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The 2013 Breast Cancer Campaign gap analysis established breast cancer research priorities without a specific focus on surgical research or the role of surgeons on breast cancer research. This Review aims to identify opportunities and priorities for research in breast surgery to complement the 2013 gap analysis. To identify these goals, research-active breast surgeons met and identified areas for breast surgery research that mapped to the patient pathway. Areas included diagnosis, neoadjuvant treatment, surgery, adjuvant therapy, and attention to special groups (eg, those receiving risk-reducing surgery). Section leads were identified based on research interests, with invited input from experts in specific areas, supported by consultation with members of the Association of Breast Surgery and Independent Cancer Patients' Voice groups. The document was iteratively modified until participants were satisfied that key priorities for surgical research were clear. Key research gaps included issues surrounding overdiagnosis and treatment; optimising treatment options and their selection for neoadjuvant therapies and subsequent surgery; reducing rates of re-operations for breast-conserving surgery; generating evidence for clinical effectiveness and cost-effectiveness of breast reconstruction, and mechanisms for assessing novel interventions; establishing optimal axillary management, especially post-neoadjuvant treatment; and defining and standardising indications for risk-reducing surgery. We propose strategies for resolving these knowledge gaps. Surgeons are ideally placed for a central role in breast cancer research and should foster a culture of engagement and participation in research to benefit patients and health-care systems. Development of infrastructure and surgical research capacity, together with appropriate allocation of research funding, is needed to successfully address the key clinical and translational research gaps that are highlighted in this Review within the next two decades.

The B-cell CLL/lymphoma 2 (BCL2) gene family encodes pro- and anti-apoptotic proteins that are critical regulators of programmed cell death. Higher levels of BCL2 expression in breast tumours have been shown to be an independent prognostic factor for improved survival from breast cancer. The promoter single nucleotide polymorphism (SNP) rs2279115 has been associated with both BCL2 expression and patient survival. The aim of this study was to attempt to replicate these observations in a cohort of 1015 UK women with breast cancer, and to compare genotype frequencies in cases and controls. In this study, 1015 breast cancer cases and 1034 control subjects were genotyped for the rs2279115 SNP by 5' nuclease PCR. Paraffin embedded tumour tissue for 342 case subjects was assembled into tissue microarrays, and the level of expression of BCL2 was established by immunohistochemistry. Kaplan Meier survival curves and Cox Proportional Hazards models were used to examine the effect of genotype on patient survival. The effect of SNP genotype on tumour BCL2 protein levels and breast cancer susceptibility was assessed by logistic regression. In this study higher BCL2 expression was significantly associated with improved survival from breast cancer (p=0.015), in keeping with previous reports. The SNP rs2279115 was not found to be associated with tumour expression of BCL2, (p=0.77), and neither was it associated with case/control status (p=0.25). There was no significant association between the SNP and overall survival (p=0.75). In conclusion, we found that higher tumour BCL2 expression is associated with improved survival from breast cancer, in keeping with previous studies. However, in contrast to a previous report, the promoter SNP rs2279115 was not associated with BCL2 expression or overall survival from breast cancer.

Background Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate mortality for patients with the major subtypes of breast cancer as classified using five immunohistochemical markers, to investigate patterns of mortality over time, and to test for heterogeneity by subtype. Methods and Findings We pooled data from more than 10,000 cases of invasive breast cancer from 12 studies that had collected information on hormone receptor status, human epidermal growth factor receptor-2 (HER2) status, and at least one basal marker (cytokeratin [CK]5/6 or epidermal growth factor receptor [EGFR]) together with survival time data. Tumours were classified as luminal and nonluminal tumours according to hormone receptor expression. These two groups were further subdivided according to expression of HER2, and finally, the luminal and nonluminal HER2-negative tumours were categorised according to expression of basal markers. Changes in mortality rates over time differed by subtype. In women with luminal HER2-negative subtypes, mortality rates were constant over time, whereas mortality rates associated with the luminal HER2-positive and nonluminal subtypes tended to peak within 5 y of diagnosis and then decline over time. In the first 5 y after diagnosis the nonluminal tumours were associated with a poorer prognosis, but over longer follow-up times the prognosis was poorer in the luminal subtypes, with the worst prognosis at 15 y being in the luminal HER2-positive tumours. Basal marker expression distinguished the HER2-negative luminal and nonluminal tumours into different subtypes. These patterns were independent of any systemic adjuvant therapy. Conclusions The six subtypes of breast cancer defined by expression of five markers show distinct behaviours with important differences in short term and long term prognosis. Application of these markers in the clinical setting could have the potential to improve the targeting of adjuvant chemotherapy to those most likely to benefit. The different patterns of mortality over time also suggest important biological differences between the subtypes that may result in differences in response to specific therapies, and that stratification of breast cancers by clinically relevant subtypes in clinical trials is urgently required. Please see later in the article for the Editors' Summary

DNA damage and replication checkpoints mediated by the ATR-CHEK1 pathway are key to the maintenance of genome stability, and both ATR and CHEK1 have been proposed as potential breast cancer susceptibility genes. Many novel variants recently identified by the large resequencing projects have not yet been thoroughly tested in genome-wide association studies for breast cancer susceptibility. We therefore used a tagging SNP (tagSNP) approach based on recent SNP data available from the 1000 genomes projects, to investigate the roles of ATR and CHEK1 in breast cancer risk and survival. ATR and CHEK1 tagSNPs were genotyped in the Sheffield Breast Cancer Study (SBCS; 1011 cases and 1024 controls) using Illumina GoldenGate assays. Untyped SNPs were imputed using IMPUTE2, and associations between genotype and breast cancer risk and survival were evaluated using logistic and Cox proportional hazard regression models respectively on a per allele basis. Significant associations were further examined in a meta-analysis of published data or confirmed in the Utah Breast Cancer Study (UBCS). The most significant associations for breast cancer risk in SBCS came from rs6805118 in ATR (p=7.6x10-5) and rs2155388 in CHEK1 (p=3.1x10-6), but neither remained significant after meta-analysis with other studies. However, meta-analysis of published data revealed a weak association between the ATR SNP rs1802904 (minor allele frequency is 12%) and breast cancer risk, with a summary odds ratio (confidence interval) of 0.90 (0.83-0.98) [p=0.0185] for the minor allele. Further replication of this SNP in larger studies is warranted since it is located in the target region of 2 microRNAs. No evidence of any survival effects of ATR or CHEK1 SNPs were identified. We conclude that common alleles of ATR and CHEK1 are not implicated in breast cancer risk or survival, but we cannot exclude effects of rare alleles and of common alleles with very small effect sizes.