Tobacco smoking, polymorphisms in carcinogen metabolism enzyme genes, and risk of localized and advanced prostate cancer: results from the California Collaborative Prostate Cancer Study

The relationship between tobacco smoking and prostate cancer (PCa) remains inconclusive. This study examined the association between tobacco smoking and PCa risk taking into account polymorphisms in carcinogen metabolism enzyme genes as possible effect modifiers (9 polymorphisms and 1 predicted phenotype from metabolism enzyme genes). The study included cases (n = 761 localized; n = 1199 advanced) and controls (n = 1139) from the multiethnic California Collaborative Case–Control Study of Prostate Cancer. Multivariable conditional logistic regression was performed to evaluate the association between tobacco smoking variables and risk of localized and advanced PCa risk. Being a former smoker, regardless of time of quit smoking, was associated with an increased risk of localized PCa (odds ratio [OR] = 1.3; 95% confidence interval [CI] = 1.0–1.6). Among non-Hispanic Whites, ever smoking was associated with an increased risk of localized PCa (OR = 1.5; 95% CI = 1.1–2.1), whereas current smoking was associated with risk of advanced PCa (OR = 1.4; 95% CI = 1.0–1.9). However, no associations were observed between smoking intensity, duration or pack-year variables, and advanced PCa. No statistically significant trends were seen among Hispanics or African-Americans. The relationship between smoking status and PCa risk was modified by the CYP1A2 rs7662551 polymorphism (P-interaction = 0.008). In conclusion, tobacco smoking was associated with risk of PCa, primarily localized disease among non-Hispanic Whites. This association was modified by a genetic variant in CYP1A2, thus supporting a role for tobacco carcinogens in PCa risk

Polymorphisms in genes involved in estrogen and progesterone metabolism and mammographic density changes in women randomized to postmenopausal hormone therapy: results from a pilot study

INTRODUCTION: Mammographic density is a strong independent risk factor for breast cancer, and can be modified by hormonal exposures. Identifying genetic variants that determine increases in mammographic density in hormone users may be important in understanding hormonal carcinogenesis of the breast. METHODS: We obtained mammograms and DNA from 232 postmenopausal women aged 45 to 75 years who had participated in one of two randomized, double-blind clinical trials with estrogen therapy (104 women, taking 1 mg/day of micronized 17β-estradiol, E2), combined estrogen and progestin therapy (34 women, taking 17β-estradiol and 5 mg/day of medroxyprogesterone acetate for 12 days/month) or matching placebos (94 women). Mammographic percentage density (MPD) was measured on baseline and 12-month mammograms with a validated computer-assisted method. We evaluated polymorphisms in genes involved in estrogen metabolism (catechol-O-methyltransferase (COMT (Val158Met)), cytochrome P450 1B1 (CYP1B1 (Val432Leu)), UDP-glucuronosyltransferase 1A1 (UGT1A1 (<7/≥ 7 TA repeats))) and progesterone metabolism (aldo-keto reductase 1C4 (AKR1C4 (Leu311Val))) with changes in MPD. RESULTS: The adjusted mean change in MPD was +4.6% in the estrogen therapy arm and +7.2% in the combined estrogen and progestin therapy arm, compared with +0.02% in the placebo arm (P = 0.0001). None of the genetic variants predicted mammographic density changes in women using estrogen therapy. Both the AKR1C4 and the CYP1B1 polymorphisms predicted mammographic density change in the combined estrogen and progestin therapy group (P < 0.05). In particular, the eight women carrying one or two low-activity AKR1C4 Val alleles showed a significantly greater increase in MPD (16.7% and 29.3%) than women homozygous for the Leu allele (4.0%). CONCLUSION: Although based on small numbers, these findings suggest that the magnitude of the increase in mammographic density in women using combined estrogen and progestin therapy may be greater in those with genetically determined lower activity of enzymes that metabolize estrogen and progesterone

Leveraging population admixture to characterize the heritability of complex traits.

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2)

Two Novel Susceptibility Loci for Prostate Cancer in Men of African Ancestry

Prostate cancer incidence is 1.6-fold higher in African Americans than in other populations. The risk factors that drive this disparity are unknown and potentially consist of social, environmental, and genetic influences. To investigate the genetic basis of prostate cancer in men of African ancestry, we performed a genome-wide association meta-analysis using two-sided statistical tests in 10 202 case subjects and 10 810 control subjects. We identified novel signals on chromosomes 13q34 and 22q12, with the risk-associated alleles found only in men of African ancestry (13q34: rs75823044, risk allele frequency = 2.2%, odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.37 to 1.76, P = 6.10 × 10−12; 22q12.1: rs78554043, risk allele frequency = 1.5%, OR = 1.62, 95% CI = 1.39 to 1.89, P = 7.50 × 10−10). At 13q34, the signal is located 5’ of the gene IRS2 and 3’ of a long noncoding RNA, while at 22q12 the candidate functional allele is a missense variant in the CHEK2 gene. These findings provide further support for the role of ancestry-specific germline variation in contributing to population differences in prostate cancer risk

Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P \u3c 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants

Genetic variants in microRNA and microRNA biogenesis pathway genes and breast cancer risk among women of African ancestry

MicroRNAs (miRNA) regulate breast biology by binding to specific RNA sequences, leading to RNA degradation and inhibition of translation of their target genes. While germline genetic variations may disrupt some of these interactions between miRNAs and their targets, studies assessing the relationship between genetic variations in the miRNA network and breast cancer risk are still limited, particularly among women of African ancestry

Fine-mapping identifies multiple prostate cancer risk loci at 5p15, one of which associates with TERT expression

Associations between single nucleotide polymorphisms (SNPs) at 5p15 and multiple cancer types have been reported. We have previously shown evidence for a strong association between prostate cancer (PrCa) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (TERT) gene that encodes TERT. To comprehensively evaluate the association between genetic variation across this region and PrCa, we performed a fine-mapping analysis by genotyping 134 SNPs using a custom Illumina iSelect array or Sequenom MassArray iPlex, followed by imputation of 1094 SNPs in 22 301 PrCa cases and 22 320 controls in The PRACTICAL consortium. Multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of TERT that independently associated with PrCa risk. Gene expression analysis of normal prostate tissue showed evidence that SNPs within one of these regions also associated with TERT expression, providing a potential mechanism for predisposition to disease