Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort.

In pre-disposed individuals, a reprogramming of the hepatic lipid metabolism may support liver cancer initiation. We conducted a high-resolution mass spectrometry based untargeted lipidomics analysis of pre-diagnostic serum samples from a nested case-control study (219 liver cancer cases and 219 controls) within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Out of 462 annotated lipids, 158 (34.2%) were associated with liver cancer risk in a conditional logistic regression analysis at a false discovery rate (FDR) <0.05. A chemical set enrichment analysis (ChemRICH) and co-regulatory set analysis suggested that 22/28 lipid classes and 47/83 correlation modules were significantly associated with liver cancer risk (FDR <0.05). Strong positive associations were observed for monounsaturated fatty acids (MUFA), triacylglycerols (TAGs) and phosphatidylcholines (PCs) having MUFA acyl chains. Negative associations were observed for sphingolipids (ceramides and sphingomyelins), lysophosphatidylcholines, cholesterol esters and polyunsaturated fatty acids (PUFA) containing TAGs and PCs. Stearoyl-CoA desaturase enzyme 1 (SCD1), a rate limiting enzyme in fatty acid metabolism and ceramidases seems to be critical in this reprogramming. In conclusion, our study reports pre-diagnostic lipid changes that provide novel insights into hepatic lipid metabolism reprogramming may contribute to a pro-cell growth and anti-apoptotic tissue environment and, in turn, support liver cancer initiation.

PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF.

We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.

Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients.

Survival rates for osteosarcoma, the most common primary bone cancer, have changed little over the past three decades and are particularly low for patients with metastatic disease. We conducted a multi-institutional genome-wide association study (GWAS) to identify germline genetic variants associated with overall survival in 632 patients with osteosarcoma, including 523 patients of European ancestry and 109 from Brazil. We conducted a time-to-event analysis and estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional hazards models, with and without adjustment for metastatic disease. The results were combined across the European and Brazilian case sets using a random-effects meta-analysis. The strongest association after meta-analysis was for rs3765555 at 9p24.1, which was inversely associated with overall survival (HR = 1.76; 95% CI 1.41-2.18, p = 4.84 × 10-7 ). After imputation across this region, the combined analysis identified two SNPs that reached genome-wide significance. The strongest single association was with rs55933544 (HR = 1.9; 95% CI 1.5-2.4; p = 1.3 × 10-8 ), which localizes to the GLDC gene, adjacent to the IL33 gene and was consistent across both the European and Brazilian case sets. Using publicly available data, the risk allele was associated with lower expression of IL33 and low expression of IL33 was associated with poor survival in an independent set of patients with osteosarcoma. In conclusion, we have identified the GLDC/IL33 locus on chromosome 9p24.1 as associated with overall survival in patients with osteosarcoma. Further studies are needed to confirm this association and shed light on the biological underpinnings of this susceptibility locus.

A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.

Genome-Wide Association Study to Identify Susceptibility Loci That Modify Radiation-Related Risk for Breast Cancer After Childhood Cancer.

Background: Childhood cancer survivors treated with chest-directed radiotherapy have substantially elevated risk for developing breast cancer. Although genetic susceptibility to breast cancer in the general population is well studied, large-scale evaluation of breast cancer susceptibility after chest-directed radiotherapy for childhood cancer is lacking. Methods: We conducted a genome-wide association study of breast cancer in female survivors of childhood cancer, pooling two cohorts with detailed treatment data and systematic, long-term follow-up: the Childhood Cancer Survivor Study and St. Jude Lifetime Cohort. The study population comprised 207 survivors who developed breast cancer and 2774 who had not developed any subsequent neoplasm as of last follow-up. Genotyping and subsequent imputation yielded 16 958 466 high-quality variants for analysis. We tested associations in the overall population and in subgroups stratified by receipt of lower than 10 and 10 or higher gray breast radiation exposure. We report P values and pooled per-allele risk estimates from Cox proportional hazards regression models. All statistical tests were two-sided. Results: Among survivors who received 10 or higher gray breast radiation exposure, a locus on 1q41 was associated with subsequent breast cancer risk (rs4342822, nearest gene PROX1 , risk allele frequency in control subjects [RAF controls ] = 0.46, hazard ratio = 1.92, 95% confidence interval = 1.49 to 2.44, P = 7.09 × 10 -9 ). Two rare variants also showed potentially promising associations (breast radiation ≥10 gray: rs74949440, 11q23, TAGLN , RAF controls = 0.02, P = 5.84 × 10 -8 ; <10 gray: rs17020562, 1q32.3, RPS6KC1 , RAF controls = 0.0005, P = 6.68 × 10 -8 ). Associations were restricted to these dose subgroups, with consistent findings in the two survivor cohorts. Conclusions: Our study provides strong evidence that germline genetics outside high-risk syndromes could modify the effect of radiation exposure on breast cancer risk after childhood cancer.

Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types.

BACKGROUND: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. METHODS: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. RESULTS: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. CONCLUSION: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.

Geographic heterogeneity of prevalence of the human herpesvirus 8 in sub-Saharan Africa: clues about etiology.

PURPOSE: Human herpesvirus 8 (HHV-8, or Kaposi sarcoma [KS]-associated herpesvirus, KSHV) is a necessary but insufficient cause of KS, as KS develops in few HHV-8-infected persons. In sub-Saharan Africa, marked differences in the geographic distribution of HHV-8 and KS suggest that environmental cofactors influence HHV-8 transmission, control, and progression to KS. However, a direct comparison of HHV-8 prevalence estimates is complicated because studies used different serologic assays and analytic methods. We assessed HHV-8 seropositivity in several African countries with heterogeneous environments and varying KS incidence using a unified approach. METHODS: HHV-8 antibodies were measured among 3196 adults (aged 20+ years) and 2404 children (aged <20 years) from five studies in four sub-Saharan countries in Africa. Serum samples were tested by the same laboratory using K8.1 and orf73 enzyme immunoassays. RESULTS: Children's HHV-8-seropositivity ranged from 18.1% in Kampala, Uganda, to 33.8% in North Mara, Tanzania, increasing steeply with age in all populations. Among adults, HHV-8-seropositivity ranged from 23.5% in Nigeria to 70.6% in rural West Nile, Uganda. It was higher in males and rural areas. CONCLUSIONS: Our data indicate that geographical exposures, gender, age, and factors correlated with rural residence impact HHV-8 seropositivity in sub-Saharan Africa.