Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) - Volume 15, Issue 2, 2017

Background: Identification of clinical and molecular biomarkers to predict dynamic response or monitor in real-time the efficacy of antiangiogenic therapy represents a major point in the treatment of patients with advanced colorectal cancer. Several studies have been conduced to identify some predictive biomarkers to select patients who will benefit from bevacizumab, the most widely used antiangiogenic monoclonal antibody. Conclusion: After a decade since the introduction of bevacizumab, no effective predictive biomarkers are available in routine clinical practice. In this review, we summarized the potential candidate dynamic biomarkers that may play a role in this setting.

Background: Sclerostin (SOST) is a secretary product of osteocytes and it is a glycoprotein expressed by SOST gene. It is well studied and established that sclerostin inhibits the bone formation by blocking Wnt signaling pathway. SOST gene acts as a potential therapeutic target for treating autosomal disorders, like van Buchem disease and sclerosteosis. Bioinformatics analysis of SOST gene shall provide exhaustive insights into the evolutionary origin of the gene sequence and its functional characteristics. Objective: In this review, we focus on presenting comprehensive information on SOST gene, its regulation and its role in van Buchem disease and sclerosteosis along with insilico analyses of its sequences. Method: The review on SOST gene and its mediation was done using various reports already published and other online resources. Bioinformatics tools and database such as BioGPS, Uniprot, MEGA and STRING were used for sequence analysis. Results: An overview on the regulation of SOST gene, its inhibitors and their pharmacological effects is presented. The molecular phylogenetic study and functional characterization suggested the conservation of SOST sequences among various species and the molecular pathway associated with SOST. Conclusion: The mediation of SOST gene under normal and disease conditions has hinted it as a potential therapeutic target, which is further confirmed through bioinformatics approaches. This paves way to attempt a possible regulation of SOST gene action involving peptide / peptidomimetic molecules and charged nanoparticles, especially during orthodontic treatments.

Background: Evidence now suggests that precision medicine, which is sometimes used interchangeably with personalized medicine, is becoming a cornerstone of medical practices by providing the right patient with the right medication at the right dose at the right time. In the light of the recent advances in biomedical computing and big data science, more and more genetic variants associated with human diseases and treatment response are being discovered in precision medicine applications by leveraging multi-omics and machine learning approaches. Objective: In this review, a key question is whether multi-omics approaches outperform the traditional single data type analysis in various multi-omics studies. Method: We focus on the most recent developments for cancer research in precision medicine using machine learning and predictive algorithms, together with multi-omics data. Results: First, we describe different machine learning approaches that are employed to assess whether biomarkers are correlated with diseases and treatment responses in various multi-omics studies. We also survey probable biomarkers that have been identified to be involved in diseases and treatment responses such as recurrence and survival in ovarian cancer. Furthermore, we summarize the limitations with respect to the mentioned multiomics studies. Finally, we address a discussion of future directions and challenges. Conclusion: Predictive models based on multi-omics data could be more powerful than those based on a single data type. Future replication studies with much larger sample sizes are essential to confirm the role of the biomarkers identified in these multi-omics studies and will seemingly have key contributions for precision medicine.

Background: Thymidylate synthase (TS) encoded by the TYMS gene is a target for cancer chemotherapeutic drugs such as 5-fluorouracil (5-FU). The overexpression of TS in several types of cancer confers poor response to 5-FU. The interpatient variation in TS levels can be due in part to functional genetic polymorphisms in TYMS gene: one of them is a variable number of 28-bp tandem repeats in its 5'-untranslated region (5'UTR), and the most common alleles are the double repeat (TSER*2) and triple repeat (TSER*3), in the second repeat of the 3R allele there is G>C SNP. In its 3' unstranslated region (3'UTR), there is a 6 bp insertion (+6bp) and deletion (-6bp) (1494del6). Several studies have identified that homozygous TSER*3R shows increased TYMS expression. Objective: The aim of this work was to predict TS expression phenotypes based on 5'UTR/3'UTR haplotypes in a population of the Central Region of Venezuela. Methods: The TSER polymorphism was analyzed by Polymerase Chain Reaction, the haplotypes inference was carried out using the PHASE 2.1 program. Based on the diplotypes analysis, the TS expression phenotypes were classified as: high, intermediate or low expression. Results: Six haplotypes were identified whose frequencies were: 2R/-6bp (5.96%), 2R/+6bp (31.70%), 3R/-6bp (31.30%), 3R/+6bp (30.0%) 4R/-6bp (0.38%), 4R/+6bp (0.57%). The frequencies of TS predicted expression phenotypes were: low (11.92%), intermediate (37.30%) and high (50.77%) the latter is associated with poor response to treatment with 5-FU. Conclusion: These results are important for pharmacogenetics, because TYMS polymorphisms are potential predictors of 5-FU therapeutic efficacy and toxicity in some cancer treatments.

Background: Single Nucleotide Polymorphisms (SNPs) in metformin transporter genes play an important role as the cause of inter-individual variability in metformin response. The SLC22A1 as an Organic Cation Transporter (OCT)1 coding gene is responsible for the metformin influx to hepatocytes, while the efflux to bile and excretion through the kidney are mainly facilitated by Multidrug and Toxin Extrusion (MATE)1 transporter coded by the SLC47A1. Objective: This study aimed to determine the distribution of allele frequencies in rs628031 A>G (M408V) OCT1 and rs2289669 G>A MATE1 among the Javanese population as the largest ethnic group in Indonesia with type-2 diabetes mellitus (T2DM). Method: The study involved 86 adult patients with T2DM. The genotyping to analyze the target SNPs used the PCR-RFLP method. Results: The frequencies of G allele of rs628031 in SLC22A1 and A allele of rs2289669 in SLC47A1 were 60.47% and 61.05% respectively. There was no significant correlation of the allele frequencies of both SLC22A1 rs628031 and SLC22A1 rs628031 between female and male patients (P>0.05). Conclusion: The allele frequency of SLC22A1 gene rs628031 in Javanese-Indonesian population was almost the same as those found in Afro-American population as well as in other Asian populations. Similar results were obtained when the A allele frequency of SLC47A1 rs2289669 was compared to those in Chinese population. Further studies are recommended to examine the correlation between these polymorphisms and the variability of pharmacokinetic profiles as well as glycemic response to metformin and its adverse reactions.

Background: A variety of genetic pathways have been described as being essential in drug abuse and myocardial ischemia (MI), including dopamine-2 receptor gene (DRD2) and serotonin transporter gene (SERT). However, there is insufficient information on how those genetic pathways could contribute to their comorbidity. Objectives: The study aimed to examine the potential association between drug abuse/myocardial ischemia comorbidity and five single nucleotide polymorphisms (SNPs) of DRD2 gene; Taq IA, Taq IB, Taq ID, -241A>G, and -141C del/ins, and two SNPs of SERT gene; 5-HTTLPR (rs25531) and an intronic variant (Stin2). Method: Fifteen Egyptian families including 211 individuals and 75 control subjects were genotyped using PCR-RFLP assay method. All individuals were assessed using drug use survey and complete cardiovascular profile. Results: The odds ratio (OR) for comorbidity with early onset MI associated with the A1A1 genotype of TaqIA was 3.9 (95% CI=1.8-8.5). However, B1B1 and B1B2 genotypes of TaqIB were significant in comorbidity of early onset drug abuse phenotype (OR =2.6, 95% CI=1.4-5.2 and OR=1.7 and 95% CI=1.2-2.5 respectively). Genotype AG of - 241A>G showed higher risk among comorbid subjects of early onset drug abuse (OR=1.9, 95% CI=1.4-2.7) and ins-containing genotypes of -141C del/ins were significant in comorbidity. Regarding SERT gene, SLA genotype of 5-HTTLPR and 10/10 of STin2 were significantly associated with comorbidity of early onset MI with OR=1.5, 95% CI=1.1-2.2 and OR=3.3, 95% CI=1.5-6.9. Conclusion: SNPs of the studied genes have made a significant contribution to comorbidity however, further studies are needed to validate the results in a wider population.

Background: Dectin-1 is a pattern recognition receptor that recognizes pathogenic fungi via carbohydrate moiety present on the cell wall. Single nucleotide polymorphisms in dectin-1 gene in an individual have been implicated in susceptibility to invasive infections in immunocompromised or immunocompetant host. Objective: We sought to analyze the deleterious nsSNP in dectin-1 gene for their susceptibility against fungal pathogens Methods: SNPs were retrieved from dbSNP database. Five in-silico algorithms (SIFT, PhD-SNP, SNAP, PolyPhen-2 and MAPP) were used to determine the functional consequences of non-synonymous SNPs. Conservation profiling of dectin-1 receptor protein was carried out using ConSurf. The three-dimensional (3D) structure of wild-type C-type lectin domain of dectin-1 protein was modeled using Phyre2 version-2, a web-based server. The protein stability was characterized using I-mutant tool. Results: A total of 91 non-synonymous SNPs were identified. Seven high-risk nonsynonymous SNPs in dectin-1 receptor were observed using five in-silico algorithms. Domain analysis resulted in the identification of C-type lectin domain. 3D structure modeling of C-type lectin domain as well as for seven variants of nsSNP in C-type lectin domain was carried out for determining their effect on structure-function in protein. Furthermore, I-Mutant revealed that the protein stability decreased which destabilized the amino acid interactions. The highest alteration in protein structures were observed in I223S (rs16910527), I158T (rs138005591) and D159G (rs 758623997) variants. Conclusion: We propose that these non-synonymous SNPs in dectin-1 receptor encoding gene could be considered for risk assessment against fungal infections.