Bocci, G. | Search bioRxiv

bioRxiv2026

Pharmacological targeting of EED is an effective therapeutic strategy in cellular models of incurable neuroendocrine prostate cancer

BackgroundNeuroendocrine Prostate Cancer (NEPC) is an incurable malignancy, originating from the trans-differentiation of prostate adenocarcinoma (PRAD). Compared to PRAD, NEPC shows over-activation of Polycomb Repressive complex-1(PRC1) and-2 (PRC2), which are multiprotein epigenetic writers that drive cancer progression via tumour suppressor gene silencing. Tazemetostat is a PRC2 inhibitor approved for the treatment of sarcomas and lymphomas. ORIC-944 is a novel EED (Embryonic Ectoderm Development) inhibitor, which is being tested in clinical trials. EED is an attractive target as it functions as a key component of both PRC1 and PRC2. Objective and MethodsWe compared the anticancer effects of tazemetostat and ORIC-944 in NEPC and PRAD cells. Cells were exposed to various concentrations of the two compounds to measure effects on cell viability (IC50) and apoptosis (flow cytometry). PRC2 inhibition was confirmed by measuring histone H3 Lys 27 trimethylation (H3K27me3) via ELISA and Western Blot. RNA Sequencing and pathway analysis was conducted to study modes of actions of tazemetostat vs ORIC-944. ResultsUnlike tazemetostat, ORIC-944 causes dose-dependent growth inhibition in both NEPC and PRAD cells. In this context, EED targeting achieves IC50 values that are comparable to those of compounds used for the clinical treatment of advanced prostate cancer. Moreover, ORIC-944 (but not tazemetostat) causes significant apoptosis in NEPC cells. Both tazemetostat and ORIC-944 reduce H3K27me3. Mechanistically, both compounds reactivate the expression of known PRC2 targets, such as genes that control neural differentiation. However, the EED inhibitor also reactivates PRC1 targets, including pro-apoptotic and anti-proliferating genes (e.g. metallothionines). This evidence suggests that EED inhibition is a promising therapeutic strategy for NEPC.

cancer biology↗

bioRxiv2021

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of more than 530 PKs have been targeted by FDA-approved medications and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on co-occurrence patterns in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. In historical data, associations between PKs and specific cancers could be predicted years in advance with good accuracy. Our model may be a tool to predict the relevance of inhibiting PKs with specific cancers.

bioinformatics↗