A Flexible Bayesian Model for Studying Gene–Environment Interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant

A regression model for risk difference estimation in population-based case-control studies clarifies gender differences in lung cancer risk of smokers and never smokers

BACKGROUND: Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case-control studies. METHODS: Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case-control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002-2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables. RESULTS: In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons. CONCLUSIONS: In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case-control studies

Lung Cancer and Occupation in a Population-based Case-Control Study

The authors examined the relation between occupation and lung cancer in the large, population-based Environment And Genetics in Lung cancer Etiology (EAGLE) case-control study. In 2002–2005 in the Lombardy region of northern Italy, 2,100 incident lung cancer cases and 2,120 randomly selected population controls were enrolled. Lifetime occupational histories (industry and job title) were coded by using standard international classifications and were translated into occupations known (list A) or suspected (list B) to be associated with lung cancer. Smoking-adjusted odds ratios and 95% confidence intervals were calculated with logistic regression. For men, an increased risk was found for list A (177 exposed cases and 100 controls; odds ratio = 1.74, 95% confidence interval: 1.27, 2.38) and most occupations therein. No overall excess was found for list B with the exception of filling station attendants and bus and truck drivers (men) and launderers and dry cleaners (women). The authors estimated that 4.9% (95% confidence interval: 2.0, 7.8) of lung cancers in men were attributable to occupation. Among those in other occupations, risk excesses were found for metal workers, barbers and hairdressers, and other motor vehicle drivers. These results indicate that past exposure to occupational carcinogens remains an important determinant of lung cancer occurrence

Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies

Despite genetics being accepted as the primary cause of familial aggregation for most diseases, it is still unclear whether afflicted families are likely to share a single highly penetrant rare variant, many minimally penetrant common variants, or a combination of the two types of variants. We therefore use recent estimates of SNP heritability and the liability threshold model to estimate the proportion of afflicted families likely to carry a rare, causal variant. We then show that Polygenic Risk Scores (PRS) may be useful for identifying families likely to carry such a rare variant and therefore for prioritizing families to include in sequencing studies with that aim. Specifically, we introduce a new statistic that estimates the proportion of individuals carrying causal rare variants based on the family structure, disease pattern, and PRS of genotyped individuals. Finally, we consider data from the MelaNostrum consortium and show that, despite an estimated PRS heritability of only 0.05 for melanoma, families carrying putative causal variants had a statistically significantly lower PRS, supporting the idea that PRS prioritization may be a useful future tool. However, it will be important to evaluate whether the presence of rare mendelian variants are generally associated with the proposed test statistic or lower PRS in future and larger studies

Contribution of Common Genetic Variants to Familial Aggregation of Disease and Implications for Sequencing Studies

Despite genetics being accepted as the primary cause of familial aggregation for most diseases, it is still unclear whether afflicted families are likely to share a single highly penetrant rare variant, many minimally penetrant common variants, or a combination of the two types of variants. We therefore use recent estimates of SNP heritability and the liability threshold model to estimate the proportion of afflicted families likely to carry a rare, causal variant. We then show that Polygenic Risk Scores (PRS) may be useful for identifying families likely to carry such a rare variant and therefore for prioritizing families to include in sequencing studies with that aim. Specifically, we introduce a new statistic that estimates the proportion of individuals carrying causal rare variants based on the family structure, disease pattern, and PRS of genotyped individuals. Finally, we consider data from the MelaNostrum consortium and show that, despite an estimated PRS heritability of only 0.05 for melanoma, families carrying putative causal variants had a statistically significantly lower PRS, supporting the idea that PRS prioritization may be a useful future tool. However, it will be important to evaluate whether the presence of rare mendelian variants are generally associated with the proposed test statistic or lower PRS in future and larger studies

Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA

Clustered somatic mutations are common in cancer genomes and previous analyses reveal several types of clustered single-base substitutions, which include doublet- and multi-base substitutions1–5, diffuse hypermutation termed omikli6, and longer strand-coordinated events termed kataegis3,7–9. Here we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome-sequenced cancers from 30 types of cancer10. Clustered mutations were highly enriched in driver genes and associated with differential gene expression and changes in overall survival. Several distinct mutational processes gave rise to clustered indels, including signatures that were enriched in tobacco smokers and homologous-recombination-deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, whereas most multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, which have previously been attributed to APOBEC3 activity6, accounted for a large proportion of clustered substitutions; however, only 16.2% of omikli matched APOBEC3 patterns. Kataegis was generated by multiple mutational processes, and 76.1% of all kataegic events exhibited mutational patterns that are associated with the activation-induced deaminase (AID) and APOBEC3 family of deaminases. Co-occurrence of APOBEC3 kataegis and extrachromosomal DNA (ecDNA), termed kyklonas (Greek for cyclone), was found in 31% of samples with ecDNA. Multiple distinct kyklonic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kyklonic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fuelling the evolution of ecDNA

Characterizing the tumor microenvironment in rare renal cancer histological types

The tumor microenvironment (TME), including immune cells, cancer-associated fibroblasts, endothelial cells, adjacent normal cells, and others, plays a crucial role in influencing tumor behavior and progression. Here, we characterized the TME in 83 primary renal tumors and matched metastatic or recurrence tissue samples (n = 15) from papillary renal cell carcinoma (pRCC) types 1 (n = 20) and 2 (n = 49), collecting duct carcinomas (CDC; n = 14), and high-grade urothelial carcinomas (HGUC; n = 5). We investigated 10 different markers of immune infiltration, vasculature, cell proliferation, and epithelial-to-mesenchymal transition by using machine learning image analysis in conjunction with immunohistochemistry. Marker expression was compared by Mann-Whitney and Kruskal-Wallis tests and correlations across markers using Spearman's rank correlation coefficient. Multivariable Poisson regression analysis was used to compare marker expression between histological types, while accounting for variation in tissue size. Several immune markers showed different rates of expression across histological types of renal carcinoma. Using pRCC1 as reference, the incidence rate ratio (IRR) of CD3+ T cells (IRR [95% confidence interval, CI] = 2.48 [1.53-4.01]) and CD20+ B cells (IRR [95% CI] = 4.38 [1.22-5.58]) was statistically significantly higher in CDC. In contrast, CD68+ macrophages predominated in pRCC1 (IRR [95% CI] = 2.35 [1.42-3.9]). Spatial analysis revealed CD3+ T-cell and CD20+ B-cell expressions in CDC to be higher at the proximal (p < 0.0001) and distal (p < 0.0001) tumor periphery than within the central tumor core. In contrast, expression of CD68+ macrophages in pRCC2 was higher in the tumor center compared to the proximal (p = 0.0451) tumor periphery and pRCC1 showed a distance-dependent reduction, from the central tumor, in CD68+ macrophages with the lowest expression of CD68 marker at the distal tumor periphery (p = 0.004). This study provides novel insights into the TME of rare kidney cancer types, which are often understudied. Our findings of differences in marker expression and localization by histological subtype could have implications for tumor progression and response to immunotherapies or other targeted therapies

SLC22A3 polymorphisms do not modify pancreatic cancer risk, but may influence overall patient survival

Expression of the solute carrier (SLC) transporter SLC22A3 gene is associated with overall survival of pancreatic cancer patients. This study tested whether genetic variability in SLC22A3 associates with pancreatic cancer risk and prognosis. Twenty four single nucleotide polymorphisms (SNPs) tagging the SLC22A3 gene sequence and regulatory elements were selected for analysis. Of these, 22 were successfully evaluated in the discovery phase while six significant or suggestive variants entered the validation phase, comprising a total study number of 1,518 cases and 3,908 controls. In the discovery phase, rs2504938, rs9364554, and rs2457571 SNPs were significantly associated with pancreatic cancer risk. Moreover, rs7758229 associated with the presence of distant metastases, while rs512077 and rs2504956 correlated with overall survival of patients. Although replicated, the association for rs9364554 did not pass multiple testing corrections in the validation phase. Contrary to the discovery stage, rs2504938 associated with survival in the validation cohort, which was more pronounced in stage IV patients. In conclusion, common variation in the SLC22A3 gene is unlikely to significantly contribute to pancreatic cancer risk. The rs2504938 SNP in SLC22A3 significantly associates with an unfavorable prognosis of pancreatic cancer patients. Further investigation of this SNP effect on the molecular and clinical phenotype is warranted

On the Interplay of Telomeres, Nevi and the Risk of Melanoma

The relationship between telomeres, nevi and melanoma is complex. Shorter telomeres have been found to be associated with many cancers and with number of nevi, a known risk factor for melanoma. However, shorter telomeres have also been found to decrease melanoma risk. We performed a systematic analysis of telomere-related genes and tagSNPs within these genes, in relation to the risk of melanoma, dysplastic nevi, and nevus count combining data from four studies conducted in Italy. In addition, we examined whether telomere length measured in peripheral blood leukocytes is related to the risk of melanoma, dysplastic nevi, number of nevi, or telomere-related SNPs. A total of 796 cases and 770 controls were genotyped for 517 SNPs in 39 telomere-related genes genotyped with a custom-made array. Replication of the top SNPs was conducted in two American populations consisting of 488 subjects from 53 melanoma-prone families and 1,086 cases and 1,024 controls from a case-control study. We estimated odds ratios for associations with SNPs and combined SNP P-values to compute gene region-specific, functional group-specific, and overall P-value using an adaptive rank-truncated product algorithm. In the Mediterranean population, we found suggestive evidence that RECQL4, a gene involved in genome stability, RTEL1, a gene regulating telomere elongation, and TERF2, a gene implicated in the protection of telomeres, were associated with melanoma, the presence of dysplastic nevi and number of nevi, respectively. However, these associations were not found in the American samples, suggesting variable melanoma susceptibility for these genes across populations or chance findings in our discovery sample. Larger studies across different populations are necessary to clarify these associations