Jianmin Wang | PubMed Health

PubMed2026

EZH2 Suppression Diversifies Prostate Cancer Lineage Variant Evolution and Lacks Efficacy in Inhibiting Disease Progression.

UNLABELLED: Advanced prostate cancer remains a leading cause of cancer-related death among men due to disease progression in nearly all patients on standard-of-care therapy targeting the androgen receptor. An important mechanism driving therapeutic resistance is lineage plasticity, which enables prostate cancer cells to reprogram into lineage variants no longer dependent on androgen receptor signaling. As inhibitors of the histone methyltransferase enhancer of zeste homolog 2 (EZH2) are being evaluated clinically for the treatment of advanced prostate cancer, we investigated in this study how EZH2 affects prostate cancer lineage plasticity. Data from genetically engineered mice and human clinical samples demonstrated that genetic or pharmacologic suppression of EZH2 altered chromatin to expand active transcription factor programs. These changes in gene expression during prostate cancer progression increased the diversity of prostate cancer lineage variants that arose. EZH2 suppression did not inhibit disease progression nor therapeutic resistance in this context. These findings advance the current understanding of prostate cancer lineage plasticity and suggest that EZH2 inhibitors may be less effective in treating prostate cancer prone to lineage plasticity. SIGNIFICANCE: EZH2 suppression diversifies prostate cancer lineage plasticity, which has implications for EZH2-targeted therapies that are being evaluated for prostate cancer treatment. See related commentary by Thienger et al., p. 827.

Enhancer of Zeste Homolog 2 Protein↗

PubMed2025

An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.

MOTIVATION: Accurate prediction of protein-ligand binding (PLB) relationships plays a crucial role in drug discovery, which helps identify drugs that modulate the activity of specific targets. Traditional biological assays for measuring PLB relationships are time consuming and costly. In addition, models for predicting PLB relationships have been developed and widely used in drug discovery tasks. However, learning more accurate PLB representations is essential to meet the stringent standards required for drug discovery. RESULTS: We propose an image-based PLB representation learning framework, called ImagePLB, which equips ligand representation learner (LRL) and protein representation learner (PRL) to accept 3D multi-view ligand images and protein graphs as input, respectively, and learns rich interaction information between ligand and protein through a binding representation learner (BRL). Considering the scarcity of protein-ligand pairs, we further propose a multi-level next trajectory prediction (MLNTP) task to pre-train ImagePLB on the 4D flexible dynamics trajectory of 16 972 complexes, including ligand level, protein level, and complex level, to learn information related to trajectories. Besides, by introducing trajectory regularization (TR), we effectively alleviate the problem of high (even almost identical) feature similarity caused by adjacent trajectories. Compared with the current state-of-the-art methods, ImagePLB has achieved competitive improvements on PLB-related prediction tasks, including protein-ligand affinity and efficacy prediction tasks. This study opens the door to the image-based PLB learning paradigm. AVAILABILITY AND IMPLEMENTATION: All data and implementation details of code can be obtained from https://github.com/HongxinXiang/ImagePLB.

Ligands↗