Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS: Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION: While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

Human papillomavirus maybe is a critical player in the regulation of chemoresistance related factors (P53, Rb, TWIST, Bcl-2, Bcl-XL, c-IAP2, cytochrome C, and caspase 3) in breast cancer.

As one of the frequent malignancies, breast cancer (BCa) is the foremost reason for cancer-related deaths among women. The role of Human papillomavirus (HPV) in chemoresistance has rarely been investigated in previous studies. The current study sets out to the possible role of HPV in BCa chemoresistance. In this research, 90 BCa tissue and 33 normal breast tissue were collected. We evaluated the presence of the HPV genome along with the viral (E2, E6, E7) and cellular gene expression associated with cell resistance to death. Statically significant differences in the prevalence of HPV between the BCa group (25.2% or 23/90) and the control group (21.8% or 7/32) were not found. HPV-16 and HPV-18 genotypes were the abundant HPV genotypes. Resistance to the Adriamycin-Cyclophosphamide (AC), paclitaxel regimen was elevated in the HPV- group (56/70) in comparison to the HPV+ group (14/70). Nevertheless, there was no significant difference in the prevalence of resistance to AC + paclitaxel + triple-negative breast cancer combination therapy between the HPV+ group (9/20) and in the HPV- group (11/20). In the BCa group in contrast to the control group, the expression level of Bcl-2, BCL-XL, and c-IAP2 demonstrated a significant decrease, while, the expression level of cytochrome C and caspase 3 was significantly increased. This study suggests that HPV infection might contribute to BCa chemoresistance through disrupt cellular genes involved in cell death.

Novel Deleterious Mutation in Steroid-5α-Reductase-2 in 46, XY Disorders of Sex Development: Case Report Study.

Background: Steroid-5α-reductase-2 (SRD5A2) and 17ß-hydroxysteroid dehydrogenase type 3 (17ß-HSD3) enzyme deficiencies are frequent causes of 46, XY disorder of sex development (46, XY DSD), where an infant with 46, XY has a female phenotype. We assessed the hydroxy-steroid-17ß-dehydrogenase-3 (HSD17B3)and SRD5A2 genes in twenty Iranian phenotypic females with 46,XY DSD. Materials and methods: All exons in HSD17B3 and SRD5A2 genes were subjected to PCR amplification followed by sequencing. Results: Of 20 identified 46, XY DSD patients, one had a homozygous missense 17ß-HSD3 mutation Ser65Leu (c.194C > T). We found 1 SRD5A2 novel homozygous missense mutation of Tyr242Asp (c.891T > G) in exon 5, which in-silico analyses revealed that this mutation may have deleterious impact on ligand binding site of SRD5A2 protein. Three other individuals harbored 17ß-HSD3 deficiencies without identified mutations. Conclusions: SRD5A2 and 17ß-HSD3 mutations are found in 10% of 46, XY DSD Iranian patients.

Altered IFN-γ Levels after Treatment of Epileptic Patients with Omega-3 Fatty Acids.

Epilepsy is a frequent chronic disorder of the brain characterized by intermittent epileptic seizures caused by hypersynchronous discharge of neurons in the brain. Studies have reported the role of cytokines in the pathogenesis of epilepsy, and a number of investigations have shown decreased levels of omega-3 fatty acids in epileptic patients. We investigated differences in serum levels of two cytokines, transforming growth factor (TGF)-ß and interferon (IFN)-γ, in 40 epileptic cases prior to and after treatment with omega-3 fatty acids. IFN-γ levels were significantly increased after the 16-week treatment period (P < 0.001). However, TGF-ß levels remained unchanged (P = 0.14). Omega-3 fatty acid treatment may alter the immune response in epileptic patients. This should be considered in prescription of omega-3 fatty acid supplements in these patients. Future studies with larger sample sizes should verify the results of the current study.