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Breast and axillary surgery after neoadjuvant systemic treatment for women with breast cancer has undergone multiple paradigm changes within the past years. In this review, we provide a state-of-the-art overview of breast and axillary surgery after neoadjuvant systemic treatment from both, a clinical routine perspective and a clinical research perspective. For axillary disease, axillary lymph node dissection, sentinel lymph node biopsy, or targeted axillary dissection are nowadays recommended depending on the lymph node status before and after neoadjuvant systemic treatment. For the primary tumor in the breast, breast conserving surgery remains the standard of care. The clinical management of exceptional responders to neoadjuvant systemic treatment is a pressing knowledge gap due to the increasing number of patients who achieve a pathologic complete response to neoadjuvant systemic treatment and for whom surgery may have no therapeutic benefit. Current clinical research evaluates whether less invasive procedures can exclude residual cancer after neoadjuvant systemic treatment as reliably as surgery to possibly omit surgery for those patients in the future.

Background Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer. Methods We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristics-curve (AUC) was the primary outcome measure. Results Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n = 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86–0.87 and for decreased satisfaction AUCs were 0.84–0.85. Conclusion Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life., Highlights • Optimal treatment for women undergoing mastectomy and reconstruction is unclear. • Can machine learning algorithms predict long-term patient-reported outcomes? • An international cohort of 1553 women was used for the algorithm development. • External validation showed good performance with AUCs of 0.84–0.87 • accurate estimates of expected quality of life may help inform treatment decisions.

Background Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in about 35% of women with breast cancer. In such cases, breast surgery may be considered overtreatment. We evaluated multivariate algorithms using patient, tumor, and vacuum-assisted biopsy (VAB) variables to identify patients with breast pCR. Methods We developed and tested four multivariate algorithms: a logistic regression with elastic net penalty, an Extreme Gradient Boosting (XGBoost) tree, Support Vector Machines (SVM), and neural network. We used data from 457 women, randomly partitioned into training and test set (2:1), enrolled in three trials with stage 1–3 breast cancer, undergoing VAB before surgery. False-negative rate (FNR) and specificity were the main outcome measures. The best performing algorithm was validated in an independent fourth trial. Results In the test set (n = 152), the logistic regression with elastic net penalty, XGboost tree, SVM, and neural network revealed an FNR of 1.2% (1 of 85 patients with missed residual cancer). Specificity of the logistic regression with elastic net penalty was 52.2% (35 of 67 women with surgically confirmed breast pCR identified), of the XGBoost tree 55.2% (37 of 67), of SVM 62.7% (42 of 67), and of the neural network 67.2% (45 of 67). External validation (n = 50) of the neural network showed an FNR of 0% (0 of 27) and a specificity of 65.2% (15 of 23). Area under the ROC curve for the neural network was 0.97 (95% CI, 0.94–1.00). Conclusion A multivariate algorithm can accurately select breast cancer patients without residual cancer after neoadjuvant treatment.

Tailoring of breast cancer treatment to the individual has especially occurred in breast cancer surgery: paradigms have changed from Halsted's radical mastectomy in 1882, to simple mastectomy, to lumpectomy. Within the next decade, we might face another paradigm change of omitting breast cancer surgery at all in case of a complete response after neoadjuvant systemic treatment. In this article, we provide an overview of the reasoning for this new paradigm change, the criticism it has evoked, and under which conditions it might be incorporated into clinical practice. We also take a look at previous paradigm changes in breast cancer surgery and the insights they provide us in the current situation on a statistical but also on a psychological level.

The glucocorticoid and mineralocorticoid receptors are known to play a crucial role in cellular responses to acute and chronic stress conditions. However, the influence of genetic variants and regulatory mechanisms within the glucocorticoid and mineralocorticoid receptor genes NR3C1 and NR3C2 is still incompletely understood. We therefore investigated putative upstream open reading frames, a motif regulating gene expression, from the 5′ untranslated regions of the predominant human glucocorticoid receptor gene NR3C1 isoform alpha variant 1 and from the human mineralocorticoid receptor NR3C2 variants 1 and 2. The in silico analysis displayed one SNP (rs10482612), being present heterozygously in about 1.2% of the world population and 1.8% of the European population (according to the NCBI database), whose minor allele ‘A’ creates an upstream start codon. Our functional analysis performed by reporter gene assay and quantitative real-time PCR confirmed that the minor allele ‘A’ of the SNP rs10482612 can indeed alter protein activity of the subsequent gene during baseline conditions and cellular stress by creating a functional uORF in the 5′UTR of the NR3C1 transcript variant 1.