VEGF 936C>T polymorphism and breast cancer risk: evidence needed further clarification

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER)

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP‑SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR‑ER analysis, six significant SNP‑SNP interaction models with seven genes (highest cross‑validation consistency, 10; classification error rates, 41.3‑21.0; and prediction error rates, 47.4‑55.3) were identified. CD4 and VEGFA genes were associated in a 2‑loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40‑2.90; P=1.71E‑04) and it also appeared in all the best 2‑7‑loci models. When the loci number increased, the classification error rates and P‑values decreased. The powers in 2‑7‑loci in all models were >0.9. The minimum classification error rate of the MDR‑ER‑generated model was shown with the 7‑loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54‑23.87; P=4.03E‑31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR‑ER can effectively and correctly identify the best SNP‑SNP interaction models in an imbalanced data set for breast cancer cases.

Vascular endothelial growth factor (VEGF) gene polymorphisms and breast cancer risk in a Chinese population

Vascular endothelial growth factor (VEGF) is a potent regulator of angiogenesis and thereby involved in the development and progression of solid tumours. Associations between three VEGF gene polymorphisms (-634 G/C, +936 C/T, and +1612 G/A) and breast cancer risk have been extensively studied, but the currently available results are inconclusive. Our aim was to investigate associations between three VEGF gene polymorphisms and breast cancer risk in Chinese Han patients. We performed a hospital-based case-control study including 680 female incident breast cancer patients and 680 female age-matched healthy control subjects. Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) analysis was performed to detect the three VEGF gene polymorphisms. We observed that women carriers of +936 TT genotypes [odds ratio (OR) =0.46, 95% confidence interval (CI) = 0.28, 0.76; P=0.002] or 936 T-allele (OR=0.81, 95% CI= 0.68, 0.98; P=0.03) had a protective effect concerning the disease. Our study suggested that the +1612G/A polymorphism was unlikely to be associated with breast cancer risk. The -634CC genotype was significantly associated with high tumor aggressiveness [large tumor size (OR=2.63, 95% CI=1.15, 6.02; P=0.02) and high histologic grade (OR=1.47, 95% CI= 1.06, 2.03; P=0.02)]. The genotypes were not related with other tumor characteristics such as regional or distant metastasis, stage at diagnosis, or estrogen or progesterone receptor status. Our study revealed that the VEGF -634 G/C and +936 C/T gene polymorphisms may be associated with breast cancer in Chinese Han patients.

Update on clinical trials: genetic targets in breast cancer

Breast cancer is the most commonly diagnosed cancer in women in United States. From data of American Cancer Society from 2007 reported total of 178,480 women diagnosed with breast cancer. The death rate from breast cancer has decreased in North America over time, but still accounts for second highest cancer death, following lung cancer. Breast cancer is staged based on tumor size, nodal involvement, and distant metastasis like any other solid tumors. However clinical staging is not the only important factor in management of breast cancer. Various molecular features divides breast cancer into many subgroups - that act differently, and respond differently from therapy. Thus the focus of breast cancer treatment has evolved focusing on specific targets. The most important biologic markers in subtyping of breast cancer so far are hormone receptor positivity and HER2/neu protein expression. Five molecular subtypes using intrinsic gene set include Basal mRNA, HER2 + mRNA, Luminal AmRNA, Luminal B mRNA, and Normal-like mRNA. In addition, better understanding of genetic target of breast cancer has given us arsenal of personalized, and more effective treatment approach.This review will focus on examples that highlight several mechanism of tumorigenesis, giving us not just understanding of gene pathways and the molecular biology, that could lead us to therapeutic target. Several important molecular targets have been investigated in preclinical and clinical trials, others are yet to be explored. We will also describe genetic mechanisms discovery related to overcoming resistance to current targeted therapies in breast cancer, including hormone receptor expression and HER 2- neu amplification. We will also review other exciting developments in understanding of breast cancer, the tumor microenvironment and cancer stem cells, and targeting agents in that area.