Anti-tumoral effect of the non-nucleoside DNMT inhibitor RG108 in human prostate cancer cells

Background: Current therapeutic strategies for advanced prostate cancer (PCa) are largely ineffective. Because aberrant DNA methylation associated with inappropriate gene-silencing is a common feature of PCa, DNA methylation inhibitors might constitute an alternative therapy. In this study we aimed to evaluate the anti-cancer properties of RG108, a novel non-nucleoside inhibitor of DNA methyltransferases (DNMT), in PCa cell lines. Methods: The anti-tumoral impact of RG108 in LNCaP, 22Rv1, DU145 and PC-3 cell lines was assessed through standard cell viability, apoptosis and cell cycle assays. Likewise, DNMT activity, DNMT1 expression and global levels of DNA methylation were evaluated in the same cell lines. The effectiveness of DNA demethylation was further assessed through the determination of promoter methylation and transcript levels of GSTP1, APC and RAR-β2, by quantitative methylation-specific PCR and RT-PCR, respectively. Results: RG108 led to a significant dose and time dependent growth inhibition and apoptosis induction in LNCaP, 22Rv1 and DU145. LNCaP and 22Rv1 also displayed decreased DNMT activity, DNMT1 expression and global DNA methylation. Interestingly, chronic treatment with RG108 significantly decreased GSTP1, APC and RAR-β2 promoter hypermethylation levels, although mRNA re-expression was only attained GSTP1 and APC. Conclusions: RG108 is an effective tumor growth suppressor in most PCa cell lines tested. This effect is likely mediated by reversion of aberrant DNA methylation affecting cancer related-genes epigenetically silenced in PCa. However, additional mechanism might underlie the anti-tumor effects of RG108. In vivo studies are now mandatory to confirm these promising results and evaluate the potential of this compound for PCa therapy.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.