Extreme discordant phenotype methodology: an intuitive approach to clinical pharmacogenetics

Genetic Determinants of Thiazide-Induced Hyperuricemia, Hyperglycemia, and Urinary Electrolyte Disturbances - A Genome-Wide Evaluation of the UK Biobank

Thiazide diuretics, widely used in hypertension, cause a variety of adverse reactions, including hyperglycemia, hyperuricemia, and electrolyte abnormalities. In this study, we aimed to identify genetic variants that interact with thiazide-use to increase the risk of these adverse reactions. Using UK Biobank data, we first performed genomewide variance quantitative trait locus (vQTL) analysis of ~ 6.2 million SNPs on 95,493 unrelated hypertensive White British participants (24,313 on self-reported bendroflumethiazide treatment at recruitment) for 2 blood (glucose and urate) and 2 urine (potassium and sodium) biomarkers. Second, we conducted direct gene-environment interaction (GEI) tests on the significant (P < 2.5 × 10-9) vQTLs, included a second UK Biobank cohort comprising 13,647 unrelated hypertensive White British participants (3,478 on thiazides other than bendroflumethiazide) and set significance at P = 0.05 divided by the number of vQTL SNPs tested for GEIs. The vQTL analysis identified eight statistically significant SNPs for blood glucose (5 SNPs) and serum urate (3 SNPs), with none being identified for the urinary biomarkers. Two of the SNPs (1 glucose SNP: CDKAL1 intron rs35612982, GEI P = 6.24 × 10-3; and 1 serum urate SNP: SLC2A9 intron rs938564, GEI P = 4.51 × 10-4) demonstrated significant GEI effects in the first, but not the second, cohort. Both genes are biologically plausible candidates, with the SLC2A9-mediated interaction having been previously reported. In conclusion, we used a two-stage approach to detect two biologically plausible genetic loci that can interact with thiazides to increase the risk of thiazide-associated biochemical abnormalities. Understanding how environmental exposures (including medications such as thiazides) and genetics interact, is an important step toward precision medicine and improved patient outcomes.

Characterizing Extreme Phenotypes for Pain Interference in Persons With Chronic Pain Following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project

OBJECTIVE

To define and characterize extreme phenotypes based on pain interference for persons with chronic pain following traumatic brain injury (TBI).

SETTING

Eighteen Traumatic Brain Injury Model System (TBIMS) Centers.

PARTICIPANTS

A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview.

PRIMARY MEASURES

The Brief Pain Inventory (BPI) interference scale, sociodemographic, injury, functional outcome, pain, and treatment characteristics.

RESULTS

Participants were predominantly male (73%), White (75%), middle-aged (mean 46 years), and who were injured in motor vehicle accidents (53%) or falls (20%). Extreme phenotypes were identified based on upper and lower 25th percentiles to create low-interference ( n = 441) and high-interference ( n = 431) extreme phenotypes. Bivariate comparisons found several sociodemographic, injury, function, pain, and treatment differences between extreme phenotype groups, including significant differences ( P < .001) on all measures of concurrent function with those in the low-interference extreme phenotype experiencing better function than those in the high-interference extreme phenotype. Lasso regression combined with logistic regression identified multivariable predictors of low- versus high-interference extreme phenotypes. Reductions in the odds of low- versus high-interference phenotypes were significantly associated with higher pain intensity (odds ratio [OR] = 0.33), having neuropathic pain (OR = 0.40), migraine headache (OR = 0.41), leg/feet pain (OR = 0.34), or hip pain (OR = 0.46), and more pain catastrophizing (OR = 0.81).

CONCLUSION

Results suggest that for those who experience current chronic pain, there is high variability in the experience and impact of pain. Future research is needed to better understand how pain experience impacts individuals with chronic pain and TBI given that pain characteristics were the primary distinguishing factors between phenotypes. The use of extreme phenotypes for pain interference may be useful to better stratify samples to determine efficacy of pain treatment for individuals with TBI.

Characterizing Extreme Phenotypes for Perceived Improvement From Treatment in Persons With Chronic Pain Following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project

OBJECTIVE

To define and characterize extreme phenotypes based on perceived improvement in pain for persons with chronic pain following traumatic brain injury (TBI).

SETTING

Eighteen Traumatic Brain Injury Model System (TBIMS) Centers.

PARTICIPANTS

A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview.

PRIMARY MEASURES

The Patient's Global Impression of Change (PGIC) related to pain treatment. Sociodemographic, injury, functional outcome, pain, and pain treatment characteristics.

RESULTS

Participants were mostly male (73%), White (75%), middle-aged (mean 46 years), injured in motor vehicle accidents (53%), or falls (20%). Extreme phenotypes were created for an extreme improvement phenotype ( n = 512, 29.8%) defined as "moderately better" or above on the PGIC and an extreme no-change group ( n = 290, 16.9%) defined as no change or worse. Least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression identified multivariable predictors of improvement versus no-change extreme phenotypes. Higher odds of extreme improvement phenotype were significantly associated with being female (odds ratio [OR] = 1.85), married versus single (OR = 2.02), better motor function (OR = 1.03), lower pain intensity (OR = 0.78), and less frequent pain, especially chest pain (OR = 0.36). Several pain treatments were associated with higher odds of being in the extreme improvement versus no-change phenotypes including pain medication (OR = 1.85), physical therapy (OR = 1.51), yoga (OR = 1.61), home exercise program (OR = 1.07), and massage (OR = 1.69).

CONCLUSION

Investigation of extreme phenotypes based on perceived improvement with pain treatment highlights the ability to identify characteristics of individuals based on pain treatment responsiveness. A better understanding of the biopsychosocial characteristics of those who respond and do not respond to pain treatments received may help inform better surveillance, monitoring, and treatment. With further research, the identification of risk factors (such as pain intensity and frequency) for treatment response/nonresponse may provide indicators to prompt changes in care for individuals with chronic pain after TBI.

Jdp2 is a spatiotemporal transcriptional activator of the AhR via the Nrf2 gene battery

BACKGROUND

Crosstalk between the aryl hydrocarbon receptor (AhR) and nuclear factor (erythroid-derived 2)-like 2 (Nrf2) signaling is called the "AhR-Nrf2 gene battery", which works synergistically in detoxification to support cell survival. Nrf2-dependent phase II gene promoters are controlled by coordinated recruitment of the AhR to adjacent dioxin responsive element (DRE) and Nrf2 recruitment to the antioxidative response element (ARE). The molecular interaction between AhR and Nrf2 members, and the regulation of each target, including phase I and II gene complexes, and their mediators are poorly understood.

METHODS

Knockdown and forced expression of AhR-Nrf2 battery members were used to examine the molecular interactions between the AhR-Nrf2 axis and AhR promoter activation. Sequential immunoprecipitation, chromatin immunoprecipitation, and histology were used to identify each protein complex recruited to their respective cis-elements in the AhR promoter. Actin fiber distribution, cell spreading, and invasion were examined to identify functional differences in the AhR-Jdp2 axis between wild-type and Jdp2 knockout cells. The possible tumorigenic role of Jdp2 in the AhR-Nrf2 axis was examined in mutant Kras-Trp53-driven pancreatic tumors.

RESULTS

Crosstalk between AhR and Nrf2 was evident at the transcriptional level. The AhR promoter was activated by phase I ligands such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) through the AhR-Jdp2-Nrf2 axis in a time- and spatial transcription-dependent manner. Jdp2 was a bifunctional activator of DRE- and ARE-mediated transcription in response to TCDD. After TCDD exposure, Jdp2 activated the AhR promoter at the DRE and then moved to the ARE where it activated the promoter to increase reactive oxygen species (ROS)-mediated functions such as cell spreading and invasion in normal cells, and cancer regression in mutant Kras-Trp53-driven pancreatic tumor cells.

CONCLUSIONS

Jdp2 plays a critical role in AhR promoter activation through the AhR-Jdp2-Nrf2 axis in a spatiotemporal manner. The AhR functions to maintain ROS balance and cell spreading, invasion, and cancer regression in a mouse model of mutant Kras-Trp53 pancreatic cancer. These findings provide new insights into the roles of Jdp2 in the homeostatic regulation of oxidative stress and in the antioxidation response in detoxification, inflammation, and cancer progression.

Characteristics of Emergency Medicine Specimen Bank Participants Compared to the Overall Emergency Department Population

INTRODUCTION

Biorepositories lack diversity both demographically and with regard to the clinical complaints of patients enrolled. The Emergency Medicine Specimen Bank (EMSB) seeks to enroll a diverse cohort of patients for discovery research in acute care conditions. Our objective in this study was to determine the differences in demographics and clinical complaints between participants in the EMSB and the overall emergency department (ED) population.

METHODS

This was a retrospective analysis of participants of the EMSB and the entire UCHealth at University of Colorado Anschutz Medical Center (UCHealth AMC) ED population across three periods: peri-EMSB; post-EMSB; and COVID-19. We compared patients consented to the EMSB to the entire ED population to determine differences in age, gender, ethnicity, race, clinical complaints, and severity of illness. We used chi-square tests to compare categorical variables and the Elixhauser Comorbidity Index to determine differences in the severity of illness between the groups.

RESULTS

Between February 5, 2018-January 29, 2022, there were 141,670 consented encounters in the EMSB, representing 40,740 unique patients and over 13,000 blood samples collected. In that same time, the ED saw approximately 188,402 unique patients for 387,590 encounters. The EMSB had significantly higher rates of participation from the following: patients 18-59 years old (80.3% vs 77.7%); White patients (52.3% vs 47.8%), and women (54.8% vs 51.1%) compared to the overall ED population. The EMSB had lower rates of participation from patients ≥70 years, Hispanic patients, Asian patients, and men. The EMSB population had higher mean comorbidity scores. During the six months after Colorado's first COVID-19 case, the rate of consented patients and samples collected increased. The odds of consent during the COVID-19 study period were 1.32 (95% CI 1.26-1.39), and the odds of sample capture were 2.19 (95% CI 2.0-2.41).

CONCLUSION

The EMSB is representative of the overall ED population for most demographics and clinical complaints.

Integration of differential expression and network structure for 'omics data analysis

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

Occipital hemi-bun development and shape covariation in a longitudinal extant human growth sample

OBJECTIVES

Although the homology of the Neanderthal occipital bun and anatomically modern human "hemi-bun" has long been debated, little is known about the developmental timing and patterning of these two patterns of prominent occipital squama convexity. In this study, occipital hemi-bun ontogeny and cranial shape covariation are assessed in a comparative extant human sample.

MATERIALS AND METHODS

Two-dimensional geometric morphometric methods were used to investigate hemi-bun development in a longitudinal sample of growth study cephalograms representing extant human subjects predominantly of European ancestry. Subjects were each measured at three distinct age points, ranging from 3.0 to 20.4 years, and two-block partial least squares analysis was used to assess patterns of covariation between midsagittal occipital bone morphology and other aspects of craniofacial shape.

RESULTS

Occipital hemi-bun morphology, when present, was found to develop early in ontogeny, in association with anteroposterior elongation of the frontal and parietal bones. No significant pattern of covariation was found between occipital hemi-bun shape and cranial/basicranial breadth, basicranial length, basicranial angle, or midfacial prognathism.

DISCUSSION

This study suggests that the occipital hemi-bun, at least in this extant human population, should not be considered an independent trait, as its development is closely linked to shape variation in the frontal and parietal bones. Importantly, these results suggest that occipital hemi-bun morphology is not significantly influenced by basicranial morphology during development, but instead covaries with changes in midsagittal neurocranial vault shape.

Quantile Function on Scalar Regression Analysis for Distributional Data

Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors to investigate their effects of imaging-based cancer heterogeneity. We call this approach quantile functional regression, regressing subject-specific marginal distributions across repeated measurements on a set of covariates, allowing us to assess which covariates are associated with the distribution in a global sense, as well as to identify distributional features characterizing these differences, including mean, variance, skewness, heavy-tailedness, and various upper and lower quantiles. To account for smoothness in the quantile functions, account for intrafunctional correlation, and gain statistical power, we introduce custom basis functions we call quantlets that are sparse, regularized, near-lossless, and empirically defined, adapting to the features of a given data set and containing a Gaussian subspace so non-Gaussianness can be assessed. We fit this model using a Bayesian framework that uses nonlinear shrinkage of quantlet coefficients to regularize the functional regression coefficients and provides fully Bayesian inference after fitting a Markov chain Monte Carlo. We demonstrate the benefit of the basis space modeling through simulation studies, and apply the method to Magnetic resonance imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate imaging-based quantile functions to various demographic, clinical, and genetic predictors, finding specific differences in tumor pixel intensity distribution between males and females and between tumors with and without DDIT3 mutations.

Radiologic image-based statistical shape analysis of brain tumours

We propose a curve-based Riemannian geometric approach for general shape-based statistical analyses of tumours obtained from radiologic images. A key component of the framework is a suitable metric that enables comparisons of tumour shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumour shapes and allows for a rich class of continuous deformations of a tumour shape. The utility of the framework is illustrated through specific statistical tasks on a data set of radiologic images of patients diagnosed with glioblastoma multiforme, a malignant brain tumour with poor prognosis. In particular, our analysis discovers two patient clusters with very different survival, subtype and genomic characteristics. Furthermore, it is demonstrated that adding tumour shape information to survival models containing clinical and genomic variables results in a significant increase in predictive power.

An African-specific profile of pharmacogene variants for rosuvastatin plasma variability: limited role for SLCO1B1 c.521T>C and ABCG2 c.421A>C

Studies in Caucasian and Asian populations consistently associated interindividual and interethnic variability in rosuvastatin pharmacokinetics to the polymorphisms SLCO1B1 c.521T>C (rs4149056 p. Val174Ala) and ABCG2 c.421C>A (rs2231142, p. Gln141Lys). To investigate the pharmacogenetics of rosuvastatin in African populations, we first screened 785 individuals from nine ethnic African populations for the SLCO1B1 c.521C and ABCG2 c.421CA variants. This was followed by sequencing whole exomes from individuals of African Bantu descent, who participated in a 20 mg rosuvastatin pharmacokinetic trial in Harare Zimbabwe. Frequencies of SLCO1B1 c.521C ranged from 0.0% (San) to 7.0% (Maasai), while ABCG2 c.421A ranged from 0.0% (Shona) to 5.0% (Kikuyu). Variants showing significant association with rosuvastatin exposure were identified in SLCO1B1, ABCC2, SLC10A2, ABCB11, AHR, HNF4A, RXRA and FOXA3, and appear to be African specific. Interindividual differences in the pharmacokinetics of rosuvastatin in this African cohort cannot be explained by the polymorphisms SLCO1B1 c.521T>C and ABCG2 c.421C>A, but appear driven by a different set of variants.

Influence of pharmacogenetic polymorphisms and demographic variables on metformin pharmacokinetics in an admixed Brazilian cohort

AIMS

To identify pharmacogenetic and demographic variables that influence the systemic exposure to metformin in an admixed Brazilian cohort.

METHODS

The extreme discordant phenotype was used to select 106 data sets from nine metformin bioequivalence trials, comprising 256 healthy adults. Eleven single-nucleotide polymorphisms in SLC22A1, SLC22A2, SLC47A1 SLC47A2 and in transcription factor SP1 were genotyped and a validated panel of ancestry informative markers was used to estimate the individual proportions of biogeographical ancestry. Two-step (univariate followed by multivariate) regression modelling was developed to identify covariates associated with systemic exposure to metformin, accessed by the area under the plasma concentration-time curve, between 0 and 48 h (AUC_0-48h ), after single oral doses of metformin (500 or 1000 mg).

RESULTS

The individual proportions of African, Amerindian and European ancestry varied widely, as anticipated from the structure of the Brazilian population The dose-adjusted, log-transformed AUC_0-48h 's (ng h ml^-1  mg^-1 ) differed largely in the two groups at the opposite ends of the distribution histogram, namely 0.82, 0.79-0.85 and 1.08, 1.06-1.11 (mean, 95% confidence interval; P = 6.10^-26 , t test). Multivariate modelling revealed that metformin AUC_0-48h increased with age, food and carriage of rs12208357 in SLC22A1 but was inversely associated with body surface area and individual proportions of African ancestry.

CONCLUSIONS

A pharmacogenetic marker in OCT1 (SLC22A1 rs12208357), combined with demographic covariates (age, body surface area and individual proportion of African ancestry) and a food effect explained 29.7% of the variability in metformin AUC_0-48h .

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Selecting cases and controls for DNA sequencing studies using family histories of disease

Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome-wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

Warfarin Anticoagulation Therapy in Caribbean Hispanics of Puerto Rico: A Candidate Gene Association Study

Existing algorithms account for ~50% of observed variance in warfarin dose requirements after including common polymorphisms. However, they do not perform as well in populations other than Caucasians, in part because some ethno-specific genetic variants are overlooked. The objective of the present study was to identify genetic polymorphisms that can explain variability in warfarin dose requirements among Caribbean Hispanics of Puerto Rico. Next-Generation Sequencing of candidate genes CYP2C9 and VKORC1 and genotyping by DMET® Plus Assay of cardiovascular patients were performed. We also aimed at characterizing the genomic structure and admixture pattern of this study cohort. Our study used the Extreme Discordant Phenotype approach to perform a case-control association analysis. The CYP2C9 variant rs2860905, which was found in all the major haplotypes occurring in the Puerto Rican population, showed stronger association with warfarin sensitivity (<4 mg/day) than common variants CYP2C9*2 and CYP2C9*3. Although, CYP2C9*2 and CYP2C9*3 are separately contained within two of the haplotypes, 10 subjects with the sensitive phenotype were carriers of only the CYP2C9 rs2860905 variant. Other polymorphisms in CES2 and ABCB1 were found to be associated with warfarin resistance. Incorporation of rs2860905 in a regression model (R2 = 0.63, MSE = 0.37) that also includes additional genetics (i.e., VKORC1-1639 G>A; CYP2C9 rs1856908; ABCB1 c.IVS9-44A>G/ rs10276036; CES2 c.269-965A>G/ rs4783745) and non-genetic factors (i.e., hypertension, diabetes and age) showed better prediction of warfarin dose requirements than CYP2C9*2 and CYP2C9*3 combined (partial R2 = 0.132 vs. 0.023 and 0.007, respectively, p < 0.001). The genetic background of Puerto Ricans in the study cohort showed a tri-hybrid admixture pattern, with a slightly higher than expected contribution of Native American ancestry (25%). The genomic diversity of Puerto Ricans is highlighted by the presence of four different major haplotype blocks in the CYP2C9 locus. Although, our findings need further replication, this study contributes to the field by identifying novel genetic variants that increase predictability of stable warfarin dosing among Caribbean Hispanics.

Identification of coagulation gene 3'UTR variants that are potentially regulated by microRNAs

MicroRNAs have been recognized as critical regulators of gene expression and might affect the risk of venous thrombosis. We aimed to identify 3' untranslated region (UTR) variants in coagulation genes that influence coagulation factor levels and venous thrombosis risk. The 3'UTR of coagulation genes were sequenced in subjects with extremely high or low plasma levels of these factors in two case-control studies. In total, 28 variants were identified. Five single nucleotide polymorphisms (SNPs) were predominantly present in one extreme level group (F2 rs1799963, F8 rs1050705 and F11 rs4253429, rs4253430 and rs1062547). Additional to F2 rs1799963, F8 rs1050705 (in men) and F11 rs4253430 were associated with an increased risk of venous thrombosis albeit confidence intervals were wide. The three F11 SNPs were in high linkage disequilibrium with functional variants rs2289252 and rs2036914. Rs1062547 and rs4253430 were associated with a significant increase of plasma FXI activity in heterozygotes and homozygotes in wild-type controls. In silico prediction revealed that these SNPs might disturb the binding sites of miR-544 and miR-513a-3p. Only miR-544 provoked a significant decrease of the luciferase activity that was not observed with a rs4253430 mutated vector. In conclusion, these results reinforce that microRNAs are candidates to play a role in haemostasis and complex disorders, such as thrombosis.

MiR-99b-5p expression and response to tyrosine kinase inhibitor treatment in clear cell renal cell carcinoma patients

A number of treatments targeting VEGF or mTOR pathways have been approved for metastatic clear cell Renal Cell Carcinoma (ccRCC), but the majority of patients show disease progression after first line therapy with a very low rate of complete or long-term responders. It has been shown that miRs may play a role in prediction of treatment response in various cancer types. The aim of our study was to identify a miR signature predictive for RCC patients' response to antiangiogenic tyrosine kinase inhibitor (TKI) treatment in the first line therapy. Sequencing of 40 paired normal/tumor formalin fixed and paraffin embedded ccRCC tissues revealed separate clustering via unsupervised dendrograms. With supervised analysis, the strongest differential expression was obtained with miR-99b-5p, which was significantly lower in patients with short progression free survival (<8 months) and TKI non-responders (progressive disease patients according to RECIST) (p<0.0001, each). Validation using RTqPCR and a second patient cohort compiled from three different hospitals (n=65) showed higher expression of miR-99b-5p in complete responders, but this trend did not reach statistical significance. It is concluded that low miR-99b-5p expression analyzed with sequencing methodology may correlate with tumor progression in TKI-treated ccRCC patients.

Head-and-neck squamous cell carcinoma risk in smokers: no association detected between phenotype and AHR, CYP1A1, CYP1A2, or CYP1B1 genotype

BACKGROUND

Head-and-neck squamous cell carcinoma (HNSCC) differs between smokers and nonsmokers in etiology and clinical presentation. Because of demonstrated unequivocal involvement in smoking-induced cancer in laboratory animals, four candidate genes--AHR, CYP1A1, CYP1A2, and CYP1B1--were selected for a clinical genotype-phenotype association study of HNSCC risk in smokers. Thirty-six single-nucleotide variants (mostly tag-SNPs) within and near these four genes [16 (AHR), 4 (CYP1A1), 4 (CYP1A2), and 12 (CYP1B1)] were chosen.

METHODS

Extreme discordant phenotype (EDP) method of analysis was used to increase statistical power. HNSCC patients--having smoked 1-40 cigarette pack-years--represented the "highly-sensitive" (HS) population; heavy smokers having smoked ≥80 cigarette-pack-years without any type of cancer comprised the "highly-resistant" (HR) group. The vast majority of smokers were intermediate and discarded from consideration. Statistical tests were performed on N = 112 HS and N = 99 HR DNA samples from whole blood.

CONCLUSIONS

Among the four genes and flanking regions--one haploblock, ACTTGATC in the 5' portion of CYP1B1, retained statistical significance after 100,000 permutations (P = 0.0042); among our study population, this haploblock was found in 36.4% of African-American, but only 1.49% of Caucasian, HNSCC chromosomes. Interestingly, in the 1000 Genomes Project database, frequency of this haplotype (in 1322 African and 1006 Caucasian chromosomes) is 0.356 and 0.003, respectively. This study represents an excellent example of "spurious association by population stratification". Considering the cohort size, we therefore conclude that the variant alleles chosen for these four genes, alone or in combinations, are not statistically significantly associated with risk of cigarette-smoking-induced HNSCC.

Altered Prefrontal Cortex Function Marks Heightened Anxiety Risk in Children

OBJECTIVE

Anxiety disorders are prevalent and cause substantial disability. An important risk factor for anxiety disorders is inhibited temperament, the tendency to be shy and to avoid new situations. Inhibited adults have heightened amygdala activation and less flexible engagement of the prefrontal cortex (PFC); however, it remains unknown whether these brain alterations are present in inhibited children before the onset of anxiety disorders.

METHOD

A total of 37 children (18 inhibited and 19 uninhibited), 8 to 10 years of age, completed a task testing anticipation and viewing of threat stimuli and social stimuli in the magnetic resonance imaging (MRI) scanner. Brain activation and functional connectivity were measured.

RESULTS

During the anticipation of threat stimuli, inhibited children failed to show the robust PFC engagement observed in the uninhibited children. In contrast, when viewing social stimuli, inhibited children had increased medial PFC and dorsolateral PFC activation. Connectivity analyses revealed a pattern of reduced connectivity between prefrontal and limbic regions and among distinct PFC regions in the inhibited group. The medial PFC emerged as a key hub of the altered PFC circuitry in inhibited children.

CONCLUSION

This study provides new evidence of a neural signature of vulnerability to anxiety disorders. By investigating both anticipation and response to images, we identified that high-risk, inhibited children have widespread alterations in PFC function and connectivity, characterized by an inability to proactively prepare for social threat combined with heightened reactivity to social stimuli. Thus, children at high risk for anxiety show significantly altered prefrontal cortical function and connectivity before the onset of anxiety disorders.

SIFORM: shared informative factor models for integration of multi-platform bioinformatic data

MOTIVATION

High-dimensional omic data derived from different technological platforms have been extensively used to facilitate comprehensive understanding of disease mechanisms and to determine personalized health treatments. Numerous studies have integrated multi-platform omic data; however, few have efficiently and simultaneously addressed the problems that arise from high dimensionality and complex correlations.

RESULTS

We propose a statistical framework of shared informative factor models that can jointly analyze multi-platform omic data and explore their associations with a disease phenotype. The common disease-associated sample characteristics across different data types can be captured through the shared structure space, while the corresponding weights of genetic variables directly index the strengths of their association with the phenotype. Extensive simulation studies demonstrate the performance of the proposed method in terms of biomarker detection accuracy via comparisons with three popular regularized regression methods. We also apply the proposed method to The Cancer Genome Atlas lung adenocarcinoma dataset to jointly explore associations of mRNA expression and protein expression with smoking status. Many of the identified biomarkers belong to key pathways for lung tumorigenesis, some of which are known to show differential expression across smoking levels. We discover potential biomarkers that reveal different mechanisms of lung tumorigenesis between light smokers and heavy smokers.

AVAILABILITY AND IMPLEMENTATION

R code to implement the new method can be downloaded from http://odin.mdacc.tmc.edu/jhhu/ CONTACT: jhu@mdanderson.org.

An exploratory approach demonstrating immune skewing and a loss of coordination among cytokines in plasma and saliva of Veterans with combat-related PTSD

Studies have suggested PTSD to be associated with an inflammatory state, although few studies have examined the balances between stimulatory and inhibitory immune mediators in PTSD. An exploratory approach was taken to assess the immune imbalances between Th1 stimulatory, inflammatory and inhibitory mediators associated with PTSD. This approach focused on a tightly-controlled and relatively homogeneous population of Veterans, all with similar levels of combat exposure in the Afghanistan and Iraq wars, but some testing negative and others testing positive for PTSD. Although the sample size was small (6 controls and 7 with PTSD) and a limitation of this study, the results showed significant imbalances in immune cytokines favoring a Th1 and inflammatory state, with reduced levels of inhibitory cytokines in Veterans with PTSD. This was particularly prominent in the saliva of PTSD subjects compared to in their plasma.

Genome-wide association study of warfarin maintenance dose in a Brazilian sample

AIM

Extreme discordant phenotype and genome-wide association (GWA) approaches were combined to explore the role of genetic variants on warfarin dose requirement in Brazilians.

METHODS

Patients receiving low (≤ 20 mg/week; n = 180) or high stable warfarin doses (≥ 42.5 mg/week; n = 187) were genotyped with Affymetrix Axiom(®) Biobank arrays. Imputation was carried out using data from the combined 1000 Genomes project.

RESULTS

Genome-wide signals (p ≤ 5 × 10(-8)) were identified in the well-known VKORC1 (lead SNP, rs749671; OR: 20.4; p = 1.08 × 10(-33)) and CYP2C9 (lead SNP, rs9332238, OR: 6.8 and p = 4.4 × 10(-13)) regions. The rs9332238 polymorphism is in virtually perfect LD with CYP2C9*2 (rs1799853) and CYP2C9*3 (rs1057910). No other genome-wide significant regions were identified in the study.

CONCLUSION

We confirmed the important role of VKORC1 and CYP2C9 polymorphisms in warfarin dose. Original submitted 14 January 2015; Revision submitted 26 May 2015.

DINGO: differential network analysis in genomics

MOTIVATION

Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.

METHOD

We propose a pathway-based differential network analysis in genomics (DINGO) model for estimating group-specific networks and making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development.

RESULTS

Simulation studies demonstrate that DINGO provides more accurate group-specific conditional dependencies than achieved by using separate estimation approaches. We apply DINGO to key signaling pathways in glioblastoma to build differential networks for long-term survivors and short-term survivors in The Cancer Genome Atlas. The hub genes found by mRNA expression, DNA copy number, methylation and microRNA expression reveal several important roles in glioblastoma progression.

AVAILABILITY AND IMPLEMENTATION

R Package at: odin.mdacc.tmc.edu/∼vbaladan.

CONTACT

veera@mdanderson.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

INTRODUCTION

Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study.

AREAS COVERED

The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits.

EXPERT OPINION

Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting

Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.

Whole-exome sequencing reveals defective CYP3A4 variants predictive of paclitaxel dose-limiting neuropathy

PURPOSE

Paclitaxel, a widely used chemotherapeutic drug, can cause peripheral neuropathies leading to dose reductions and treatment suspensions and decreasing the quality of life of patients. It has been suggested that genetic variants altering paclitaxel pharmacokinetics increase neuropathy risk, but the major causes of interindividual differences in susceptibility to paclitaxel toxicity remain unexplained. We carried out a whole-exome sequencing (WES) study to identify genetic susceptibility variants associated with paclitaxel neuropathy.

EXPERIMENTAL DESIGN

Blood samples from 8 patients with severe paclitaxel-induced peripheral neuropathy were selected for WES. An independent cohort of 228 cancer patients with complete paclitaxel neuropathy data was used for variant screening by DHPLC and association analysis. HEK293 cells were used for heterologous expression and characterization of two novel CYP3A4 enzymes.

RESULTS

WES revealed 2 patients with rare CYP3A4 variants, a premature stop codon (CYP3A4*20 allele) and a novel missense variant (CYP3A4*25, p.P389S) causing reduced enzyme expression. Screening for CYP3A4 variants in the independent cohort revealed three additional CYP3A4*20 carriers, and two patients with missense variants exhibiting diminished enzyme activity (CYP3A4*8 and the novel CYP3A4*27 allele, p.L475V). Relative to CYP3A4 wild-type patients, those carrying CYP3A4 defective variants had more severe neuropathy (2- and 1.3-fold higher risk of neuropathy for loss-of-function and missense variants, respectively, P = 0.045) and higher probability of neuropathy-induced paclitaxel treatment modifications (7- and 3-fold higher risk for loss-of-function and missense variants, respectively, P = 5.9 × 10(-5)).

CONCLUSION

This is the first description of a genetic marker associated with paclitaxel treatment modifications caused by neuropathy. CYP3A4 defective variants may provide a basis for paclitaxel treatment individualization.

Pharmacogenetics of coumarin anticoagulants in Brazilians

INTRODUCTION

Coumarin vitamin K antagonists are the mainstay of anticoagulant therapy in atrial fibrillation, prosthetic heart valves and thromboembolic conditions. Concerns with these drugs include large inter-individual variability in dose requirements, narrow therapeutic index and need to monitor prothrombin time repeatedly.

AREAS COVERED

Pharmacogenetic studies and dosing algorithms for warfarin and phenprocoumon.

EXPERT OPINION

Gene candidate studies in Brazilian patients verified consistently the association of warfarin and pheprocoumon stable dose requirements with CYP2C9 and VKORC1 polymorphisms, and minor or no influence of other pharmacogenes (e.g., CYP4F2 and F7). The predictive power of warfarin and phenprocoumon dosing algorithms developed for Brazilians compares favorably with those reported for other populations. A warfarin dosing algorithm derived for an admixed cohort performed equally well in self-reported White and Black patients, in marked contrast with the considerably poorer performance of other warfarin algorithms in patients of African descent compared to those of European ancestry. This discrepancy is ascribed to the extensive European/African admixture among Brazilians. Prospective studies of clinical utility of coumarin dosing algorithms, in the context of the Brazilian Public Health System, would represent an important counterpart to recently published trials in European and North American cohorts with predominant or exclusive European ancestry.

Nutri-metabolomics applied to taste perception phenotype: human subjects with high and low sensitivity to taste of fat differ in salivary response to oleic acid

Saliva has different functions in the mouth and is involved, for example, in taste perception. Saliva composition can also be modified rapidly by taste stimulation. It remains unclear, however, whether the perceived intensity of a tastant may modulate this response. Based on increasing evidence that fat can be perceived by the taste system and that fat taste perception may be associated with fat intake, the aim of this work was to study if stimulation by a fatty acid (oleic acid) modifies saliva composition differently in subjects highly (sensitive+) or weakly (sensitive-) sensitive to that taste. For that purpose, saliva of two groups of subjects was collected after stimulation by either a control emulsion or an emulsion containing 5.61 mM oleic acid. Saliva was analyzed by 2D electrophoresis and (1)H NMR spectroscopy. The results show that sensitive+ and sensitive- subjects differ in their salivary response in terms of proteome and metabolome composition. Oppositely to sensitive- subjects, sensitive+ subjects responded to oleic acid by increased abundance of polymeric immunoglobulin receptor, rab GDP dissociation inhibitor beta, and organic acids, and decreased abundance of metabolites characteristic of mucins. The results highlight that modification of saliva composition by taste stimulation may be modulated by taste perception.

Neurocircuitry underlying risk and resilience to social anxiety disorder

BACKGROUND

Almost half of children with an inhibited temperament will develop social anxiety disorder by late adolescence. Importantly, this means that half of children with an inhibited temperament will not develop social anxiety disorder. Studying adults with an inhibited temperament provides a unique opportunity to identify neural signatures of both risk and resilience to social anxiety disorder.

METHODS

Functional magnetic resonance imaging (fMRI) was used to measure brain activation during the anticipation of viewing fear faces in 34 young adults (17 inhibited, 17 uninhibited). To identify neural signatures of risk, we tested for group differences in functional activation and connectivity in regions implicated in social anxiety disorder, including the prefrontal cortex, amygdala, and insula. To identify neural signatures of resilience, we tested for correlations between brain activation and both emotion regulation and social anxiety scores.

RESULTS

Inhibited subjects had greater activation of a prefrontal network when anticipating viewing fear faces, relative to uninhibited subjects. No group differences were identified in the amygdala. Inhibited subjects had more negative connectivity between the rostral anterior cingulate cortex (ACC) and the bilateral amygdala. Within the inhibited group, those with fewer social anxiety symptoms and better emotion regulation skills had greater ACC activation and greater functional connectivity between the ACC and amygdala.

CONCLUSIONS

These findings suggest that engaging regulatory prefrontal regions during anticipation may be a protective factor, or putative neural marker of resilience, in high-risk individuals. Cognitive training targeting prefrontal cortex function may provide protection against anxiety, especially in high-risk individuals, such as those with inhibited temperament.

Improved drug therapy: triangulating phenomics with genomics and metabolomics

Embracing the complexity of biological systems has a greater likelihood to improve prediction of clinical drug response. Here we discuss limitations of a singular focus on genomics, epigenomics, proteomics, transcriptomics, metabolomics, or phenomics-highlighting the strengths and weaknesses of each individual technique. In contrast, 'systems biology' is proposed to allow clinicians and scientists to extract benefits from each technique, while limiting associated weaknesses by supplementing with other techniques when appropriate. Perfect predictive modeling is not possible, whereas modeling of intertwined phenomic responses using genomic stratification with metabolomic modifications may greatly improve predictive values for drug therapy. We thus propose a novel-integrated approach to personalized medicine that begins with phenomic data, is stratified by genomics, and ultimately refined by metabolomic pathway data. Whereas perfect prediction of efficacy and safety of drug therapy is not possible, improvements can be achieved by embracing the complexity of the biological system. Starting with phenomics, the combination of linking metabolomics to identify common biologic pathways and then stratifying by genomic architecture, might increase predictive values. This systems biology approach has the potential, in specific subsets of patients, to avoid drug therapy that will be either ineffective or unsafe.

Structural and functional bases of inhibited temperament

Children born with an inhibited temperament are at heightened risk for developing anxiety, depression and substance use. Inhibited temperament is believed to have a biological basis; however, little is known about the structural brain basis of this vulnerability trait. Structural MRI scans were obtained from 84 (44 inhibited, 40 uninhibited) young adults. Given previous findings of amygdala hyperactivity in inhibited individuals, groups were compared on three measures of amygdala structure. To identify novel substrates of inhibited temperament, a whole brain analysis was performed. Functional activation and connectivity were examined across both groups. Inhibited adults had larger amygdala and caudate volume and larger volume predicted greater activation to neutral faces. In addition, larger amygdala volume predicted greater connectivity with subcortical and higher order visual structures. Larger caudate volume predicted greater connectivity with the basal ganglia, and less connectivity with primary visual and auditory cortex. We propose that larger volume in these salience detection regions may result in increased activation and enhanced connectivity in response to social stimuli. Given the strong link between inhibited temperament and risk for psychiatric illness, novel therapeutics that target these brain regions and related neural circuits have the potential to reduce rates of illness in vulnerable individuals.

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma

Purpose

To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance.

Methods

We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenotypes selected from an independent cohort (n = 101). In vitro experiments were conducted to study the role of miR-942 in sunitinib resistance.

Results

TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p = 0.0074). High expression of miR-942, miR-628-5p, miR-133a, and miR-484 was significantly associated with decreased time to progression and overall survival. These microRNAs were also overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive cell line. MiR-942 overexpression in Caki-2 up-regulates MMP-9 and VEGF secretion which, in turn, promote HBMEC endothelial migration and sunitinib resistance.

Conclusions

We identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity or resistance to sunitinib. MiR-942 was the best predictor of efficacy. We describe a novel paracrine mechanism through which high miR-942 levels in MRCC cells up-regulates MMP-9 and VEGF secretion to enhance endothelial migration and sunitinib resistance. Our results support further validation of these miRNA in clinical confirmatory studies.

Pharmacogenomics of warfarin in populations of African descent

Warfarin is the most commonly prescribed oral anticoagulant worldwide despite its narrow therapeutic index and the notorious inter- and intra-individual variability in dose required for the target clinical effect. Pharmacogenetic polymorphisms are major determinants of warfarin pharmacokinetic and dynamics and included in several warfarin dosing algorithms. This review focuses on warfarin pharmacogenomics in sub-Saharan peoples, African Americans and admixed Brazilians. These 'Black' populations differ in several aspects, notably their extent of recent admixture with Europeans, a factor which impacts on the frequency distribution of pharmacogenomic polymorphisms relevant to warfarin dose requirement for the target clinical effect. Whereas a small number of polymorphisms in VKORC1 (3673G > A, rs9923231), CYP2C9 (alleles *2 and *3, rs1799853 and rs1057910, respectively) and arguably CYP4F2 (rs2108622), may capture most of the pharmacogenomic influence on warfarin dose variance in White populations, additional polymorphisms in these, and in other, genes (e.g. CALU rs339097) increase the predictive power of pharmacogenetic warfarin dosing algorithms in the Black populations examined. A personalized strategy for initiation of warfarin therapy, allowing for improved safety and cost-effectiveness for populations of African descent must take into account their pharmacogenomic diversity, as well as socio-economical, cultural and medical factors. Accounting for this heterogeneity in algorithms that are 'friendly' enough to be adopted by warfarin prescribers worldwide requires gathering information from trials at different population levels, but demands also a critical appraisal of racial/ethnic labels that are commonly used in the clinical pharmacology literature but do not accurately reflect genetic ancestry and population diversity.

Epigenome-Wide Association Studies (EWAS) in Cancer

After completion of the human genome, genome-wide association studies were conducted to identify single nucleotide polymorphisms (SNPs) associated with cancer initiation and progression. Most of the studies identified SNPs that were located outside the coding region, and the odds ratios were too low to implement in clinical practice. Although the genome gives information about genome sequence and structure, the human epigenome provides functional aspects of the genome. Epigenome-wide association studies (EWAS) provide an opportunity to identify genome-wide epigenetic variants that are associated with cancer. However, there are problems and issues in implementing EWAS to establish an association between epigenetic profiles and cancer. Few challenges include selection and handling of samples, choice of population and sample size, accurate measurement of exposure, integrating data, and insufficient information about the role of repeat sequences. The current status of EWAS, challenges in the field, and their potential solutions are discussed in this article.

Cancer pharmacogenomics: strategies and challenges

Genetic variation influences the response of an individual to drug treatments. Understanding this variation has the potential to make therapy safer and more effective by determining selection and dosing of drugs for an individual patient. In the context of cancer, tumours may have specific disease-defining mutations, but a patient's germline genetic variation will also affect drug response (both efficacy and toxicity), and here we focus on how to study this variation. Advances in sequencing technologies, statistical genetics analysis methods and clinical trial designs have shown promise for the discovery of variants associated with drug response. We discuss the application of germline genetics analysis methods to cancer pharmacogenomics with a focus on the special considerations for study design.

A non-synonymous single nucleotide polymorphism in an OPRM1 splice variant is associated with fentanyl-induced emesis in women undergoing minor gynaecological surgery

BACKGROUND

Fentanyl-induced emesis (FIE) is a distressing adverse effect in the postoperative setting. The genetic basis of FIE remains largely unknown, therefore, we examined whether it was associated with specific genetic variants of OPRM1, the gene encoding the main receptor target of fentanyl.

METHODS

In this prospective case-control study, 193 women undergoing gynaecological surgery under a standardized anaesthetic with a low emetogenic risk were enrolled. Inclusion and exclusion criteria were designed to select extreme phenotypes as well as to ensure that most major confounders for FIE were either excluded or present in all patients. To control for unforeseen intra- and postoperative confounders for FIE, only 161 patients were further analysed, out of which 10 were categorized as having FIE, defined by the presence of at least one of three symptoms: nausea, vomiting or retching that was likely to be fentanyl-related. To identify SNPs relevant to FIE in our population, DNA from 40 controls and 10 cases was sequenced at the following OPRM1 regions: 3 kbp of the promoter, main and alternative exons as well as 2 kbp of the 3' downstream region. The genotype of the significant SNP was further determined in the remaining 111 controls.

RESULTS

The incidence of FIE was 6.2%. Initial sequencing of 10 cases and 40 controls identified 25 SNPs. Only rs540825, a non-synonymous SNP in the splice variant, MOR1X, showed a significant association with FIE post-Bonferroni correction. This SNP was further examined in the remaining 111 controls which confirmed its significant association with FIE (p = 0.019 post-Bonferroni, OR: 5.6, 95% CI: 1.42-21.91).

CONCLUSIONS

This is the first report of an association between the occurrence of FIE in Chinese women undergoing gynaecological surgery and an OPRM1 splice variant SNP, rs540825.

Food-induced brain responses and eating behaviour

The brain governs food intake behaviour by integrating many different internal and external state and trait-related signals. Understanding how the decisions to start and to stop eating are made is crucial to our understanding of (maladaptive patterns of) eating behaviour. Here, we aim to (1) review the current state of the field of 'nutritional neuroscience' with a focus on the interplay between food-induced brain responses and eating behaviour and (2) highlight research needs and techniques that could be used to address these. The brain responses associated with sensory stimulation (sight, olfaction and taste), gastric distension, gut hormone administration and food consumption are the subject of increasing investigation. Nevertheless, only few studies have examined relations between brain responses and eating behaviour. However, the neural circuits underlying eating behaviour are to a large extent generic, including reward, self-control, learning and decision-making circuitry. These limbic and prefrontal circuits interact with the hypothalamus, a key homeostatic area. Target areas for further elucidating the regulation of food intake are: (eating) habit and food preference formation and modification, the neural correlates of self-control, nutrient sensing and dietary learning, and the regulation of body adiposity. Moreover, to foster significant progress, data from multiple studies need to be integrated. This requires standardisation of (neuroimaging) measures, data sharing and the application and development of existing advanced analysis and modelling techniques to nutritional neuroscience data. In the next 20 years, nutritional neuroscience will have to prove its potential for providing insights that can be used to tackle detrimental eating behaviour.

Prediction of drug response and safety in clinical practice

Many clinicians hoped that the completion of the Human Genome Project would result in "individualized drug therapy," i.e., determining the right medication at the right dose 100% of the time based upon the individual's genetics. The pharmacogenomic prediction of drug efficacy and safety has not become a reality due to continuing realization of the complexity dictating the human-drug interaction. New methods of metabolomics, proteomics, and transcriptomics that account for this complexity hold promise for translational researchers hoping to increase drug efficacy and decrease drug toxicity.

Integrating diverse genomic data using gene sets

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.

CYP2D6 genotype and smoking influence fluvoxamine steady-state concentration in Japanese psychiatric patients: lessons for genotype-phenotype association study design in translational pharmacogenetics

The CYP2D6 enzyme is a capacity-limited high-affinity drug elimination pathway that metabolizes numerous psychiatric medicines. The capacity-limited nature of this enzyme suggests that drug dose may serve as an important factor that influence genotype-phenotype associations. However, dose dependency of CYP2D6 genotype contributions to drug elimination, and its interaction with environmental factors (e.g., smoking) did not receive adequate attention in translational study designs. Fluvoxamine is a selective serotonin reuptake inhibitor antidepressant. Fluvoxamine concentration is one of the factors previously linked to clinical remission in moderate to severe depression. We investigated the joint effect of smoking (an inducer of CYP1A2) and CYP2D6 genotype on interindividual variability in fluvoxamine steady-state concentration. Fluvoxamine concentration was measured in 87 patients treated with 50, 100, 150 or 200 mg/d. While CYP2D6 genotype significantly influenced fluvoxamine concentration in all four dose groups (p < 0.05), the percentage variance explained (R²) by CYP2D6 decreased as the dose of fluvoxamine increased. Smoking status (nonsmokers vs. smoking 20 or more cigarettes/d) significantly affected fluvoxamine concentration in the 50 mg/d group only (p = 0.005). Together, CYP2D6 genotype and smoking status explained 23% of the variance in fluvoxamine concentration but only at the low 50 mg/d dose group. These findings contribute to evidence-based and personalized choice of fluvoxamine dose using smoking status and CYP2D6 genetic variation. Additionally, these data lend evidence for drug dose as an important variable in translational pharmacogenetic study design and pharmaceutical phenotype associations with capacity-limited drug metabolism pathways such as CYP2D6.

Expectation and temperament moderate amygdala and dorsal anterior cingulate cortex responses to fear faces

A chronic tendency to avoid novelty is often the result of a temperamental bias called inhibited temperament, and is associated with increased risk for anxiety disorders. Neuroimaging studies have demonstrated that an inhibited temperament is associated with increased amygdalar blood-oxygenation-level-dependent (BOLD) response to unfamiliar faces that were not expected; however, the effects of variations in expectancy remain unknown. Using functional magnetic resonance imaging (fMRI), we studied BOLD response to infrequently encountered fear faces that were either expected or not expected in 42 adults with an inhibited or an uninhibited temperament. Individuals with an inhibited temperament had greater amygdala, but less dorsal anterior cingulate cortex (dACC), BOLD response when the stimuli were expected. In contrast, those with an uninhibited temperament had a smaller amygdala but larger dorsal anterior cingulate cortex BOLD response when expecting to see fear faces. These findings demonstrate temperament differences in expectancy effects and provide preliminary evidence for the dACC as a neural substrate mediating differences in inhibited temperament. Enhanced amygdala sensitivity coupled with weak inhibitory control from the dACC may form a neural circuit mediating behaviors characteristic of inhibited temperament and risk for anxiety disorders.

Pharmacogenomics of mood stabilizers in the treatment of bipolar disorder

Bipolar disorder (BD) is a chronic and often severe psychiatric illness characterized by manic and depressive episodes. Among the most effective treatments, mood stabilizers represent the keystone in acute mania, depression, and maintenance treatment of BD. However, treatment response is a highly heterogeneous trait, thus emphasizing the need for a structured informational framework of phenotypic and genetic predictors. In this paper, we present the current state of pharmacogenomic research on long-term treatment in BD, specifically focusing on mood stabilizers. While the results provided so far support the key role of genetic factors in modulating the response phenotype, strong evidence for genetic predictors is still lacking. In order to facilitate implementation of pharmacogenomics into clinical settings (i.e., the creation of personalized therapy), further research efforts are needed.

Selection of extreme phenotypes: the role of clinical observation in translational research

Systematic collection of phenotypes and their correlation with molecular data has been proposed as a useful method to advance in the study of disease. Although some databases for animal species are being developed, progress in humans is slow, probably due to the multifactorial origin of many human diseases and to the intricacy of accurately classifying phenotypes, among other factors. An alternative approach has been to identify and to study individuals or families with very characteristic, clinically relevant phenotypes. This strategy has shown increased efficiency to identify the molecular features underlying such phenotypes. While on most occasions the subjects selected for these studies presented harmful phenotypes, a few studies have been performed in individuals with very favourable phenotypes. The consistent results achieved suggest that it seems logical to further develop this strategy as a methodology to study human disease, including cancer. The identification and the study with high-throughput techniques of individuals showing a markedly decreased risk of developing cancer or of cancer patients presenting either an unusually favourable prognosis or striking responses following a specific treatment, might be promising ways to maximize the yield of this approach and to reveal the molecular causes that explain those phenotypes and thus highlight useful therapeutic targets. This manuscript reviews the current status of selection of extreme phenotypes in cancer research and provides directions for future development of this methodology.

Prenatal pulmonary hypertension index: novel prenatal predictor of severe postnatal pulmonary artery hypertension in antenatally diagnosed congenital diaphragmatic hernia

PURPOSE

This study aim to assess the potential of prenatal predictors of postnatal severe pulmonary artery hypertension (PAH) in isolated left congenital diaphragmatic hernia (CDH) and to define a new prenatal pulmonary hypertension index (PPHI).

METHODS

A retrospective chart review of CDH patients between May 2005 and October 2008 was conducted. Ten patients with systemic/suprasystemic and 9 patients with subsystemic pulmonary hypertension at 3 weeks of age were identified. Diameters of the right pulmonary artery, left pulmonary artery (LPA(d)), aorta, and the length of vermis of the cerebellum were obtained from prenatal magnetic resonance imaging to calculate the PPHI [=(LPA(d)/length of vermis of the cerebellum) x 10] and the modified McGoon index (MGI) [=(diameter of the right pulmonary artery + LPA(d))/diameter of aorta]. Prenatal pulmonary hypertension index and MGI were compared with lung-to-head ratio, percent predicted lung volume, and total lung volume for pulmonary hypertension and survival.

RESULTS

The PPHI and MGI had a significant, negative correlation with pulmonary hypertension (r = -0.61, P = .005, and r = -0.72, P < .005, respectively). The PPHI and MGI are significantly lower in the systemic/suprasystemic PAH group compared with the subsystemic PAH group (1.11 +/- 0.32 versus 1.63 +/- 0.28, P = .004, and 0.71 +/- 0.15 versus 1.05 +/- 0.11, P < .001, respectively). There were no significant differences between the groups comparing the lung-to-head ratio, percent predicted lung volume, and total lung volume.

CONCLUSION

Both PPHI and MGI accurately predict the severity of postnatal PAH in isolated left CDH.