Neuroendocrine gene subsets are uniquely dysregulated in prostate adenocarcinoma.

Prostate cancer has heterogeneous growth patterns, and its prognosis is the poorest when it progresses to a neuroendocrine phenotype. Using bioinformatic analysis, we evaluated RNA expression of neuroendocrine genes in a panel of five different cancer types: prostate adenocarcinoma, breast cancer, kidney chromophobe, kidney renal clear cell carcinoma and kidney renal papillary cell carcinoma. Our results show that specific neuroendocrine genes are significantly dysregulated in these tumors, suggesting that they play an active role in cancer progression. Among others, synaptophysin (SYP), a conventional neuroendocrine marker, is upregulated in prostate adenocarcinoma (PRAD) and breast cancer (BRCA). Our analysis shows that SYP is enriched in small extracellular vesicles (sEVs) derived from plasma of PRAD patients, but it is absent in sEVs derived from plasma of healthy donors. Similarly, classical sEV markers are enriched in sEVs derived from plasma of prostate cancer patients, but weakly detectable in sEVs derived from plasma of healthy donors. Overall, our results pave the way to explore new strategies to diagnose these diseases based on the neuroendocrine gene expression in patient tumors or plasma sEVs.

Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal.

PURPOSE: Many efforts have been made to explore the potential of deep learning and artificial intelligence (AI) in disciplines such as medicine, including ophthalmology. This systematic review aims to evaluate the reporting quality of randomised controlled trials (RCTs) that evaluate AI technologies applied to ophthalmology. METHODS: A comprehensive search of three relevant databases (EMBASE, Medline, Cochrane) from 1 January 2010 to 5 February 2022 was conducted. The reporting quality of these papers was scored using the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) checklist and further risk of bias was assessed using the RoB-2 tool. RESULTS: The initial search yielded 2973 citations from which 5 articles satisfied the inclusion/exclusion criteria. These articles featured AI technologies applied to diabetic retinopathy screening, ophthalmologic education, fungal keratitis detection and paediatric cataract diagnosis. None of the articles reported all items in the CONSORT-AI checklist. The overall mean CONSORT-AI score of the included RCTs was 53% (range 37%-78%). The individual scores of the articles were 37% (19/51), 39% (20), 49% (25), 61% (31) and 78% (40). All articles were scored as being moderate risk, or 'some concerns present', regarding potential risk of bias according to the RoB-2 tool. CONCLUSION: A small number of RCTs have been published to date on the applications of AI in ophthalmology and vision science. Adherence to the 2020 CONSORT-AI reporting guidelines is suboptimal with notable reporting items often missed. Greater adherence will help facilitate reproducibility of AI research which can be a stimulus for more AI-based RCTs and clinical applications in ophthalmology.

Chemokine receptor CXCR7 activates Aurora Kinase A and promotes neuroendocrine prostate cancer growth.

CXCR7 is an atypical chemokine receptor that recruits ß-arrestin (ARRB2) and internalizes into clathrin-coated intracellular vesicles where the complex acts as a scaffold for cytoplasmic kinase assembly and signal transduction. Here, we report that CXCR7 was elevated in the majority of prostate cancer (PCa) cases with neuroendocrine features (NEPC). CXCR7 markedly induced mitotic spindle and cell cycle gene expression. Mechanistically, we identified Aurora Kinase A (AURKA), a key regulator of mitosis, as a novel target that was bound and activated by the CXCR7-ARRB2 complex. CXCR7 interacted with proteins associated with microtubules and golgi, and, as such, the CXCR7-ARRB2-containing vesicles trafficked along the microtubules to the pericentrosomal golgi apparatus, where the complex interacted with AURKA. Accordingly, CXCR7 promoted PCa cell proliferation and tumor growth, which was mitigated by AURKA inhibition. In summary, our study reveals a critical role of CXCR7-ARRB2 in interacting and activating AURKA, which can be targeted by AURKA inhibitors to benefit a subset of patients with NEPC.

Palmitoyl acyltransferase ZDHHC7 inhibits androgen receptor and suppresses prostate cancer.

The hormonal transcription factor androgen receptor (AR) is a master regulator of prostate cancer (PCa). Protein palmitoylation, which attaches a palmitate fatty acid to a substrate protein, is mediated by a class of 23 ZDHHC (Zinc-Finger DHHC motif)-family palmitoyltransferases. Although palmitoylation has been shown to modify many proteins and regulate diverse cellular processes, little is known about ZDHHC genes in cancer. Here we examined ZDHHC family gene expression in human tissue panels and identified ZDHHC7 as a PCa-relevant member. RNA-seq analyses of PCa cells with ZDHHC7 de-regulation revealed global alterations in androgen response and cell cycle pathways. Mechanistically, ZDHHC7 inhibits AR gene transcription and therefore reduces AR protein levels and abolishes AR signaling in PCa cells. Accordingly, ZDHHC7 depletion increased the oncogenic properties of PCa cells, whereas restoring ZDHHC7 is sufficient to suppress PCa cell proliferation and invasion in vitro and mitigate xenograft tumor growth in vivo. Lastly, we demonstrated that ZDHHC7 is downregulated in human PCa compared to benign-adjacent tissues, and its loss is associated with worse clinical outcomes. In summary, our study reveals a global role of ZDHHC7 in inhibiting androgen response and suppressing PCa progression and identifies ZDHHC7 loss as a biomarker for aggressive PCa and a target for therapeutic intervention.

Artificial intelligence applications in brachytherapy: A literature review.

PURPOSE: Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS: We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS: A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS: AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.

PALI1 promotes tumor growth through competitive recruitment of PRC2 to G9A-target chromatin for dual epigenetic silencing.

PALI1 is a newly identified accessory protein of the Polycomb repressive complex 2 (PRC2) that catalyzes H3K27 methylation. However, the roles of PALI1 in cancer are yet to be defined. Here, we report that PALI1 is upregulated in advanced prostate cancer (PCa) and competes with JARID2 for binding to the PRC2 core subunit SUZ12. PALI1 further interacts with the H3K9 methyltransferase G9A, bridging the formation of a unique G9A-PALI1-PRC2 super-complex that occupies a subset of G9A-target genes to mediate dual H3K9/K27 methylation and gene repression. Many of these genes are developmental regulators required for cell differentiation, and their loss in PCa predicts poor prognosis. Accordingly, PALI1 and G9A drive PCa cell proliferation and invasion in vitro and xenograft tumor growth in vivo. Collectively, our study shows that PALI1 harnesses two central epigenetic mechanisms to suppress cellular differentiation and promote tumorigenesis, which can be targeted by dual EZH2 and G9A inhibition.

FOXA1 inhibits hypoxia programs through transcriptional repression of HIF1A.

Intratumoral hypoxia is associated with castration-resistant prostate cancer (CRPC), a lethal disease. FOXA1 is an epithelial transcription factor that is down-regulated in CRPC. We have previously reported that FOXA1 loss induces epithelial-mesenchymal transition (EMT) and cell motility through elevated TGFß signaling. However, whether FOXA1 directly regulates hypoxia pathways of CRPC tumors has not been previously studied. Here we report that FOXA1 down-regulation induces hypoxia transcriptional programs, and FOXA1 level is negatively correlated with hypoxia markers in clinical prostate cancer (PCa) samples. Mechanistically, FOXA1 directly binds to an intragenic enhancer of HIF1A to inhibit its expression, and HIF1A, in turn, is critical in mediating FOXA1 loss-induced hypoxia gene expression. Further, we identify CCL2, a chemokine ligand that modulates tumor microenvironment and promotes cancer progression, as a crucial target of the FOXA1-HIF1A axis. We found that FOXA1 loss leads to immunosuppressive macrophage infiltration and increased cell invasion, dependent on HIF1A expression. Critically, therapeutic targeting of HIF1A-CCL2 using pharmacological inhibitors abolishes FOXA1 loss-induced macrophage infiltration and PCa cell invasion. In summary, our study reveals an essential role of FOXA1 in controlling the hypoxic tumor microenvironment and establishes the HIF1A-CCL2 axis as one mechanism of FOXA1 loss-induced CRPC progression.

Immuno-Thrombotic Complications of COVID-19: Implications for Timing of Surgery and Anticoagulation.

Early in the coronavirus disease 2019 (COVID-19) pandemic, global governing bodies prioritized transmissibility-based precautions and hospital capacity as the foundation for delay of elective procedures. As elective surgical volumes increased, convalescent COVID-19 patients faced increased postoperative morbidity and mortality and clinicians had limited evidence for stratifying individual risk in this population. Clear evidence now demonstrates that those recovering from COVID-19 have increased postoperative morbidity and mortality. These data-in conjunction with the recent American Society of Anesthesiologists guidelines-offer the evidence necessary to expand the early pandemic guidelines and guide the surgeon's preoperative risk assessment. Here, we argue elective surgeries should still be delayed on a personalized basis to maximize postoperative outcomes. We outline a framework for stratifying the individual COVID-19 patient's fitness for surgery based on the symptoms and severity of acute or convalescent COVID-19 illness, coagulopathy assessment, and acuity of the surgical procedure. Although the most common manifestation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is COVID-19 pneumonitis, every system in the body is potentially afflicted by an endotheliitis. This endothelial derangement most often manifests as a hypercoagulable state on admission with associated occult and symptomatic venous and arterial thromboembolisms. The delicate balance between hyper and hypocoagulable states is defined by the local immune-thrombotic crosstalk that results commonly in a hemostatic derangement known as fibrinolytic shutdown. In tandem, the hemostatic derangements that occur during acute COVID-19 infection affect not only the timing of surgical procedures, but also the incidence of postoperative hemostatic complications related to COVID-19-associated coagulopathy (CAC). Traditional methods of thromboprophylaxis and treatment of thromboses after surgery require a tailored approach guided by an understanding of the pathophysiologic underpinnings of the COVID-19 patient. Likewise, a prolonged period of risk for developing hemostatic complications following hospitalization due to COVID-19 has resulted in guidelines from differing societies that recommend varying periods of delay following SARS-CoV-2 infection. In conclusion, we propose the perioperative, personalized assessment of COVID-19 patients' CAC using viscoelastic hemostatic assays and fluorescent microclot analysis.

HOXB13 suppresses de novo lipogenesis through HDAC3-mediated epigenetic reprogramming in prostate cancer.

HOXB13, a homeodomain transcription factor, critically regulates androgen receptor (AR) activities and androgen-dependent prostate cancer (PCa) growth. However, its functions in AR-independent contexts remain elusive. Here we report HOXB13 interaction with histone deacetylase HDAC3, which is disrupted by the HOXB13 G84E mutation that has been associated with early-onset PCa. Independently of AR, HOXB13 recruits HDAC3 to lipogenic enhancers to catalyze histone deacetylation and suppress lipogenic regulators such as fatty acid synthase. Analysis of human tissues reveals that the HOXB13 gene is hypermethylated and downregulated in approximately 30% of metastatic castration-resistant PCa. HOXB13 loss or G84E mutation leads to lipid accumulation in PCa cells, thereby promoting cell motility and xenograft tumor metastasis, which is mitigated by pharmaceutical inhibition of fatty acid synthase. In summary, we present evidence that HOXB13 recruits HDAC3 to suppress de novo lipogenesis and inhibit tumor metastasis and that lipogenic pathway inhibitors may be useful to treat HOXB13-low PCa.

Loss of miR-24-3p promotes epithelial cell apoptosis and impairs the recovery from intestinal inflammation.

While apoptosis plays a significant role in intestinal homeostasis, it can also be pathogenic if overactive during recovery from inflammation. We recently reported that microRNA-24-3p (miR-24-3p) is elevated in the colonic epithelium of ulcerative colitis patients during active inflammation, and that it reduced apoptosis in vitro. However, its function during intestinal restitution following inflammation had not been examined. In this study, we tested the influence of miR-24-3p on mucosal repair by studying recovery from colitis in both novel miR-24-3p knockout and miR-24-3p-inhibited mice. We observed that knockout mice and mice treated with a miR-24-3p inhibitor had significantly worsened recovery based on weight loss, colon length, and double-blinded histological scoring. In vivo and in vitro analysis of miR-24-3p inhibition in colonic epithelial cells revealed that inhibition promotes apoptosis and increases levels of the pro-apoptotic protein BIM. Further experiments determined that silencing of BIM reversed the pro-apoptotic effects of miR-24-3p inhibition. Taken together, these data suggest that miR-24-3p restrains intestinal epithelial cell apoptosis by targeting BIM, and its loss of function is detrimental to epithelial restitution following intestinal inflammation.

Posttranslational regulation of FOXA1 by Polycomb and BUB3/USP7 deubiquitin complex in prostate cancer.

Forkhead box protein A1 (FOXA1) is essential for androgen-dependent prostate cancer (PCa) growth. However, how FOXA1 levels are regulated remains elusive and its therapeutic targeting proven challenging. Here, we report FOXA1 as a nonhistone substrate of enhancer of zeste homolog 2 (EZH2), which methylates FOXA1 at lysine-295. This methylation is recognized by WD40 repeat protein BUB3, which subsequently recruits ubiquitin-specific protease 7 (USP7) to remove ubiquitination and enhance FOXA1 protein stability. They functionally converge in regulating cell cycle genes and promoting PCa growth. FOXA1 is a major therapeutic target of the inhibitors of EZH2 methyltransferase activities in PCa. FOXA1-driven PCa growth can be effectively mitigated by EZH2 enzymatic inhibitors, either alone or in combination with USP7 inhibitors. Together, our study reports EZH2-catalyzed methylation as a key mechanism to FOXA1 protein stability, which may be leveraged to enhance therapeutic targeting of PCa using enzymatic EZH2 inhibitors.

Activation of MAPK Signaling by CXCR7 Leads to Enzalutamide Resistance in Prostate Cancer.

Castration-resistant prostate cancer (CRPC) that has developed resistance to the new-generation androgen receptor (AR) antagonist enzalutamide is a lethal disease. Transcriptome analysis of multiple prostate cancer models identified CXCR7, an atypical chemokine receptor, as one of the most upregulated genes in enzalutamide-resistant cells. AR directly repressed CXCR7 by binding to an enhancer 110 kb downstream of the gene and expression was restored upon androgen deprivation. We demonstrate that CXCR7 is a critical regulator of prostate cancer sensitivity to enzalutamide and is required for CRPC growth in vitro and in vivo. Elevated CXCR7 activated MAPK/ERK signaling through ligand-independent, but ß-arrestin 2-dependent mechanisms. Examination of patient specimens showed that CXCR7 and pERK levels increased significantly from localized prostate cancer to CRPC and further upon enzalutamide resistance. Preclinical studies revealed remarkable efficacies of MAPK/ERK inhibitors in suppressing enzalutamide-resistant prostate cancer. Overall, these results indicate that CXCR7 may serve as a biomarker of resistant disease in patients with prostate cancer and that disruption of CXCR7 signaling may be an effective strategy to overcome resistance. SIGNIFICANCE: These findings identify CXCR7-mediated MAPK activation as a mechanism of resistance to second-generation antiandrogen therapy, highlighting the therapeutic potential of MAPK/ERK inhibitors in CRPC.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning.

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI50 values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI50 thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI50 values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Targeting FOXA1-mediated repression of TGF-ß signaling suppresses castration-resistant prostate cancer progression.

Prostate cancer (PC) progressed to castration resistance (CRPC) is a fatal disease. CRPC tumors develop resistance to new-generation antiandrogen enzalutamide through lineage plasticity, characterized by epithelial-mesenchymal transition (EMT) and a basal-like phenotype. FOXA1 is a transcription factor essential for epithelial lineage differentiation. Here, we demonstrate that FOXA1 loss leads to remarkable upregulation of transforming growth factor beta 3 (TGFB3), which encodes a ligand of the TGF-ß pathway. Mechanistically, this is due to genomic occupancy of FOXA1 on an upstream enhancer of the TGFB3 gene to directly inhibit its transcription. Functionally, FOXA1 downregulation induces TGF-ß signaling, EMT, and cell motility, which is effectively blocked by the TGF-ß receptor I inhibitor galunisertib (LY2157299). Tissue microarray analysis confirmed reduced levels of FOXA1 protein and a concordant increase in TGF-ß signaling, indicated by SMAD2 phosphorylation, in CRPC as compared with primary tumors. Importantly, combinatorial LY2157299 treatment sensitized PC cells to enzalutamide, leading to synergistic effects in inhibiting cell invasion in vitro and xenograft CRPC tumor growth and metastasis in vivo. Therefore, our study establishes FOXA1 as an important regulator of lineage plasticity mediated in part by TGF-ß signaling, and supports a novel therapeutic strategy to control lineage switching and potentially extend clinical response to antiandrogen therapies.

Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator.

Enhancer of Zeste 2 (EZH2) is the enzymatic subunit of Polycomb Repressive Complex 2 (PRC2), which catalyzes histone H3 lysine 27 trimethylation (H3K27me3) at target promoters for gene silencing. Here, we report that EZH2 activates androgen receptor (AR) gene transcription through direct occupancy at its promoter. Importantly, this activating role of EZH2 is independent of PRC2 and its methyltransferase activities. Genome-wide assays revealed extensive EZH2 occupancy at promoters marked by either H3K27ac or H3K27me3, leading to gene activation or repression, respectively. Last, we demonstrate enhanced efficacy of enzymatic EZH2 inhibitors when used in combination with AR antagonists in blocking the dual roles of EZH2 and suppressing prostate cancer progression in vitro and in vivo. Taken together, our study reports EZH2 as a transcriptional activator, a key target of which is AR, and suggests a drug-combinatory approach to treat advanced prostate cancer.

TRIM28 protects TRIM24 from SPOP-mediated degradation and promotes prostate cancer progression.

TRIM24 is an effector substrate of the E3 ubiquitin ligase adaptor SPOP and becomes stabilized in prostate cancer (PCa) with SPOP mutations. However, how TRIM24 protein is regulated in the vast majority of SPOP-wildtype PCa is unknown. Here we report TRIM28 as a critical upstream regulator of TRIM24. TRIM28 protein interacts with TRIM24 to prevent its ubiquitination and degradation by SPOP. Further, TRIM28 facilitates TRIM24 occupancy on the chromatin and, like TRIM24, augments AR signaling. TRIM28 promotes PCa cell proliferation in vitro and xenograft tumor growth in vivo. Importantly, TRIM28 is upregulated in aggressive PCa and associated with elevated levels of TRIM24 and worse clinical outcome. TRIM24 and AR coactivated gene signature of SPOP-mutant PCa is similarly activated in human PCa with high TRIM28 expression. Taken together, this study provides a novel mechanism to broad TRIM24 protein stabilization and establishes TRIM28 as a promising therapeutic target.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.