negative impacts of neglecting PGx, particularly in cancer biology. Precision oncology has recently emerged, challenging its own reliable frameworks to ensure stability and broad implementation. ^{5 , 6} Remarkably, cancer pain management (CPM) has introduced interesting alternatives to opioid prescribing for alleviating cancer-related pains. Therefore, pain-relieving drugs in CPM must be precisely prescribed based on each individual’s PGx profile. ⁷ Notably, no study has reported the possible risks of cancer induction resulting from common drug metabolizing genes involved in pain, anti-inflammation, and immunomodulation through PGx detection.

The identification of variants in actionable pharmacogenes and their clinical application may be beneficial in medication prescription, dosage optimization, and the avoidance of ADRs. The approach utilized in the pharmacogenetic routine at the moment is the study of single nucleotide variations (SNVs) with high frequency in genetic panels. ⁸ Thus, by saving time in a proactive panel-based strategy, logic and cost-effectiveness will be maximized. ⁹ Preventive testing by a PGx-based panel can be used to minimize ADRs and improve the treatment efficacy. ²

Current research and contentious issues have limited the use of pharmacogenomics as a pharmacological guiding tool in clinical trials. Thus, this study aimed to investigate the WES results of unrelated Western Iranians through a PGx-based panel containing variants involved in pain, anti-inflammatory and immunomodulating agents (PAIma) signaling pathways. To test the plausible impacts of population stratifications and to introduce rapid clinical testing of pharmacogenomic variations, the present study additionally set up a Multiplex Multiplex-Amplification-Refractory Mutation System (ARMS) PCR test on the unrelated healthy northern Iranians.

Material and Methods

Sampling and Data Collection

The studied population was 200 individuals including 100 healthy people who were referred to the Medical Genetic Laboratory (in the West of Iran, Kermanshah Province) and 100 healthy people from Guilan province (North of Iran). A written informed consent was obtained from each subject. This study was approved by the Research Ethics Committee of Isfahan University and the Biomedical Research Ethics Committee (code: IR.UI.REC.1402.092). The inclusion criteria were as follows: no abnormal clinical characteristics and disease-associated manifestations, normal results of the blood test, age higher than 20, and no consanguinity (relative) status. The exclusion criteria were considered as the healthy subjects with a patient child, subjects with relative parents, subjects who had taken drugs that were not related to the candidate genes, and subjects younger than 20 years old. The cut-off for age range was due to the majority of referred cases to the medical genetic laboratory; this frequently happens because healthy couples (older than 20 years old) request a carrier screening exome test. Finally, 100 WES tests were selected for the NGS analysis. The basic data was extracted from PharmGKB (https://www.pharmgkb.org/). The rationale behind this choice was that a few organizations give explicit guidelines for adjusting drug doses or recommending alternative treatments based on an individual’s genetic data. PharmGKB annotates clinical guidelines developed by the Clinical Pharmacogenetic Implementation Consortium (CPIC), the Dutch Pharmacogenetics Working Group (DPWG), and other professional organizations such as the Canadian Pharmacogenomics Network for Drug Safety (CPNDS) and the French National Network of Pharmacogenetic (RNPGx). The recommendations released by CPIC are developed in partnership with PharmGKB. PAIma category has 37 signaling pathways (21 curated pathways) and is among the most potential evidence-based and challenging pathways classified by PharmGKB. First of all, each of the 21 curated pathways in the PAIma category was investigated, and by filtering the unrepeated, significant, structural, and splicing variants, 55,590 annotations were found among PGx variants and Food and Drug Administration (FDA)-approved drugs. Finally, 128 genes had at least one nsSNV playing a potential role in the PAIma category. Further NGS analyses were performed on this PGx panel.

WES Tests and NGS Analyzing Strategies

All WES tests were done per the following pipeline: A filter-based method was employed for the extraction and purification of genomic deoxyribonucleic acid (gDNA) from the subjects’ blood samples, which was then evaluated. For DNA preparation, 1.0 g of gDNA was utilized. The Agilent SureSelect Human All ExonV7 Kit (Agilent Technologies, CA, USA) was applied to generate sequencing datasets, and x-index codes were attached to the sample attribute sequences. Using a hydrodynamic shear procedure (Covaris, Massachusetts, USA), the DNAs were fragmented into 180-280 bp segments. The reactions of exonuclease/polymerase attenuated the residual overhangs, and enzymes were removed from the nest. Adapter oligonucleotides were ligated after adenylation of the 3’ ends of the DNA pieces; DNA pieces with adaptor molecules linked at both ends were selectively chosen in a PCR reaction. Collected libraries were enriched with index tags in a PCR reaction to be ready for hybridization. The products were purified via the Beckman Coulter AMPure XP system (USA) and quantified with the Agilent Bioanalyzer 2100 System (USA) and the Agilent High Sensitivity DNA Assay (USA). The Illumina NovaSeq 6000 sequencer was fed with validated libraries. Data quality control, analysis, and interpretation were then carried out on a Unix-based operating system operating on a Generation G9 from an HP server (USA). NGS analyzing was carried out on every FASTQ file by Ubuntu (version 22.04.2) through a filtered-based command-line step and employing genomic packages including fastqc, IlluQC, Cutadapt, Alignment, Post-Alignment, Base Quality Score Recalibration (BQSR), Variant-calling, Variant Quality Score Recalibration (VQSR), Annotation with ANNOVAR, and Filtering. Annotation was performed via three levels including Gene-level (refGene), Region-level (cytoBand), and Frequency-level (cytoBand, exac03, dbnsfp30a, avsnp150, clinvar_20221231, regSNP-intron, and ICGC28) databases (See https://annovar.openbioinformatics.org/en/latest/user-guide/download/). ¹⁰ Following merging the candidate panel of 128 PAIma genes on the Variant Call Format (VCF) files, the commands were applied based on the variant (Reference Sequence) RS IDs, and the genotypes were extracted for each individual’s variant of interest. Remarkably, to validate the exome results, 10% of the samples were checked by Sanger sequencing.

Rapid Detection by Multiplex-ARMS PCR

Using PAIma high-risk variants, a rapid PGx test was established to provide a time- and cost-saving pharmacogenomics-based test. This set of variants (six actionable variants) was selected from the most important genes of PAIma that had the most differences in minor allele frequency (MAF) compared to the reference genome (available at http://asia.ensembl.org/index.html; Cambridge, UK., and https://www.ncbi.nlm.nih.gov/snp/; NCBI, USA). To be more specific, the selection of these six variants comes from the comparison between the reference genome data (available in public databases) and acquired MAFs of 100 WES tests by calculating the combined frequencies of major and minor alleles according to the genotypic data; the most differed MAFs were selected for designing Multiplex PCR. This study was done to focus more on determining whether there is any plausible population stratification between northern and western Iranians or not by examining a 6-variant set on another Iranian ethnicity -the Guilaks (who reside in northern Iran). A set of primers were designed for six variants including rs1695 (GSTP1), rs628031 (SLC22A1), rs17863778 (UGT1A7), rs16947 (CYP2D6), rs2257401 (CYP3A7), and rs2515641 (CYP2E1). To optimize PCR conditions to achieve uniform amplification across all target variants, we confidently optimized the PCR conditions for Multiplex-ARMS PCR and randomly double-checked some variants in random samples, which indicated the same results.

In Silico Investigations

The primary in silico investigations were performed on 128 candidate PAIma genes to uncover the novel interactions in different levels, including Protein-Protein Interactions (PPIs), Gene-miRNA Interactions (GMIs) by the STRING database version 12 (https://string-db.org/), ¹¹ and miRTargetLink2 version 2.0 (https://ccb-compute.cs.uni-saarland.de/mirtargetlink2). ¹² Moreover, a deep in silico analysis was designed through Enrichment Analysis (EA) applying Enrichr (https://maayanlab.cloud/Enrichr/) to investigate the known and unknown interplays among these three pathways. ¹³ The P values used in this study are calculated through a standard statistical method utilized by most enrichment analysis tools including Fisher’s exact test or the hypergeometric test. This is a binomial quantity test assuming a binomial distribution and independence for the likelihood of any gene categorized in any set. The Q value also, is an adjusted P value measured by the Benjamini-Hochberg method for the correction of multiple hypotheses testing.

Results

Data Mining and Analyzing the WES Results

Data mining of 21 curated pathways in the PAIma category from the PharmGKB database represented 55,590 annotations, 900 significant variants impacting the FDA-approved drugs, and 128 genes. Following multiple filtrations, 128 genes remained, which comprised the primary gene list for the WES test analysis. Discarding the non-coding and synonymous variants, 54 candidate variants were found, which showed differences with the reference genome (hg38) based on different MAFs estimated for all 100 WES results. The PAIma panel contained 128 genes including ABCB1, ABCC2, ABCC3, ABCC4, ABCG2, AKR1B1, AKR1C3, AMACR, ATF2, ATF3, BATF, CES1, CES2, CNR1, CNR2, CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C18, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP2E1, CYP3A, CYP3A4, CYP3A5, CYP3A7, FAAH, FKBP1A, FOS, FOSB, FOSL1, FOSL2, GSTA1, GSTM1, GSTP1, GSTT1, HPGDS, IL2, IMPDH1, IMPDH2, JDP2, JUN, JUNB, JUND, MAF, MAFA, MAFB, MAFF, MAFG, MAFK, MAP2K3, MAP2K4, MAP2K6, MAP2K7, MAP3K1, MAP3K11, MAP3K7, MAPK14, MAPK8, NFATC1, NFATC2, NFATC4, NFKB1, NFKB2, NOS1, NOS2, NOS3, NRL, PLA2G2A, PLA2G4A, PPIA, PPP3CA, PPP3CB, PPP3CC, PPP3R1, PPP3R2, PTGDR, PTGDR2, PTGDS, PTGER1, PTGER2, PTGER3, PTGER4, PTGES, PTGES2, PTGES3, PTGFR, PTGIR, PTGIS, PTGS1, PTGS2, REL, RELA, RELB, S1PR1, S1PR3, S1PR5, SLC22A1, SLC22A11, SLC22A6, SLC22A7, SLC22A8, SLC22A9, SLCO1B1, SLCO1B3, SLCO2B1, SULT1A1, SULT1A3, SULT1A4, SULT1E1, SULT2A1, TBXA2R, TBXAS1, TGFB1, UGT1A1, UGT1A10, UGT1A3, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT2B15, UGT2B17, UGT2B4, and UGT2B7. Genotype assessments of nsSNVs indicated 54 nsSNVs in the PAIma panel. Additionally, the MAFs of western Iranians revealed that 10 nsSNVs had a MAF of higher than 0.1 and four variants had a MAF of lower than 0.05. According to the function of variants, 48 variants out of 54, were nsSNVs. Furthermore, six variants were either splicing (rs2270860, rs776746, and rs4513095) or highly structure-damaging including two stop-gained (rs17863778 and rs145014075) and one frameshift (rs11572078) variants (table 1). It was found that some nsSNVs had overlapping roles as either functional (missense) or regulatory (promoter, Transcription binding site, enhancer, and CTCF) variants. As mentioned before, rs17863778 (UGT1A7) and rs145014075 (CYP2A6) are stop-gained mutations, rs11572078 (CYP2C8) is a frameshift, and rs2270860 (SLC22A7), rs776746 (CYP3A; CYP3A5), and rs4513095 (CES1) are splicing variants.

Multiplex-ARMS PCR as a Rapid PGx Test

To investigate the potentiality of the most different variants compared with the reference genome (GRCh38.p14; from the HapMap project, https://www.genome.gov/10001688/international-hapmap-project), and/or the 1000 Genomes project) and test the possibility of population stratification, the current study designed a Multiplex-ARMS PCR mini-panel as a rapid test on 100 samples from northern Iranians containing the important variants having major pharmacological impacts in the PAIma pathways. Genotyping was set up using ARMS-PCR protocol including 95 °C for 5 min, 95 °C for 40 sec; 59 °C (annealing time) for 40 sec, 72 °C for 40 sec (30 cycles), 72 °C for 5 min, and 4 °C for 5 min as holding time. This rapid test included various variants involved in neoplasm, type 2 diabetes, Human Immunodeficiency Virus (HIV) infection, expression level of CYP2D6 in the liver, toxic liver disease, and transplantation (and kidney transplantation specifically). Investigating the results of 100 WES tests, six nsSNVs showed the highest differences with reference genomes, which were selected for designing the Multiplex-ARMS PCR. These variants are rs1695 (GSTP1), rs628031 (SLC22A1), rs17863778 (UGT1A7), rs16947 (CYP2D6), rs2257401 (CYP3A7), and rs2515641 (CYP2E1) (figure 1). The MAFs of rs1695 were estimated 0.33 (reference genome), 0.21 (WES tests; western Iranians), and 0.36 (Multiplex-ARMS PCR; northern Iranians). The MAFs of rs628031 were estimated 0.39 (reference genome), 0.66 (western Iranians), and 0.52 (northern Iranians). The MAFs of rs17863778 were estimated 0.4 (reference genome), 0.56 (western Iranians), and 0.38 (northern Iranians). The MAFs of rs16947 were estimated 0.32 (reference genome), 0.6 (western Iranians), and 0.48 (northern Iranians). For rs2257401, the reference, western, and northern Iranians MAFs were 0.48, 0.14, and 0.28, respectively. Finally, for rs2515641, the reference, western, and northern Iranians MAFs were 0.14, 0.2, and 0.24, respectively (table 2). To test the reliability of Multiplex-ARMS test genotypes, ten samples were confirmed by Sanger sequencing. The sequences of the Multiplex-ARMS PCR will be available upon request.

Figure 1. The Gel Electrophoresis image of Multiplex-ARMS PCR products for five samples including six pharmacogenomics-based variants, including rs1695 GSTP1; 92 base pairs (bp)], rs628031 SLC22A1; 180 bp], rs17863778 UGT1A7; 288 bp], rs16947 CYP2D6; 387 bp], rs2257401 CYP3A7; 459 bp], and rs2515641 CYP2E1; 508 bp]. Numbers 1 and 2 represent the major allele and minor for the 1st sample, and the other numbers following this major-minor order to the last numbers 9 and 10 for the 5th sample.

In Silico Findings

Protein-Protein Interactions (PPIs)

STRING database represents a unique network with completely linked 128 proteins of coding genes. This finding is a strong clue for including three pathways in one categorization as well as showing certain genes that have various variants in all these three pathways. PPI enrichment P value was lower than 1.0E-16 (figure 2).

Figure 2. The STRING model of 128 genes involved in the PAIma signaling pathways.

Gene-miRNA Interactions (GMIs)

For investigating the regulatory interactions among the PAIma genes, miRTargetLink2 (version 2) was employed to find the potential miRNAs associations. By adjusting the strong validated evidence only, the output indicated a two-circled concentric model of GMIs with the inner circle having the most interactions. In the inner circle of concentric model, three miRNAs had the greatest interactions including hsa-miR-146a-5p associated with the PTGS2, TGFB1, PTGES2, and NOS1 genes; hsa-miR-155-5p correlated with the MAFB, FOS, JUN, NFKB1, MAPK14, and NOS3 genes; and hsa-miR199a-5p linked with the MAFB, PTGES2, NFKB1, MAP3K11, JUNB, and SULT1E1 genes (figure 3).

Figure 3. The concentric model of 128 PAIma genes, visualized by miRTargetLink2 and indicating A) The complete concentric model, and B) the inner circle of the concentric model.

Enrichment Analysis (EA)

Enrichr was applied to test the EA of 128 PGx-associated genes. Results were extracted from three layers including Pathway analysis, Gene Ontology (GO), and Disease/Drugs analysis (DDA). Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases are considered for pathway analysis. According to Reactome and KEGG, the most significant pathways were Drug ADME (R-HSA-9748784) (Q value=2.13E-55) and Chemical carcinogenesis (Q value=3.64E-42), respectively. Notably, biological oxidations R-HSA-211859 (Q value=1.83E-40) and drug metabolism (Q value=1.11E-39) were in the third and fourth places (table 3). The most significant processes in GO analyses were as follows: Arachidonic Acid Metabolic Process (GO:0019369) with a Q value of 3.58E-26 in GO Biological Process, Endoplasmic Reticulum Membrane (GO:0005789) with a Q value of 7.33E-11 in GO Cellular Component, and Heme Binding (GO:0020037) with a q-value of 1.62E-15 and Prostaglandin Receptor Activity (GO:0004955) (Q value=1.72E-15) in GO Molecular Function were found for candidate genes. Lastly, Disease Drugs Analysis (DDA) indicated a high risk of various cancers based on DisGeNET and GeDiPNet. As shown in table 4, the most significant phenotype result from the interplays among candidate genes is Mammary Neoplasm (Q value=4.32E-31) according to the DisGeNET database and also the top-scored disease risk. Additionally, based on GeDiPNET database, Lung Neoplasms were scored as the high-risk phenotype (Q value=5.44E-15).

Discussion

This study conducted a multiplex-ARMS PCR as a PGx rapid test by genotyping six important PGx variants. Deep Enrichment Analysis predicted a high risk of chemical carcinogenesis and various cancer types, remarkably mammary and lung neoplasms, which may result from the drug metabolism processes of the 128 refined candidate genes.

To have a comprehensive overview of PGx literature, the PubMed search engine was filtered in the title for the two main keywords including “pharmacogenomics” and “exome”. Results revealed 68 publications (2012-2023). Among them, 51 publications were related. Remarkably, nine of them had a panel-based strategy, but none of them mentioned a pathway-based strategy of analysis; thus, the present study might be the first PGx report to employ a pathway-based strategy of analysis on exome results. Several studies examined exome results and identified pharmacogenes, which mostly focused on the dispersed introduction of pharmacogenes and pharmacogenetic variants in various populations such as Spanish, Colombian, Thai, Dutch, American, Korean, and Canadian. ^{14 - 19}

Recent reports represented increasing attention to testing the population stratifications by investigating PGx panels. PGx-based panels according to these studies will be more powerful than other investigations introducing a set of pharmacogenes. Using the WES datasets of the Qatari population, Sivadas and others studied the PGx impacts of commonly prescribed antiplatelet drugs, warfarin, and clopidogrel. ²⁰ Nagar and colleagues hypothesized that genetic ancestry would give better precision for stratifying PGx risks because it is a more reliable factor for genetic heterogeneity than self-identified race/ethnicity (SIRE), which has an important social component. ²¹ The current study is inconsistent with these reports.

We selected the PAIma pathways because this was the most challenging categorization in PharmGKB (which in turn is based on CPIC, DPWG, and other aforementioned well-known guidelines). Another challenging issue was that this categorization includes 21 curated pathways such as the Diclofenac pathway, which in turn has various genes involved in different phenotypes; for instance, ABCC2 (associated with epilepsy), CYP2B6 (associated with HIV infections), SLC22A7 (associated with colorectal neoplasms), and SLCO1B1 (associated with hypercholesterolemia). Western and northern Iranian ethnicities are among the most important gene pools of Iran. By comparing their MAFs for the same variants, possible population stratifications were checked. As the most important gene in the heart of pharmacogenomics, CYP2D6 is included in this rapid test, which shows individuals carrying the genotypes AA+AG are associated with decreased expression of CYP2D6 in human liver cells compared to genotype GG. The other five variants of this test are associated with colorectal neoplasms (rs1695), type 2 diabetes (rs628031), HIV Infections (rs17863778), kidney transplantation (specifically) and transplantation (generally) (rs2257401), and Acute Myeloid Leukemia (AML) risk (rs2515641). An additional important response is that all cancers are along with various kinds of pain including chronic or acute pains during cancer metastasis, cancer therapies, or post-operative cancer pains. Therefore, there are increasing numbers of research and review articles concentrated on CPM. Opioids such as morphine and methadone are widely used in alleviating the pain of cancers and due to their addictive consequences, personalized medicine procedures are suggested for minimizing the side effects of opioid consumption. ^{7 , 22 , 23} Despite the experimental and clinical reports indicating critical associations between pain and cancer, ^{24 - 26} there is no study concentrated on the pharmacogenomics impacts of pain, inflammation, and immunomodulation pathways on cancer incidence and chemical carcinogenesis; thus, the deep in silico findings of this study for the first time uncovered the potential cancer risks resulting from PGx-associated variants, which need clinical follow-up and future studies on this new predictive application of PGx in cancer biology and CPM.

Remarkably, there were some limitations, which are highly recommended to be considered in future studies such as larger sample size, genotyping of the suggested panel, and included variants, specifically six important variants in the other Iranian ethnicities (central, southern, and eastern Iranians) and other ancestries including East Asians (Japanese, Chinese, Indians) European, Hispanic, and Americans. Additionally, to cover and investigate other variants in the intronic locations and non-exonic regions, future researchers can utilize the WGS technique or SNP-array technologies. Furthermore, incorporating experimental validation alongside computational analysis would enhance the robustness of current results and will improve the in-silico suggestions. Finally, while WES offers advantages in studying protein-coding regions, it’s important to note that comprehensive detection of structural variants (SVs) and copy number variations (CNVs), particularly within pharmacogenes, is better suited for techniques such as WGS and long-read sequencing due to their broader coverage of the entire genome.

Conclusion

This study suggested a PGx panel containing 128 genes involved in PAIma pathways and also a time and cost-saving Multiplex-ARMS PCR test for genotyping six important PGx variants as a rapid test conducted on northern Iranians. Interestingly, primary and deep in silico findings proposed the chemical carcinogenesis and cancer risks resulting from dysregulations in drug metabolisms of pain, inflammation, and immunomodulation pathways, which play roles in novel unknown mechanisms that need to be studied soon. Notably, the PAIma genes and their included variants are presented as the first signaling-based PGx panel, which can be considered by working groups and utilized by NGS analyzers.

Acknowledgment

We are very thankful for the financial supports of Research and Technology Department of University of Isfahan and our colleagues in the medical genetic laboratory of Dr. Shaveisi-zadeh in Kermanshah for their collaboration in data preparation and kind helps. Moreover, a special thanks to our colleagues in Cellular and Molecular Research Center (CMRC), Guilan University of Medical Sciences.

Authors’ Contribution

A.S: acquisition, analysis, and interpretation of data for the work, drafting the work; M.M: design of the work, reviewing the work critically for important intellectual content; P.K: interpretation of data for the work, reviewing the work critically for important intellectual content. All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of Interest:

None declared.

References

1. Kalow W, Tang BK, Endrenyi L. Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. Pharmacogenetics. 1998; 8:283-9. DOI | PubMed
2. Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, Basford MA, et al. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin Pharmacol Ther. 2012; 92:235-42. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
3. Pavlopoulos GA, Oulas A, Iacucci E, Sifrim A, Moreau Y, Schneider R, et al. Unraveling genomic variation from next generation sequencing data. BioData Min. 2013; 6:13. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
4. Hovelson DH, Xue Z, Zawistowski M, Ehm MG, Harris EC, Stocker SL, et al. Characterization of ADME gene variation in 21 populations by exome sequencing. Pharmacogenet Genomics. 2017; 27:89-100. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
5. Boscolo Bielo L, Trapani D, Repetto M, Crimini E, Valenza C, Belli C, et al. Variant allele frequency: a decision-making tool in precision oncology? Trends Cancer. 2023; 9:1058-68. DOI | PubMed
6. Rulten SL, Grose RP, Gatz SA, Jones JL, Cameron AJM. The Future of Precision Oncology. Int J Mol Sci. 2023; 24Publisher Full Text | DOI | PubMed [ PMC Free Article ]
7. Raad M, Lopez WOC, Sharafshah A, Assefi M, Lewandrowski KU. Personalized Medicine in Cancer Pain Management. J Pers Med. 2023; 13Publisher Full Text | DOI | PubMed [ PMC Free Article ]
8. van der Lee M, Allard WG, Bollen S, Santen GWE, Ruivenkamp CAL, Hoffer MJV, et al. Repurposing of Diagnostic Whole Exome Sequencing Data of 1,583 Individuals for Clinical Pharmacogenetics. Clin Pharmacol Ther. 2020; 107:617-27. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
9. Roden DM, Van Driest SL, Mosley JD, Wells QS, Robinson JR, Denny JC, et al. Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome. Clin Pharmacol Ther. 2018; 103:787-94. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
10. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38:e164. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
11. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023; 51:D638-D46. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
12. Kern F, Aparicio-Puerta E, Li Y, Fehlmann T, Kehl T, Wagner V, et al. miRTargetLink 2.0-interactive miRNA target gene and target pathway networks. Nucleic Acids Res. 2021; 49:W409-W16. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
13. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene Set Knowledge Discovery with Enrichr. Curr Protoc. 2021; 1:e90. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
14. Mizzi C, Peters B, Mitropoulou C, Mitropoulos K, Katsila T, Agarwal MR, et al. Personalized pharmacogenomics profiling using whole-genome sequencing. Pharmacogenomics. 2014; 15:1223-34. DOI | PubMed
15. Yang W, Wu G, Broeckel U, Smith CA, Turner V, Haidar CE, et al. Comparison of genome sequencing and clinical genotyping for pharmacogenes. Clin Pharmacol Ther. 2016; 100:380-8. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
16. Yan C, Pattabiraman N, Goecks J, Lam P, Nayak A, Pan Y, et al. Impact of germline and somatic missense variations on drug binding sites. Pharmacogenomics J. 2017; 17:128-36. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
17. Pandi MT, Williams MS, van der Spek P, Koromina M, Patrinos GP. Exome-Wide Analysis of the DiscovEHR Cohort Reveals Novel Candidate Pharmacogenomic Variants for Clinical Pharmacogenomics. Genes (Basel). 2020; 11Publisher Full Text | DOI | PubMed [ PMC Free Article ]
18. Han SM, Park J, Lee JH, Lee SS, Kim H, Han H, et al. Targeted Next-Generation Sequencing for Comprehensive Genetic Profiling of Pharmacogenes. Clin Pharmacol Ther. 2017; 101:396-405. DOI | PubMed
19. Gulilat M, Lamb T, Teft WA, Wang J, Dron JS, Robinson JF, et al. Targeted next generation sequencing as a tool for precision medicine. BMC Med Genomics. 2019; 12:81. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
20. Sivadas A, Sharma P, Scaria V. Landscape of warfarin and clopidogrel pharmacogenetic variants in Qatari population from whole exome datasets. Pharmacogenomics. 2016; 17:1891-901. DOI | PubMed
21. Nagar SD, Conley AB, Jordan IK. Population structure and pharmacogenomic risk stratification in the United States. BMC Biol. 2020; 18:140. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
22. Fassoulaki A, Melemeni A, Staikou C, Triga A, Sarantopoulos C. Acute postoperative pain predicts chronic pain and long-term analgesic requirements after breast surgery for cancer. Acta Anaesthesiol Belg. 2008; 59:241-8. PubMed
23. Preux C, Bertin M, Tarot A, Authier N, Pinol N, Brugnon D, et al. Prevalence of Opioid Use Disorder among Patients with Cancer-Related Pain: A Systematic Review. J Clin Med. 2022; 11Publisher Full Text | DOI | PubMed [ PMC Free Article ]
24. Check DK, Avecilla RAV, Mills C, Dinan MA, Kamal AH, Murphy B, et al. Opioid Prescribing and Use Among Cancer Survivors: A Mapping Review of Observational and Intervention Studies. J Pain Symptom Manage. 2022; 63:e397-e417. DOI | PubMed
25. Yang GS, Barnes NM, Lyon DE, Dorsey SG. Genetic Variants Associated with Cancer Pain and Response to Opioid Analgesics: Implications for Precision Pain Management. Semin Oncol Nurs. 2019; 35:291-9. Publisher Full Text | DOI | PubMed [ PMC Free Article ]
26. Sharma M, Kantorovich S, Lee C, Anand N, Blanchard J, Fung ET, et al. An observational study of the impact of genetic testing for pain perception in the clinical management of chronic non-cancer pain. J Psychiatr Res. 2017; 89:65-72. DOI | PubMed