Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

Rifampin causes drug interactions by altering hepatic drug metabolism. Because microRNAs (miRNAs) have been shown to regulate genes involved in drug metabolism, we determined the effect of rifampin on the expression of hepatic miRNAs. Primary human hepatocytes from seven subjects were treated with rifampin, and the expression of miRNA and cytochrome P450 (P450) mRNAs was measured by TaqMan assays and RNA-seq, respectively. Rifampin induced the expression of 10 clinically important and 13 additional P450 genes and repressed the expression of 9 other P450 genes (P < 0.05). Rifampin induced the expression of 33 miRNAs and repressed the expression of 35 miRNAs (P < 0.05). Several of these changes were highly negatively correlated with the rifampin-induced changes in the expression of their predicted target P450 mRNAs, supporting the possibility of miRNA-induced regulation of P450 mRNA expression. In addition, several other miRNA changes were positively correlated with the changes in P450 mRNA expression, suggesting similar regulatory mechanisms. Despite the interindividual variability in the rifampin effects on miRNA expression, principal components analysis clearly separated the rifampin-treated samples from the controls. In conclusion, rifampin treatment alters miRNA expression patterns in human hepatocytes, and some of the changes were correlated with the rifampin-induced changes in expression of the P450 mRNAs they are predicted to target.

Background: Cancer pharmacogenetic studies use archival tumor samples as a DNA source when germline DNA is unavailable. Genotyping DNA from formalin‐fixed paraffin embedded tumors (FFPE‐T) may be inaccurate due to FFPE storage, genetic aberrations, and/or insufficient DNA extraction. Our objective was to assess the extent and source of genotyping inaccuracy from FFPE‐T DNA and demonstrate analytical validity of FFPE‐T genotyping of candidate single nucleotide polymorphisms (SNPs) for pharmacogenetic analyses.Methods: Cancer pharmacogenetics SNPs were genotyped by Sequenom MassARRAYs in DNA harvested from matched FFPE‐T, FFPE lymph node (FFPE‐LN), and whole blood leukocyte samples obtained from breast cancer patients. No‐ and discordant‐call rates were calculated for each tissue type and SNP. Analytical validity was defined as any SNP with <5% discordance between FFPE‐T and blood and <10% discordance plus no‐calls.Results: Matched samples from 114 patients were genotyped for 247 SNPs. No‐call rate in FFPE‐T was greater than FFPE‐LN and blood (4.3% vs. 3.0% vs. 0.5%, p < 0.001). Discordant‐call rate between FFPE‐T and blood was very low, but greater than that between FFPE‐LN and blood (1.1% vs. 0.3%, p < 0.001). Samples with heterozygous genotypes were more likely to be no‐ or discordantly‐called in either tissue (p < 0.001). Analytical validity of FFPE‐T genotyping was demonstrated for 218 (88%) SNPs.Conclusions: No‐ and discordant‐call rates were below concerning thresholds, confirming that most SNPs can be accurately genotyped from FFPE‐T on our Sequenom platform. FFPE‐T is a viable DNA source for prospective–retrospective pharmacogenetic analyses of clinical trial cohorts.

Rifampin is a pleiotropic inducer of multiple drug metabolizing enzymes and transporters. This work utilized a global approach to evaluate rifampin effects on conjugating enzyme gene expression with relevance to human xeno‐ and endo‐biotic metabolism. Primary human hepatocytes from 7 subjects were treated with rifampin (10 μmol/L, 24 hours). Standard methods forRNA‐seq library construction,EZBead preparation, and NextGen sequencing were used to measure UDP‐glucuronosyl transferaseUGT, sulfonyltransferaseSULT, N acetyltransferaseNAT, and glutathione‐S‐transferaseGST mRNAexpression compared to vehicle control (0.01% MeOH). Rifampin‐induced (>1.25‐fold)mRNAexpression of 13 clinically important phaseIIdrug metabolizing genes and repressed (>1.25‐fold) the expression of 3 genes (P < .05). Rifampin‐induced miRNAexpression changes correlated withmRNAchanges and miRNAs were identified that may modulate conjugating enzyme expression.NAT2 gene expression was most strongly repressed (1.3‐fold) by rifampin whileUGT1A4 andUGT1A1 genes were most strongly induced (7.9‐ and 4.8‐fold, respectively). Physiologically based pharmacokinetic modeling (PBPK) was used to simulate the clinical consequences of rifampin induction ofCYP3A4‐ andUGT1A4‐mediated midazolam metabolism. Simulations evaluating isolatedUGT1A4 induction predicted increased midazolam N‐glucuronide exposure (~4‐fold) with minimal reductions in parent midazolam exposure (~10%). Simulations accounting for simultaneous induction of bothCYP3A4 andUGT1A4 predicted a ~10‐fold decrease in parent midazolam exposure with only a ~2‐fold decrease in midazolam N‐glucuronide metabolite exposure. These data reveal differential effects of rifampin on the human conjugating enzyme transcriptome and potential associations with miRNAs that form the basis for future mechanistic studies to elucidate the interplay of conjugating enzyme regulatory elements.

Discovery of clinical and genetic predictors of exemestane pharmacokinetics was attempted in 246 postmenopausal patients with breast cancer enrolled on a prospective clinical study. A sample was collected 2 h after exemestane dosing at a 1- or 3-month study visit to measure drug concentration. The primary hypothesis was that patients carrying the low-activity CYP3A4*22 (rs35599367) single-nucleotide polymorphism (SNP) would have greater exemestane concentration. Additional SNPs in genes relevant to exemestane metabolism (CYP1A1/2, CYP1B1, CYP3A4, CYP4A11, AKR1C3/4, AKR7A2) were screened in secondary analyses and adjusted for clinical covariates. CYP3A4*22 was associated with a 54% greater exemestane concentration (P<0.01). Concentration was greater in patients who reported White race, had elevated aminotransferases, renal insufficiency, lower body mass index and had not received chemotherapy (all P<0.05), and CYP3A4*22 maintained significance after adjustment for covariates (P<0.01). These genetic and clinical predictors of exemestane concentration may be useful for treatment individualization in patients with breast cancer.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.