Prostate cancer (PCa) is a major concern in public health, with many genetically distinct subsets. Genomic alterations in PCa are extraordinarily complex, and both germline and somatic mutations are of great importance in the development of this tumor. The aim of this review is to provide an overview of genetic changes that can occur in the development of PCa and their role in potential therapeutic approaches. Various pathways and mechanisms proposed to play major roles in PCa are described in detail to provide an overview of current knowledge.

Purpose of review: Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer. Recent findings: We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans. Summary: It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.

Substaging of T1 urothelial cancer is associated with tumor progression and its reporting is recommended by international guidelines. However, it has not been integrated in risk stratification tools and there is no agreement on the best method to use for its reporting. We aimed to investigate the applicability, interobserver variability, and prognostic value of histological landmark based and micrometric (aggregate linear length of invasive carcinoma (ALLICA), microscopic vs. extensive system, Rete Oncologica Lombarda (ROL) system) substaging methods. A total of 79 patients with the primary diagnosis of T1 urothelial cancer treated with conventional transurethral resection and adjuvant BCG therapy between 2000 and 2020 at the Medical University of Vienna were included. The anatomical and metrical substaging systems were evaluated using agreement rate, Cohen’s kappa, Kendall’s tau, and Spearman rank correlation. Prognostic value for high-grade recurrence or T2 progression was evaluated in uni- and multivariable analysis. Applicability and reproducibility were good to moderate and varied between substaging methods. Obstacles are mainly due to fragmentation of samples. Anatomical substaging was associated with progression in univariable and multivariable analysis. In our cohort, we could only identify anatomical landmark–based substaging to be prognostic for T2 progression. A major obstacle for proper pathological assessment is fragmentation of samples due to operational procedure. Avoiding such fragmentation might improve reproducibility and significance of pathological T1 substaging of urothelial cancer.

Tumor staging of prostate cancer is a fundamental principle in management and therapy, with a hallmark being tumor growth beyond the organ boundary. Often, this is referred to as “capsule penetration”, suggesting the existence of a true prostatic capsule that would facilitate the determination of tumor penetration. In fact, the prostate does not have a true capsule and, depending on the anatomic area, it blends with the surrounding fibrous, adipose and muscular tissue. This makes it sometimes difficult or impossible to unequivocally identify extraprostatic tumor extension. It is necessary to appreciate this difficulty in order to better understand the significance of extraprostatic tumor extension.

Background: Tumor-associated macrophages (TAMs) are key drivers of the immunosuppressive tumor microenvironment (TME), supporting tumor progression through diverse functions. However, mechanistic studies of TAM polarization remain limited by the lack of physiologically relevant human model systems that capture stromal-immune interactions and macrophage heterogeneity. Methods: We established advanced human co-culture systems that integrate healthy donor-derived macrophages with patient-derived organoids and tumoroids (PDOs and PDTs), as well as matched normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs). These multicellular models enabled the investigation of interactions among stromal, epithelial, and immune cells within tumor and adjacent normal tissue environments. Results: The co-culture systems recapitulated distinct macrophage states associated with tumor and adjacent normal environments and identified fibroblasts as major regulators of macrophage phenotypes. CAFs promoted macrophage metabolic remodeling characterized by altered lipid handling and enrichment of TAM-like signatures. Mechanistically, we identified thrombospondin 1 (TSP1) as a CAF-secreted factor linked to metabolic priming. Recombinant TSP1 induced transient lipid accumulation followed by mitochondrial remodeling. In tumor co-culture conditions, CD36 inhibition reduced lipid accumulation in macrophages, supporting a role for TSP1-linked lipid crosstalk in stromal-immune interactions. Conclusion: Our study establishes advanced patient-derived co-culture models as a platform to investigate human TAM biology and stromal-immune interactions in CRC. Using these systems, we identify a fibroblast-associated TSP1-lipid axis linked to macrophage metabolic remodeling and TAM-like polarization, highlighting stromal metabolic communication as a potential targetable feature of the CRC microenvironment.

Purpose: Understanding the impact of gene expression in pathological processes, such as carcinogenesis, is crucial for understanding the biology of cancer and advancing personalised medicine. Yet, current methods lack biologically-informed-omics approaches to stratify cancer patients effectively, limiting our ability to dissect the underlying molecular mechanisms. Results: To address this gap, we present a novel workflow for the stratification and combined analysis of RNA-Seq and DNA methylation samples. Leveraging MSigDB curated gene sets, and utilising graph machine learning and ensemble clustering, we identify specific pathways and functional genes. We applied our workflow to analyse nearly a thousand prostate cancer samples, focusing on the varying expression of the \textit{FOLH1} gene. We identified pathways such as the PI3K-AKT-mTOR gene sets as well as signatures linked to prostate tumour aggressiveness. Conclusion: Our comprehensive approach provides a novel tool to identify disease-relevant functions of genes of interest (GOI) in large datasets. This integrated approach offers a valuable framework for understanding the role of the expression variation of a GOI in complex diseases and for informing on targeted therapeutic strategies.