Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

IMPLICATIONS: Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Distinct mesenchymal cell states mediate prostate cancer progression.

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Evaluating the Learning Curve for In-office Freehand Cognitive Fusion Transperineal Prostate Biopsy.

OBJECTIVE: To define the learning curve of the in-office, freehand MRI-ultrasound cognitive fusion transperineal prostate biopsy (CTPB) by assessing cancer detection, biopsy core quantity and quality, procedure times, and complications over the initial experience. METHODS: We reviewed 110 consecutive CTPB performed March 2021-September 2022 by a urologist inexperienced with the PrecisionPoint platform. The study period was divided into quarters to assess for temporal variation in outcomes. Univariable and multivariable analysis modeled the learning curve. RESULTS: Across quarters, there were no differences in the detection of clinically significant prostate cancer (Q1:50%, Q2:52%, Q3:50%, Q4:48%, P > .9) or Gleason grade group upgrading by targeted vs systematic biopsy (P = .6). Median procedure times improved with experience (Q1:17 minutes, Q2:14 minutes, Q3:12 minutes, Q4:13 minutes, P = .018). On multivariable analysis, procedure times decreased by 1minute per 20 cases (P < .001). On linear regression, CTPB procedure times approximated transrectal biopsy times after 90 cases (P < .001). The histopathologic core quality did not differ, as evidenced by consistent core length (P = .13) and presence of minimal fibromuscular tissue (P > .9). The most common complications, hematuria and hematospermia, were similar across quarters (P = .7, P = .3, respectively). There was a single episode of urinary retention and no reported infections. CONCLUSION: There is no evidence of a learning curve for CTPB as shown by consistent clinically significant prostate cancer detection, high-quality biopsy cores, and low complications. However, CTPB procedural times begin to approximate cognitive targeted transrectal biopsy times after 90 cases.

Distinct mesenchymal cell states mediate prostate cancer progression.

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.