Identification of favorable subgroups for alternative anti‑androgen therapy in castration‑resistant prostate cancer

Updates on Overcoming Bicalutamide Resistance: A Glimpse into Resistance to a Novel Antiandrogen

The standard androgen deprivation therapy for advanced prostate cancer includes the use of bicalutamide, which is a well-known antagonist of androgen receptors. Despite numerous benefits of the drugs in prostate cancer treatment, there is always a risk of developing a resistant phenotype, which paves the way for a more aggressive and low-survival type of prostate cancer. Over the years, many studies have investigated the candidate mechanisms of such resistance and have managed to find possible therapeutic solutions. In this Review, we shed light on the heterogeneous dynamics of progression to resistance against bicalutamide treatment, referring to the most recent studies and the approaches that have been so far discussed. This Review tries to offer a deep and comprehensive understanding about how the resistant cells become sensitive to the drug and what corresponding pathways lead to an appropriate solution for the antiandrogen resistance challenge.

DNA Methylation Modification Regulator-Mediated Molecular Clusters and Tumor Metabolic Characterization in Prostate Cancer

Background

An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear.

Methods

We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets.

Results

Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment.

Conclusion

Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa.