Citations

Abstract Although the Sonic hedgehog (SHH) signaling pathway has been implicated in promoting malignant phenotypes of prostate cancer, details on how it is activated and exerts its oncogenic role during prostate cancer development and progression is less clear. Here, we show that GLI3, a key SHH pathway effector, is transcriptionally upregulated during androgen deprivation and posttranslationally stabilized in prostate cancer cells by mutation of speckle-type POZ protein (SPOP). GLI3 is a substrate of SPOP-mediated proteasomal degradation in prostate cancer cells and prostate cancer driver mutations in SPOP abrogate GLI3 degradation. Functionally, GLI3 is necessary and sufficient for the growth and migration of androgen receptor (AR)–positive prostate cancer cells, particularly under androgen-depleted conditions. Importantly, we demonstrate that GLI3 physically interacts and functionally cooperates with AR to enrich an AR-dependent gene expression program leading to castration-resistant growth of xenografted prostate tumors. Finally, we identify an AR/GLI3 coregulated gene signature that is highly correlated with castration-resistant metastatic prostate cancer and predictive of disease recurrence. Together, these findings reveal that hyperactivated GLI3 promotes castration-resistant growth of prostate cancer and provide a rationale for therapeutic targeting of GLI3 in patients with castration-resistant prostate cancer (CRPC). Implications: We describe two clinically relevant mechanisms leading to hyperactivated GLI3 signaling and enhanced AR/GLI3 cross-talk, suggesting that GLI3-specific inhibitors might prove effective to block prostate cancer development or delay CRPC.

Background . There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods . A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results . Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion . The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion . Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

AbstractDNA‐binding proteins, including transcription factors (TFs), play essential roles in various cellular processes and pathogenesis of diseases, deeming to be potential therapeutic targets. However, these proteins are generally considered undruggable as they lack an enzymatic catalytic site or a ligand‐binding pocket. Proteolysis‐targeting chimera (PROTAC) technology has been developed by engineering a bifunctional molecule chimera to bring a protein of interest (POI) to the proximity of an E3 ubiquitin ligase, thus inducing the ubiquitination of POI and further degradation through the proteasome pathway. Here, the development of oligonucleotide‐based PROTAC (O'PROTACs), a class of noncanonical PROTACs in which a TF‐recognizing double‐stranded oligonucleotide is incorporated as a binding moiety of POI is reported. It is demonstrated that O'PROTACs of lymphoid enhancer‐binding factor 1 (LEF1) and ETS‐related gene (ERG), two highly cancer‐related transcription factors, successfully promote degradation of these proteins, impede their transcriptional activity, and inhibit cancer cell growth in vitro and in vivo. The programmable nature of O'PROTACs indicates that this approach is also applicable to destruct other TFs. O'PROTACs not only can serve as a research tool but also can be harnessed as a therapeutic arsenal to target DNA binding proteins for effective treatment of diseases such as cancer.

AbstractObjectiveWith COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.Materials and MethodsPerformance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.ResultsThis rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.DiscussionOpportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.ConclusionThis rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

Abstract Androgen receptor (AR) is a major survival factor for prostate cancer. Inflammation is implicated in many cancer types, including prostate cancer. Activation of MAP3K7 (also termed TAK1) and downstream IκB kinase β (IKKβ) by proinflammatory cytokines such as TNFα stimulates NF-κB survival pathways. Paradoxically, MAP3K7 is often deleted in human prostate cancer. Here, we demonstrate that AR protein expression is lower in inflammatory tumor areas compared with non-inflammatory tissues in patients with prostate cancer. Map3k7 knockout increased AR protein levels and activity in the mouse prostate, and MAP3K7 and AR protein levels were inversely correlated in prostate cancer patient specimens. TNFα treatment increased AR protein ubiquitination and proteasomal degradation. Mechanistically, activation of IKKβ by TNFα induced phosphorylation and TRCP1/2 E3 ligase–mediated polyubiquitination and degradation of AR protein. TNFα suppressed prostate cancer proliferation, which could be rescued by blockade of AR degradation. These findings reveal a previously unrecognized tumor suppressive function of the inflammation-activated MAP3K7–IKKβ axis in degrading AR protein. Moreover, they suggest that aberrant elevation of AR protein could be a prognostic biomarker and therapeutic target for MAP3K7-deficient prostate cancer. Significance: This study identifies that MAP3K7–IKKβ signaling plays a tumor-suppressive role in prostate cancer by degrading AR, revealing potential prognostic and therapeutic strategies for MAP3K7-deficient tumors.

SPOP mutation disables 53BP1 ubiquitination and exclusion from DNA double-strand break sites and causes genome instability.

PURPOSEHepatocellular carcinoma (HCC) has well-defined environmental risk factors. In addition, epidemiologic studies have suggested hereditary risk factors. The goals of this study were to determine the rate of pathogenic and likely pathogenic (P/LP) germline variants in cancer predisposition genes in patients with HCC, possible enrichment of P/LP variants in particular genes, and potential impact on clinical management.MATERIALS AND METHODSA prospective study at a tertiary medical center enrolled 217 patients with a personal history of HCC. Multigene panel testing was performed for 134 cancer predisposition genes in all patients. The rate of P/LP variants was compared with population rates. A separate retrospective cohort included 219 patients with HCC who underwent testing at a commercial laboratory.RESULTSIn the prospective cohort, P/LP germline variants were identified in 25 of 217 patients with HCC (11.5%). Four patients (1.8%) had P/LP variants in the highly penetrant cancer genes BRCA2 (n = 2), MSH6 (n = 1), and PMS2 (n = 1). In addition, multiple patients had P/LP variants in FANCA (n = 5) and BRIP1 (n = 4), which were significantly enriched in HCC compared with the general population. Detection of P/LP variants led to changes in clinical management in regard to therapy selection, screening recommendations, and cascade testing of relatives. In a separate retrospective analysis of 219 patients with HCC, 30 (13.7%) were positive for P/LP variants including 13 (5.9%) with highly penetrant genes APC (n = 2), BRCA1 (n = 1), BRCA2 (n = 6), MSH2 (n = 2), or TP53 (n = 2).CONCLUSIONP/LP germline variants in cancer predisposition genes were detected in 11%-14% of patients with HCC. Inherited genetics should not be overlooked in HCC as there are important implications for precision treatment, future risk of cancers, and familial cancer risk.

Abstract Molecular mechanisms underlying intratumoral androgenesis and aberrant androgen receptor (AR) activation in prostate cancer remain poorly understood. Here we demonstrate that ectopic expression of the E3 ubiquitin ligase adaptor speckle-type poxvirus and zinc finger domain protein (SPOP) stabilizes 17βHSD4. SPOP bound a functional substrate-binding consensus (SBC) motif 315RATST319 in 17βHSD4 and promoted nondegradable K27- and K29-linked polyubiquitination of 17βHSD4. The effect of SPOP was antagonized by serum- and glucocorticoid kinase-3 (SGK3)-mediated phosphorylation of serine 318 (S318) in the SBC and S318 phosphorylation-dependent binding of SKP2 E3 ligase and subsequent K48-linked polyubiquitination and proteasomal degradation of 17βHSD4. Prostate cancer–associated SPOP mutations impaired the SPOP-17βHSD4 interaction, caused 17βHSD4 protein destruction in prostate cancer cells in culture and patient specimens, and increased testosterone production and prostate cancer cell growth in vitro and in mouse models. Thus, we have identified SPOP and SKP2 as two essential E3 ubiquitin ligases that exert opposite effects on 17βHSD4 protein degradation and intratumoral androgenesis in prostate cancer cells. We further demonstrate that SPOP mutations or SKP2 overexpression contribute to prostate cancer progression by decreasing 17βHSD4 expression and increasing intratumoral androgen synthesis. Significance: This study reveals a novel mechanism of aberrant AR activation in SPOP-mutated prostate cancer and uncovers putative biomarkers for effective treatment by AR-targeted therapies.