Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers.

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.

Socioeconomic status and lung cancer: unraveling the contribution of genetic admixture.

OBJECTIVES: We examined the relationship between genetic ancestry, socioeconomic status (SES), and lung cancer among African Americans and Latinos. METHODS: We evaluated SES and genetic ancestry in a Northern California lung cancer case-control study (1998-2003) of African Americans and Latinos. Lung cancer case and control participants were frequency matched on age, gender, and race/ethnicity. We assessed case-control differences in individual admixture proportions using the 2-sample t test and analysis of covariance. Logistic regression models examined associations among genetic ancestry, socioeconomic characteristics, and lung cancer. RESULTS: Decreased Amerindian ancestry was associated with higher education among Latino control participants and greater African ancestry was associated with decreased education among African lung cancer case participants. Education was associated with lung cancer among both Latinos and African Americans, independent of smoking, ancestry, age, and gender. Genetic ancestry was not associated with lung cancer among African Americans. CONCLUSIONS: Findings suggest that socioeconomic factors may have a greater impact than genetic ancestry on lung cancer among African Americans. The genetic heterogeneity and recent dynamic migration and acculturation of Latinos complicate recruitment; thus, epidemiological analyses and findings should be interpreted cautiously.

CYP1A1/2 haplotypes and lung cancer and assessment of confounding by population stratification.

Prior studies of lung cancer and CYP1A1/2 in African-American and Latino populations have shown inconsistent results and have not yet investigated the haplotype block structure of CYP1A1/2 or addressed potential population stratification. To investigate haplotypes in the CYP1A1/2 region and lung cancer in African-Americans and Latinos, we conducted a case-control study (1998-2003). African-Americans (n = 535) and Latinos (n = 412) were frequency matched on age, sex, and self-reported race/ethnicity. We used a custom genotyping panel containing 50 single nucleotide polymorphisms in the CYP1A1/2 region and 184 ancestry informative markers selected to have large allele frequency differences between Africans, Europeans, and Amerindians. Latinos exhibited significant haplotype main effects in two blocks even after adjusting for admixture [odds ratio (OR), 2.02; 95% confidence interval (95% CI), 1.28-3.19 and OR, 0.55; 95% CI, 0.36-0.83], but no main effects were found among African-Americans. Adjustment for admixture revealed substantial confounding by population stratification among Latinos but not African-Americans. Among Latinos and African-Americans, interactions between smoking level and haplotypes were not statistically significant. Evidence of population stratification among Latinos underscores the importance of adjusting for admixture in lung cancer association studies, particularly in Latino populations. These results suggest that a variant occurring within the CYP1A2 region may be conferring an increased risk of lung cancer in Latinos.

Strengths and limitations of breast cancer risk assessment.

PURPOSE/OBJECTIVES: To evaluate current definitions of breast cancer risk and breast cancer risk assessment models, including the Gail, Claus, and BRCAPRO models, and discuss potential markers to enhance and standardize individual risk assessment. DATA SOURCES: Published articles, conference proceedings, and textbooks. DATA SYNTHESIS: Defining high risk for breast cancer development is explored, and options for high-risk women are discussed. The risk factors frequently used for risk evaluation, including age, age at menarche, age at first live birth, past history of breast biopsy, family history of breast cancer, and the presence of atypical hyperplasia, are reviewed. CONCLUSIONS: Current models of breast cancer risk assessment are limited. Exploring the progression from healthy tissue to malignancy through techniques such as fine needle aspiration, ductal lavage, and nipple aspiration may lead to more precise individualized risk prediction. IMPLICATIONS FOR NURSING: More accurate information regarding personal breast cancer risk is necessary. Oncology nurses may facilitate the use of appropriate tools that provide the most individualized risk assessment.

Nipple aspirate fluid cytology and the Gail model for breast cancer risk assessment in a screening population.

BACKGROUND: Recent guidelines suggest that chemoprevention with tamoxifen may be appropriate for women who have a 5-year risk of breast cancer greater than 1.66% calculated using the Gail model. OBJECTIVES: To determine whether nipple aspirate fluid (NAF) cytology combined with the Gail model provides breast cancer risk assessment that is superior to either method alone. METHODS: Prospective observational cohort of 6,904 asymptomatic women. Breast cancer cases were identified through follow-up with the women and linkage to cancer registries. We used proportional hazards modeling to recalculate the coefficients for the predictor variables used in the Gail model. NAF cytology was added to create a second model. The two models were compared using the concordance statistic (c-statistic). RESULTS: During 14.6 years of follow-up, 400 women were diagnosed with breast cancer. There were 940 (14%) women with hyperplasia and 109 (1.6%) women with atypical hyperplasia found in NAF. Adding NAF cytology results to the Gail model significantly improved the model fit (P < 0.0001). The c-statistic for the Gail model was 0.62, indicating only modest discriminatory accuracy. Adding NAF cytology to the model increased the c-statistic to 0.64. NAF cytology results had the largest effect on discriminatory accuracy among women in the upper third of Gail model risk. The relative incidence for the highest quintile of risk score compared with the lowest quintile was 7.2 for the Gail model and 8.0 for the model including NAF cytology. CONCLUSION: NAF cytology has the potential to improve prediction models of breast cancer incidence, particularly for high-risk women.