Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype

Purpose: To investigate the proportion of breast cancers arising inpatients with germ line BRCA1 and BRCA2 mutations expressing basal markers and developing predictive tests for identification of high-risk patients. Experimental Design: Histopathologic material from 182 tumors in BRCA1 mutation carriers, 63 BRCA2 carriers, and 109 controls, collected as part of the international Breast Cancer Linkage Consortium were immunohistochemically stained for CK14, CK5/6, CK17, epidermal growth factor receptor (EGFR), and osteonectin. Results: All five basal markers were commoner in BRCA1 tumors than in control tumors (CK14: 61% versus 12%; CK5/6: 58% versus 7%; CK17: 53% versus 10%; osteonectin: 43% versus 19%; EGFR: 67% versus 21%; P < 0.0001 in each case). In a multivariate analysis, CK14, CK5/6, and estrogen receptor (ER) remained significant predictors of BRCA1 carrier status. In contrast, the frequency of basal markers in BRCA2 tumors did not differ significant from controls. Conclusion: The use of cytokeratin staining in combination with ER and morphology provides a more accurate predictor of BRCA1 mutation status than previously available, that may be useful in selecting patients for BRCA1 mutation testing. The high percentage of BRCA1 cases positive for EGFR suggests that specific anti-tyrosine kinase therapy may be of potential benefit in these patients.

Cashew nut allergy is associated with a high risk of anaphylaxis

Cashew allergy is an evolving clinical problem. A retrospective chart review of 213 children with peanut or tree nut allergy was undertaken over a 42 month period. Anaphylaxis to cashew nut was more common than to peanut ( 74.1% v 30.5%). Children with cashew allergy are at risk of anaphylaxis.

Reduced nephron number and glomerulomegaly in Australian Aborigines: A group at high risk for renal disease and hypertension

Aborigines in remote areas of Australia have much higher rates of renal disease, as well as hypertension and cardiovascular disease, than non-Aboriginal Australians. We compared kidney findings in Aboriginal and non-Aboriginal people in one remote region. Glomerular number and mean glomerular volume were estimated with the disector/fractionator combination in the right kidney of 19 Aborigines and 24 non-Aboriginal people undergoing forensic autopsy for sudden or unexpected death in the Top End of the Northern Territory. Aborigines had 30% fewer glomeruli than non-Aborigines-202000 fewer glomeruli per kidney, or an estimated 404000 fewer per person (P=0.036). Their mean glomerular volume was 27% larger (P=0.016). Glomerular number was significantly correlated with adult height, inferring a relationship with birthweight, which, on average, is much lower in Aboriginal than non-Aboriginal people. Aboriginal people with a history of hypertension had 30% fewer glomeruli than those without-250000 fewer per kidney (P=0.03), or 500000 fewer per person, and their mean glomerular volume was about 25% larger. The lower nephron number in Aboriginal people is compatible with their susceptibility to renal failure. The additional nephron deficit associated with hypertension is compatible with other reports. Lower nephron numbers are probably due in part to reduced nephron endowment, which is related to a suboptimal intrauterine environment. Compensatory glomerular hypertrophy in people with fewer nephrons, while minimizing loss of total filtering surface area, might be exacerbating nephron loss. Optimization of fetal growth should ultimately reduce the florid epidemic of renal disease, hypertension, and cardiovascular disease.

Cadmium in the environment and high risk population groups

Cadmium has been widely used in various industries for the past fifty years, with current world production standing at around 16,755 tonnes per year. Very little cadmium is ever recycled and the ultimate fate of all cadmium is the environment. In view of reports that cadmium in the environment is increasing, this thesis aims to identify population groups 'at risk' of receiving dietary intakes of cadmium up to or above the current Food and Agricultural Organisation/World Health Organisation maximum tolerable intake of 70 ug/day. The study involves the investigation of one hundred households (260 individuals) who grow a large proportion of their vegetable diet in garden soils in the Borough of Walsall, part of an urban/industrial area in the United Kingdom. Measurements were made of the cadmium levels in atmospheric deposition, soil, house dust, diet and urine from the participants. Atmospheric deposition of cadmium was found to be comparable with other urban/industrial areas in the European Community, with deposition rates as high as 209 g ha-1 yr-1. The garden soils of the study households were found to contain up to 33 mg kg-1 total cadmium, eleven times the highest level usually found in agricultural soils. Dietary intakes of cadmium by the residents from food were calculated to be as high as 68 ug/day. It is suggested that with intakes from other sources, such as air, adventitious ingestion, smoking and occupational exposure, total intakes of cadmium may reach or exceed the FAO/WHO limit. Urinary excretion of cadmium amongst a non-smoking, non-occupationally exposed sub-group of the study population was found to be significantly higher than that of a similar urban population who did not rely on home-produced vegetables. The results from this research indicate that present levels of cadmium in urban/industrial areas can increase dietary intakes and body burdens of cadmium. As cadmium serves no useful biological function and has been found to be highly toxic, it is recommended that policy measures to reduce human exposure on the European scale be considered.

New biomarkers and risk stratification in atrial fibrillation:simplicity and practicality matter

Advances in our understanding of pathological mechanisms can inform the identification of various biomarkers for risk stratification, monitoring drug efficacy and toxicity; and enabling careful monitoring of polypharmacy. Biomarkers in the broadest sense refer to 'biological markers' and this can be blood-based (eg. fibrin D-dimer, von Willebrand factor, etc) urine-based (eg. thromboxane), or even related to cardiac or cerebral imaging(1). Most biomarkers offer improvements over clinical risk scores in predicting high risk patients - at least statistically - but usually at the loss of simplicity and practicality for easy application in everyday clinical practice. Given the various biomarkers can be informed by different aspects of pathophysiology (e.g. inflammation, clotting, collagen turnover) they can nevertheless contribute to a better understanding of underlying disease processes(2). Indeed, many age-related diseases share common modifiable underpinning mechanisms e.g. inflammation, oxidative stress and visceral adiposity.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.