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IntroductionDetection of serum biomarkers for early diagnosis of breast cancer remains an important goal. Changes in the structure of O-linked glycans occur in all breast cancers resulting in the expression of glycoproteins that are antigenically distinct. Indeed, the serum assay widely used for monitoring disease progression in breast cancer (CA15.3), detects a glycoprotein (MUC1), but elevated levels of the antigen cannot be detected in early stage patients. However, since the immune system acts to amplify the antigenic signal, antibodies can be detected in sera long before the antigen. We have exploited the change in O-glycosylation to measure autoantibody responses to cancer-associated glycoforms of MUC1 in sera from early stage breast cancer patients.MethodsWe used a microarray platform of 60mer MUC1 glycopeptides, to confirm the presence of autoantibodies to cancer associated glycoforms of MUC1 in a proportion of early breast cancer patients (54/198). Five positive sera were selected for detailed definition of the reactive epitopes using on chip glycosylation technology and a panel of glycopeptides based on a single MUC1 tandem repeat carrying specific glycans at specific sites. Based on these results, larger amounts of an extended repertoire of defined MUC1 glycopeptides were synthesised, printed on microarrays, and screened with sera from a large cohort of breast cancer patients (n = 395), patients with benign breast disease (n = 108) and healthy controls (n = 99). All sera were collected in the 1970s and 1980s and complete clinical follow-up of breast cancer patients is available.ResultsThe presence and level of autoantibodies was significantly higher in the sera from cancer patients compared with the controls, and a highly significant correlation with age was observed. High levels of a subset of autoantibodies to the core3MUC1 (GlcNAcβ1-3GalNAc-MUC1) and STnMUC1 (NeuAcα2,6GalNAc-MUC1) glycoforms were significantly associated with reduced incidence and increased time to metastasis.ConclusionsAutoantibodies to specific cancer associated glycoforms of MUC1 are found more frequently and at higher levels in early stage breast cancer patients than in women with benign breast disease or healthy women. Association of strong antibody response with reduced rate and delay in metastases suggests that autoantibodies can affect disease progression.

We found no evidence that the level of melatonin is strongly associated with the risk for breast cancer.

Mammographic features are known to be associated with breast cancer but the magnitude of the effect differs markedly from study to study. Methods to assess mammographic features range from subjective qualitative classifications to computer-automated quantitative measures. We used data from the UK Guernsey prospective studies to examine the relative value of these methods in predicting breast cancer risk. In all, 3,211 women ages z35 years who had a mammogram taken in 1986 to 1989 were followed-up to the end of October 2003, with 111 developing breast cancer during this period. Mammograms were classified using the subjective qualitative Wolfe classification and several quantitative mammographic features measured using computer-based techniques. Breast cancer risk was positively associated with high-grade Wolfe classification, percent breast density and area of dense tissue, and negatively associated with area of lucent tissue, fractal dimension, and lacunarity. Inclusion of the quantitative measures in the same model identified area of dense tissue and lacunarity as the best predictors of breast cancer, with risk increasing by 59% [95% confidence interval (95% CI), 29-94%] per SD increase in total area of dense tissue but declining by 39% (95% CI, 53-22%) per SD increase in lacunarity, after adjusting for each other and for other confounders. Comparison of models that included both the qualitative Wolfe classification and these two quantitative measures to models that included either the qualitative or the two quantitative variables showed that they all made significant contributions to prediction of breast cancer risk. These findings indicate that breast cancer risk is affected not only by the amount of mammographic density but also by the degree of heterogeneity of the parenchymal pattern and, presumably, by other features captured by the Wolfe classification.