Background: Patients with locally advanced or recurrent prostate cancer typically undergo androgen deprivation therapy (ADT), but the benefits are often short-lived and the responses variable. ADT failure results in castration-resistant prostate cancer (CRPC), which inevitably leads to metastasis. We hypothesized that differences in tumor transcriptional programs may reflect differential responses to ADT and subsequent metastasis. Results: We performed whole transcriptome analysis of 20 patient-matched Pre-ADT biopsies and 20 Post-ADT prostatectomy specimens, and identified two subgroups of patients (high impact and low impact groups) that exhibited distinct transcriptional changes in response to ADT. We found that all patients lost the AR-dependent subtype (PCS2) transcriptional signatures. The high impact group maintained the more aggressive subtype (PCS1) signal, while the low impact group more resembled an AR-suppressed (PCS3) subtype. Computational analyses identified transcription factor coordinated groups (TFCGs) enriched in the high impact group network. Leveraging a large public dataset of over 800 metastatic and primary samples, we identified 33 TFCGs in common between the high impact group and metastatic lesions, including SOX4/FOXA2/GATA4, and a TFCG containing JUN, JUNB, JUND, FOS, FOSB, and FOSL1. The majority of metastatic TFCGs were subsets of larger TFCGs in the high impact group network, suggesting a refinement of critical TFCGs in prostate cancer progression. Conclusions: We have identified TFCGs associated with pronounced initial transcriptional response to ADT, aggressive signatures, and metastasis. Our findings suggest multiple new hypotheses that could lead to novel combination therapies to prevent the development of CRPC following ADT.

This work introduces TrialSieve, a novel framework for biomedical information extraction that enhances clinical meta-analysis and drug repurposing. By extending traditional PICO (Patient, Intervention, Comparison, Outcome) methodologies, TrialSieve incorporates hierarchical, treatment group-based graphs, enabling more comprehensive and quantitative comparisons of clinical outcomes. TrialSieve was used to annotate 1609 PubMed abstracts, 170,557 annotations, and 52,638 final spans, incorporating 20 unique annotation categories that capture a diverse range of biomedical entities relevant to systematic reviews and meta-analyses. The performance (accuracy, precision, recall, F1-score) of four natural-language processing (NLP) models (BioLinkBERT, BioBERT, KRISSBERT, PubMedBERT) and the large language model (LLM), GPT-4o, was evaluated using the human-annotated TrialSieve dataset. BioLinkBERT had the best accuracy (0.875) and recall (0.679) for biomedical entity labeling, whereas PubMedBERT had the best precision (0.614) and F1-score (0.639). Error analysis showed that NLP models trained on noisy, human-annotated data can match or, in most cases, surpass human performance. This finding highlights the feasibility of fully automating biomedical information extraction, even when relying on imperfectly annotated datasets. An annotator user study (n = 39) revealed significant (p < 0.05) gains in efficiency and human annotation accuracy with the unique TrialSieve tree-based annotation approach. In summary, TrialSieve provides a foundation to improve automated biomedical information extraction for frontend clinical research.

Noninvasive Quantification of Hepatic Steatosis: Relationship Between Obesity Status and Liver Fat Content