Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage.

Importance: Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. Objective: To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. Design, Setting, and Participants: In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Exposure: Diagnosis of prostate cancer. Main Outcomes and Measures: The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. Results: A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. Conclusions and Relevance: This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Usp25-Erlin1/2 activity limits cholesterol flux to restrict virus infection.

Reprogramming lipid metabolic pathways is a critical feature of activating immune responses to infection. However, how these reconfigurations occur is poorly understood. Our previous screen to identify cellular deubiquitylases (DUBs) activated during influenza virus infection revealed Usp25 as a prominent hit. Here, we show that Usp25-deleted human lung epithelial A549 cells display a >10-fold increase in pathogenic influenza virus production, which was rescued upon reconstitution with the wild type but not the catalytically deficient (C178S) variant. Proteomic analysis of Usp25 interactors revealed a strong association with Erlin1/2, which we confirmed as its substrate. Newly synthesized Erlin1/2 were degraded in Usp25-/- or Usp25C178S cells, activating Srebp2, with increased cholesterol flux and attenuated TLR3-dependent responses. Our study therefore defines the function of a deubiquitylase that serves to restrict a range of viruses by reprogramming lipid biosynthetic flux to install appropriate inflammatory responses.

A novel prostate cancer subtyping classifier based on luminal and basal phenotypes.

BACKGROUND: Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought. METHODS: Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts. RESULTS: Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01-0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09-0.51). CONCLUSIONS: With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features. PLAIN LANGUAGE SUMMARY: Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors-the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.

Altered ISGylation drives aberrant macrophage-dependent immune responses during SARS-CoV-2 infection.

Ubiquitin-like protein ISG15 (interferon-stimulated gene 15) (ISG15) is a ubiquitin-like modifier induced during infections and involved in host defense mechanisms. Not surprisingly, many viruses encode deISGylating activities to antagonize its effect. Here we show that infection by Zika, SARS-CoV-2 and influenza viruses induce ISG15-modifying enzymes. While influenza and Zika viruses induce ISGylation, SARS-CoV-2 triggers deISGylation instead to generate free ISG15. The ratio of free versus conjugated ISG15 driven by the papain-like protease (PLpro) enzyme of SARS-CoV-2 correlates with macrophage polarization toward a pro-inflammatory phenotype and attenuated antigen presentation. In vitro characterization of purified wild-type and mutant PLpro revealed its strong deISGylating over deubiquitylating activity. Quantitative proteomic analyses of PLpro substrates and secretome from SARS-CoV-2-infected macrophages revealed several glycolytic enzymes previously implicated in the expression of inflammatory genes and pro-inflammatory cytokines, respectively. Collectively, our results indicate that altered free versus conjugated ISG15 dysregulates macrophage responses and probably contributes to the cytokine storms triggered by SARS-CoV-2.