The Target for Statins,HMG-CoA Reductase,Is Expressed in Ductal Carcinoma-In Situ and May Predict Patient Response to Radiotherapy

Background

Patients with ductal carcinoma-in-situ (DCIS) are currently not prescribed adjuvant systemic treatment after surgery and radiotherapy. Prediction of DCIS patients who would benefit from radiotherapy is warranted. Statins have been suggested to exert radio-sensitizing effects. The target for cholesterol-lowering statins is HMG-CoA reductase (HMGCR), the rate-limiting enzyme in the mevalonate pathway. The aim of this study was to examine HMGCR expression in DCIS and study its treatment predictive value.

Methods

A population-based cohort including 458 women diagnosed with primary DCIS between 1986 and 2004 were followed until November 2011 to study long-term survival. Tumor tissue microarrays were constructed, and immunohistochemical analyses were performed to detect cytoplasmic protein expression of HMGCR. The association between DCIS HMGCR expression and invasive breast cancer recurrence-free survival (RFS_inv) and overall survival (OS) was analyzed by Kaplan–Meier curves, log rank test, and Cox proportional hazard analysis.

Results

HMGCR was strongly expressed in 24 % of the assessed DCIS samples, moderately expressed in 46 %, and weakly expressed in 23 %; no expression was detected in 7 % of the samples. During the follow-up time (median 13.8 years), 61 patients were diagnosed with an invasive breast cancer recurrence, and 80 patients died. A crude analysis showed no survival benefit from radiotherapy. However, patients with strong HMGCR expression showed an improved RFS_inv (log rank, p = 0.03) and OS (log rank, p = 0.04) after radiotherapy. No statistically significant interaction was observed for HMGCR and radiotherapy (RFS_inv p = 0.69 and OS p = 0.29).

Conclusions

This study demonstrates HMGCR expression in DCIS and suggests HMGCR as a predictive marker of response to postoperative radiotherapy in DCIS, although the test for interaction was nonsignificant. Future DCIS studies addressing the potential of statin treatment targeting HMGCR are warranted.     

Association between lung function decline and obstructive sleep apnoea: the ALEC study

Sleep and Breathing - To study changes in lung function among individuals with a risk of obstructive sleep apnoea (OSA), and if asthma affected this relationship. We used data from the European...     

Analogical Mapping with Sparse Distributed Memory: A Simple Model that Learns to Generalize from Examples

We present a computational model for the analogical mapping of compositional structures that combines two existing ideas known as holistic mapping vectors and sparse distributed memory. The model enables integration of structural and semantic constraints when learning mappings of the type $x_i \rightarrow y_i$ and computing analogies $x_j \rightarrow y_j$ for novel inputs x _j . The model has a one-shot learning process, is randomly initialized, and has three exogenous parameters: the dimensionality $\mathcal{D}$ of representations, the memory size S, and the probability χ for activation of the memory. After learning three examples, the model generalizes correctly to novel examples. We find minima in the probability of generalization error for certain values of χ, S, and the number of different mapping examples learned. These results indicate that the optimal size of the memory scales with the number of different mapping examples learned and that the sparseness of the memory is important. The optimal dimensionality of binary representations is of the order 10⁴, which is consistent with a known analytical estimate and the synapse count for most cortical neurons. We demonstrate that the model can learn analogical mappings of generic two-place relationships, and we calculate the error probabilities for recall and generalization.