The Emergence of Precision Urologic Oncology: A Collaborative Review on Biomarker-driven Therapeutics

Symphony in the crowd: Key genetic alterations in prostate cancer

Androgen receptor (AR) signaling have been frequently targeted for treating prostate cancer (PCa). Even though primarily patients receive a good therapeutic outcome by targeting AR signaling axis, eventually it emerges resistance by altering the genetic makeup of prostate cells. However, to develop an effective therapeutic regime, it is essential to recognize key genetic alterations in PCa. The most common genetic alterations that give rise to distinct androgen different differentiation states are gene fusion of TMPRSS2 with ETS family genes, deletion, or mutation of tumor suppressor PTEN and TP53 gene, amplification or splicing of AR, altered DNA repair genes. In this review, we describe key genes and genetic changes that have been recognized to contribute to altered prostate environment.

How to read a next-generation sequencing report—what oncologists need to know

Next-generation sequencing (NGS) of tumor cell-derived DNA/RNA to screen for targetable genomic alterations is now widely available and has become part of routine practice in oncology. NGS testing strategies depend on cancer type, disease stage and the impact of results on treatment selection. The European Society for Medical Oncology (ESMO) has recently published recommendations for the use of NGS in patients with advanced cancer. We complement the ESMO recommendations with a practical review of how oncologists should read and interpret NGS reports. A concise and straightforward NGS report contains details of the tumor sample, the technology used and highlights not only the most important and potentially actionable results, but also other pathogenic alterations detected. Variants of unknown significance should also be listed. Interpretation of NGS reports should be a joint effort between molecular pathologists, tumor biologists and clinicians. Rather than relying and acting on the information provided by the NGS report, oncologists need to obtain a basic level of understanding to read and interpret NGS results. Comprehensive annotated databases are available for clinicians to review the information detailed in the NGS report. Molecular tumor boards do not only stimulate debate and exchange, but may also help to interpret challenging reports and to ensure continuing medical education.

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NGS is routinely carried out in the diagnostic work-up of several cancer types.

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In many other malignancies NGS is carried out after exhaustion of standard therapy options.

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Minimal requirements for the NGS report are detailed in this review.

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Interpretation of NGS reports can be challenging and require interdisciplinary discussion.

Relative Quantification of Proteins in Formalin-Fixed Paraffin-Embedded Breast Cancer Tissue Using Multiplexed Mass Spectrometry Assays

The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative, 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched immunohistochemistry or chromogenic in situ hybridization data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in triple-negative breast cancer samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.

A Novel Angiogenesis-Related Gene Signature to Predict Biochemical Recurrence of Patients with Prostate Cancer following Radical Therapy

Background

Prostate cancer (PCa) is one of the most common malignancies in males; we aim to establish a novel angiogenesis-related gene signature for biochemical recurrence (BCR) prediction in PCa patients following radical therapy.

Methods

Gene expression profiles and corresponding clinicopathological data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We quantified the levels of various cancer hallmarks and identified angiogenesis as the primary risk factor for BCR. Then machine learning methods combined with Cox regression analysis were used to screen prognostic genes and construct an angiogenesis-related gene signature, which was further verified in external cohorts. Furthermore, estimation of immune cell abundance and prediction of drug responses were also conducted to detect potential mechanisms.

Results

Angiogenesis was regarded as the leading risk factor for BCR in PCa patients (HR = 1.58, 95% CI: 1.38–1.81), and a novel prognostic signature based on three genes (NRP1, JAG2, and VCAN) was developed in the training cohort and successfully validated in another three independent cohorts. In all datasets, this signature was identified as a prognostic factor with promising and robust predictive values. Moreover, it also predicted higher infiltration of regulatory T cells and M2-polarized macrophages and increased drug sensitivity of angiogenesis inhibitors in high-risk patients.

Conclusions

The angiogenesis-related three-gene signature may serve as an independent prognostic factor for BCR, which would contribute to risk stratification and personalized management of PCa patients after radical therapy in clinical practice.

Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis

OBJECTIVE

Glioma is the most common intracranial primary malignancy, but its pathogenesis remains unclear.

METHODS

We integrated four eligible glioma microarray datasets from the gene expression omnibus database using the robust rank aggregation method to identify a group of significantly differently expressed genes (DEGs) between glioma and normal samples. We used these DEGs to explore key genes closely associated with glioma survival through weighted gene co-expression network analysis. We then constructed validations of prognosis and survival analyses for the key genes via multiple databases. We also explored their potential biological functions using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA).

RESULTS

We selected DLGAP5, CDCA8, NCAPH, and CCNB2, as four genes that were abnormally up-regulated in glioma samples, for verification. They showed high levels of isocitrate dehydrogenase gene mutation and tumor grades, as well as good prognostic and diagnostic value for glioma. Their methylation levels were generally lower in glioma samples. GSEA and GSVA analyses suggested the genes were closely involved with glioma proliferation.

CONCLUSION

These findings provide new insights into the pathogenesis of glioma. The hub genes have the potential to be used as diagnostic and therapeutic markers.

Assessing the Potential Prognostic and Immunological Role of TK1 in Prostate Cancer

Background: It has been reported that thymidine kinase 1 (TK1) was up-regulated in multiple malignancies and participated in the regulation of tumor malignant behavior. However, its specific role in prostate cancer (PCa) remains unclear. Methods: TK1 expression in PCa patients and cell lines was identified via crossover analysis of the public datasets. A series of in vitro experiments and in vivo models was applied to investigate the function of TK1 in PCa. Functional enrichment analyses were further conducted to explore the underlying mechanism. Additionally, TISIDB was applied to explore the correlation between TK1 expression and tumor-infiltrating lymphocytes, immune subtypes, and immune regulatory factors. Results: TK1 expression was significantly up-regulated in PCa patients and cell lines. TK1 ablation inhibited tumor cell proliferation and migration potential, and in vivo experiments showed that TK1 inactivation can significantly restrain tumor growth. Functional enrichment analysis revealed TK1-related hub genes (AURKB, CCNB2, CDC20, CDCA5, CDK1, CENPA, CENPM, KIF2C, NDC80, NUF2, PLK1, SKA1, SPC25, ZWINT), and found that TK1 was closely involved in the regulation of cell cycle. Moreover, elevated mRNA expression of TK1 was related with higher Gleason score, higher clinical stage, higher pathological stage, higher lymph node stage, shorter overall survival, and DFS in PCa patients. Particularly, TK1 represented attenuated expression in C3 PCa and was related with infiltration of CD4+, CD8+ T cells, and dendritic cells as well as immunomodulator expression. Conclusion: Our study indicates that TK1 is a prognostic predictor correlated with poor outcomes of PCa patients, and for the first time represented that TK1 can promote the progression of PCa. Therefore, TK1 may be a potential diagnostic and prognostic biomarker, as well as a therapeutic target for PCa.

Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy

Background

This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients.

Methods

Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO‐Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence‐free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs).

Results

Notably, the risk score could significantly identify BCRFS by time‐dependent receiver operating characteristic (t‐ROC) curves in the training set (3‐year area under the curve (AUC) = 0.820, 5‐year AUC = 0.809) and the validation set (3‐year AUC = 0.723, 5‐year AUC = 0.733).

Conclusions

Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa.

Expression of phosphorylated p21-activated kinase 4 is associated with aggressive histologic characteristics and poor prognosis in patients with surgically treated renal cell carcinoma

Purpose

P21-activated kinase 4 (PAK4), a serine/threonine kinase that regulates a number of fundamental cellular processes, has been suggested as a prognostic factor for various human tumors. The aim of the present study was to evaluate the clinical implications of phospho-Ser474 PAK4 (pPAK4^S474), an activated form of PAK4, in surgically treated renal cell carcinoma (RCC).

Materials and Methods

Samples from 131 patients with surgically treated RCC were immunostained to detect PAK4 and pPAK4^S474. Expression of PAK4 and pPAK4^S474 was compared with clinicopathological characteristics and survival after nephrectomy.

Results

PAK4 and pPAK4^S474 were expressed predominantly in the nucleus. Overall, 57.3% (75/131) and 24.4% (29/119) of specimens exhibited high expression of pPAK4^S474 and PAK4, respectively. High expression of pPAK4^S474 was associated with adverse pathologic characteristics, including advanced tumor stage and grade (p=0.036 and p=0.002, respectively), whereas this association was not significant for PAK4 expression (each p>0.05). Kaplan-Meier estimates showed that high expression of pPAK4^S474 was associated with shorter recurrence-free survival in a subgroup with localized RCC and with cancer-specific survival in the total RCC cohort (log-rank test: p=0.001 and p=0.005, respectively), whereas PAK4 expression was not. Multivariate Cox regression analysis identified that high pPAK4^S474 expression was an independent predictor of recurrence in the subgroup with localized RCC.

Conclusions

pPAK4^S474 may be a more accurate prognostic factor than total PAK4 in RCC patients. This marker would be useful for identifying patients with pathologically localized disease who may require further interventions.

Functional Assessment of Four Novel Immune-Related Biomarkers in the Pathogenesis of Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC.

Methods

The Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC.

Results

Four hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results.

Conclusion

The four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.

Overexpression of TICRR and PPIF confer poor prognosis in endometrial cancer identified by gene co-expression network analysis

The incidence of endometrial cancer (EC) is intensively increasing. However, due to the complexity and heterogeneity of EC, the molecular targeted therapy is still limited. The reliable and accurate biomarkers for tumor progression are urgently demanded. After normalizing the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized limma and WGCNA packages to identify differentially expressed genes (DEGs). The copy number variations of candidate genes were investigated by cBioPortal. Enrichment pathways analysis was performed by ClueGO and CluePedia. The methylation status was explored by UALCAN. ROC curve and survival analysis were conducted by SPSS and Kaplan–Meier. Infiltration immune cells in microenvironment were analyzed by TISIDB. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were applied to explore potential biological pathways. Immunohistochemistry staining (IHC), cell proliferation, cell apoptosis, colony formation, migration, invasion and scratch-wound assays were performed to investigate the function of key genes in vitro. In this study, four expression profile datasets were integrated to identify candidate genes. Combined with WGCNA analysis, the top ten candidates were screened out, whose abnormal methylation patterns were extremely correlated with their expression level and they were associated with tumor grades and predicted poor survival. GSEA and GSVA demonstrated they were involved in DNA replication and cell cycle transition in EC. Gene silencing of TICRR and PPIF dramatically inhibited cell growth, migration and epithelial-mesenchymal transition (EMT) and enhanced progesterone sensitivity. Additionally, from DrugBank database, cyclosporine may be effective for PPIF targeted therapy. By integrative bioinformatics analysis and in vitro experiments, our study shed novel light on the molecular mechanisms of EC. TICRR and PPIF may promise to be potential therapeutic targets for endometrial cancer.

Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Background

Long noncoding RNA (lncRNA) is generally identified as competing endogenous RNA (ceRNA) that plays a vital role in the pathogenesis of kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma with poor prognosis and unclear pathogenesis. This study established a novel ceRNA network and thus identified a three-lncRNA prognostic model in KIRC patients.

Methods

Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) database. The lncATLAS was applied to determine the differentially expressed lncRNAs (DElncRNAs) of the cytoplasm. The miRcode, miRDB, miRTarBase, and TargetScan databases were utilized to predict the interactions of DElncRNAs, DEmiRNAs, and DEmRNAs. Cytoscape was used to construct the ceRNA network. Then, a lncRNA prognostic model (LPM) was constructed based on ceRNA-related lncRNA that was significantly related to overall survival (OS), and its predictive ability was evaluated. Moreover, an LPM-based nomogram model was constructed. The significantly different expression of genes in the LPM was validated in an independent clinical cohort (N = 21) by quantitative RT-PCR.

Results

A novel ceRNA regulatory network, including 73 lncRNAs, 8 miRNAs, and 21 mRNAs was constructed. Functional enrichment analysis indicated that integral components of membrane and PI3K-Akt signaling pathway represented the most significant GO terms and pathway, respectively. The LPM established based on three lncRNAs (MIAT, LINC00460, and LINC00443) of great prognostic value from the ceRNA network was proven to be independent of conventional clinical parameters to differentiate patients with low or high risk of poor survival, with the AUC of 1-, 5- and 10-year OS were 0.723, 0.714 and 0.826 respectively. Furthermore, the nomogram showed a better predictive value in KIRC patients than individual prognostic parameters. The expression of MIAT and LINC00460 was significantly upregulated in the KIRC samples, while the expression of LINC00443 was significantly downregulated compared with the adjacent normal samples in the clinical cohort, TCGA, and GTEx.

Conclusion

This LPM based on three-lncRNA could serve as an independent prognostic factor with a tremendous predictive ability for KIRC patients, and the identified novel ceRNA network may provide insight into the prognostic biomarkers and therapeutic targets of KIRC.

The ProBio trial: molecular biomarkers for advancing personalized treatment decision in patients with metastatic castration-resistant prostate cancer

BACKGROUND

Multiple therapies exist for patients with metastatic castration-resistant prostate cancer (mCRPC). However, their improvement on progression-free survival (PFS) remains modest, potentially explained by tumor molecular heterogeneity. Several prognostic molecular biomarkers have been identified for mCRPC that may have predictive potential to guide treatment selection and prolong PFS. We designed a platform trial to test this hypothesis.

METHODS

The Prostate-Biomarker (ProBio) study is a multi-center, outcome-adaptive, multi-arm, biomarker-driven platform trial for tailoring treatment decisions for men with mCRPC. Treatment decisions in the experimental arms are based on biomarker signatures defined as mutations in certain genes/pathways suggested in the scientific literature to be important for treatment response in mCRPC. The biomarker signatures are determined by targeted sequencing of circulating tumor and germline DNA using a panel specifically designed for mCRPC.

DISCUSSION

Patients are stratified based on the sequencing results and randomized to either current clinical practice (control), where the treating physician decides treatment, or to molecularly driven treatment selection based on the biomarker profile. Outcome-adaptive randomization is implemented to early identify promising treatments for a biomarker signature. Biomarker signature-treatment combinations graduate from the platform when they demonstrate 85% probability of improving PFS compared to the control arm. Graduated combinations are further evaluated in a seamless confirmatory trial with fixed randomization. The platform design allows for new drugs and biomarkers to be introduced in the study.

CONCLUSIONS

The ProBio design allows promising treatment-biomarker combinations to quickly graduate from the platform and be confirmed for rapid implementation in clinical care.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT03903835. Date of registration: April 4, 2019. Status: Recruiting.

Identification of castration-resistant prostate cancer-related hub genes using weighted gene co-expression network analysis

Prostate cancer is the most common malignancy in urinary system and brings heavy burdens in men. We downloaded gene expression profile of mRNA and related clinical data of GSE70768 data set from public database. Weighted gene co‐expression network analysis (WGCNA) was used to identify the relationships between gene modules and clinical features, as well as the candidate genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were developed to investigate the potential functions of related hub genes. Importantly, basic experiments were performed to verify the relationship between hub genes and the phenotype previously identified. Lastly, copy number variation (CNV) analysis was conducted to explore the genetical alteration. WGCNA identified that black module was the most relevant module which was tightly related to castration‐resistant prostate cancer (CRPC) phenotype. KEGG and GO analysis results revealed genes in black module were mainly related to RNA splicing. Additionally, 9 genes were chosen as hub genes and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1), golgin A8 family member B (GOLGA8B) and mitogen‐activated protein kinase 8 interacting protein 3 (MAPK8IP3) were identified to be associated with PCa progression and prognosis. Moreover, all above three genes were highly expressed in CRPC‐like cells and their suppression led to hindered cell proliferation in vitro. Finally, CNV analysis found that amplification was the main type of alteration of the 3 hub genes. Our study found that HNRNPA2B1, GOLGA8B and MAPK8IP3 were identified to be tightly associated with tumour progression and prognosis, and further researches are needed before clinical application.

Challenges, applications and future directions of precision medicine in prostate cancer - the role of organoids and patient-derived xenografts

OBJECTIVE

To provide a clinically relevant outline of various current precision medicine principles and available evidence on the application and potential for a precision medicine approach in prostate cancer.

METHODS

Narrative review of the current literature in the field.

CONCLUSION

Precision medicine is the concept of individualising patient management based on specific tumour characteristics and biology, rather than traditional histological subtypes. The overall aim is to personalise management to individual patients, to provide the right cancer treatment, to the right patient, at the right time. While the approach aims to improve clinical outcomes, decrease morbidity and improve survival in men with advanced prostate cancer, its clinical application is in its infancy. It does however show great promise in this and other cancers, and will continue to be an area of active research and clinical investigation.

Development and validation of hub genes for lymph node metastasis in patients with prostate cancer

Lymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis. We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co-expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein-protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well-distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa.

Merging new-age biomarkers and nanodiagnostics for precision prostate cancer management

The accurate identification and stratified treatment of clinically significant early-stage prostate cancer have been ongoing concerns since the outcomes of large international prostate cancer screening trials were reported. The controversy surrounding clinical and cost benefits of prostate cancer screening has highlighted the lack of strategies for discriminating high-risk disease (that requires early treatment) from low-risk disease (that could be managed using watchful waiting or active surveillance). Advances in molecular subtyping and multiomics nanotechnology-based prostate cancer risk delineation can enable refinement of prostate cancer molecular taxonomy into clinically meaningful and treatable subtypes. Furthermore, the presence of intertumoural and intratumoural heterogeneity in prostate cancer warrants the development of novel nanodiagnostic technologies to identify clinically significant prostate cancer in a rapid, cost-effective and accurate manner. Circulating and urinary next-generation prostate cancer biomarkers for disease molecular subtyping and the newest complementary nanodiagnostic platforms for enhanced biomarker detection are promising tools for precision prostate cancer management. However, challenges in merging both aspects and clinical translation still need to be overcome.

Histone deacetylase inhibitors dysregulate DNA repair proteins and antagonize metastasis-associated processes

Purpose

We set out to determine whether clinically tested epigenetic drugs against class I histone deacetylases (HDACs) affect hallmarks of the metastatic process.

Methods

We treated permanent and primary renal, lung, and breast cancer cells with the class I histone deacetylase inhibitors (HDACi) entinostat (MS-275) and valproic acid (VPA), the replicative stress inducer hydroxyurea (HU), the DNA-damaging agent cis-platinum (L-OHP), and the cytokine transforming growth factor-β (TGFβ). We used proteomics, quantitative PCR, immunoblot, single cell DNA damage assays, and flow cytometry to analyze cell fate after drug exposure.

Results

We show that HDACi interfere with DNA repair protein expression and trigger DNA damage and apoptosis alone and in combination with established chemotherapeutics. Furthermore, HDACi disrupt the balance of cell adhesion protein expression and abrogate TGFβ-induced cellular plasticity of transformed cells.

Conclusion

HDACi suppress the epithelial–mesenchymal transition (EMT) and compromise the DNA integrity of cancer cells. These data encourage further testing of HDACi against tumor cells.

Electronic supplementary material

The online version of this article (10.1007/s00432-019-03118-4) contains supplementary material, which is available to authorized users.

Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods

Background

Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method.

Methods

Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.

Results

Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in “oxidation-reduction process” and “positive regulation of transcription from RNA polymerase II promoter”; KEGG pathway analysis enriched mostly in “metabolic pathways” and “protein digestion and absorption.” Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.

Conclusions

This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

The pathogenic mechanisms of prostate cancer (PCa) remain to be defined. In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate 10 eligible PCa microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between tumor samples and normal, matched specimens. To explore potential associations between gene sets and PCa clinical features and to identify hub genes, we utilized WGCNA to construct gene co-expression networks incorporating the DEGs screened with the use of RRA. From the key module, we selected LMNB1, TK1, ZWINT, and RACGAP1 for validation. We found that these genes were up-regulated in PCa samples, and higher expression levels were associated with higher Gleason scores and tumor grades. Moreover, ROC and K-M plots indicated these genes had good diagnostic and prognostic value for PCa. On the other hand, methylation analyses suggested that the abnormal up-regulation of these four genes likely resulted from hypomethylation, while GSEA and GSVA for single hub gene revealed they all had a close association with proliferation of PCa cells. These findings provide new insight into PCa pathogenesis, and identify LMNB1, TK1, RACGAP1 and ZWINT as candidate biomarkers for diagnosis and prognosis of PCa.

Challenges and Chances in the Preclinical to Clinical Translation of Anticancer Metallodrugs

Despite being “sentenced to death” for quite some time, anticancer platinum compounds are still the most frequently prescribed cancer therapies in the oncological routine and recent exciting news from late-stage clinical studies on combinations of metallodrugs with immunotherapies suggest that this situation will not change soon. It is perhaps surprising that relatively simple molecules like cisplatin, discovered over 50 years ago, are still widely used clinically, while none of the highly sophisticated metal compounds developed over the last decade, including complexes with targeting ligands and multifunctional (nano)formulations, have managed to obtain clinical approval. In this book chapter, we summarize the current status of ongoing clinical trials for anticancer metal compounds and discuss the reasons for previous failures, as well as new opportunities for the clinical translation of metal complexes.

Development of a Highly Multiplexed SRM Assay for Biomarker Discovery in Formalin-Fixed Paraffin-Embedded Tissues

The search for novel and clinically relevant biomarkers still represents a major clinical challenge and mass-spectrometry-based technologies are essential tools to help in this process. In this application, we demonstrate how selected reaction monitoring (SRM) can be applied in a highly multiplexed way to analyze formalin-fixed paraffin-embedded (FFPE) tissues. Such an assay can be used to analyze numerous samples for narrowing down a list of potential biomarkers to the most relevant candidates. The use of FFPE tissues is of high relevance in this context as large sample collections linked with valuable clinical information are available in hospitals around the world. Here we describe in detail how we proceeded to develop such an assay for 200 proteins in breast tumor FFPE tissues. We cover the selection of suitable peptides, which are different in FFPE compared to fresh frozen tissues and show how we deliberately biased our assay toward proteins with a high probability of being measurable in human clinical samples.

Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis

BACKGROUND

There are multiple existing and emerging therapeutic avenues for metastatic prostate cancer, with a common denominator, which is the need for predictive biomarkers. Circulating tumor DNA (ctDNA) has the potential to cost-efficiently accelerate precision medicine trials to improve clinical efficacy and diminish costs and toxicity. However, comprehensive ctDNA profiling in metastatic prostate cancer to date has been limited.

METHODS

A combination of targeted and low-pass whole genome sequencing was performed on plasma cell-free DNA and matched white blood cell germline DNA in 364 blood samples from 217 metastatic prostate cancer patients.

RESULTS

ctDNA was detected in 85.9% of baseline samples, correlated to line of therapy and was mirrored by circulating tumor cell enumeration of synchronous blood samples. Comprehensive profiling of the androgen receptor (AR) revealed a continuous increase in the fraction of patients with intra-AR structural variation, from 15.4% during first-line metastatic castration-resistant prostate cancer therapy to 45.2% in fourth line, indicating a continuous evolution of AR during the course of the disease. Patients displayed frequent alterations in DNA repair deficiency genes (18.0%). Additionally, the microsatellite instability phenotype was identified in 3.81% of eligible samples (≥ 0.1 ctDNA fraction). Sequencing of non-repetitive intronic and exonic regions of PTEN, RB1, and TP53 detected biallelic inactivation in 47.5%, 20.3%, and 44.1% of samples with ≥ 0.2 ctDNA fraction, respectively. Only one patient carried a clonal high-impact variant without a detectable second hit. Intronic high-impact structural variation was twice as common as exonic mutations in PTEN and RB1. Finally, 14.6% of patients presented false positive variants due to clonal hematopoiesis, commonly ignored in commercially available assays.

CONCLUSIONS

ctDNA profiles appear to mirror the genomic landscape of metastatic prostate cancer tissue and may cost-efficiently provide somatic information in clinical trials designed to identify predictive biomarkers. However, intronic sequencing of the interrogated tumor suppressors challenges the ubiquitous focus on coding regions and is vital, together with profiling of synchronous white blood cells, to minimize erroneous assignments which in turn may confound results and impede true associations in clinical trials.

New insights into long noncoding RNAs and pseudogenes in prognosis of renal cell carcinoma

Background

Increasing evidence suggests a critical role for long noncoding RNAs (LncRNAs) and pseudogenes in cancer. Renal cell carcinoma (RCC), the most common primary renal neoplasm, is highly aggressive and difficult to treat because of its resistance to chemotherapy and radiotherapy. Despite many identified LncRNAs and pseudogenes, few have been clearly elucidated.

Methods

This study provides new insights into LncRNAs and pseudogenes in the prognosis of RCC. We searched an online database to interrogate alterations and clinical data on cBioPortal. We analysed LncRNA and pseudogene signatures to predict the prognosis of RCC based on a Cox model. We also found potential serum biomarkers of RCC and validated them in 32 RCC patients, as well as healthy controls.

Results

Alterations were found in 2553 LncRNAs and 8901 pseudogenes and occurred in up to 23% of all cases. Among these, 27 LncRNAs and 45 pseudogenes were closely related to prognosis. We also identified signatures of LncRNAs and pseudogenes that can predict overall survival and recurrence of RCC. We then validated the relative levels of these LncRNAs and pseudogenes in the serum of 32 patients. Six of these, including LINC00520, PIK3CD-AS1, LINC01559, CEACAM22P, MSL3P1 and TREML3P, could be non-invasive biomarkers of RCC. Finally, we selected PIK3CD-AS1 to determine its role in RCC and found that upregulation of PIK3CD-AS1 was closely associated with higher tumour stage and metastasis.

Conclusions

These signatures of LncRNAs and pseudogenes can predict overall survival and recurrence of RCC. LINC00520, PIK3CD-AS1, LINC01559, CEACAM22P, MSL3P1 and TREML3P could be non-invasive biomarkers of RCC. These data suggest the important roles of LncRNAs and pseudogenes in RCC, and therefore provides us new insights into the prognosis of RCC.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0652-6) contains supplementary material, which is available to authorized users.

Genetics and biology of prostate cancer

Despite the high long-term survival in localized prostate cancer, metastatic prostate cancer remains largely incurable even after intensive multimodal therapy. The lethality of advanced disease is driven by the lack of therapeutic regimens capable of generating durable responses in the setting of extreme tumor heterogeneity on the genetic and cell biological levels. Here, we review available prostate cancer model systems, the prostate cancer genome atlas, cellular and functional heterogeneity in the tumor microenvironment, tumor-intrinsic and tumor-extrinsic mechanisms underlying therapeutic resistance, and technological advances focused on disease detection and management. These advances, along with an improved understanding of the adaptive responses to conventional cancer therapies, anti-androgen therapy, and immunotherapy, are catalyzing development of more effective therapeutic strategies for advanced disease. In particular, knowledge of the heterotypic interactions between and coevolution of cancer and host cells in the tumor microenvironment has illuminated novel therapeutic combinations with a strong potential for more durable therapeutic responses and eventual cures for advanced disease. Improved disease management will also benefit from artificial intelligence-based expert decision support systems for proper standard of care, prognostic determinant biomarkers to minimize overtreatment of localized disease, and new standards of care accelerated by next-generation adaptive clinical trials.

Upregulation of lncRNA-ATB by Transforming Growth Factor β1 (TGF-β1) Promotes Migration and Invasion of Papillary Thyroid Carcinoma Cells

Background

lncRNA-ATB plays an oncogenic role in various types of malignancies, but its involvement in papillary thyroid carcinoma (PTC) cells, which is a main type of thyroid cancer, is unknown.

Material/Methods

A total of 76 patients with PTC and 28 people with normal physiological conditions were included in this study. Tumor tissues and adjacent healthy tissues were collected from PTC patients and blood was extracted from both patients and healthy controls. Expression of lncRNA-ATB in those tissues was detected by qRT-PCR. All patients were followed up for 5 years and diagnostic and prognostic values of serum lncRNA-ATB for PTC were investigated by ROC curve analysis and survival curve analysis, respectively. lncRNA-ATB overexpression PTC cell lines were established and effects of lncRNA-ATB overexpression on cell migration and invasion were investigated by Transwell cell migration and invasion assay, respectively. Effects of lncRNA-ATB overexpression on TGF-β1 expression were investigated by Western blot.

Results

lncRNA-ATB expression level was higher in tumor tissues than in adjacent healthy tissues in most PTC patients. Serum level of lncRNA-ATB was higher in cancer patients than in healthy control. Serum lncRNA-ATB can be used to accurately predict PTC and its prognosis. lncRNA-ATB overexpression promoted tumor cell migration and invasion, lncRNA-ATB overexpression showed no significant effects on TGF-β1 expression, and TGF-β1 treatment increased the expression level of lncRNA-ATB.

Conclusion

Upregulation of lncRNA-ATB by TGF-β1 promotes migration and invasion of PTC cells.

Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioinformatics Methods

OBJECTIVE

To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer.

METHODS

Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data.

RESULTS

We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival.

CONCLUSIONS

We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.

Elevation of Peripheral BDNF Promoter Methylation Predicts Conversion from Amnestic Mild Cognitive Impairment to Alzheimer's Disease: A 5-Year Longitudinal Study

Epigenetic aberrations have been identified as biomarkers to predict the risk of Alzheimer's disease (AD). This study aimed to evaluate whether altered DNA methylation status of BDNF promoter could be used as potential epigenetic biomarkers for predicting the progression from amnestic mild cognitive impairment (aMCI) to AD. A total of 506 aMCI patients and 728 cognitively normal controls were recruited in the cross-sectional analyses. Patients (n = 458) from aMCI cohort were classified into two groups after 5-year follow-up: aMCI-stable group (n = 330) and AD-conversion group (n = 128). DNA methylation of BDNF promoter was detected by bisulfite-PCR amplification and pyrosequencing. The DNA methylation levels of CpG1 and CpG2 in promoter I and CpG5 and CpG6 in promoter IV of BDNF gene were significantly higher in the aMCI group than in the control group at baseline and also were increased in the conversion group compared with the non-conversion group at 5-year follow up time point. CpG5 in BDNF promoter IV had the highest AUC of 0.910 (95% CI: 0.817-0.983, p < 0.05). Kaplan-Meier analysis showed a significant AD conversion propensity for aMCI patients with high methylation levels of CpG5 (HR = 1.96, 95% CI: 1.07-2.98, p < 0.001). Multivariate Cox regression analysis revealed elevated methylation status of CpG5 was a significant independent predictor for AD conversion (HR = 3.51, p = 0.013). These results suggest that elevation of peripheral BDNF promoter methylation might be used as potential epigenetic biomarkers for predicting the conversion from aMCI to AD.

CXCR1 expression predicts benefit from tyrosine kinase inhibitors therapy in patients with metastatic renal cell carcinoma

PURPOSE

CXCR1 signaling promotes tumor progression in various cancers, and clinical trial has proved efficacy of CXCR1 inhibitor in metastatic breast cancer. Therefore, we investigated the prognostic value of CXCR1 in patients with metastatic renal cell carcinoma (mRCC) receiving tyrosine kinase inhibitors (TKIs) therapy.

MATERIALS AND METHODS

Patients treated with sunitinib or sorafenib were retrospectively enrolled (n = 111). CXCR1 expression was assessed by immunohistochemical staining of tissue microarrays of primary tumor, and its association with prognosis and therapeutic response were evaluated. To explore possible mechanism related to CXCR1 expression, gene set enrichment analysis was performed based on The Cancer Genome Atlas cohort.

RESULTS

High CXCR1 expression was associated with poorer overall survival (P = 0.015) and was an independent prognostic factor for patients with mRCC treated by TKIs (Hazard Ratio = 1.683, 95% Confidence Interval: 1.109-2.553, P = 0.014). CXCR1 expression was also associated with worse therapeutic response of TKIs (P = 0.017). Thirteen pathways, including hypoxia and angiogenesis, were identified to be enriched in CXCR1 positive patients.

CONCLUSIONS

High CXCR1 expression indicates reduced benefit from TKIs therapy in patients with mRCC. The mechanism may be attributed to the enriched pathways of hypoxia and angiogenesis in CXCR1 positive patients. CXCR1 may be a potential therapeutic target for mRCC, but further studies are required.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models

Background

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.

Methods

Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.

Results

Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.

Conclusions

Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12874-017-0437-y) contains supplementary material, which is available to authorized users.

The role of pazopanib on tumour angiogenesis and in the management of cancers: A review

Pazopanib is a relatively new compound to be introduced into the chemotherapy field. It is thought to have decent anti-angiogenic properties, which gives an additional hope for the treatment of certain types of cancers. A systematic review solely discussing about pazopanib and its anti-angiogenic effect is yet to be published to date, despite several relevant clinical trials being conducted over the recent years. In this review, we aim to investigate the mechanism of pazopanib's anti-angiogenic effect and its effectiveness in treating several cancers. We have included, in this study, findings from electronically searchable data from randomized clinical trials, clinical studies, cohort studies and other relevant articles. A total of 352 studies were included in this review. From the studies, the effect of pazopanib in various cancers or models was observed and recorded. Study quality is indefinite, with a few decent quality articles. The most elaborately studied cancers include renal cell carcinoma, solid tumors, advanced solid tumors, soft tissue sarcoma, breast cancer and gynecological cancers. In addition, several less commonly studied cancers are included in the studies as well. Pazopanib had demonstrated its anti-angiogenic effect based on favorable results observed in cancers, which are caused by angiogenesis-related mechanisms, such as renal cell carcinoma, solid tumors, advanced solid tumors and soft tissue sarcoma. This review was conducted to study, analyze and review the anti-angiogenic properties of pazopanib in various cancers. The results obtained can provide a decent reference when considering treatment options for angiogenesis-related malignancies. Furthermore, the definite observations of the anti-angiogenic effects of pazopanib could provide newer insights leading to the future development of drugs of the same mechanism with increased efficiency and reduced adverse effects.

Store-Operated Ca2+ Entry as a Prostate Cancer Biomarker - a Riddle with Perspectives

Purpose of Review

Store-operated calcium entry (SOCE) is dysregulated in prostate cancer, contributing to increased cellular migration and proliferation and preventing cancer cell apoptosis. We here summarize findings on gene expression levels and functions of SOCE components, stromal interaction molecules (STIM1 and STIM2), and members of the Orai protein family (Orai1, 2, and 3) in prostate cancer. Moreover, we introduce new research models that promise to provide insights into whether dysregulated SOCE signaling has clinically relevant implications in terms of increasing the migration and invasion of prostate cancer cells.

Recent Findings

Recent reports on Orai1 and Orai3 expression levels and function were in part controversial probably due to the heterogeneous nature of prostate cancer. Lately, in prostate cancer cells, transient receptor melastatin 4 channel was shown to alter SOCE and play a role in migration and proliferation. We specifically highlight new cancer research models: a subpopulation of cells that show tumor initiation and metastatic potential in mice and zebrafish models.

Summary

This review focuses on SOCE component dysregulation in prostate cancer and analyzes several preclinical, cellular, and animal cancer research models.

A bioinformatics-to-clinic sequential approach to analysis of prostate cancer biomarkers using TCGA datasets and clinical samples: a new method for precision oncology?

Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa. The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa. We identified PEG10 as a candidate biomarker. Data from clinical samples suggested that increased expression of PEG10 at the time of initial diagnosis was linked to shorter survival time. Interestingly, PEG10 overexpression also correlated with expression of chromogranin A and synaptophysin, markers for neuroendocrine prostate cancer, a type of treatment-resistant prostate cancer. These results indicate that PEG10 is a novel biomarker for shorter survival of patients with PACa. Also, PEG10 expression at the time of initial diagnosis may predict focal neuroendocrine differentiation of PACa. Thus, PEG10 may be an attractive target for biomarker-driven cancer therapy. Thus, bioinformatics-to-clinic sequential analysis is a valid tool for identifying targets for precision oncology.

Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes

Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the secreting cells. So far, biomarkers for pathological conditions have been mainly searched from their protein, (mi)RNA, DNA and lipid cargo. Here, we explored the small molecule metabolites from urinary and platelet EVs relative to their matched source samples. As a proof-of-concept study of intra-EV metabolites, we compared alternative normalization methods to profile urinary EVs from prostate cancer patients before and after prostatectomy and from healthy controls.

Methods: We employed targeted ultra-performance liquid chromatography-tandem mass spectrometry to profile over 100 metabolites in the isolated EVs, original urine samples and platelets. We determined the enrichment of the metabolites in the EVs and analyzed their subcellular origin, pathways and relevant enzymes or transporters through data base searches. EV- and urine-derived factors and ratios between metabolites were tested for normalization of the metabolomics data.

Results: Approximately 1 x 10¹⁰ EVs were sufficient for detection of metabolite profiles from EVs. The profiles of the urinary and platelet EVs overlapped with each other and with those of the source materials, but they also contained unique metabolites. The EVs enriched a selection of cytosolic metabolites including members from the nucleotide and spermidine pathways, which linked to a number of EV-resident enzymes or transporters. Analysis of the urinary EVs from the patients indicated that the levels of glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine were 2-26-fold lower in all pre-prostatectomy samples compared to the healthy control and post-prostatectomy samples (p < 0.05). These changes were only detected from EVs by normalization to EV-derived factors or with metabolite ratios, and not from the original urine samples.

Conclusions: Our results suggest that metabolite analysis of EVs from different samples is feasible using a high-throughput platform and relatively small amount of sample material. With the knowledge about the specific enrichment of metabolites and normalization methods, EV metabolomics could be used to gain novel biomarker data not revealed by the analysis of the original EV source materials.

A BAP1 Mutation-specific MicroRNA Signature Predicts Clinical Outcomes in Clear Cell Renal Cell Carcinoma Patients with Wild-type BAP1

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent histologic subtype of kidney cancers in adults, which could be divided into two distinct subgroups according to the BRCA1 associated protein-1 (BAP1) mutation status. In the current study, we comprehensively analyzed the genome-wide microRNA (miRNA) expression profiles in ccRCC, with the aim to identify the differentially expressed miRNAs between BAP1 mutant and wild-type tumors, and generate a BAP1 mutation-specific miRNA signature for ccRCC patients with wild-type BAP1.

Methods: The BAP1 mutation status and miRNA profiles in BAP1 mutant and wild-type tumors were analyzed. Subsequently, the association of the differentially expressed miRNAs with patient survival was examined, and a BAP1 mutation-specific miRNA signature was generated and examined with Kaplan-Meier survival, univariate and multivariate Cox regression analyses. Finally, the bioinformatics methods were adopted for the target prediction of selected miRNAs and functional annotation analyses.

Results: A total of 350 treatment-naïve primary ccRCC patients were selected from The Cancer Genome Atlas project, among which 35 (10.0%) subjects carried mutant BAP1 and had a shorter overall survival (OS) time. Furthermore, 33 miRNAs were found to be differentially expressed between BAP1 mutant and wild-type tumors, among which 11 (miR-149, miR-29b-2, miR-182, miR-183, miR-21, miR-365-2, miR-671, miR-365-1, miR-10b, miR-139, and miR-181a-2) were significantly associated with OS in ccRCC patients with wild-type BAP1. Finally, a BAP1 mutation-specific miRNA signature consisting of 11 miRNAs was generated and validated as an independent prognostic parameter.

Conclusions: In summary, our study identified a total of 33 miRNAs differentially expressed between BAP1 mutant and wild-type tumors, and generated a BAP1 mutation-specific miRNA signature including eleven miRNAs, which could serve as a novel prognostic biomarker for ccRCC patients with wild-type BAP1.

Synergistic Activity with NOTCH Inhibition and Androgen Ablation in ERG-Positive Prostate Cancer Cells

The oncogenic activation of the ETS-related gene (ERG) due to gene fusions is present in over half of prostate cancers in Western countries. Because of its high incidence and oncogenic role, ERG and components of ERG network have emerged as potential drug targets for prostate cancer. Utilizing gene expression datasets, from matched normal and prostate tumor epithelial cells, an association of NOTCH transcription factors with ERG expression status was identified, confirming that NOTCH factors are direct transcriptional targets of ERG. Inhibition of ERG in TMPRSS2-ERG-positive VCaP cells led to decreased levels of NOTCH1 and 2 proteins and downstream transcriptional targets and partially recapitulated the phenotypes associated with ERG inhibition. Regulation of NOTCH1 and 2 genes by ERG were also noted with ectopic ERG expression in LNCaP (ERG-negative prostate cancer) and RWPE-1 (benign prostate-derived immortalized) cells. Furthermore, inhibition of NOTCH by the small-molecule γ-secretase inhibitor 1, GSI-1, conferred an increased sensitivity to androgen receptor (AR) inhibitors (bicalutamide and enzalutamide) or the androgen biosynthesis inhibitor (abiraterone) in VCaP cells. Combined treatment with bicalutamide and GSI-1 showed strongest inhibition of AR, ERG, NOTCH1, NOTCH2, and PSA protein levels along with decreased cell growth, cell survival, and enhanced apoptosis. Intriguingly, this effect was not observed in ERG-negative prostate cancer cells or immortalized benign/normal prostate epithelial cells. These data underscore the synergy of AR and NOTCH inhibitors in reducing the growth of ERG-positive prostate cancer cells.Implications: Combinational targeting of NOTCH and AR signaling has therapeutic potential in advanced ERG-driven prostate cancers. Mol Cancer Res; 15(10); 1308-17. ©2017 AACR.

Advances in the pharmacotherapy of chronic heart failure with preserved ejection fraction: an ideal opportunity for precision medicine

INTRODUCTION

Heart failure with preserved ejection fraction (HFpEF), which comprises approximately 50% of all heart failure patients, is a challenging and complex clinical syndrome that is often thought to lack effective treatments. Areas covered: Despite the common mantra that HFpEF has no effective treatments, closer inspection of HFpEF clinical trials reveals that several of the drugs tested are associated with benefits in exercise capacity and quality of life, and reduction in heart failure hospitalization. Here we review major randomized controlled trials in HFpEF, focusing on renin-angiotensin-aldosterone system antagonists, organic nitrates, digoxin, beta-blockers, and phosphodiesterase-5 inhibitors. In addition, we review several classes of drugs currently in development for HFpEF such as neprilysin inhibitors, inorganic nitrates (nitrites), and soluble guanylate cyclase stimulators. Expert opinion: HFpEF should not be viewed as lacking effective treatments. While there have been no breakthrough clinical trials showing a reduction in mortality, several existing medications are likely to benefit specific subgroups of HFpEF patients. HFpEF is now well known to be a heterogeneous syndrome; thus, the clinical management of HFpEF patients and future HFpEF clinical trials will both likely require a nuanced, phenotype-specific approach instead of a one-size-fits-all tactic. Drug development for HFpEF therefore represents an exciting opportunity for personalized medicine.