The Genetics of Breast Cancer: What the Surgical Oncologist Needs to Know

Genetic cancer predisposition syndromes among older adults

Earlier age at onset is one characteristic of hereditary cancer syndromes, so most studies of genetic testing have focused on young patients with cancer. However, recent studies of multigene panel tests in unselected cancer populations have detected a considerable proportion of older patients with germline pathogenic variants (PVs) in cancer susceptibility genes. As the number of older patients with cancer continues to rise, clinicians should be aware of genetic/genomic cancer risk assessment (GCRA) criteria in both young and older adults. Identifying individuals with a germline PV in a cancer susceptibility gene may be important for precision therapy of current cancers and screening and prevention of new primary cancers, as well as cascade testing to identify high cancer risks for family members. Typically, hereditary predisposition germline genetic testing has been recommended for patients with early onset cancers and/or a family history of cancer. However, more recently international guidelines recommend testing for potential therapeutic intervention regardless of age for some tumors frequently seen in older patients, such as epithelial ovarian, pancreatic, and metastatic prostate and breast cancers. GCRA in older patients may present challenges including: clonal hematopoiesis (CH) confounding test interpretation, ethical aspects (autonomy, nonmaleficence, beneficence), patient health status, comorbidities, as well as lack of insurance coverage. These factors should be considered during genetic counseling and when considering cancer screening and risk reduction procedures. This manuscript reviews available data on common hereditary cancer syndromes in older patients and provides tools to help providers perform GCRA in this population.

ATPase copper transporter A, negatively regulated by miR-148a-3p, contributes to cisplatin resistance in breast cancer cells

BACKGROUND

Breast cancer is the leading cause of death among women. Cisplatin is an effective drug for breast cancer, but resistance often develops during long term chemotherapy. While the mechanism of chemotherapy resistance is still not fully understood.

METHODS

Survival analyses of ATP7A and ATP7B were used to evaluate their effects on the development of Breast invasive carcinoma (BRCA). Immunostaining, flow cytometry, and IC50 assay were utilized to examine the effects of ATP7A-siRNA combined with cisplatin on apoptosis in breast cancer cells. Q-PCR, western blotting, and dual-luciferase assay were employed to confirm ATP7A is a novel target gene of miR-148a-3p.

RESULTS

In this current study, we identified knocking-down ATP7A could enhance cytotoxicity treatment of cisplatin in breast cancer cells. We also demonstrated miR-148a-3p overexpression in BRCA cells increased the sensitivity to cisplatin, and subsequently enhanced DNA damage and apoptosis. Moreover, we found ATP7A is a novel target gene of miR-148a-3p. In brief, our results showed miR-148a could accelerate chemotherapy induced-apoptosis in breast cancer cells by inhibiting ATP7A expression.

CONCLUSIONS

Our results highlight that inhibition of ATP7A is a potential strategy for targeting breast cancer resistant to cisplatin, and we provided an interesting method to compare the involvement of various genes in the assessment of cisplatin resistance.

Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R² = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.