Association between 5p12 genomic markers and breast cancer susceptibility: evidence from 19 case-control studies

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants

The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.

Associations of Genetic Variants at Nongenic Susceptibility Loci with Breast Cancer Risk and Heterogeneity by Tumor Subtype in Southern Han Chinese Women

Current understanding of cancer genomes is mainly “gene centric.” However, GWAS have identified some nongenic breast cancer susceptibility loci. Validation studies showed inconsistent results among different populations. To further explore this inconsistency and to investigate associations by intrinsic subtype (Luminal-A, Luminal-B, ER−&PR−&HER2+, and triple negative) among Southern Han Chinese women, we genotyped five nongenic polymorphisms (2q35: rs13387042, 5p12: rs981782 and rs4415084, and 8q24: rs1562430 and rs13281615) using MassARRAY IPLEX platform in 609 patients and 882 controls. Significant associations with breast cancer were observed for rs13387042 and rs4415084 with OR (95% CI) per-allele 1.29 (1.00–1.66) and 0.83 (0.71–0.97), respectively. In subtype specific analysis, rs13387042 (per-allele adjusted OR = 1.36, 95% CI = 1.00–1.87) and rs4415084 (per-allele adjusted OR = 0.82, 95% CI = 0.66–1.00) showed slightly significant association with Luminal-A subtype; however, only rs13387042 was associated with ER−&PR−&HER2+ tumors (per-allele adjusted OR = 1.55, 95% CI = 1.00–2.40), and none of them were linked to Luminal-B and triple negative subtype. Collectively, nongenic SNPs were heterogeneous according to the intrinsic subtype. Further studies with larger datasets along with intrinsic subtype categorization should explore and confirm the role of these variants in increasing breast cancer risk.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Introduction

Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.

Methods

We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.

Results

In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m² versus <25 kg/m²; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m². The SNP panel appeared to have predictive ability among both white and black women.

Conclusions

Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Comparing the value of mammographic features and genetic variants in breast cancer risk prediction

The goal of this study was to compare the value of mammographic features and genetic variants for breast cancer risk prediction with Bayesian reasoning and information theory. We conducted a retrospective case-control study, collecting mammographic findings and high-frequency/low-penetrance genetic variants from an existing personalized medicine data repository. We trained and tested Bayesian networks for mammographic findings and genetic variants respectively. We found that mammographic findings had a higher discriminative ability than genetic variants for improving breast cancer risk prediction in terms of the area under the ROC curve. We compared the value of each mammographic feature and genetic variant for breast risk prediction in terms of mutual information, with and without consideration of interactions of those risk factors. We also identified the interactions between mammographic features and genetic variants in an attempt to prioritize mammographic features and genetic variants to efficiently predict the risk of breast cancer.

Follow-up of breast papillary lesion on core needle biopsy: experience in African-American population

Background

The optimal course of clinical follow-up after a diagnosis of breast papillary lesion on a core needle biopsy (CNB) remains elusive. In particular, no reports in literature have addressed this question in African-American population. We describe our experience with breast papillary lesions in a primarily African-American population.

Methods

A search of our database for breast papillary lesions diagnosed on CNB between September 2002 and September 2012 was conducted. Cases were categorized into benign, atypical, and malignant. CK5/6 and CK903 stains were performed when necessary.

Results

A total of 64 breast papillary lesions were diagnosed on CNB, including 55 (86%) benign papillary lesions, 6 (9%) atypical lesions, and 3 (5%) intraductal papillary carcinomas. Of these 64 patients, 29 patients (25 African-Americans, 3 Hispanics, 1 Asian American) underwent lumpectomy within 6 months after CNB. Pathology of the lumpectomy showed: five of the 25 (20%) benign papillary lesions on needle biopsy were upgraded to intraductal or invasive papillary carcinoma; 2 of the 3 atypical papillary lesion cases on core biopsy were upgraded (67%), one into intraductal papillary carcinoma, the other invasive papillary carcinoma; the only case of malignant papillary lesion on CNB remained as intraductal papillary carcinoma on lumpectomy. The rate of upgrade in lumpectomy/mastectomy was 25%. CK5/6 and CK903 immunostains were performed on all seven core needle biopsies that were later upgraded.

Conclusions

In our predominantly African-American urban population, 25% of benign or atypical papillary lesions diagnosed on CNB was upgraded in the final excisional examination. Early excision of all papillary lesions diagnosed on CNB may be justified in this patient population.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/7950117821177201

Quantitative assessment of the influence of common variations (rs8034191 and rs1051730) at 15q25 and lung cancer risk

Several genome-wide association studies on lung cancer (LC) have reported similar findings of a new susceptibility locus, 15q25. After that, a number of studies reported that rs8034191 and rs1051730 polymorphisms at chromosome 15q25 have been implicated in LC risk. However, studies have yielded contradictory results. To derive a more precise estimation of the relationship, a meta-analysis of 43,742 LC cases and 58,967 controls from 17 published case-control studies was performed. Overall, significantly elevated LC risk was associated with rs8034191-C (OR = 1.26, 95% CI 1.22-1.31, P < 10(-5)) and rs105173-A variant (OR = 1.28, 95% CI 1.20-1.36, P < 10(-5)) when all studies were pooled into the meta-analysis. In the subgroup analysis by ethnicity, significantly increased risks were found for rs8034191 and rs105173 polymorphisms among Caucasians and African American, while no significant associations were observed for the two polymorphisms in East Asians. In addition, we found that rs8034191 and rs105173 confer risk, for both adenocarcinoma and squamous cell carcinoma when stratified by histological types of LC. Furthermore, our results on stratified analysis according to smoking status showed an increased LC risk in ever-smokers, while no associations were detected among never-smokers for the two polymorphisms. In conclusion, this meta-analysis demonstrated that the two common variations (rs8034191 and rs1051730) at 15q25 are a risk factor associated with increased LC susceptibility, but these associations vary in different ethnic populations.