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PURPOSE Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application. METHODS Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team. RESULTS 233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p

Unlike single-gene mutations leading to Mendelian conditions, common human diseases are likely emergent phenomena arising from multilayer, multiscale and highly interconnected interactions. Atrial and ventricular septal defects are the most common forms of cardiac congenital anomalies in humans. Atrial septal defects (ASD) show an open communication between left and right atria postnatally, potentially resulting in serious hemodynamic consequences if untreated. A milder form of atrial septal defect, patentforamen ovale(PFO), exists in about one quarter of the human population, strongly associated with ischaemic stroke and migraine. The anatomic liabilities and genetic and molecular basis of atrial septal defects remain unclear. Here, we advance our previous analysis of atrial septal variation through quantitative trait locus (QTL) mapping of an advanced intercross line (AIL) established between the inbred QSi5 and 129T2/SvEms mouse strains, that show extremes of septal phenotypes. Analysis resolved 37 unique septal QTL with high overlap between QTL for distinct septal traits. Whole genome sequencing of parental strains identified high confidence candidate deleterious variants, including in known human congenital heart disease genes, whereas transcriptome analysis of developing septa revealed networks involving ribosome, nucleosome, mitochondrial and extracellular matrix biosynthesis underlying septal variation. Analysis of variant architecture across different gene features, including enhancers and promoters, provided evidence for involvement of non-coding as well as protein coding variants. Our study provides the first high resolution picture of genetic complexity and network liability underlying common congenital heart disease, with relevance to human ASD and PFO.

Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application.Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team.233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p 0.001) were the strongest discriminators of risk.Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.

Nuclear receptors (NRs) have been targets of intensive drug development for decades due to their roles as key regulators of multiple developmental, physiological and disease processes. In breast cancer, expression of the estrogen and progesterone receptor remains clinically important in predicting prognosis and determining therapeutic strategies. More recently, there is growing evidence supporting the involvement of multiple nuclear receptors other than the estrogen and progesterone receptors, in the regulation of various processes important to the initiation and progression of breast cancer. We review new insights into the mechanisms of action of NRs made possible by recent advances in genomic technologies and focus on the emerging functional roles of NRs in breast cancer biology, including their involvement in circadian regulation, metabolic reprogramming and breast cancer migration and metastasis.

Although molecular signatures based on transcript expression in breast cancer samples have provided new insights into breast cancer classification and prognosis, there are acknowledged limitations in current signatures. To provide rational, pathway‐based signatures of disrupted physiology in cancer tissues that may be relevant to prognosis, this study has directly quantitated changed gene expression, between normal breast and cancer tissue, as a basis for signature development. The nuclear receptor (NR) family of transcription factors, and their coregulators, are fundamental regulators of every aspect of metazoan life, and were rigorously quantified in normal breast tissues and ERα positive and ERα negative breast cancers. Coregulator expression was highly correlated with that of selected NR in normal breast, particularly from postmenopausal women. These associations were markedly decreased in breast cancer, and the expression of the majority of coregulators was down‐regulated in cancer tissues compared with normal. While in cancer the loss of NR‐coregulator associations observed in normal breast was common, a small number of NR (Rev‐ERBβ, GR, NOR1, LRH‐1 and PGR) acquired new associations with coregulators in cancer tissues. Elevated expression of these NR in cancers was associated with poorer outcome in large clinical cohorts, as well as suggesting the activation of ERα ‐related, but ERα‐independent, pathways in ERα negative cancers. In addition, the combined expression of small numbers of NR and coregulators in breast cancer was identified as a signature predicting outcome in ERα negative breast cancer patients, not linked to proliferation and with predictive power superior to existing signatures containing many more genes. These findings highlight the power of predictive signatures derived from the quantitative determination of altered gene expression between normal breast and breast cancers. Taken together, the findings of this study identify networks of NR‐coregulator associations active in normal breast but disrupted in breast cancer, and moreover provide evidence that signatures based on NR networks disrupted in cancer can provide important prognostic information in breast cancer patients.

Cumulative exposure to estrogen (E) and progesterone (P) over the menstrual cycle significantly influences the risk of developing breast cancer. Despite the dogma that PR in the breast merely serves as a marker of an active estrogen receptor (ER), and as an inhibitor of the proliferative actions of E, it is now clear that in the breast P increases proliferation independently of E action. We show here that the progesterone receptor (PR) and ER are expressed in different epithelial populations, and target non-overlapping pathways in the normal human breast. In breast cancer, PR becomes highly correlated with ER, and this convergence is associated with signaling pathways predictive of disease metastasis. These data challenge the established paradigm that ER and PR function co-operatively in normal breast, and have significant implications not only for our understanding of normal breast biology, but also for diagnosis, prognosis and/or treatment options in breast cancer patients.