Search

Background: This in silico study investigated the association between the local biosynthesis of cholesterol and mammographic density, the major risk of developing breast cancer, as a function of the three cellular components of breast tissue (epithelium, fatty, and non-fatty stroma). Methods: The study compared the expression of 7 genes (HMGCR, FDPS, FDFT1, GGPS1, SQLE, LSS, and SREBF2) involved in the de novo cholesterol biosynthesis, first, according to the radiological density (dense vs. non-dense breast) and, then, according to the cellular components of breast tissue, regardless of the radiological classification. Results: HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in radiologically dense than in non-dense breasts (-1.70 vs. -1.41, P=0.0028; -1.20 vs. -1.11, P=0.0501; -3.63 vs. -3.31 P=0.0003; -0.92 vs. -0.76, P=0.0271, respectively). When the samples were reclassified based on their cellular components as highly fatty and highly non-fatty, HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in highly non-fatty samples (-1.48 vs. -1.94, P<0.0001; -3.39 vs. -4.18, P<0.0001; -0.77 vs. -0.94, P=0.0103, respectively), whereas LSS was overexpressed in high fatty ones (0.28 vs. -0.60, P<0.0001). Besides, while in the highly non-fatty subgroup, SREBF2 was positively associated with both HMGCR (r=0.53, P<0.0001) and SQLE (r=0.73, P<0.0001), in the highly fatty subgroup, these positive correlations disappeared (SREBF2*HMGCR: r=-0.19, P=0.3026) or substantially decreased (SREBF2*SQLE: r=0.41, P=0.0173). Conclusion: Findings provide a compelling biological explanation for the clinical evidence that women with radiologically dense breasts are at a higher risk of developing cancer compared to those with non-dense breasts because of the prevalence of non-fatty tissue, where the altered expression of genes leading to an increased cholesterol production can contribute to the transformation of epithelial cells, and support the use of mammographic density as a reliable surrogate marker to identify women who may benefit from a preventive treatment aimed at reducing cholesterol production. [ABSTRACT FROM AUTHOR]

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/ alkylating agent-based chemotherapy. [ABSTRACT FROM AUTHOR]

A biological characterisation carried out on 14 007 primary breast cancers provided evidence in favour of a relation between advanced age and favourable features (positivity for oestrogen/progesterone receptors, low proliferative rate, absence of p53 accumulation, bcl-2 overexpression, diploid DNA content), showed a similar pattern of association between patho-biological variables regardless of patient age, and demonstrated a relation between biological variables and disease outcome in the elderly, comparable to that already reported for younger patients. In fact, oestrogen receptor and proliferative activity provided independent prognostic information either in node-negative or in node-positive tumours treated with radical or conservative surgery plus radiotherapy, alone or followed by adjuvant hormonal therapy. It would be thus reasonable to use biomarkers as a complement to clinico-pathological features in a ‘risk-factor profile system’ even for elderly patients, upon their validation in prospective studies and after assessing the cost-benefit of treatments planned on the basis of biological information. [Copyright &y& Elsevier]

The article presents a response to the research on the limits of predictive models using microarray data for breast cancer clinical treatment. The author finds that the criticism and the limited data previously presented do not affect his conclusions that the performance of some multigene biomarker classifiers was unsatisfactory. He suggested that more validation studies are needed.