JUN mediates the senescence associated secretory phenotype and immune cell recruitment to prevent prostate cancer progression.

BACKGROUND: Prostate cancer develops through malignant transformation of the prostate epithelium in a stepwise, mutation-driven process. Although activator protein-1 transcription factors such as JUN have been implicated as potential oncogenic drivers, the molecular programs contributing to prostate cancer progression are not fully understood. METHODS: We analyzed JUN expression in clinical prostate cancer samples across different stages and investigated its functional role in a Pten-deficient mouse model. We performed histopathological examinations, transcriptomic analyses and explored the senescence-associated secretory phenotype in the tumor microenvironment. RESULTS: Elevated JUN levels characterized early-stage prostate cancer and predicted improved survival in human and murine samples. Immune-phenotyping of Pten-deficient prostates revealed high accumulation of tumor-infiltrating leukocytes, particularly innate immune cells, neutrophils and macrophages as well as high levels of STAT3 activation and IL-1ß production. Jun depletion in a Pten-deficient background prevented immune cell attraction which was accompanied by significant reduction of active STAT3 and IL-1ß and accelerated prostate tumor growth. Comparative transcriptome profiling of prostate epithelial cells revealed a senescence-associated gene signature, upregulation of pro-inflammatory processes involved in immune cell attraction and of chemokines such as IL-1ß, TNF-α, CCL3 and CCL8 in Pten-deficient prostates. Strikingly, JUN depletion reversed both the senescence-associated secretory phenotype and senescence-associated immune cell infiltration but had no impact on cell cycle arrest. As a result, JUN depletion in Pten-deficient prostates interfered with the senescence-associated immune clearance and accelerated tumor growth. CONCLUSIONS: Our results suggest that JUN acts as tumor-suppressor and decelerates the progression of prostate cancer by transcriptional regulation of senescence- and inflammation-associated genes. This study opens avenues for novel treatment strategies that could impede disease progression and improve patient outcomes.

The functional interlink between AR and MMP9/VEGF signaling axis is mediated through PIP5K1α/pAKT in prostate cancer.

Currently, no effective targeted therapeutics exists for treatment of metastatic prostate cancer (PCa). Given that matrix metalloproteinases 9 (MMP9) and its associated vascular endothelial growth factor (VEGF) are critical for tumor vascularization and invasion under castration-resistant condition, it is therefore of great importance to define the functional association and interplay between androgen receptor (AR) and MMP9 and their associated key survival and invasion pathways in PCa cells. Here, we found that there was a significant correlation between MMP9 and AR protein expression in primary and metastatic PCa tissues, and a trend that high level of MMP9 expression was associated with poor prognosis. We demonstrated that constitutive activation of AR increased expression of MMP9 and VEGF/VEGF receptors. We further showed that AR exerts its effect on MMP9/VEGF signaling axis through PIP5K1α/AKT. We showed that MMP9 physically interacted with PIP5K1α via formation of protein-protein complexes. Furthermore, elevated expression of MMP9 enhanced ability of AR to activate its target gene cyclin A1. The elevated sequential activation of AR/PIP5K1α/AKT/MMP9/VEGF signaling axis contributed to increased invasiveness and growth of metastatic tumors. Conversely, treatment with PIP5K1α inhibitor significantly suppressed invasiveness of PCa cells expressing constitutively activated AR, this was coincident with its inhibitory effect of this inhibitor on AR/MMP9/VEGF pathways. Our results suggest that AR and MMP9-associated network proteins may be effectively targeted by blocking PIP5K1α/AKT pathways using PIP5K1α inhibitor in metastatic PCa.

Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up.

BACKGROUND: Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. METHODS: Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. RESULTS: The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963-0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929-0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956-0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980-0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947-0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. CONCLUSIONS: The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

The interplay between AR, EGF receptor and MMP-9 signaling pathways in invasive prostate cancer.

BACKGROUND: Metastatic Prostate cancer (PCa) cells have gained survival and invasive advantages. Epidermal growth factor (EGF) receptor is a receptor tyrosine kinase, which may mediate signalling to promote progression and invasion of various cancers. In this study, we uncovered the molecular mechanisms underlying the interconnection among the androgen receptor (AR), matrix metalloproteinase-9 (MMP9) and EGFR in promoting PCa progression. METHODS: Immunohistochemical analysis of the tissue microarrays consisting of primary and metastatic PCa tissues was performed. The clinical importance of EGFR and its association with survivals were analyzed using three cohorts from MSKCC Prostate Oncogenome Project dataset (For primary tumors, n = 181; for metastatic tumors n = 37) and The Cancer Genome Atlas Prostate Adenocarcinoma Provisional dataset (n = 495). Targeted overexpression or inhibition of the proteins of interests was introduced into PCa cell lines. Treatment of PCa cell lines with the compounds was conducted. Immunoblot analysis was performed. RESULTS: We showed that AR, MMP-9 and EGFR are interconnect factors, which may cooperatively promote PCa progression. Altered EGFR expression was associated with poor disease-free survival in PCa patients. Induced overexpression of AR led to an increase in the expression of EGFR, p-GSK-3ß and decrease in p27 expression in PCa cell lines in the presence of androgen stimulation. Overexpression of MMP9 significantly induced EGFR expression in PCa cells. Inhibition of PIP5K1α, a lipid kinase that acts upstream of PI3K/AKT greatly reduced expressions of AR, MMP-9 and EGFR. CONCLUSIONS: Our findings also suggest that PCa cells may utilize AR, EGFR and MMP-9 pathways in androgen-dependent as well as in castration-resistant conditions. Our data suggest a new therapeutic potential to block cancer metastasis by targeting AR, EGFR and MMP-9 pathways in subsets of PCa patients.

Expression of NAD(P)H quinone dehydrogenase 1 (NQO1) is increased in the endometrium of women with endometrial cancer and women with polycystic ovary syndrome.

OBJECTIVE: Women with a prior history of polycystic ovary syndrome (PCOS) have an increased risk of endometrial cancer (EC). AIM: To investigate whether the endometrium of women with PCOS possesses gene expression changes similar to those found in EC. DESIGN AND METHODS: Patients with EC, PCOS and control women unaffected by either PCOS or EC were recruited into a cross-sectional study at the Nottingham University Hospital, UK. For RNA sequencing, representative individual endometrial biopsies were obtained from women with EC, PCOS and a woman unaffected by PCOS or EC. Expression of a subset of differentially expressed genes identified by RNA sequencing, including NAD(P)H quinone dehydrogenase 1 (NQO1), was validated by quantitative reverse transcriptase PCR validation (n = 76) and in the cancer genome atlas UCEC (uterine corpus endometrioid carcinoma) RNA sequencing data set (n = 381). The expression of NQO1 was validated by immunohistochemistry in EC samples from a separate cohort (n = 91) comprised of consecutive patients who underwent hysterectomy at St Mary's Hospital, Manchester, between 2011 and 2013. A further 6 postmenopausal women with histologically normal endometrium who underwent hysterectomy for genital prolapse were also included. Informed consent and local ethics approval were obtained for the study. RESULTS: We show for the first that NQO1 expression is significantly increased in the endometrium of women with PCOS and EC. Immunohistochemistry confirms significantly increased NQO1 protein expression in EC relative to nonmalignant endometrial tissue (P < .0001). CONCLUSIONS: The results obtained here support a previously unrecognized molecular link between PCOS and EC involving NQO1.

The role of PI3K/AKT-related PIP5K1α and the discovery of its selective inhibitor for treatment of advanced prostate cancer.

Nitrogen-containing heterocyclic compounds are an important class of molecules that are commonly used for the synthesis of candidate drugs. Phosphatidylinositol-4-phosphate 5-kinase-α (PIP5Kα) is a lipid kinase, similar to PI3K. However, the role of PIP5K1α in oncogenic processes and the development of inhibitors that selectively target PIP5K1α have not been reported. In the present study we report that overexpression of PIP5K1α is associated with poor prognosis in prostate cancer and correlates with an elevated level of the androgen receptor. Overexpression of PIP5K1α in PNT1A nonmalignant cells results in an increased AKT activity and an increased survival, as well as invasive malignant phenotype, whereas siRNA-mediated knockdown of PIP5K1α in aggressive PC-3 cells leads to a reduced AKT activity and an inhibition in tumor growth in xenograft mice. We further report a previously unidentified role for PIP5K1α as a druggable target for our newly developed compound ISA-2011B using a high-throughput KINOMEscan platform. ISA-2011B was discovered during our synthetic studies of C-1 indol-3-yl substituted 1,2,3,4-tetrahydroisoquinolines via a Pictet-Spengler approach. ISA-2011B significantly inhibits growth of tumor cells in xenograft mice, and we show that this is mediated by targeting PIP5K1α-associated PI3K/AKT and the downstream survival, proliferation, and invasion pathways. Further, siRNA-mediated knockdown of PIP5K1α exerts similar effects on PC3 cells as ISA-2011B treatment, significantly inhibiting AKT activity, increasing apoptosis and reducing invasion. Thus, PIP5K1α has high potential as a drug target, and compound ISA-2011B is interesting for further development of targeted cancer therapy.

Use of two gene panels for prostate cancer diagnosis and patient risk stratification.

Currently, no ideal prostate cancer (PCa) diagnostic or prognostic test is available due to the lack of biomarkers with high sensitivity and specificity. There is an unmet medical need to develop combinations of multiple biomarkers which may have higher accuracy in detection of PCa and stratification of aggressive and indolent cancer patients. The aim of this study was to test two biomarker gene panels in distinguishing PCa from benign prostate and high-risk, aggressive PCa from low-risk, indolent PCa, respectively. We identified a five-gene panel that can be used to distinguish PCa from benign prostate. The messenger RNA (mRNA) expression signature of the five genes was determined in 144 PCa and benign prostate specimens from prostatectomy. We showed that the five-gene panel distinguished PCa from benign prostate with sensitivity of 96.59 %, specificity of 92.86 %, and area under the curve (AUC) of 0.992 (p < 0.0001). The five-gene panel was further validated in a 137 specimen cohort and showed sensitivity of 84.62 %, specificity of 91.84 %, and AUC of 0.942 (p < 0.0001). To define subtypes of PCa for treatment guidance, we examined mRNA expression signature of an eight-gene panel in 87 PCa specimens from prostatectomy. The signature of the eight-gene panel was able to distinguish aggressive PCa (Gleason score >6) from indolent PCa (Gleason score ≤6) with sensitivity of 90.28 %, specificity of 80.00 %, and AUC of 0.967 (p < 0.0001). This panel was further validated in a 158 specimen cohort and showed significant difference between aggressive PCa and indolent PCa with sensitivity of 92.57 %, specificity of 70.00 %, and AUC of 0.962 (p < 0.0001). Our findings in assessing multiple biomarkers in combination may provide new tools to detect PCa and distinguish aggressive and indolent PCa for precision and personalized treatment. The two biomarker panels may be used in clinical settings for accurate PCa diagnosis and patient risk stratification for biomarker-guided treatment.

Vitronectin: a promising breast cancer serum biomarker for early diagnosis of breast cancer in patients.

Breast cancer is the most common cancer in women worldwide, identification of new biomarkers for early diagnosis and detection will improve the clinical outcome of breast cancer patients. In the present study, we determined serum levels of vitronectin (VN) in 93 breast cancer patients, 30 benign breast lesions, 9 precancerous lesions, and 30 healthy individuals by enzyme-linked immunosorbent assays. Serum VN level was significantly higher in patients with stage 0-I primary breast cancer than in healthy individuals, patients with benign breast lesion or precancerous lesions, as well as those with breast cancer of higher stages. Serum VN level was significantly and negatively correlated with tumor size, lymph node status, and clinical stage (p < 0.05 in all cases). In addition, VN displayed higher area under curve (AUC) value (0.73, 95 % confidence interval (CI) [0.62-0.84]) than carcinoembryonic antigen (CEA) (0.64, 95 % CI [0.52-0.77]) and cancer antigen 15-3 (CA 15-3) (0.69, 95 % CI [0.58-0.81]) when used to distinguish stage 0-I cancer and normal control. Importantly, the combined use of three biomarkers yielded an improvement in receiver operating characteristic curve with an AUC of 0.83, 95 % CI [0.74-0.92]. Taken together, our current study showed for the first time that serum VN is a promising biomarker for early diagnosis of breast cancer when combined with CEA and CA15-3.

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis.

BACKGROUND: Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. METHODS: An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. RESULTS: An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. CONCLUSIONS: The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

The KDM5B and KDM1A lysine demethylases cooperate in regulating androgen receptor expression and signalling in prostate cancer.

Histone H3 lysine 4 (H3K4) methylation is key epigenetic mark associated with active transcription and is a substrate for the KDM1A/LSD1 and KDM5B/JARID1B lysine demethylases. Increased expression of KDM1A and KDM5B is implicated in many cancer types, including prostate cancer (PCa). Both KDM1A and KDM5B interact with AR and promote androgen regulated gene expression. For this reason, there is great interested in the development of new therapies targeting KDM1A and KDM5B, particularly in the context of castrate resistant PCa (CRPC), where conventional androgen deprivation therapies and androgen receptor signalling inhibitors are no longer effective. As there is no curative therapy for CRPC, new approaches are urgently required to suppress androgen signalling that prevent, delay or reverse progression to the castrate resistant state. While the contribution of KDM1A to PCa is well established, the exact contribution of KDM5B to PCa is less well understood. However, there is evidence that KDM5B is implicated in numerous pro-oncogenic mechanisms in many different types of cancer, including the hypoxic response, immune evasion and PI3/AKT signalling. Here we elucidate the individual and cooperative functions of KDM1A and KDM5B in PCa. We show that KDM5B mRNA and protein expression is elevated in localised and advanced PCa. We show that the KDM5 inhibitor, CPI-455, impairs androgen regulated transcription and alternative splicing. Consistent with the established role of KDM1A and KDM5B as AR coregulators, we found that individual pharmacologic inhibition of KDM1A and KDM5 by namoline and CPI-455 respectively, impairs androgen regulated transcription. Notably, combined inhibition of KDM1A and KDM5 downregulates AR expression in CRPC cells. Furthermore, combined KDM1A and KDM5 inhibition impairs PCa cell proliferation and invasion more than individual inhibition of KDM1A and KDM5B. Collectively our study has identified individual and cooperative mechanisms involving KDM1A and KDM5 in androgen signalling in PCa. Our findings support the further development of KDM1A and KDM5B inhibitors to treat advanced PCa. Further work is now required to confirm the therapeutic feasibility of combined inhibition of KDM1A and KDM5B as a novel therapeutic strategy for targeting AR positive CRPC.

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer.

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Sialyl-Tn Antigen-Imprinted Dual Fluorescent Core-Shell Nanoparticles for Ratiometric Sialyl-Tn Antigen Detection and Dual-Color Labeling of Cancer Cells.

Sialyl-Tn (STn or sialyl-Thomsen-nouveau) is a carbohydrate antigen expressed by more than 80% of human carcinomas. We here report a strategy for ratiometric STn detection and dual-color cancer cell labeling, particularly, by molecularly imprinted polymers (MIPs). Imprinting was based on spectroscopic studies of a urea-containing green-fluorescent monomer 1 and STn-Thr-Na (sodium salt of Neu5Acα2-6GalNAcα-O-Thr). A few-nanometer-thin green-fluorescent polymer shell, in which STn-Thr-Na was imprinted with 1, other comonomers, and a cross-linker, was synthesized from the surface of red-emissive carbon nanodot (R-CND)-doped silica nanoparticles, resulting in dual fluorescent STn-MIPs. Dual-color labeling of cancer cells was achieved since both red and green emissions were detected in two separate channels of the microscope and an improved accuracy was obtained in comparison with single-signal MIPs. The flow cytometric cell analysis showed that the binding of STn-MIPs was significantly higher (p < 0.001) than that of non-imprinted polymer (NIP) control particles within the same cell line, allowing to distinguish populations. Based on the modularity of the luminescent core-fluorescent MIP shell architecture, the concept can be transferred in a straightforward manner to other target analytes.

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients.

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

Bacterial protein MakA causes suppression of tumour cell proliferation via inhibition of PIP5K1α/Akt signalling.

Recently, we demonstrated that a novel bacterial cytotoxin, the protein MakA which is released by Vibrio cholerae, is a virulence factor, causing killing of Caenorhabditis elegans when the worms are grazing on the bacteria. Studies with mammalian cell cultures in vitro indicated that MakA could affect eukaryotic cell signalling pathways involved in lipid biosynthesis. MakA treatment of colon cancer cells in vitro caused inhibition of growth and loss of cell viability. These findings prompted us to investigate possible signalling pathways that could be targets of the MakA-mediated inhibition of tumour cell proliferation. Initial in vivo studies with MakA producing V. cholerae and C. elegans suggested that the MakA protein might target the PIP5K1α phospholipid-signalling pathway in the worms. Intriguingly, MakA was then found to inhibit the PIP5K1α lipid-signalling pathway in cancer cells, resulting in a decrease in PIP5K1α and pAkt expression. Further analyses revealed that MakA inhibited cyclin-dependent kinase 1 (CDK1) and induced p27 expression, resulting in G2/M cell cycle arrest. Moreover, MakA induced downregulation of Ki67 and cyclin D1, which led to inhibition of cell proliferation. This is the first report about a bacterial protein that may target signalling involving the cancer cell lipid modulator PIP5K1α in colon cancer cells, implying an anti-cancer effect.

Fluorescent Molecularly Imprinted Polymer Layers against Sialic Acid on Silica-Coated Polystyrene Cores-Assessment of the Binding Behavior to Cancer Cells.

Sialic acid (SA) is a monosaccharide usually linked to the terminus of glycan chains on the cell surface. It plays a crucial role in many biological processes, and hypersialylation is a common feature in cancer. Lectins are widely used to analyze the cell surface expression of SA. However, these protein molecules are usually expensive and easily denatured, which calls for the development of alternative glycan-specific receptors and cell imaging technologies. In this study, SA-imprinted fluorescent core-shell molecularly imprinted polymer particles (SA-MIPs) were employed to recognize SA on the cell surface of cancer cell lines. The SA-MIPs improved suspensibility and scattering properties compared with previously used core-shell SA-MIPs. Although SA-imprinting was performed using SA without preference for the α2,3- and α2,6-SA forms, we screened the cancer cell lines analyzed using the lectins Maackia Amurensis Lectin I (MAL I, α2,3-SA) and Sambucus Nigra Lectin (SNA, α2,6-SA). Our results show that the selected cancer cell lines in this study presented a varied binding behavior with the SA-MIPs. The binding pattern of the lectins was also demonstrated. Moreover, two different pentavalent SA conjugates were used to inhibit the binding of the SA-MIPs to breast, skin, and lung cancer cell lines, demonstrating the specificity of the SA-MIPs in both flow cytometry and confocal fluorescence microscopy. We concluded that the synthesized SA-MIPs might be a powerful future tool in the diagnostic analysis of various cancer cells.

Comparative pathology of dog and human prostate cancer.

Though relatively rare in dogs, prostate cancer (PCa) is the most common non-cutaneous cancer in men. Human and canine prostate glands share many functional, anatomical and physiological features. Due to these similarities, canine PCa has been proposed as a model for PCa in men. PCa is typically androgen-dependent at diagnosis in men and for this reason, androgen deprivation therapies (ADT) are important treatments for advanced PCa in men. In contrast, there is some evidence that PCa is diagnosed more commonly in castrate dogs, at which point, limited therapeutic options are available. In men, a major limitation of current ADT is that progression to a lethal and incurable form of PCa, termed castrate-resistant prostate cancer (CRPC), is common. There is, therefore, an urgent need for a better understanding of the mechanism of PCa initiation and progression to CRPC to enable the development of novel therapeutic approaches. This review focuses on the functional, physiological, endocrine and histopathological similarities and differences in the prostate gland of these species. In particular, we focus on common physiological roles for androgen signalling in the prostate of men and dogs, we review the short- and longer-term effects of castration on PCa incidence and progression in the dog and relate how this knowledge may be relevant to understanding the mechanisms of CRPC in men.

PIP5K1α is Required for Promoting Tumor Progression in Castration-Resistant Prostate Cancer.

PIP5K1α has emerged as a promising drug target for the treatment of castration-resistant prostate cancer (CRPC), as it acts upstream of the PI3K/AKT signaling pathway to promote prostate cancer (PCa) growth, survival and invasion. However, little is known of the molecular actions of PIP5K1α in this process. Here, we show that siRNA-mediated knockdown of PIP5K1α and blockade of PIP5K1α action using its small molecule inhibitor ISA-2011B suppress growth and invasion of CRPC cells. We demonstrate that targeted deletion of the N-terminal domain of PIP5K1α in CRPC cells results in reduced growth and migratory ability of cancer cells. Further, the xenograft tumors lacking the N-terminal domain of PIP5K1α exhibited reduced tumor growth and aggressiveness in xenograft mice as compared to that of controls. The N-terminal domain of PIP5K1α is required for regulation of mRNA expression and protein stability of PIP5K1α. This suggests that the expression and oncogenic activity of PIP5K1α are in part dependent on its N-terminal domain. We further show that PIP5K1α acts as an upstream regulator of the androgen receptor (AR) and AR target genes including CDK1 and MMP9 that are key factors promoting growth, survival and invasion of PCa cells. ISA-2011B exhibited a significant inhibitory effect on AR target genes including CDK1 and MMP9 in CRPC cells with wild-type PIP5K1α and in CRPC cells lacking the N-terminal domain of PIP5K1α. These results indicate that the growth of PIP5K1α-dependent tumors is in part dependent on the integrity of the N-terminal sequence of this kinase. Our study identifies a novel functional mechanism involving PIP5K1α, confirming that PIP5K1α is an intriguing target for cancer treatment, especially for treatment of CRPC.

FcγRIIIa receptor interacts with androgen receptor and PIP5K1α to promote growth and metastasis of prostate cancer.

Low-affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) is a cell surface protein that belongs to a family of Fc receptors that facilitate the protective function of the immune system against pathogens. However, the role of FcγRIIIa in prostate cancer (PCa) progression remained unknown. In this study, we found that FcγRIIIa expression was present in PCa cells and its level was significantly higher in metastatic lesions than in primary tumors from the PCa cohort (P = 0.006). PCa patients with an elevated level of FcγRIIIa expression had poorer biochemical recurrence (BCR)-free survival compared with those with lower FcγRIIIa expression, suggesting that FcγRIIIa is of clinical importance in PCa. We demonstrated that overexpression of FcγRIIIa increased the proliferative ability of PCa cell line C4-2 cells, which was accompanied by the upregulation of androgen receptor (AR) and phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), which are the key players in controlling PCa progression. Conversely, targeted inhibition of FcγRIIIa via siRNA-mediated knockdown or using its inhibitory antibody suppressed growth of xenograft PC-3 and PC-3M prostate tumors and reduced distant metastasis in xenograft mouse models. We further showed that elevated expression of AR enhanced FcγRIIIa expression, whereas inhibition of AR activity using enzalutamide led to a significant downregulation of FcγRIIIa protein expression. Similarly, inhibition of PIP5K1α decreased FcγRIIIa expression in PCa cells. FcγRIIIa physically interacted with PIP5K1α and AR via formation of protein-protein complexes, suggesting that FcγRIIIa is functionally associated with AR and PIP5K1α in PCa cells. Our study identified FcγRIIIa as an important factor in promoting PCa growth and invasion. Further, the elevated activation of FcγRIII and AR and PIP5K1α pathways may cooperatively promote PCa growth and invasion. Thus, FcγRIIIa may serve as a potential new target for improved treatment of metastatic and castration-resistant PCa.

The METTL3 RNA Methyltransferase Regulates Transcriptional Networks in Prostate Cancer.

Prostate cancer (PCa) is a leading cause of cancer-related deaths and is driven by aberrant androgen receptor (AR) signalling. For this reason, androgen deprivation therapies (ADTs) that suppress androgen-induced PCa progression either by preventing androgen biosynthesis or via AR signalling inhibition (ARSi) are common treatments. The N6-methyladenosine (m6A) RNA modification is involved in regulating mRNA expression, translation, and alternative splicing, and through these mechanisms has been implicated in cancer development and progression. RNA-m6A is dynamically regulated by the METTL3 RNA methyltransferase complex and the FTO and ALKBH5 demethylases. While there is evidence supporting a role for aberrant METTL3 in many cancer types, including localised PCa, the wider contribution of METTL3, and by inference m6A, in androgen signalling in PCa remains poorly understood. Therefore, the aim of this study was to investigate the expression of METTL3 in PCa patients and study the clinical and functional relevance of METTL3 in PCa. It was found that METTL3 is aberrantly expressed in PCa patient samples and that siRNA-mediated METTL3 knockdown or METTL3-pharmacological inhibition significantly alters the basal and androgen-regulated transcriptome in PCa, which supports targeting m6A as a novel approach to modulate androgen signalling in PCa.