The role of HMGCR expression in combination therapy of simvastatin and FAC treated locally advanced breast cancer patients.

OBJECTIVE: Several studies have shown the role of statin added to the patient's chemotherapy regimen and the role of Hydroxymethylglutaryl-CoA Reductase (HMGCR) expression in predicting breast cancer patient outcomes. In our previous study, adding statins improved clinical and pathological responses in LABC patients. Furthermore, we planned to study statin's role as a combination to neoadjuvant chemotherapy (NAC) in treating locally advanced breast cancers on the basis of HMGCR expression. Moreover, we aimed to study the association between the patients' clinicopathological characteristics and HMGCR expression. METHODS: This study is a randomized, double-blinded, placebo-controlled trial in two health centers in Indonesia. Each patient enrolled with written informed consent and then randomized to receive either simvastatin 40 mg/day or a placebo, combined with the fluorouracil, adriamycin, and cyclophosphamide (FAC) NAC. RESULTS: HMGCR was associated with low staging and normal serum cholesterol in the high Ki67 level group (p = 0.042 and p = 0.021, respectively). The pre-and post-chemotherapy tumor sizes are significantly correlated in two groups (HMGCR negative expression, p = 0.000 and HMGCR moderate expression, p = 0.001) with a more considerable average decrease in tumor size compared to HMGCR strong expression group. CONCLUSION: Statin therapy might work better in HMGCR-negative or low-expression tumors, although HGMCR expression is associated with better clinical parameters in our study.

A topological data analytic approach for discovering biophysical signatures in protein dynamics.

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.