Prognostic value of pretreatment inflammatory markers in localised prostate cancer before radical prostatectomy.

PURPOSE: There is growing evidence of an association between inflammatory processes and cancer development and progression. In different solid tumor entities, a pronounced inflammatory response is associated with worse oncological outcome. In this study, we aim to evaluate the prognostic role of clinically established pretreatment inflammatory markers in patients with localised prostate cancer (PCa) before radical prostatectomy (RP). METHODS: A total of 641 men met our inclusion criteria and were followed prospectively for a median of 2.85 years. Univariable logistic and Cox regression analysis were performed to analyse associations between preoperative inflammatory markers and tumor characteristics, and biochemical recurrence free survival (BRFS). RESULTS: Median age at RP was 64 years. Gleason Score (GS) 7a (263, 41%) was the most prevalent histology, whereas high-risk PCa (≥ GS 8) was present in 156 (24%) patients. Lympho-nodal metastasis and positive surgical margin (PSM) were detected in 69 (11%) and 180 (28%) patients, respectively. No statistically relevant association could be shown between pretreatment inflammatory markers with worse pathological features like higher tumor stage or grade, nodal positive disease or PSM (for all p > 0.05). Additionally, pretreatment inflammatory markers were not associated with a shorter BRFS (p > 0.05). Known risk factors (tumor grade, tumor stage, nodal positivity and positive surgical margins) were all associated with a shorter BRFS (for all p < 0.0001). CONCLUSION: In this large prospective cohort, preoperative inflammatory markers were not associated with worse outcome.

Digital pathology with artificial intelligence analysis provides insight to the efficacy of anti-fibrotic compounds in human 3D MASH model.

Metabolic dysfunction-associated steatohepatitis (MASH) is a severe liver disease characterized by lipid accumulation, inflammation and fibrosis. The development of MASH therapies has been hindered by the lack of human translational models and limitations of analysis techniques for fibrosis. The MASH three-dimensional (3D) InSight™ human liver microtissue (hLiMT) model recapitulates pathophysiological features of the disease. We established an algorithm for automated phenotypic quantification of fibrosis of Sirius Red stained histology sections of MASH hLiMTs model using a digital pathology quantitative single-fiber artificial intelligence (AI) FibroNest™ image analysis platform. The FibroNest™ algorithm for MASH hLiMTs was validated using anti-fibrotic reference compounds with different therapeutic modalities-ALK5i and anti-TGF-ß antibody. The phenotypic quantification of fibrosis demonstrated that both reference compounds decreased the deposition of fibrillated collagens in alignment with effects on the secretion of pro-collagen type I/III, tissue inhibitor of metalloproteinase-1 and matrix metalloproteinase-3 and pro-fibrotic gene expression. In contrast, clinical compounds, Firsocostat and Selonsertib, alone and in combination showed strong anti-fibrotic effects on the deposition of collagen fibers, however less pronounced on the secretion of pro-fibrotic biomarkers. In summary, the phenotypic quantification of fibrosis of MASH hLiMTs combined with secretion of pro-fibrotic biomarkers and transcriptomics represents a promising drug discovery tool for assessing anti-fibrotic compounds.