A candidate gene approach to searching for low-penetrance breast and prostate cancer genes

CYP2C8 rs11572080 and CYP3A4 rs2740574 risk genotypes in paclitaxel-treated premenopausal breast cancer patients

Breast cancer (BC) is the most prevalent malignancy in women globally. At time of diagnosis, premenopausal BC is considered more aggressive and harder to treat than postmenopausal cases. Cytochrome P450 (CYP) enzymes are responsible for phase I of estrogen metabolism and thus, they are prominently involved in the pathogenesis of BC. Moreover, CYP subfamily 2C and 3A play a pivotal role in the metabolism of taxane anticancer agents. To understand genetic risk factors that may have a role in pre-menopausal BC we studied the genotypic variants of CYP2C8, rs11572080 and CYP3A4, rs2740574 in female BC patients on taxane-based therapy and their association with menopausal status. Our study comprised 105 female patients with histologically proven BC on paclitaxel-therapy. They were stratified into pre-menopausal (n = 52, 49.5%) and post-menopausal (n = 53, 50.5%) groups. Genotyping was done using TaqMan assays and employed on Quantstudio 12 K flex real-time platform. Significant increased frequencies of rs11572080 heterozygous CT genotype and variant T allele were established in pre-menopausal group compared to post-menopausal group (p = 0.023, 0.01, respectively). Moreover, logistic regression analysis revealed a significant association between rs11572080 CT genotype and premenopausal BC. However, regarding rs2740574, no significant differences in genotypes and allele frequencies between both groups were detected. We reported a significant association between CYP2C8 genotypic variants and premenopausal BC risk in Egyptian females. Further studies on larger sample sizes are still needed to evaluate its importance in early prediction of BC in young women and its effect on treatment outcome.

The Significance of Tumor Microenvironment Score for Breast Cancer Patients

Purpose

This study was designed to clarify the prognostic value of tumor microenvironment score and abnormal genomic alterations in TME for breast cancer patients.

Method

The TCGA-BRCA data were downloaded from TCGA and analyzed with R software. The results from analyses were further validated using the dataset from GSE96058, GSE124647, and GSE25066.

Results

After analyzing the TCGA data and verifying it with the GEO data, we developed a TMEscore model based on the TME infiltration pattern and validated it in 3273 breast cancer patients. The results suggested that our TMEscore model has high prognostic value. TME features with the TMEscore model can help to predict breast cancer patients' response to immunotherapy and provide new strategies for breast cancer treatment. Signature 24 was first found in breast cancer. In focal SCNAs, a total of 95 amplified genes and 169 deletion genes in the TMEscore high group were found to be significantly related to the prognosis of breast cancer patients, while 61 amplified genes and 174 deletion genes in the TMEscore low group were identified. LRRC48, CFAP69, and cg25726128 were first discovered and reported to be related to the survival of breast cancer patients. We identified specific mutation signatures that correlate with TMEscore and prognosis.

Conclusion

TMEscore model has high predictive value regarding prognosis and patients' response to immunotherapy. Signature 24 was first found in breast cancer. Specific mutation signatures that correlate with TMEscore and prognosis might be used for providing additional indicators for disease evaluation.

Discovery of primary prostate cancer biomarkers using cross cancer learning

Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of life effects. Further, there is ever increasing evidence of metastasis and higher mortality when hormone-sensitive or castration-resistant PCa tumors are treated indistinctively. Hence, the critical need is to discover clinically-relevant and actionable PCa biomarkers by better understanding the biology of PCa. In this paper, we have discovered novel biomarkers of PCa tumors through cross-cancer learning by leveraging the pathological and molecular similarities in the DNA repair pathways of ovarian, prostate, and breast cancer tumors. Cross-cancer disease learning enriches the study population and identifies genetic/phenotypic commonalities that are important across diseases with pathological and molecular similarities. Our results show that ADIRF, SLC2A5, C3orf86, HSPA1B are among the most significant PCa biomarkers, while MTRNR2L1, EEPD1, TEPP and VN1R2 are jointly important biomarkers across prostate, breast and ovarian cancers. Our validation results have further shown that the discovered biomarkers can predict the disease state better than any randomly selected subset of differentially expressed prostate cancer genes.

Integrative genomic analysis implicates ERCC6 and its interaction with ERCC8 in susceptibility to breast cancer

Up to 30% of all breast cancer cases may be inherited and up to 85% of those may be due to segregation of susceptibility genes with low and moderate risk [odds ratios (OR) ≤ 3] for (mostly peri- and post-menopausal) breast cancer. The majority of low/moderate-risk genes, particularly those with minor allele frequencies (MAF) of < 30%, have not been identified and/or validated due to limitations of conventional association testing approaches, which include the agnostic nature of Genome Wide Association Studies (GWAS). To overcome these limitations, we used a hypothesis-driven integrative genomics approach to test the association of breast cancer with candidate genes by analyzing multi-omics data. Our candidate-gene association analyses of GWAS datasets suggested an increased risk of breast cancer with ERCC6 (main effect: 1.29 ≤ OR ≤ 2.91, 0.005 ≤ p ≤ 0.04, 11.8 ≤ MAF ≤ 40.9%), and implicated its interaction with ERCC8 (joint effect: 3.03 ≤ OR ≤ 5.31, 0.01 ≤ pinteraction ≤ 0.03). We found significant upregulation of ERCC6 (p = 7.95 × 10-6) and ERCC8 (p = 4.67 × 10-6) in breast cancer and similar frequencies of ERCC6 (1.8%) and ERCC8 (0.3%) mutations in breast tumors to known breast cancer susceptibility genes such as BLM (1.9%) and LSP1 (0.3%). Our integrative genomics approach suggests that ERCC6 may be a previously unreported low- to moderate-risk breast cancer susceptibility gene, which may also interact with ERCC8.

Breast and prostate cancers harbor common somatic copy number alterations that consistently differ by race and are associated with survival

Background

Pan-cancer studies of somatic copy number alterations (SCNAs) have demonstrated common SCNA patterns across cancer types, but despite demonstrable differences in aggressiveness of some cancers by race, pan-cancer SCNA variation by race has not been explored. This study investigated a) racial differences in SCNAs in both breast and prostate cancer, b) the degree to which they are shared across cancers, and c) the impact of these shared, race-differentiated SCNAs on cancer survival.

Methods

Utilizing data from The Cancer Genome Atlas (TCGA), SCNAs were identified using GISTIC 2.0, and in each tumor type, differences in SCNA magnitude between African Americans (AA) and European Americans (EA) were tested using linear regression. Unsupervised hierarchical clustering of the copy number of genes residing in race-differentiated SCNAs shared between tumor types was used to identify SCNA-defined patient groups, and Cox proportional hazards regression was used to test for association between those groups and overall/progression-free survival (PFS).

Results

We identified SCNAs that differed by race in breast (n = 58 SCNAs; permutation p < 10^− 4) and prostate tumors (n = 78 SCNAs; permutation p = 0.006). Six race-differentiated SCNAs common to breast and prostate found at chromosomes 5q11.2-q14.1, 5q15-q21.1, 8q21.11-q21.13, 8q21.3-q24.3, 11q22.3, and 13q12.3-q21.3 had consistent differences by race across both tumor types, and all six were of higher magnitude in AAs, with the chromosome 8q regions being the only amplifications. Higher magnitude copy number differences in AAs were also identified at two of these race-differentiated SCNAs in two additional hormonally-driven tumor types: endometrial (8q21.3-q24.3 and 13q12.3-q21.3) and ovarian (13q12.3-q21.3) cancers. Race differentiated SCNA-defined patient groups were significantly associated with survival differences in both cancer types, and these groups also differentiated within triple negative breast cancers based on PFS. While the frequency of the SCNA-defined patient groups differed by race, their effects on survival did not.

Conclusions

This study identified race-differentiated SCNAs shared by two related cancers. The association of SCNA-defined patient groups with survival demonstrates the clinical significance of combinations of these race-differentiated genomic aberrations, and the higher frequency of these alterations in AA relative to EA patients may explain racial disparities in risk of aggressive breast and prostate cancer.

Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer's and cancer

INTRODUCTION

We compared genetic variants between Alzheimer's disease (AD) and two age-related cancers-breast and prostate -to identify single-nucleotide polymorphisms (SNPs) that are associated with inverse comorbidity of AD and cancer.

METHODS

Bayesian multinomial regression was used to compare sex-stratified cases (AD and cancer) against controls in a two-stage study. A ±500 KB region around each replicated hit was imputed and analyzed after merging individuals from the two stages. The microRNAs (miRNAs) that target the genes involving these SNPs were analyzed for miRNA family enrichment.

RESULTS

We identified 137 variants with inverse odds ratios for AD and cancer located on chromosomes 19, 4, and 5. The mapped miRNAs within the network were enriched for miR-17 and miR-515 families.

DISCUSSION

The identified SNPs were rs4298154 (intergenic), within TOMM40/APOE/APOC1, MARK4, CLPTM1, and near the VDAC1/FSTL4 locus. The miRNAs identified in our network have been previously reported to have inverse expression in AD and cancer.

Electrophoretic mobility shift assays implicate XRCC2:rs3218550C>T as a potential low-penetrant susceptibility allele for sporadic breast cancer

OBJECTIVE

A previous study undertaken at our centre to identify common genetic variants associated with sporadic breast cancer in Sri Lankan women showed that the T allele of rs3218550, located in the 3'untranslated region of X-ray repair cross-complementing gene-2 (XRCC2), increased breast cancer risk by 1.5-fold. Dual luciferase reporter assays performed in MCF-7 breast cancer cells showed a putative transcriptional repressor effect exerted mainly by the T allele. Electrophoretic mobility shift assays were conducted to further investigate the interaction of this variant with DNA-binding protein, using nuclear protein extracts derived from MCF-7 cells.

RESULTS

An allele-specific differential binding was observed. The T allele resulted in differential DNA-protein complex binding as evidenced by the presence of multiple bands of increased intensity compared to the wild-type C allele. This implies possible alteration in binding of regulatory proteins by the variant allele. These results implicate XRCC2:rs3218550C>T as a potential low-penetrant susceptibility allele for sporadic breast cancer. XRCC2 is known to play an essential role in homologous recombination repair of DNA double-strand breaks. It is plausible that this variant may be exerting regulatory effects on XRCC2 gene expression leading to altered DNA repair capacity. Further functional studies are warranted to validate this finding.

The role of plasma microseminoprotein-beta in prostate cancer: an observational nested case-control and Mendelian randomization study in the European prospective investigation into cancer and nutrition

BACKGROUND

Microseminoprotein-beta (MSP), a protein secreted by the prostate epithelium, may have a protective role in the development of prostate cancer. The only previous prospective study found a 2% reduced prostate cancer risk per unit increase in MSP. This work investigates the association of MSP with prostate cancer risk using observational and Mendelian randomization (MR) methods.

PATIENTS AND METHODS

A nested case-control study was conducted with the European Prospective Investigation into Cancer and Nutrition (EPIC) with 1871 cases and 1871 matched controls. Conditional logistic regression analysis was used to investigate the association of pre-diagnostic circulating MSP with risk of incident prostate cancer overall and by tumour subtype. EPIC-derived estimates were combined with published data to calculate an MR estimate using two-sample inverse-variance method.

RESULTS

Plasma MSP concentrations were inversely associated with prostate cancer risk after adjusting for total prostate-specific antigen concentration [odds ratio (OR) highest versus lowest fourth of MSP = 0.65, 95% confidence interval (CI) 0.51-0.84, Ptrend = 0.001]. No heterogeneity in this association was observed by tumour stage or histological grade. Plasma MSP concentrations were 66% lower in rs10993994 TT compared with CC homozygotes (per allele difference in MSP: 6.09 ng/ml, 95% CI 5.56-6.61, r2=0.42). MR analyses supported a potentially causal protective association of MSP with prostate cancer risk (OR per 1 ng/ml increase in MSP for MR: 0.96, 95% CI 0.95-0.97 versus EPIC observational: 0.98, 95% CI 0.97-0.99). Limitations include lack of complete tumour subtype information and more complete information on the biological function of MSP.

CONCLUSIONS

In this large prospective European study and using MR analyses, men with high circulating MSP concentration have a lower risk of prostate cancer. MSP may play a causally protective role in prostate cancer.

Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities

Collaborative study designs (CSDs) that combine individual-level data from multiple independent contributing studies (ICSs) are becoming much more common due to their many advantages: increased statistical power through large sample sizes; increased ability to investigate effect heterogeneity due to diversity of participants; cost-efficiency through capitalizing on existing data; and ability to foster cooperative research and training of junior investigators. CSDs also present surmountable political, logistical and methodological challenges. Data harmonization may result in a reduced set of common data elements, but opportunities exist to leverage heterogeneous data across ICSs to investigate measurement error and residual confounding. Combining data from different study designs is an art, which motivates methods development. Diverse study samples, both across and within ICSs, prompt questions about the generalizability of results from CSDs. However, CSDs present unique opportunities to describe population health across person, place and time in a consistent fashion, and to explicitly generalize results to target populations of public health interest. Additional analytic challenges exist when analysing CSD data, because mechanisms by which systematic biases (e.g. information bias, confounding bias) arise may vary across ICSs, but multidisciplinary research teams are ready to tackle these challenges. CSDs are a powerful tool that, when properly harnessed, permits research that was not previously possible.

Association between the insulin-like growth factor 1 gene rs2195239 and rs2162679 polymorphisms and cancer risk: a meta-analysis

Background

Many epidemiological studies have suggested that insulin-like growth factor1 (IGF1) gene single-nucleotide polymorphisms (SNPs) may be associated with cancer risk. Among several commonly studied polymorphisms in IGF1 gene, rs2195239 and rs2162679 attracted many attentions. So we perform a meta-analysis to determine potential associations between IGF1 rs2195239 and rs2162679 polymorphisms and cancer risk.

Methods

We retrieved relevant articles from the PubMed, Embase, and Web of Science databases up to April 30, 2018. Ultimately, thirteen studies were included in the present meta-analysis, which involved 12,515 cases and 19,651 controls. The odd ratios (ORs) and their 95% confidence intervals (CIs) were pooled to estimate the strength of the associations.

Results

rs2195239 reduces the overall cancer risk in homozygote model, as well as reducing cancer risk in Asian populations in allele, homozygote, and recessive models. No significant relationship was found between rs2195239 and breast or pancreatic cancer risk. rs2162679 reduces the overall cancer risk in allele, homozygote, dominant, and recessive models, as well as reducing cancer risk in Asian populations in allele, homozygote, and recessive models.

Conclusions

IGF1 rs2195239 and rs2162679 were associated with overall cancer risk based on present studies.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0749-3) contains supplementary material, which is available to authorized users.

The National Cancer Institute Cohort Consortium: An International Pooling Collaboration of 58 Cohorts from 20 Countries

Cohort studies have been central to the establishment of the known causes of cancer. To dissect cancer etiology in more detail-for instance, for personalized risk prediction and prevention, assessment of risks of subtypes of cancer, and assessment of small elevations in risk-there is a need for analyses of far larger cohort datasets than available in individual existing studies. To address these challenges, the NCI Cohort Consortium was founded in 2001. It brings together 58 cancer epidemiology cohorts from 20 countries to undertake large-scale pooling research. The cohorts in aggregate include over nine million study participants, with biospecimens available for about two million of these. Research in the Consortium is undertaken by >40 working groups focused on specific cancer sites, exposures, or other research areas. More than 180 publications have resulted from the Consortium, mainly on genetic and other cancer epidemiology, with high citation rates. This article describes the foundation of the Consortium; its structure, governance, and methods of working; the participating cohorts; publications; and opportunities. The Consortium welcomes new members with cancer-oriented cohorts of 10,000 or more participants and an interest in collaborative research. Cancer Epidemiol Biomarkers Prev; 27(11); 1307-19. ©2018 AACR.

Molecular evaluation of glutathione S transferase family genes in patients with sporadic colorectal cancer

AIM

To evaluate the association between polymorphisms in glutathione S transferases (GSTs) and the risk of sporadic colorectal cancer (SCRC), tumor progression and the survival of patients.

METHODS

A case-control study of 970 individuals from the Brazilian population was conducted (232 individuals from the case group with colorectal cancer and 738 individuals from the control group without a history of cancer). PCR multiplex and PCR-RFLP techniques were used to genotype the GST polymorphisms. The tumors were categorized according to the TNM classification: tumor extension (T), affected lymph nodes (N), and presence of metastasis (M). Logistic regression, multiple logistic regression and survival analysis were used to analyze the data. The results are presented in terms of odds ratio (OR) and 95% confidence interval (CI). The level of significance was set at 5% (P ≤ 0.05).

RESULTS

Age equal to or over 62 years (OR = 8.79; 95%CI: 5.90-13.09, P < 0.01) and female gender (OR = 2.91; 95%CI: 1.74-4.37; P < 0.01) were associated with increased risk of SCRC. Analysis of the polymorphisms revealed an association between the GSTM1 polymorphisms and a risk of SCRC (OR = 1.45; 95%CI: 1.06-2.00; P = 0.02), as well as between GSTT1 and a reduced risk of the disease (OR = 0.65; 95%CI: 0.43-0.98; P = 0.04). An interaction between the presence of the wild-type allele of GSTP1 Ile105Val polymorphism and tobacco consumption on risk of SCRC (OR = 2.33; 95%CI: 1.34-4.05; P = 0.05) was observed. There was an association between the GSTM1 null genotype and the presence of advanced tumors (OR = 2.33; 95%CI: 1.23-4.41; P = 0.009), as well as increased risk of SCRC in the presence of a combination of GSTT1 non-null/GSTM1 null genotypes (OR = 1.50; 95%CI: 1.03-2.19; P = 0.03) and GSTT1 non-null/GSTM1 null/GSTP1 Val* (OR = 1.85; 95%CI: 1.01-3.36, P = 0.04). Combined GSTT1 non-null/GSTM1 null genotypes (OR = 2.40; 95%CI: 1.19-4.85; P = 0.01) and GSTT1 non-null/GSTM1 null/GSTP1 Val* (OR = 2.92; 95%CI: 1.05-8.12; P = 0.04) were associated with tumor progression. Polymorphisms were not associated with the survival of patients with SCRC.

CONCLUSION

Females aged 62 years or older are more susceptible to SCRC. Polymorphisms of GSTT1 and GSTM1 null genotypes modulated the susceptibility to SCRC in the population studied.

A comprehensive analysis of polymorphic variants in steroid hormone and insulin-like growth factor-1 metabolism and risk of in situ breast cancer: Results from the Breast and Prostate Cancer Cohort Consortium

We assessed the association between 1,414 single nucleotide polymorphisms (SNPs) in genes involved in synthesis and metabolism of steroid hormones and insulin-like growth factor 1, and risk of breast cancer in situ (BCIS), with the aim of determining whether any of these were disease specific. This was carried out using 1,062 BCIS cases and 10,126 controls as well as 6,113 invasive breast cancer cases from the Breast and Prostate Cancer Cohort Consortium (BPC3). Three SNPs showed at least one nominally significant association in homozygous minor versus homozygous major models. ACVR2A-rs2382112 (OR_hom  = 3.05, 95%CI = 1.72-5.44, P_hom  = 1.47 × 10^-4 ), MAST2-rs12124649 (OR_hom  = 1.73, 95% CI =1.18-2.54, P_hom  = 5.24 × 10^-3 ), and INSR-rs10500204 (OR_hom  = 1.96, 95% CI = 1.44-2.67, P_hom =1.68 × 10^-5 ) were associated with increased risk of BCIS; however, only the latter association was significant after correcting for multiple testing. Furthermore, INSR-rs10500204 was more strongly associated with the risk of BCIS than invasive disease in case-only analyses using the homozygous minor versus homozygous major model (OR_hom  = 1.78, 95% CI = 1.30-2.44, P_hom  = 3.23 × 10^-4 ). The SNP INSR-rs10500204 is located in an intron of the INSR gene and is likely to affect binding of the promyelocytic leukemia (PML) protein. The PML gene is known as a tumor suppressor and growth regulator in cancer. However, it is not clear on what pathway the A-allele of rs10500204 could operate to influence the binding of the protein. Hence, functional studies are warranted to investigate this further.

Validating a breast cancer score in Spanish women. The MCC-Spain study

A breast-risk score, published in 2016, was developed in white-American women using 92 genetic variants (GRS92), modifiable and non-modifiable risk factors. With the aim of validating the score in the Spanish population, 1,732 breast cancer cases and 1,910 controls were studied. The GRS92, modifiable and non-modifiable risk factor scores were estimated via logistic regression. SNPs without available genotyping were simulated as in the aforementioned 2016 study. The full model score was obtained by combining GRS92, modifiable and non-modifiable risk factor scores. Score performances were tested via the area under the ROC curve (AUROC), net reclassification index (NRI) and integrated discrimination improvement (IDI). Compared with non-modifiable and modifiable factor scores, GRS92 had higher discrimination power (AUROC: 0.6195, 0.5885 and 0.5214, respectively). Adding the non-modifiable factor score to GRS92 improved patient classification by 23.6% (NRI = 0.236), while the modifiable factor score only improved it by 7.2%. The full model AUROC reached 0.6244. A simulation study showed the ability of the full model for identifying women at high risk for breast cancer. In conclusion, a model combining genetic and risk factors can be used for stratifying women by their breast cancer risk, which can be applied to individualizing genetic counseling and screening recommendations.

Interactions Between Genome-Wide Significant Genetic Variants and Circulating Concentrations of 25-Hydroxyvitamin D in Relation to Prostate Cancer Risk in the National Cancer Institute BPC3

Genome-wide association studies (GWAS) have identified over 100 single nucleotide polymorphisms (SNPs) associated with prostate cancer. However, information on the mechanistic basis for some associations is limited. Recent research has been directed towards the potential association of vitamin D concentrations and prostate cancer, but little is known about whether the aforementioned genetic associations are modified by vitamin D. We investigated the associations of 46 GWAS-identified SNPs, circulating concentrations of 25-hydroxyvitamin D (25(OH)D), and prostate cancer (3,811 cases, 511 of whom died from the disease, compared with 2,980 controls-from 5 cohort studies that recruited participants over several periods beginning in the 1980s). We used logistic regression models with data from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) to evaluate interactions on the multiplicative and additive scales. After allowing for multiple testing, none of the SNPs examined was significantly associated with 25(OH)D concentration, and the SNP-prostate cancer associations did not differ by these concentrations. A statistically significant interaction was observed for each of 2 SNPs in the 8q24 region (rs620861 and rs16902094), 25(OH)D concentration, and fatal prostate cancer on both multiplicative and additive scales (P ≤ 0.001). We did not find strong evidence that associations between GWAS-identified SNPs and prostate cancer are modified by circulating concentrations of 25(OH)D. The intriguing interactions between rs620861 and rs16902094, 25(OH)D concentration, and fatal prostate cancer warrant replication.

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

IMPORTANCE

An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention.

OBJECTIVE

To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors.

DESIGN, SETTING, AND PARTICIPANTS

Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality.

EXPOSURES

Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors.

MAIN OUTCOMES AND MEASURES

Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking).

RESULTS

The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population.

CONCLUSIONS AND RELEVANCE

This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

The Impact of the Nurses' Health Study on Population Health: Prevention, Translation, and Control

OBJECTIVES

To summarize the overall impact of the Nurses' Health Study (NHS) over the past 40 years on the health of populations through its contributions on prevention, translation, and control.

METHODS

We performed a narrative review of the findings of the NHS, NHS II, and NHS3 between 1976 and 2016.

RESULTS

The NHS has generated significant findings about the associations between (1) smoking and type 2 diabetes, cardiovascular diseases, colorectal and pancreatic cancer, psoriasis, multiple sclerosis, and eye diseases; (2) physical activity and cardiovascular diseases, breast cancer, psoriasis, and neurodegeneration; (3) obesity and cardiovascular diseases, numerous cancer sites, psoriasis, multiple sclerosis, kidney stones, and eye diseases; (4) oral contraceptives and cardiovascular disease, melanoma, and breast, colorectal, and ovarian cancer; (5) hormone therapy and cardiovascular diseases, breast and endometrial cancer, and neurodegeneration; (6) endogenous hormones and breast cancer; (7) dietary factors and type 2 diabetes, cardiovascular diseases, breast and pancreatic cancer, non-Hodgkin's lymphoma, neurodegeneration, multiple sclerosis, kidney stones, and eye diseases; and (8) sleep and shift work and chronic diseases.

CONCLUSIONS

The NHS findings have influenced public health policy and practice both locally and globally to improve women's health.

The Cancer Epidemiology Descriptive Cohort Database: A Tool to Support Population-Based Interdisciplinary Research

BACKGROUND

We report on the establishment of a web-based Cancer Epidemiology Descriptive Cohort Database (CEDCD). The CEDCD's goals are to enhance awareness of resources, facilitate interdisciplinary research collaborations, and support existing cohorts for the study of cancer-related outcomes.

METHODS

Comprehensive descriptive data were collected from large cohorts established to study cancer as primary outcome using a newly developed questionnaire. These included an inventory of baseline and follow-up data, biospecimens, genomics, policies, and protocols. Additional descriptive data extracted from publicly available sources were also collected. This information was entered in a searchable and publicly accessible database. We summarized the descriptive data across cohorts and reported the characteristics of this resource.

RESULTS

As of December 2015, the CEDCD includes data from 46 cohorts representing more than 6.5 million individuals (29% ethnic/racial minorities). Overall, 78% of the cohorts have collected blood at least once, 57% at multiple time points, and 46% collected tissue samples. Genotyping has been performed by 67% of the cohorts, while 46% have performed whole-genome or exome sequencing in subsets of enrolled individuals. Information on medical conditions other than cancer has been collected in more than 50% of the cohorts. More than 600,000 incident cancer cases and more than 40,000 prevalent cases are reported, with 24 cancer sites represented.

CONCLUSIONS

The CEDCD assembles detailed descriptive information on a large number of cancer cohorts in a searchable database.

IMPACT

Information from the CEDCD may assist the interdisciplinary research community by facilitating identification of well-established population resources and large-scale collaborative and integrative research. Cancer Epidemiol Biomarkers Prev; 25(10); 1392-401. ©2016 AACR.

Interactions between breast cancer susceptibility loci and menopausal hormone therapy in relationship to breast cancer in the Breast and Prostate Cancer Cohort Consortium

Current use of menopausal hormone therapy (MHT) has important implications for postmenopausal breast cancer risk, and observed associations might be modified by known breast cancer susceptibility loci. To provide the most comprehensive assessment of interactions of prospectively collected data on MHT and 17 confirmed susceptibility loci with invasive breast cancer risk, a nested case-control design among eight cohorts within the NCI Breast and Prostate Cancer Cohort Consortium was used. Based on data from 13,304 cases and 15,622 controls, multivariable-adjusted logistic regression analyses were used to estimate odds ratios (OR) and 95 % confidence intervals (CI). Effect modification of current and past use was evaluated on the multiplicative scale. P values <1.5 × 10(-3) were considered statistically significant. The strongest evidence of effect modification was observed for current MHT by 9q31-rs865686. Compared to never users of MHT with the rs865686 GG genotype, the association between current MHT use and breast cancer risk for the TT genotype (OR 1.79, 95 % CI 1.43-2.24; P interaction = 1.2 × 10(-4)) was less than expected on the multiplicative scale. There are no biological implications of the sub-multiplicative interaction between MHT and rs865686. Menopausal hormone therapy is unlikely to have a strong interaction with the common genetic variants associated with invasive breast cancer.

Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer

Risk assessment for prostate cancer is challenging due to its genetic heterogeneity. In this study, our goal was to develop an operational framework to select and evaluate gene variants that may contribute to familial prostate cancer risk. Drawing on orthogonal sources, we developed a candidate list of genes relevant to prostate cancer, then analyzed germline exomes from 12 case-only prostate cancer patients from high-risk families to identify patterns of protein-damaging gene variants. We described an average of 5 potentially disruptive variants in each individual and annotated them in the context of public databases representing human variation. Novel damaging variants were found in several genes of relevance to prostate cancer. Almost all patients had variants associated with defects in DNA damage response. Many also had variants linked to androgen signaling. Treatment of primary T-lymphocytes from these prostate cancer patients versus controls with DNA damaging agents showed elevated levels of the DNA double strand break (DSB) marker γH2AX (p < 0.05), supporting the idea of an underlying defect in DNA repair. This work suggests the value of focusing on underlying defects in DNA damage in familial prostate cancer risk assessment and demonstrates an operational framework for exome sequencing in case-only prostate cancer genetic evaluation.

Hormone-related pathways and risk of breast cancer subtypes in African American women

We sought to investigate genetic variation in hormone pathways in relation to risk of overall and subtype-specific breast cancer in women of African ancestry (AA). Genotyping and imputation yielded data on 143,934 SNPs in 308 hormone-related genes for 3663 breast cancer cases (1098 ER-, 1983 ER+, 582 ER unknown) and 4687 controls from the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium. AMBER includes data from four large studies of AA women: the Carolina Breast Cancer Study, the Women's Circle of Health Study, the Black Women's Health Study, and the Multiethnic Cohort Study. Pathway- and gene-based analyses were conducted, and single-SNP tests were run for the top genes. There were no strong associations at the pathway level. The most significantly associated genes were GHRH, CALM2, CETP, and AKR1C1 for overall breast cancer (gene-based nominal p ≤ 0.01); NR0B1, IGF2R, CALM2, CYP1B1, and GRB2 for ER+ breast cancer (p ≤ 0.02); and PGR, MAPK3, MAP3K1, and LHCGR for ER- disease (p ≤ 0.02). Single-SNP tests for SNPs with pairwise linkage disequilibrium r (2) < 0.8 in the top genes identified 12 common SNPs (in CALM2, CETP, NR0B1, IGF2R, CYP1B1, PGR, MAPK3, and MAP3K1) associated with overall or subtype-specific breast cancer after gene-level correction for multiple testing. Rs11571215 in PGR (progesterone receptor) was the SNP most strongly associated with ER- disease. We identified eight genes in hormone pathways that contain common variants associated with breast cancer in AA women after gene-level correction for multiple testing.

Association of breast cancer risk loci with breast cancer survival

The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele =0.70; 95% CI: 0.58-0.85; ptrend  = 2.84 × 10(-4) ; HRheterozygotes  = 0.71; 95% CI: 0.55-0.92; HRhomozygotes  = 0.48; 95% CI: 0.31-0.76; p2DF  = 1.45 × 10(-3) ). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04-1.15; ptrend  = 6.6 × 10(-4) ; HRheterozygotes  = 0.96 95% CI: 0.90-1.03; HRhomozygotes  = 1.21; 95% CI: 1.09-1.35; p2DF =1.25 × 10(-4) ). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662.

Genetic risk variants associated with in situ breast cancer

INTRODUCTION

Breast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.

METHODS

To evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.

RESULTS

We found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11-1.38, P = 1.27 x 10(-4)) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99-1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09-1.42, P = 1.07 x 10(-3)). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.

CONCLUSIONS

Our study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.

Vitamin D-associated genetic variation and risk of breast cancer in the breast and prostate cancer cohort consortium (BPC3)

BACKGROUND

Two recent genome-wide association studies (GWAS) identified SNPs in or near four genes related to circulating 25-hydroxyvitamin D [25(OH)D] concentration. To examine the hypothesized inverse relationship between vitamin D status and breast cancer, we studied the associations between SNPs in these genes and breast cancer risk in a large pooled study of 9,456 cases and 10,816 controls from six cohorts.

METHODS

SNP markers localized to each of four genes (GC, CYP24A1, CYP2R1, and DHCR7) previously associated with 25(OH)D were genotyped and examined both individually and as a 4-SNP polygenic score. Logistic regression was used to estimate the associations between the genetic variants and risk of breast cancer.

RESULTS

We found no association between any of the four SNPs or their polygenic score and breast cancer risk.

CONCLUSIONS

Our findings do not support an association between vitamin D status, as reflected by 25(OH)D-related genotypes, and breast cancer risk.

IMPACT

These findings may contribute to future meta-analyses and scientific review articles, and provide new data about the association between vitamin D-related genes and breast cancer. Cancer Epidemiol Biomarkers Prev; 24(3); 627-30. ©2014 AACR.

Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium

Additive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 × 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.

Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women

We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case-case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (Pinteraction = 8.84 × 10(-4)) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.

Promoter methylation and polymorphism of E-cadherin gene may confer a risk to prostate cancer: a meta-analysis based on 22 studies

Emerging evidence has suggested that -160C/A polymorphism and promoter methylation of E-cadherin gene may contribute to the risk of prostate cancer. However, the results are still conflicting. We aim to systematically evaluate the potential of promoter methylation and polymorphism in E-cadherin gene to confer a risk to prostate cancer through meta-analysis. PubMed, Embase, Web of Science, Cochrane Library, and Chinese National Knowledge Infrastructure (CNKI) databases were searched to identify eligible studies published before April 1, 2014. Pooled odds ratios (ORs) with their 95 % confidence intervals (95 % CIs) were calculated by using the random-effect model or the fixed-effect model, according to heterogeneity test. Subgroup analyses were also performed to explore the potential sources of heterogeneity. Sensitivity and publication bias analyses were used to test the robustness of our results. We performed a meta-analysis of 22 included studies, with 11 on -160C/A polymorphism and another 11 on promoter methylation of E-cadherin gene. Our meta-analysis results suggested that E-cadherin -160C/A polymorphism may be a potential risk factor for prostate cancer. Furthermore, we observed that the frequencies of promoter methylation of E-cadherin gene in the prostate cancer tissues were significantly higher than those of normal tissues, indicating that promoter methylation of E-cadherin gene may play an important role in prostate carcinogenesis. In conclusion, the present meta-analysis provides further evidence that promoter methylation and -160C/A polymorphism of E-cadherin gene may confer a risk to prostate cancer. Identifying these risk factors for prostate cancer will improve early detection, allow for selective chemoprevention, and provide further insights into its disease mechanisms.

Testing calibration of risk models at extremes of disease risk

Risk-prediction models need careful calibration to ensure they produce unbiased estimates of risk for subjects in the underlying population given their risk-factor profiles. As subjects with extreme high or low risk may be the most affected by knowledge of their risk estimates, checking the adequacy of risk models at the extremes of risk is very important for clinical applications. We propose a new approach to test model calibration targeted toward extremes of disease risk distribution where standard goodness-of-fit tests may lack power due to sparseness of data. We construct a test statistic based on model residuals summed over only those individuals who pass high and/or low risk thresholds and then maximize the test statistic over different risk thresholds. We derive an asymptotic distribution for the max-test statistic based on analytic derivation of the variance-covariance function of the underlying Gaussian process. The method is applied to a large case-control study of breast cancer to examine joint effects of common single nucleotide polymorphisms (SNPs) discovered through recent genome-wide association studies. The analysis clearly indicates a non-additive effect of the SNPs on the scale of absolute risk, but an excellent fit for the linear-logistic model even at the extremes of risks.

Effect of lipidated gonadotropin-releasing hormone peptides on receptor mediated binding and uptake into prostate cancer cells in vitro

Gonadotropin-releasing hormone (GnRH) receptors are overexpressed on many cancer cells but not on primary cell lines. This study was designed to investigate the targeting ability and uptake of dendritic lipidated [Gln(1)]-GnRH peptide analogues on receptor-positive prostate cancer PC-3 cells relative to receptor-negative ovarian carcinoma SKOV-3 cells for potential application in drug delivery. Direct antiproliferative effect of these was investigated on three GnRH-receptor positive cancer cells, PC-3, LNCaP and DU145. A significant dose dependent growth inhibitory effect was produced in DU145 cells by 5 dendrimers giving an IC50 value of 22-35 μM. All compounds were non-toxic to the normal peripheral blood mononuclear cells.

FROM THE CLINICAL EDITOR

This study demonstrates the use of specific dendritic lapidated GnRH analogues in growth inhibition of GnRH receptor positive prostate cancer cell lines, suggesting potential future clinical use of this or similar strategies to address GnRH receptor positive cancer cells.

Prostate cancer (PCa) risk variants and risk of fatal PCa in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

BACKGROUND

Screening and diagnosis of prostate cancer (PCa) is hampered by an inability to predict who has the potential to develop fatal disease and who has indolent cancer. Studies have identified multiple genetic risk loci for PCa incidence, but it is unknown whether they could be used as biomarkers for PCa-specific mortality (PCSM).

OBJECTIVE

To examine the association of 47 established PCa risk single-nucleotide polymorphisms (SNPs) with PCSM.

DESIGN, SETTING, AND PARTICIPANTS

We included 10 487 men who had PCa and 11 024 controls, with a median follow-up of 8.3 yr, during which 1053 PCa deaths occurred.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The main outcome was PCSM. The risk allele was defined as the allele associated with an increased risk for PCa in the literature. We used Cox proportional hazards regression to calculate the hazard ratios of each SNP with time to progression to PCSM after diagnosis. We also used logistic regression to calculate odds ratios for each risk SNP, comparing fatal PCa cases to controls.

RESULTS AND LIMITATIONS

Among the cases, we found that 8 of the 47 SNPs were significantly associated (p<0.05) with time to PCSM. The risk allele of rs11672691 (intergenic) was associated with an increased risk for PCSM, while 7 SNPs had risk alleles inversely associated (rs13385191 [C2orf43], rs17021918 [PDLIM5], rs10486567 [JAZF1], rs6465657 [LMTK2], rs7127900 (intergenic), rs2735839 [KLK3], rs10993994 [MSMB], rs13385191 [C2orf43]). In the case-control analysis, 22 SNPs were associated (p<0.05) with the risk of fatal PCa, but most did not differentiate between fatal and nonfatal PCa. Rs11672691 and rs10993994 were associated with both fatal and nonfatal PCa, while rs6465657, rs7127900, rs2735839, and rs13385191 were associated with nonfatal PCa only.

CONCLUSIONS

Eight established risk loci were associated with progression to PCSM after diagnosis. Twenty-two SNPs were associated with fatal PCa incidence, but most did not differentiate between fatal and nonfatal PCa. The relatively small magnitudes of the associations do not translate well into risk prediction, but these findings merit further follow-up, because they may yield important clues about the complex biology of fatal PCa.

PATIENT SUMMARY

In this report, we assessed whether established PCa risk variants could predict PCSM. We found eight risk variants associated with PCSM: One predicted an increased risk of PCSM, while seven were associated with decreased risk. Larger studies that focus on fatal PCa are needed to identify more markers that could aid prediction.

Insulin-like growth factor pathway genetic polymorphisms, circulating IGF1 and IGFBP3, and prostate cancer survival

BACKGROUND

The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown.

METHODS

Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided.

RESULTS

The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P trend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r 2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r 2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P trend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P trend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P trend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality.

CONCLUSIONS

The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.

Insulin-like Growth Factor Pathway Genetic Polymorphisms, Circulating IGF1 and IGFBP3, and Prostate Cancer Survival

Background

The insulin-like growth factor (IGF) signaling pathway has been implicated in prostate cancer (PCa) initiation, but its role in progression remains unknown.

Methods

Among 5887 PCa patients (704 PCa deaths) of European ancestry from seven cohorts in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium, we conducted Cox kernel machine pathway analysis to evaluate whether 530 tagging single nucleotide polymorphisms (SNPs) in 26 IGF pathway-related genes were collectively associated with PCa mortality. We also conducted SNP-specific analysis using stratified Cox models adjusting for multiple testing. In 2424 patients (313 PCa deaths), we evaluated the association of prediagnostic circulating IGF1 and IGFBP3 levels and PCa mortality. All statistical tests were two-sided.

Results

The IGF signaling pathway was associated with PCa mortality (P = .03), and IGF2-AS and SSTR2 were the main contributors (both P = .04). In SNP-specific analysis, 36 SNPs were associated with PCa mortality with P trend less than .05, but only three SNPs in the IGF2-AS remained statistically significant after gene-based corrections. Two were in linkage disequilibrium (r 2 = 1 for rs1004446 and rs3741211), whereas the third, rs4366464, was independent (r 2 = 0.03). The hazard ratios (HRs) per each additional risk allele were 1.19 (95% confidence interval [CI] = 1.06 to 1.34; P trend = .003) for rs3741211 and 1.44 (95% CI = 1.20 to 1.73; P trend < .001) for rs4366464. rs4366464 remained statistically significant after correction for all SNPs (P trend.corr = .04). Prediagnostic IGF1 (HRhighest vs lowest quartile = 0.71; 95% CI = 0.48 to 1.04) and IGFBP3 (HR = 0.93; 95% CI = 0.65 to 1.34) levels were not associated with PCa mortality.

Conclusions

The IGF signaling pathway, primarily IGF2-AS and SSTR2 genes, may be important in PCa survival.

A genome-wide "pleiotropy scan" does not identify new susceptibility loci for estrogen receptor negative breast cancer

Approximately 15–30% of all breast cancer tumors are estrogen receptor negative (ER−). Compared with ER-positive (ER+) disease they have an earlier age at onset and worse prognosis. Despite the vast number of risk variants identified for numerous cancer types, only seven loci have been unambiguously identified for ER-negative breast cancer. With the aim of identifying new susceptibility SNPs for this disease we performed a pleiotropic genome-wide association study (GWAS). We selected 3079 SNPs associated with a human complex trait or disease at genome-wide significance level (P<5×10⁻⁸) to perform a secondary analysis of an ER-negative GWAS from the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), including 1998 cases and 2305 controls from prospective studies. We then tested the top ten associations (i.e. with the lowest P-values) using three additional populations with a total sample size of 3509 ER+ cases, 2543 ER− cases and 7031 healthy controls. None of the 3079 selected variants in the BPC3 ER-GWAS were significant at the adjusted threshold. 186 variants were associated with ER− breast cancer risk at a conventional threshold of P<0.05, with P-values ranging from 0.049 to 2.3×10⁻⁴. None of the variants reached statistical significance in the replication phase. In conclusion, this study did not identify any novel susceptibility loci for ER-breast cancer using a “pleiotropic approach”.

Genome-wide association studies and the clinic: a focus on breast cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.

Factors associated with oxidative stress and cancer risk in the Breast and Prostate Cancer Cohort Consortium

Both endogenous factors (genomic variations) and exogenous factors (environmental exposures, lifestyle) impact the balance of reactive oxygen species (ROS). Variants of the ND3 (rs2853826; G10398A) gene of the mitochondrial genome, manganese superoxide dismutase (MnSOD; rs4880 Val16Ala) and glutathione peroxidase (GPX-1; rs1050450 Pro198Leu), are purported to have functional effects on regulation of ROS balance. In this study, we examined associations of breast and prostate cancer risks and survival with these variants, and interactions between rs4880-rs1050450, and alcohol consumption-rs2853826. Nested case-control studies were conducted in the Breast and Prostate Cancer Cohort Consortium (BPC3), consisting of nine cohorts. The analyses included over 10726 post-menopausal breast and 7532 prostate cancer cases with matched controls. Logistic regression models were used to evaluate associations with risk, and proportional hazard models were used for survival outcomes. We did not observe significant interactions between polymorphisms in MnSOD and GPX-1, or between mitochondrial polymorphisms and alcohol intake and risk of either breast (p-interaction of 0.34 and 0.98, respectively) or prostate cancer (p-interaction of 0.49 and 0.50, respectively). We observed a weak inverse association between prostate cancer risk and GPX-1 Leu198Leu carriers (OR 0.87, 95% CI 0.79-0.97, p = 0.01). Overall survival among women with breast cancer was inversely associated with G10398 carriers who consumed alcohol (HR 0.66 95% CI 0.49-0.88). Given the high power in our study, it is unlikely that interactions tested have more than moderate effects on breast or prostate cancer risk. Observed associations need both further epidemiological and biological confirmation.

Challenges of SNP genotyping and genetic variation: its future role in diagnosis and treatment of cancer

Thorough annotation of common germline genetic variation in the human genome has generated a foundation for the investigation of the contribution of genetics to the etiology and pathogenesis of cancer. For many malignancies, it has become increasingly apparent that numerous alleles, with small-to-moderate effects, additively contribute to cancer susceptibility. The most common genetic variant in the genome, the single nucleotide polymorphism, is of special interest for the study of susceptibility to and protection from cancer. Similarly, intense effort has focused on genetic variants that can predict either response or toxicity to therapeutic interventions. This review discusses the challenges and prospects of genetic association studies in cancer research. On the basis of recent changes in genomics and high-throughput genotyping platforms, future genetic findings of association studies could impact clinical care and public health screening.

Leveraging family history in population-based case-control association studies

Population-based epidemiologic studies often gather information from study participants on disease history among their family members. Although investigators widely recognize that family history will be associated with genotypes of the participants at disease susceptibility loci, they commonly ignore such information in primary genetic association analyses. In this report, we propose a simple approach to association testing by incorporating family history information as a "phenotype." We account for the expected attenuation in strength of association of the genotype of study participants with family history under Mendelian transmission. The proposed analysis can be performed using standard statistical software adopting either a meta- or pooled-analysis framework. Re-analysis of a total of 115 known susceptibility single-nucleotide polymorphisms, discovered through genome-wide association studies for several disease traits, indicates that incorporation of family history information can increase efficiency by as much as 40%. Efficiency gain depends on the type of design used for conducting the primary study, extent of family history, and accuracy and completeness of reporting.

Collaborative cancer epidemiology in the 21st century: the model of cancer consortia

During the last two decades, epidemiology has undergone a rapid evolution toward collaborative research. The proliferation of multi-institutional, interdisciplinary consortia has acquired particular prominence in cancer research. Herein, we describe the characteristics of a network of 49 established cancer epidemiology consortia (CEC) currently supported by the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI). This collection represents the largest disease-based research network for collaborative cancer research established in population sciences. We describe the funding trends, geographic distribution, and areas of research focus. The CEC have been partially supported by 201 grants and yielded 3,876 publications between 1995 and 2011. We describe this output in terms of interdisciplinary collaboration and translational evolution. We discuss challenges and future opportunities in the establishment and conduct of large-scale team science within the framework of CEC, review future prospects for this approach to large-scale, interdisciplinary cancer research, and describe a model for the evolution of an integrated Network of Cancer Consortia optimally suited to address and support 21st-century epidemiology.

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

OBJECTIVE

There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status.

MATERIAL AND METHODS

Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.

RESULTS

We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.

DISCUSSION AND CONCLUSIONS

Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

One thousand genomes imputation in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer genome-wide association study

BACKGROUND

Genotype imputation substantially increases available markers for analysis in genome-wide association studies (GWAS) by leveraging linkage disequilibrium from a reference panel. We sought to (i) investigate the performance of imputation from the August 2010 release of the 1000 Genomes Project (1000GP) in an existing GWAS of prostate cancer, (ii) look for novel associations with prostate cancer risk, (iii) fine-map known prostate cancer susceptibility regions using an approximate Bayesian framework and stepwise regression, and (iv) compare power and efficiency of imputation and de novo sequencing.

METHODS

We used 2,782 aggressive prostate cancer cases and 4,458 controls from the NCI Breast and Prostate Cancer Cohort Consortium aggressive prostate cancer GWAS to infer 5.8 million well-imputed autosomal single nucleotide polymorphisms (SNPs).

RESULTS

Imputation quality, as measured by correlation between imputed and true allele counts, was higher among common variants than rare variants. We found no novel prostate cancer associations among a subset of 1.2 million well-imputed low-frequency variants. At a genome-wide sequencing cost of $2,500, imputation from SNP arrays is a more powerful strategy than sequencing for detecting disease associations of SNPs with minor allele frequencies (MAF) above 1%.

CONCLUSIONS

1000GP imputation provided dense coverage of previously identified prostate cancer susceptibility regions, highlighting its potential as an inexpensive first-pass approach to fine mapping in regions such as 5p15 and 8q24. Our study shows 1000GP imputation can accurately identify low-frequency variants and stresses the importance of large sample size when studying these variants.

Genetic variation in the vitamin d pathway in relation to risk of prostate cancer--results from the breast and prostate cancer cohort consortium

BACKGROUND

Studies suggest that vitamin D status may be associated with prostate cancer risk although the direction and strength of this association differs between experimental and observational studies. Genome-wide association studies have identified genetic variants associated with 25-hydroxyvitamin D [25(OH)D] status. We examined prostate cancer risk in relation to single-nucleotide polymorphisms (SNP) in four genes shown to predict circulating levels of 25(OH)D.

METHODS

SNP markers localized to each of four genes (GC, CYP24A1, CYP2R1, and DHCR7) previously associated with 25(OH)D were genotyped in 10,018 cases and 11,052 controls from the National Cancer Institute (NCI) Breast and Prostate Cancer Cohort Consortium. Logistic regression was used to estimate the individual and cumulative association between genetic variants and risk of overall and aggressive prostate cancer.

RESULTS

We observed a decreased risk of aggressive prostate cancer among men with the allele in rs6013897 near CYP24A1 associated with lower serum 25(OH)D [per A allele, OR, 0.86; 95% confidence interval (CI), 0.80-0.93; Ptrend = 0.0002) but an increased risk for nonaggressive disease (per A allele: OR, 1.10; 95% CI, 1.04-1.17; Ptrend = 0.002). Examination of a polygenic score of the four SNPs revealed statistically significantly lower risk of aggressive prostate cancer among men with a greater number of low vitamin D alleles (OR for 6-8 vs. 0-1 alleles, 0.66; 95% CI, 0.44-0.98; Ptrend = 0.003).

CONCLUSIONS

In this large, pooled analysis, genetic variants related to lower 25(OH)D levels were associated with a decreased risk of aggressive prostate cancer.

IMPACT

Our genetic findings do not support a protective association between loci known to influence vitamin D levels and prostate cancer risk.

Plasma carotenoid- and retinol-weighted multi-SNP scores and risk of breast cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

BACKGROUND

Dietary and circulating carotenoids have been inversely associated with breast cancer risk, but observed associations may be due to confounding. Single-nucleotide polymorphisms (SNPs) in β-carotene 15,15'-monooxygenase 1 (BCMO1), a gene encoding the enzyme involved in the first step of synthesizing vitamin A from dietary carotenoids, have been associated with circulating carotenoid concentrations and may serve as unconfounded surrogates for those biomarkers. We determined associations between variants in BCMO1 and breast cancer risk in a large cohort consortium.

METHODS

We used unconditional logistic regression to test four SNPs in BCMO1 for associations with breast cancer risk in 9,226 cases and 10,420 controls from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also tested weighted multi-SNP scores composed of the two SNPs with strong, confirmed associations with circulating carotenoid concentrations.

RESULTS

Neither the individual SNPs nor the weighted multi-SNP scores were associated with breast cancer risk [OR (95% confidence interval) comparing extreme quintiles of weighted multi-SNP scores = 1.04 (0.94-1.16) for β-carotene, 1.08 (0.98-1.20) for α-carotene, 1.04 (0.94-1.16) for β-cryptoxanthin, 0.95 (0.87-1.05) for lutein/zeaxanthin, and 0.92 (0.83-1.02) for retinol]. Furthermore, no associations were observed when stratifying by estrogen receptor status, but power was limited.

CONCLUSIONS

Our results do not support an association between SNPs associated with circulating carotenoid concentrations and breast cancer risk.

IMPACT

Future studies will need additional genetic surrogates and/or sample sizes at least three times larger to contribute evidence of a causal link between carotenoids and breast cancer.

Insulin-like growth factor pathway genes and blood concentrations, dietary protein and risk of prostate cancer in the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)

It has been hypothesized that a high intake of dairy protein may increase prostate cancer risk by increasing the production of insulin-like growth factor 1 (IGF-1). Several single nucleotide polymorphisms (SNPs) have been weakly associated with circulating concentrations of IGF-1 and IGF binding protein 3 (IGFBP-3), but none of these SNPs was associated with risk of prostate cancer. We examined whether an association between 16 SNPs associated with circulating IGF-1 or IGFBP-3 concentrations and prostate cancer exists within subgroups defined by dietary protein intake in 5,253 cases and 4,963 controls of European ancestry within the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). The BPC3 includes nested case-control studies within large North-American and European cohorts. Per-allele odds ratios for prostate cancer for the SNPs were compared across tertiles of protein intake, which was expressed as the percentage of energy derived from total, animal, dairy or plant protein sources, using conditional logistic regression models. Total, animal, dairy and plant protein intakes were significantly positively associated with blood IGF-1 (p < 0.01), but not with IGFBP-3 concentrations (p > 0.10) or with risk of prostate cancer (p > 0.20). After adjusting for multiple testing, the SNP-prostate cancer associations did not differ by intakes of protein, although two interactions by intake of plant protein were of marginal statistical significance [SSTR5 (somatostatin receptor 5)-rs197056 (uncorrected p for interaction, 0.001); SSTR5-rs197057 (uncorrected p for interaction, 0.002)]. We found no strong evidence that the associations between 16 IGF pathway SNPs and prostate cancer differed by intakes of dietary protein.

Informed conditioning on clinical covariates increases power in case-control association studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10⁻⁹). The improvement varied across diseases with a 16% median increase in χ² test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.

This work describes a new methodology for analyzing genome-wide case-control association studies of diseases with strong correlations to clinical covariates, such as age in prostate cancer and body mass index in type 2 diabetes. Currently, researchers either ignore these clinical covariates or apply approaches that ignore the disease's prevalence and the study's ascertainment strategy. We take an alternative approach, leveraging external prevalence information from the epidemiological literature and constructing a statistic based on the classic liability threshold model of disease. Our approach not only improves the power of studies that ascertain individuals randomly or based on the disease phenotype, but also improves the power of studies that ascertain individuals based on both the disease phenotype and clinical covariates. We apply our statistic to seven datasets over six different diseases and a variety of clinical covariates. We found that there was a substantial improvement in test statistics relative to current approaches at known associated variants. This suggests that novel loci may be identified by applying our method to existing and future association studies of these diseases.