Mutation analysis of the BCCIP gene for breast cancer susceptibility in breast/ovarian cancer families

Expression and Significance of BCCIP and Glutathione Peroxidase 4 in Clear Cell Renal Cell Carcinoma

In this retrospective study involving 112 patients with clear cell renal cell carcinoma (ccRCC), we analyzed clinical significance and prognostic value of the expression of BCCIP protein interacting with BRCA2 and CDKN1A and glutathione peroxidase 4 (GPX4). The expressions of mRNA and the corresponding proteins were evaluated using reverse transcription PCR and immunohistochemistry. In comparison with control samples of renal peritumoral tissue, the expressions of BCCIP and its mRNA in the tumor tissues were significantly down-regulated, while the expressions of GPX4 and the corresponding mRNA were significantly up-regulated. The down-regulation of BCCIP expression was closely related to histological grade, TNM stage, and lymph node metastasis (p<0.05). The GPX4 overexpression was closely related to tumor size, TNM stage, and the presence of distant metastasis. The Kaplan-Meier survival analysis showed that tumor size, TNM stage, lymph node metastasis, distant metastasis, expressions of BCCIP and GPX4 correlated with progression-free survival (p<0.05). Multivariate Cox regression showed that down-regulation of BCCIP expression and overexpression of GPX4, TNM stage, and distant metastasis were independent prognostic factors of progression-free survival. Thus, down-regulation of BCCIP expression and overexpression of GPX4 are indicatives of progression of ccRCC with poor prognosis. Hence, the control of expression of these proteins can be considered as a novel target for the treatment of ccRCC.

Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

Background

Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score.

Methods

Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve.

Results

The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10^− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%).

Conclusion

Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

Roles of BCCIP deficiency in mammary tumorigenesis

Background

Dysregulated DNA repair and cell proliferation controls are essential driving forces in mammary tumorigenesis. BCCIP was originally identified as a BRCA2 and CDKN1A interacting protein that has been implicated in maintenance of genomic stability, cell cycle regulation, and microtubule dynamics. The aims of this study were to determine whether BCCIP deficiency contributes to mammary tumorigenesis, especially for a subset of breast cancers with 53BP1 abnormality, and to reveal the mechanistic implications of BCCIP in breast cancer interventions.

Methods

We analyzed the BCCIP protein level in 470 cases of human breast cancer to determine the associations between BCCIP and 53BP1, p53, and subtypes of breast cancer. We further constructed a unique BCCIP knockdown mouse model to determine whether a partial BCCIP deficiency leads to spontaneous breast cancer formation.

Results

We found that the BCCIP protein level is downregulated in 49% of triple-negative breast cancer and 25% of nontriple-negative breast cancer. The downregulation of BCCIP is mutually exclusive with p53 mutations but concurrent with 53BP1 loss in triple-negative breast cancer. In a K14-Cre-mediated conditional BCCIP knockdown mouse model, we found that BCCIP downregulation causes a formation of benign modules in the mammary glands, resembling the epidermal inclusion cyst of the breast. However, the majority of these benign lesions remain indolent, and only ~ 10% of them evolve into malignant tumors after a long latency. This tumor progression is associated with a loss of 53BP1 and p16 expression. BCCIP knockdown did not alter the latency of mammary tumor formation induced by conditional Trp53 deletion.

Conclusions

Our data suggest a confounding role of BCCIP deficiency in modulating breast cancer development by enhancing tumor initiation but hindering progression. Furthermore, secondary genetic alternations may overcome the progression suppression imposed by BCCIP deficiency through a synthetic viability mechanism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0907-5) contains supplementary material, which is available to authorized users.

Modeling of Cancer Classifier to Predict Site of Origin

Cancer classification based on site of origin is very significant research issue for prediction and treatment of cancer. This paper is addressing the problem of cancer classification for Homo Sapiens genes composed of amino acid chain. Cancer gene network is realized by equivalent electrical circuits based on hydrophilic/ hydrophobic property of amino acid and a classifier is modeled to determine the cancer origin. The phase value, peak gain value and shape of Nyquist curve of network model are investigated to characterize different types of cancer gene origins. The model achieves 81.09% of classification accuracy and proves to be more sensitive and simple, since it shows 69% better performance compare to the existing nucleotide based method. The proposed classifier successfully predicts the site of origin of 93 cancer gene samples.