RESUMO
Inflammation is a common condition of prostate tissue, whose impact on carcinogenesis is highly debated. Microbial colonization is a well-documented cause of a small percentage of prostatitis cases, but it remains unclear what underlies the majority of sterile inflammation reported. Here, androgen- independent fluctuations of PSA expression in prostate cells have lead us to identify a prominent function of the Transient Receptor Potential Cation Channel Subfamily M Member 8 (TRPM8) gene in sterile inflammation. Prostate cells secret TRPM8 RNA into extracellular vesicles (EVs), which primes TLR3/NF-kB-mediated inflammatory signaling after EV endocytosis by epithelial cancer cells. Furthermore, prostate cancer xenografts expressing a translation-defective form of TRPM8 RNA contain less collagen type I in the extracellular matrix, significantly more infiltrating NK cells, and larger necrotic areas as compared to control xenografts. These findings imply sustained, androgen-independent expression of TRPM8 constitutes as a promoter of anticancer innate immunity, which may constitute a clinically relevant condition affecting prostate cancer prognosis.
Assuntos
Neoplasias da Próstata , Canais de Cátion TRPM , Humanos , Masculino , Androgênios , Inflamação/genética , Fator Regulador 3 de Interferon , Proteínas de Membrana , NF-kappa B/genética , Neoplasias da Próstata/genética , Receptor 3 Toll-Like/genética , Canais de Cátion TRPM/genética , AnimaisRESUMO
Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.
RESUMO
Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.