Tracking prostate cancer development at the single-cell level

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Preclinical Models in Prostate Cancer: Resistance to AR Targeting Therapies in Prostate Cancer

Prostate cancer is an androgen-driven tumor. Different prostate cancer therapies consequently focus on blocking the androgen receptor pathway. Clinical studies reported tumor resistance mechanisms by reactivating and bypassing the androgen pathway. Preclinical models allowed the identification, confirmation, and thorough study of these pathways. This review looks into the current and future role of preclinical models to understand resistance to androgen receptor-targeted therapies. Increasing knowledge on this resistance will greatly improve insights into tumor pathophysiology and future treatment strategies in prostate cancer.