Mary Wilson

Biomarkers of dietary energy restriction in women at increased risk of breast cancer.

Dietary energy restriction (DER) reduces risk of spontaneous mammary cancer in rodents. In humans, DER in premenopausal years seems to reduce risk of postmenopausal breast cancer. Markers of DER are required to develop acceptable DER regimens for breast cancer prevention. We therefore examined markers of DER in the breast, adipose tissue, and serum. Nineteen overweight or obese women at moderately increased risk of breast cancer (lifetime risk, 1 in 6 to 1 in 3) ages between 35 and 45 were randomly allocated to DER [liquid diet, 3,656 kJ/d (864 kcal/d); n = 10] or asked to continue their normal eating patterns (n = 9) for one menstrual cycle. Biopsies of the breast and abdominal fat were taken before and after the intervention. RNA was extracted from whole tissues and breast epithelium (by laser capture microdissection) and hybridized to Affymetrix GeneChips. Longitudinal plasma and urine samples were collected before and after intervention, and metabolic profiles were generated using gas chromatography-mass spectrometry. DER was associated with significant reductions in weight [−7.0 (±2.3) kg] and in alterations of serum biomarkers of breast cancer risk (insulin, leptin, total and low-density lipoprotein cholesterol, and triglycerides). In both abdominal and breast tissues, as well as isolated breast epithelial cells, genes involved in glycolytic and lipid synthesis pathways (including stearoyl-CoA desaturase, fatty acid desaturase, and aldolase C) were significantly down-regulated. We conclude that reduced expressions of genes in the lipid metabolism and glycolytic pathways are detectable in breast tissue following DER, and these may represent targets for DER mimetics as effective chemoprophylactic agents.

Use of Volumetric Breast Density Measures for the Prediction of Weight and Body Mass Index

Body Mass Index (BMI) is an important confounding factor for breast density assessment, particularly where a relative measure (percentage density) is used. Since height and weight are not routinely collected at screening, we investigated the relationship between breast and fat volumes computed by QuantraTM and VolparaTM and weight/BMI in 6898 women for whom self-reported values are available. A significant positive correlation was found between breast volume and fat volume with both weight and BMI. BMI and VolparaTM average fat volume showed the strongest positive relationship (r = 0.728, p<0.001). Using these results we predicted weight and BMI for a separate group of women; these showed moderate intraclass correlation (ICC) agreement with self-reported weight and BMI. The strongest relationship was with weight predicted using QuantraTM average fat volume (ICC = 0.634, CI = 0.573-0.689, p<0.001), however our results suggest that it is not possible to accurately predict individuals’ weight and BMI from volumetric breast density measures.

Mammography reading with computer-aided detection (CAD): single view vs two views

Two-view mammography is known to be more effective than one-view in increasing breast cancer detection and reducing recall rates. In addition, there is evidence that computer aided detection (CAD) systems are able to prompt malignant abnormalities that have been overlooked by a human reader. Using data from the UK NHS Breast Screening Programme (NHSBSP) we compared double reading with single reading using a CAD system, to assess the relationship between CAD and number of views in terms of the sensitivity of the screening regime to cancer detection and the recall rate of normal cases. CAD appeared to contribute to an increased cancer detection rate with single-view mammography without significantly increasing the recall rate. For two-view mammography, there was no significant change in sensitivity using CAD but a significantly higher recall rate. However, single-view mammography was used in incident rounds in which previous mammograms were available whereas two-view mammography was used in the prevalent round where no previous mammograms were available.

Mammographic Density Over Time in Women With and Without Breast Cancer

This study compared mammographic density over time between women who developed breast cancer cases and women who did not controls. Cases had an initial negative mammographic screen and another three years later when cancer was diagnosed. Cases were matched to three controls with two successive negative screens by age, year of mammogram, BMI, parity, menopausal status and HRT use. Mammographic density was measured by VolparaTM. There was a significant reduction in percentage density in the affected breast for cases 5.2 to 4.8i?ź%, pi?ź<i?ź0.001 and for the same matched breast in controls 4.9 to 4.5, pi?ź<i?ź0.001. Similar results were found for the unaffected breast. After adjusting for density measures at the initial screen, case-control status was only significantly associated with fibroglandular volume in the unaffected breast adjusted mean 45.8i?źcm3 in cases, 44.0i?źcm3 in controls, pi?ź=i?ź0.008. The results suggest changes in mammographic density may be less important than initial mammographic density.

Should We Adjust Visually Assessed Mammographic Density for Observer Variability

This study aimed to determine whether correcting for observer variability alters estimations of breast cancer risk associated with mammographic density. A case control design examined the relationship between mammographic density, measured by visual analogue scales VAS, and the risk of breast cancer after correcting for observer variability. Mammographic density was assessed by two observers and average scores V2 were adjusted to correct for observer variability V2ad. Two case-control sets were identified: i breast cancer detected during screening at entry and ii breast cancer detected subsequently. Cases were matched to three controls. In the first case-control set the odds ratio for breast cancer was 4.6 95i?ź%CI 2.8---7.5 for the highest compared to the lowest quintile of V2, and was attenuated for V2ad OR 3.1, 95i?ź%CI 1.9---4.8. Similar findings were observed for the second case-control set. Not adjusting for observer variability may lead to an overestimate of the risk of breast cancer.