Discovery and Biosynthesis of the Cytotoxic Polyene Terpenomycin in Human Pathogenic Nocardia.

Nocardia are opportunistic human pathogens that can cause a range of debilitating and difficult to treat infections of the lungs, brain, skin, and soft tissues. Despite their close relationship to the well-known secondary metabolite-producing genus, Streptomyces, comparatively few natural products are known from the Nocardia, and even less is known about their involvement in the pathogenesis. Here, we combine chemistry, genomics, and molecular microbiology to reveal the production of terpenomycin, a new cytotoxic and antifungal polyene from a human pathogenic Nocardia terpenica isolate. We unveil the polyketide synthase (PKS) responsible for terpenomycin biosynthesis and show that it combines several unusual features, including "split", skipped, and iteratively used modules, and the use of the unusual extender unit methoxymalonate as a starter unit. To link genes to molecules, we constructed a transposon mutant library in N. terpenica, identifying a terpenomycin-null mutant with an inactivated terpenomycin PKS. Our findings show that the neglected actinomycetes have an unappreciated capacity for the production of bioactive molecules with unique biosynthetic pathways waiting to be uncovered and highlights these organisms as producers of diverse natural products.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Genomic Testing in Localized Prostate Cancer Can Identify Subsets of African Americans With Aggressive Disease.

BACKGROUND: Personalized genomic classifiers have transformed the management of prostate cancer (PCa) by identifying the most aggressive subsets of PCa. Nevertheless, the performance of genomic classifiers to risk classify African American men is thus far lacking in a prospective setting. METHODS: This is a prospective study of the Decipher genomic classifier for National Comprehensive Cancer Network low- and intermediate-risk PCa. Study-eligible non-African American men were matched to African American men. Diagnostic biopsy specimens were processed to estimate Decipher scores. Samples accrued in NCT02723734, a prospective study, were interrogated to determine the genomic risk of reclassification (GrR) between conventional clinical risk classifiers and the Decipher score. RESULTS: The final analysis included a clinically balanced cohort of 226 patients with complete genomic information (113 African American men and 113 non-African American men). A higher proportion of African American men with National Comprehensive Cancer Network-classified low-risk (18.2%) and favorable intermediate-risk (37.8%) PCa had a higher Decipher score than non-African American men. Self-identified African American men were twice more likely than non-African American men to experience GrR (relative risk [RR] = 2.23, 95% confidence interval [CI] = 1.02 to 4.90; P = .04). In an ancestry-determined race model, we consistently validated a higher risk of reclassification in African American men (RR = 5.26, 95% CI = 1.66 to 16.63; P = .004). Race-stratified analysis of GrR vs non-GrR tumors also revealed molecular differences in these tumor subtypes. CONCLUSIONS: Integration of genomic classifiers with clinically based risk classification can help identify the subset of African American men with localized PCa who harbor high genomic risk of early metastatic disease. It is vital to identify and appropriately risk stratify the subset of African American men with aggressive disease who may benefit from more targeted interventions.

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

Substantial Gleason reclassification in Black men with national comprehensive cancer network low-risk prostate cancer - A propensity score analysis.

BACKGROUND: Emerging evidence suggests that a subset of Black men with National Comprehensive Cancer Network (NCCN) low-risk prostate cancer (PCa) may harbor high volume and genomically aggressive disease. However, limited, and ambiguous research exist to evaluate the risk of extreme Gleason reclassification in Black men with low-risk PCa. METHODS: This retrospective cohort study included 45,674 low-risk PCa patients who underwent prostatectomy and were not on active surveillance, from National Cancer Database (NCDB). A propensity score matched-pair design was employed, and the final cohort was limited to 1:1 matched 12,340 patients. Gleason score reclassification was used as primary endpoint. As such, any migration to pathologic Gleason score ≥7(3 + 4) was identified as overall, whereas migration to ≥7(4 + 3) was defined as extreme reclassification. A conditional Poisson regression model was used to estimate the risk of reclassification. Whereas spline model was used to estimate the impact of increasing time to treatment as a non-linear function on Gleason reclassification between race group. RESULTS: Upon matching there were no differences in the baseline characteristics between race groups. In a matched cohort, higher proportion of low-risk Black men (6.6%) reported extreme reclassification to pathologic Gleason score than White men (5.0%), p < 0.001. In a conditional Poisson regression model adjusted for time to treatment, the risk of overall (RR = 1.09, 95% CI, 1.05-1.13, p < 0.001) and extreme (RR = 1.30, 95% CI, 1.12-1.50, p = 0.004) reclassification was significantly higher in Black men as compared to their White counterpart. In spline model, the probability of Gleason reclassification in Black men was elevated with increasing time to treatment, especially after 180 days (53% vs. 43% between Black and White men). CONCLUSION: Risk of Gleason score reclassification is disparately elevated in Black men with low-risk PCa. Furthermore, time to treatment can non-linearly impact Gleason reclassification in Black men.

Mitochondrial DNA sequence variation and risk of glioma.

BACKGROUND: Malignant gliomas are the most common primary adult brain tumors, with a poor prognosis and ill-defined etiology. Mitochondrial DNA (mtDNA) sequence variation has been linked with certain cancers; however, research on glioma is lacking. METHODS: We examined the association of common (minor allele frequency ≥ 5%) germline mtDNA variants and haplogroups with glioma risk in 1,566 glioma cases and 1,017 controls from a US case-control study, and 425 glioma cases and 1,534 matched controls from the UK Biobank cohort (UKB). DNA samples were genotyped using the UK Biobank array that included a set of common and rare mtDNA variants. Risk associations were examined separately for glioblastoma (GBM) and lower grade tumors (non-GBM). RESULTS: In the US study, haplogroup W was inversely associated with glioma when compared with haplogroup H (OR = 0.43, 95%CI: 0.23-0.79); this association was not demonstrated in the UKB (OR = 1.07, 95%CI: 0.47-2.43). In the UKB, the variant m.3010G > A was significantly associated with GBM (OR = 1.32; 95%CI: 1.01-1.73; p = 0.04), but not non-GBM (1.23; 95%CI: 0.78-1.95; p = 0.38); no similar association was observed in the US study. In the US study, the variant m.14798 T > C, was significantly associated with non-GBM (OR = 0.72; 95%CI: 0.53-0.99), but not GBM (OR = 0.86; 95%CI: 0.66-1.11), whereas in the UKB, a positive association was observed between this variant and GBM (OR = 1.46; 95%CI: 1.06-2.02) but not non-GBM (OR = 0.92; 95%CI: 0.52-1.63). None of these associations were significant after adjustment for multiple testing. CONCLUSION: The association of inherited mtDNA variation, including rare and singleton variants, with glioma risk merits further study.

spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data.

SUMMARY: Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley's K, Besag's L, Macron's M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time. AVAILABILITY AND IMPLEMENTATION: spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mitochondrial DNA sequence variation and risk of meningioma.

BACKGROUND: Risk factors for meningioma include female gender, African American race, high body mass index (BMI), and exposure to ionizing radiation. Although genome-wide association studies (GWAS) have identified two nuclear genome risk loci for meningioma (rs12770228 and rs2686876), the relation between mitochondrial DNA (mtDNA) sequence variants and meningioma is unknown. METHODS: We examined the association of 42 common germline mtDNA variants (minor allele frequency ≥ 5%), haplogroups, and genes with meningioma in 1080 controls and 478 meningioma cases from a case-control study conducted at medical centers in the southeastern United States. Associations were examined separately for meningioma overall and by WHO grade (n = 409 grade I and n = 69 grade II/III). RESULTS: Overall, meningioma was significantly associated with being female (OR 2.85; 95% CI 2.21-3.69), self-reported African American race (OR 2.38, 95% CI 1.41-3.99), and being overweight (OR 1.48; 95% CI 1.11-1.97) or obese (OR 1.70; 95% CI 1.25-2.31). The variant m.16362T > C (rs62581341) in the mitochondrial control region was positively associated with grade II/III meningiomas (OR 2.33; 95% CI 1.14-4.77), but not grade I tumors (OR 0.99; 95% CI 0.64-1.53). Haplogroup L, a marker for African ancestry, was associated with meningioma overall (OR 2.92; 95% CI 1.01-8.44). However, after stratifying by self-reported race, this association was only apparent among the few self-reported Caucasians with this haplogroup (OR 6.35; 95% CI 1.56-25.9). No other mtDNA variant, haplogroup, or gene was associated with meningioma. CONCLUSION: Common mtDNA variants and major mtDNA haplogroups do not appear to have associations with the odds of developing meningioma.

A prospective study of inflammatory biomarkers and growth factors and risk of glioma in the UK Biobank.

PURPOSE: The role of growth factors and inflammation in the onset of glioma is poorly understood, and conflicting reports of associations of circulating IGF-1 and inflammatory biomarkers with glioma risk exist in the literature. We examined associations between C-reactive protein (CRP), white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), and insulin-like growth factor-1 (IGF-1) and glioma risk in the UK Biobank cohort. METHODS: Hazard ratios (HR) and 95% confidence intervals (CI) for glioma according to circulating biomarkers concentrations were calculated using Cox proportional hazards regression, adjusted for age, sex, race, and education. Analyses were conducted separately for glioma overall and by glioma subtype. RESULTS: We identified 417 incident glioma cases among 428,537 participants with 3,255,815 person-years of follow up. Weak, non-significant associations were observed with increasing levels of these biomarkers for risk of glioma overall or by glioma subtype. Among women only, IGF-1 in the highest quartile was positively associated with glioma risk compared to the lowest quartile (HR=1.64, 95%CI: 1.03-2.60, p-trend=0.08), as was NLR (HR=1.54, 95%CI: 1.00-2.39, p-trend=0.05). CONCLUSION: In this prospective cohort, we found no significant associations between the inflammatory biomarkers CRP and WBC and the development of glioma. NLR and IGF-1 were associated with risk in women, but not men. When considered with previous studies, further investigation of NLR and IGF-1 as markers of glioma risk appears warranted, particularly in women.

Prospective study of sleep duration and glioma risk.

PURPOSE: Both long and short sleep duration have been linked with risk of some cancers, but evidence for glioma is lacking. METHODS: Using prospective data from the UK Biobank (UKB), the Nurses' Health Study (NHS), and the Health Professionals Follow-Up Study (HPFS), we examined the association between self-reported hours of sleep and incident glioma in multivariable-adjusted Cox proportional hazards models. RESULTS: In the UKB, compared to 7 h, sleep durations of < 7 h (HR = 0.90; 95% CI 0.70-1.16) or > 7 h (HR = 1.05; 95% CI 0.85-1.30) were not significantly associated with glioma risk. Likewise, no significant associations were found between sleep duration and glioma risk in the NHS/HPFS for either < 7 h (HR = 0.93; 95% CI 0.69-1.26) or > 7 h (HR = 1.22; 95% CI 0.94-1.57), compared to 7 h. Results were similar for low-grade and high-grade glioma, did not materially change after lagging 2 years, or after accounting for factors known to disrupt sleep. CONCLUSION: Sleep duration was not associated with incident glioma in either the UKB or the NHS/HPFS cohorts.

Pre-diagnostic circulating concentrations of fat-soluble vitamins and risk of glioma in three cohort studies.

Few prospective studies have evaluated the relation between fat-soluble vitamins and glioma risk. Using three cohorts-UK Biobank (UKB), Nurses' Health Study (NHS), and Health Professionals Follow-Up Study (HPFS), we investigated associations of pre-diagnostic concentrations of fat-soluble vitamins D, A, and E with incident glioma. In 346,785 participants (444 cases) in UKB, associations with vitamin D (25-hydroxyvitamin D [25(OH)D]) were evaluated by Cox proportional hazards regression. In NHS (52 cases, 104 controls) and HPFS (32 cases, 64 controls), associations with 25(OH)D, vitamin A (retinol), and vitamin E (α- and γ-tocopherol) were assessed using conditional logistic regression. Our results suggested plasma concentrations of 25(OH)D and retinol were not associated with glioma risk. Comparing the highest to lowest tertile, the multivariable hazard ratio (MVHR) for 25(OH)D was 0.87 (95% confidence interval [CI] 0.68-1.11) in UKB and the multivariable risk ratio (MVRR) was 0.97 (95% CI 0.51-1.85) in NHS and HPFS. In NHS and HPFS, the MVRR for the same comparison for retinol was 1.16 (95% CI 0.56-2.38). Nonsignificant associations were observed for α-tocopherol (MVRRtertile3vs1 = 0.61, 95% CI 0.29-1.32) and γ-tocopherol (MVRR tertile3vs1 = 1.30, 95% CI 0.63-2.69) that became stronger in 4-year lagged analyses. Further investigation is warranted on a potential association between α- and γ-tocopherol and glioma risk.

Circulating lipids and glioma risk: results from the UK Biobank, Nurses' Health Study, and Health Professionals Follow-Up Study.

PURPOSE: Evidence is mixed on whether cholesterol plays a role in the pathogenesis of glioma. We explored the associations between circulating lipids and glioma risk in three prospective cohorts. METHODS: Using prospective data from the UK Biobank, we examined the associations of total cholesterol (TC), high- and low-density lipoprotein cholesterol (HDL-C, LDL-C), and triglycerides (TG) with glioma risk in multivariable (MV)-adjusted Cox proportional hazards models. Within the Nurses' Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS), we carried out a matched, nested case-control study to examine these same associations. RESULTS: In the UK Biobank, 490 gliomas accrued over 2,358,964 person-years. TC was not significantly associated with glioma risk (MV HR = 1.20, 95% CI 0.89-1.61 for highest quartile vs. lowest, p-trend = 0.24). In 4-year lagged analyses (n = 229), higher TC was associated with significantly higher risk of glioma in men (MV HR = 2.26, 95% CI 1.32-3.89, p-trend = 0.002) but not women (MV HR = 1.28, 95% CI 0.61-2.68, p-trend = 0.72); similar findings emerged for HDL-C and, to a lesser extent, LDL-C. In the NHS/HPFS, no significant associations were found between cholesterol and glioma risk. No significant associations were identified for TG. CONCLUSION: In the UK Biobank, higher prediagnostic TC and HDL-C levels were associated with higher risk of glioma in 4-year lagged analyses, but not in non-lagged analyses, in men only. These findings merit further investigation, given that there are few risk factors and no reliable biomarkers of risk identified for glioma.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Accessible Pipeline for Translational Research Using TCGA: Examples of Relating Gene Mechanism to Disease-Specific Outcomes.

Bioinformatic scientists are often asked to do widespread analyses of publicly available datasets in order to identify genetic alterations in cancer for genes of interest; therefore, we sought to create a set of tools to conduct common statistical analyses of The Cancer Genome Atlas (TCGA) data. These tools have been developed in response to requests from our collaborators to ask questions, validate findings, and better understand the function of their gene of interest. We describe here what data we have used, how to obtain it, and what figures we have found useful.

A prospective study of coffee and tea consumption and the risk of glioma in the UK Biobank.

BACKGROUND: Coffee and tea have been hypothesised to reduce the risk of some cancers; however, their impact on glioma is less well studied. METHODS: We examined associations between self-reported intake of tea and coffee in relation to glioma risk in the UK Biobank. We identified 487 incident glioma cases among 379,259 participants. Hazard ratios (HR) and 95% confidence intervals (CI) for glioma according to caffeinated beverage consumption were calculated using Cox proportional hazards regression with adjustment for age, gender, race and education; daily cups of tea or coffee were included in models considering the other beverage. RESULTS: Consuming 4 or more cups of tea was associated with reduced risk of glioma when compared to no tea consumption (HR = 0.69; 95% CI, 0.51-0.94). A significant inverse association was observed for glioblastoma (HR = 0.93 per 1 cup/d increment; 95% CI, 0.89-0.98) and among males for all gliomas combined (HR = 0.95 per 1 cup/d increment; 95% CI, 0.90-1.00). A suggestive inverse association was also observed with greater consumption of coffee (HR = 0.71; 95% CI, 0.49-1.05 for >4 versus 0 cups/d). Results were not materially changed with further adjustment for smoking, alcohol and body mass index. Associations were similar in 2-year and 3-year lagged analyses. CONCLUSIONS: In this prospective study, we found a significant inverse association between tea consumption and the risk of developing glioma, and a suggestive inverse association for the consumption of coffee. Further investigation on the possible preventive role of caffeine in glioma is warranted.

Commercial Gene Expression Tests for Prostate Cancer Prognosis Provide Paradoxical Estimates of Race-Specific Risk.

BACKGROUND: Commercial gene expression signatures of prostate cancer prognosis were developed and validated in cohorts of predominantly European American men (EAM). Limited research exists on the value of such signatures in African American men (AAM), who have poor prostate cancer outcomes. We explored differences in gene expression between EAM and AAM for three commercially available panels recommended by the National Comprehensive Cancer Network for prostate cancer prognosis. METHODS: A total of 232 EAM and 95 AAM patients provided radical prostatectomy specimens. Gene expression was quantified using NanoString for 60 genes spanning the Oncotype DX Prostate, Prolaris, and Decipher panels. A continuous expression-based risk score was approximated for each. Differential expression, intrapanel coexpression, and risk by race were assessed. RESULTS: Clinical and pathologic features were similar between AAM and EAM. Differential expression by race was observed for 48% of genes measured, although the magnitudes of expression differences were small. Coexpression patterns were more strongly preserved by race group for Oncotype DX and Decipher than Prolaris. Poorer prognosis was estimated in EAM versus AAM for Oncotype DX (P < 0.001), whereas negligible prognostic differences were predicted between AAM and EAM using Prolaris or Decipher (P > 0.05). CONCLUSIONS: Because of observed racial differences across three commercial gene expression panels for prostate cancer prognosis, caution is warranted when applying these panels in clinical decision-making in AAM. IMPACT: Differences in gene expression by race for three commercial panels for prostate cancer prognosis indicate that further study of their effectiveness in AAM with long-term follow-up is warranted.

Methylmercury exposure, genetic variation in metabolic enzymes, and the risk of glioma.

Methylmercury (MeHg) is an environmental neurotoxin with human exposure mainly from dietary intake of contaminated fish. Exposure to MeHg has been implicated in neurological damage, but research on its role in cancers, specifically glioma, is limited. In a glioma case-control study, we examined associations between toenail mercury (Hg) and glioma risk. We also examined genetic polymorphisms in 13 genes related to MeHg metabolism for association with glioma risk; genetic associations were also studied in the UK Biobank cohort. Median toenail Hg in cases and controls, respectively, was 0.066 µg/g and 0.069 µg/g (interquartile range (IQR): 0.032-0.161 and 0.031-0.150 µg/g). Toenail Hg was not found to be significantly associated with glioma risk (Odds Ratio: 1.02; 95% Confidence Interval: 0.91, 1.14; p = 0.70 in analysis for ordinal trend with increasing quartile of toenail MeHg). No genetic variant was statistically significant in both of the studies; one variant, rs11859163 (MMP2) had a combined p-value of 0.02 though it was no longer significant after adjustment for multiple testing (Bonferroni corrected p = 1). This study does not support the hypothesis that exposure to MeHg plays a role in the development of glioma at levels of exposure found in this study population.

epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology.

SUMMARY: Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery. AVAILABILITY AND IMPLEMENTATION: epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/ where we have additionally made publicly available the source code and a Docker containerized version of the application.

Toenail selenium, genetic variation in selenoenzymes and risk and outcome in glioma.

BACKGROUND: Selenium is an essential trace element obtained through diet that plays a critical role in DNA synthesis and protection from oxidative damage. Selenium intake and polymorphisms in selenoproteins have been linked to the risk of certain cancers though data for glioma are sparse. METHODS: In a case-control study of glioma, we examined the associations of selenium in toenails and genetic variants in the selenoenzyme pathway with the risk of glioma and patient survival. A total of 423 genetic variants in 29 candidate genes in the selenoenzyme pathway were studied in 1547 glioma cases and 1014 healthy controls. Genetic associations were also examined in the UK Biobank cohort comprised of 313,868 persons with 322 incident glioma cases. Toenail selenium was measured in a subcohort of 300 glioma cases and 300 age-matched controls from the case-control study. RESULTS: None of the 423 variants studied were consistently associated with glioma risk in the case-control and cohort studies. Moreover, toenail selenium in the case-control study had no significant association with glioma risk (p trend = 0.70) or patient survival among 254 patients with high grade tumors (p trend = 0.70). CONCLUSION: The present study offers no support for the hypothesis that selenium plays a role in the onset of glioma or patient outcome.