Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel

Prostate-specific Membrane Antigen: Alpha-labeled Radiopharmaceuticals

Novel prostate-specific membrane antigen (PSMA) ligands labeled with α-emitting radionuclides are sparking a growing interest in prostate cancer treatment. These targeted alpha therapies (TATs) have attractive physical properties that deem them effective in progressive metastatic castrate-resistant prostate cancer (mCRPC). Among the PSMA TAT radiopharmaceuticals, [225Ac]Ac-PSMA has been used extensively on a compassionate basis and is currently undergoing phase I trials. Notably, TAT has the potential to improve quality of life and has favorable antitumor activity and outcomes in multiple scenarios other than in mCRPC. In addition, resistance mechanisms to TAT may be amenable to combination therapies.

Claudins in Cancer: A Current and Future Therapeutic Target

Claudins are a family of 27 proteins that have an important role in the formation of tight junctions. They also have an important function in ion exchange, cell mobility, and the epithelial-to-mesenchymal transition, the latter being very important in cancer invasion and metastasis. Therapeutic targeting of claudins has been investigated to improve cancer outcomes. Recent evidence shows improved outcomes when combining monoclonal antibodies against claudin 18.2 with chemotherapy for patients with gastroesophageal junction cancer. Currently, chimeric antigen receptor T-cells targeting claudin 18 are under investigation. In this review, we will discuss the major functions of claudins, their distribution in the normal as well as cancerous tissues, and their effect in cancer metastasis, with a special focus on the therapeutic targeting of claudins to improve cancer outcomes.

Prostate cancer: Molecular aspects, consequences, and opportunities of the multifocal nature

Prostate cancer is unique compared to other major cancers due to the presence of multiple primary malignant foci in the majority of patients at the time of diagnosis. Each malignant focus has distinct somatic mutations and gene expression patterns, which represents a challenge for the development of prognostic tests for localized prostate cancer. Additionally, the molecular heterogeneity of advanced prostate cancer has important implications for management, particularly for patients with metastatic and locally recurrent cancer. Studies have shown that prostate cancers with mutations in DNA damage response genes are more sensitive to drugs inhibiting the poly ADP-ribose polymerase (PARP) enzyme. However, testing for such mutations should consider both spatial and temporal heterogeneity. Here, we summarize studies where multiregional genomics and transcriptomics analyses have been performed for primary prostate cancer. We further discuss the vast interfocal heterogeneity and how prognostic biomarkers and a molecular definition of the index tumor should be developed. The concept of focal treatments in prostate cancer has been evolving as a demand from patients and clinicians and is one example where there is a need for defining an index tumor. Here, biomarkers must have proven value for individual malignant foci. The potential discovery and implementation of biomarkers that are agnostic to heterogeneity are also explored as an alternative to multisample testing. Thus, deciding upon whole-organ treatment, such as radical prostatectomy, should depend on information from biomarkers which are informative for the whole organ.

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.

METHODS

Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.

RESULTS

We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.

CONCLUSIONS

Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Multiplex protein analysis and ensemble machine learning methods of fine needle aspirates from prostate cancer patients reveal potential diagnostic signatures associated with tumour grade

BACKGROUND

Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression.

METHODS

In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models.

RESULTS

Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort.

CONCLUSIONS

Our pilot study represents a "proof of concept" and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.

Prediction of prostate cancer biochemical recurrence by using discretization supports the critical contribution of the extra-cellular matrix genes

Due to its complexity, much effort has been devoted to the development of biomarkers for prostate cancer that have acquired the utmost clinical relevance for diagnosis and grading. However, all of these advances are limited due to the relatively large percentage of biochemical recurrence (BCR) and the limited strategies for follow up. This work proposes a methodology that uses discretization to predict prostate cancer BCR while optimizing the necessary variables. We used discretization of RNA-seq data to increase the prediction of biochemical recurrence and retrieve a subset of ten genes functionally known to be related to the tissue structure. Equal width and equal frequency data discretization methods were compared to isolate the contribution of the genes and their interval of action, simultaneously. Adding a robust clinical biomarker such as prostate specific antigen (PSA) improved the prediction of BCR. Discretization allowed classifying the cancer patients with an accuracy of 82% on testing datasets, and 75% on a validation dataset when a five-bin discretization by equal width was used. After data pre-processing, feature selection and classification, our predictions had a precision of 71% (testing dataset: MSKCC and GSE54460) and 69% (Validation dataset: GSE70769) should the patients present BCR up to 24 months after their final treatment. These results emphasize the use of equal width discretization as a pre-processing step to improve classification for a limited number of genes in the signature. Functionally, many of these genes have a direct or expected role in tissue structure and extracellular matrix organization. The processing steps presented in this study are also applicable to other cancer types to increase the speed and accuracy of the models in diverse datasets.

Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings

PURPOSE

To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy.

MATERIAL AND METHODS

Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI).

RESULTS

195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features.

CONCLUSION

Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.

Translational Research for Identifying Potential Early-stage Prostate Cancer Biomarkers

BACKGROUND/AIM

Prostate cancer (PCa) is one of the most common types of cancer in men. Prostate-specific antigen (PSA) is currently the only biomarker used to screen for the risk of developing PCa. Because PSA tests may show false positives, identifying novel PCa-specific biomarkers would improve prediction and diagnosis at an early stage. Previously, we identified a number of genes/microRNAs (miRNAs) in prostate tissue as potential biomarkers of chronic prostatitis in a rat model of chemical-induced prostatitis. The current study aimed to evaluate their potential for use as translational, diagnostic markers in humans.

MATERIALS AND METHODS

We performed quantitative polymerase chain reaction analysis using pathologically clear (normal) or confirmed PCa tissue samples from the same patients (N=18 per group).

RESULTS

Levels (relative fold changes) of bone morphogenetic protein 7 (BMP7) transcripts were significantly lower in PCa tissues, compared with clear tissues, in a paired t-test (p=0.0075). Although neural cell adhesion molecule 1 (NCAM1) transcripts tended to be altered in PCa tissues, statistically insignificant differences were observed (p=0.0521). No statistically significant differences were observed for the other genes/miRNAs analyzed in PCa tissues due to a high degree of individual variance in expression.

CONCLUSION

Similar to the results previously observed in rats, changes in the levels of BMP7 and NCAM1 transcripts were evident in human PCa tissues, suggesting that these genes may serve as potential diagnostic biomarkers during the early stages of PCa. Further studies are needed to determine the potential use of these molecules as biomarkers.

Statin Use and the Risk of Prostate Cancer Biochemical Recurrence Following Definitive Therapy: A Systematic Review and Meta-Analysis of Cohort Studies

Background

Numerous studies have reported the role of statins on biochemical recurrence (BCR) among patients with prostate cancer (PCa) after definite treatment. However, the conclusions of these studies are contradictory. We aimed to determine the effect of statins on BCR of PCa using a systematic review and meta-analysis.

Methods

We searched PubMed (Medline) and other databases for cohort studies evaluating the effect of statins on the BCR of patients with PCa between January 1, 2000, and December 31, 2021. The random effects (RE) model and quality effects (QE) model were used to calculate the pooled hazard ratio (pHR) and pooled risk ratio (pRR) and their 95% confidence interval (95% CI).

Results

A total of 33 cohort studies were finally selected and included in this systematic review and meta-analysis. Statin use was significantly associated with a 14% reduction in the HR of BCR (pHR: 0.86, 95% CI: 0.78 to 0.95, I² = 64%, random effects model, 31 studies) and a 26% reduction in the RR of BCR (pRR: 0.74, 95% CI: 0.57 to 0.94, 24,591 patients, I² = 88%, random effects model, 15 studies) among patients with PCa. The subgroup analyses showed that statins could result in 22% reduction in the HR of BCR (pHR: 0.78, 95% CI: 0.61 to 0.98, I² = 57%, random effects model) among patients accepting radiotherapy (RT).

Conclusions

Our study suggests that statins have a unique role in the reduction of BCR in patients with PCa after definite treatment, especially RT. In the future, more clinical trials and in vitro and animal experiments are needed to further verify the effects of statins in PCa and the potential mechanisms.

Expressed prognostic biomarkers for primary prostate cancer independent of multifocality and transcriptome heterogeneity

The majority of prostate cancer patients are diagnosed with multiple primary malignant foci. The distinct foci are exceptionally heterogeneous with regard to DNA mutations, but whether this is recapitulated at the transcriptome level remains unknown. In this study, inter- and intrafocal heterogeneity has been assessed by whole-transcriptome sequencing of 87 tissue samples from 23 patients with localized prostate cancer treated with radical prostatectomy. From each patient, multiple samples were taken from one or more malignant foci, in addition to one sample from benign prostate tissue. Transcriptomic profiles of different malignant foci from the same patient showed a similar level of heterogeneity as tumors from different patients. This applies to expression of genes, fusion genes, and somatic mutations. Within-patient pair-wise analyses identified expression patterns linked to ETS status and extraprostatic extension. A set of 62 genes were found with low intrapatient heterogeneity and high interpatient heterogeneity, retaining stable expression profiles across foci within the same patient. Among these, 16 genes are associated with biochemical recurrence in a separately published study and are therefore nominated as biomarkers with prognostic value regardless of which malignant focus is sampled. In conclusion, an extensive heterogeneity in multifocal prostate cancer is confirmed at the gene expression level. Diagnostic biomarkers were identified for ETS positive samples and samples from extraprostatic extensions. Finally, prognostic biomarkers independent of multifocal heterogeneity were found.

Update of the keratin gene family: evolution, tissue-specific expression patterns, and relevance to clinical disorders

Intermediate filament (IntFil) genes arose during early metazoan evolution, to provide mechanical support for plasma membranes contacting/interacting with other cells and the extracellular matrix. Keratin genes comprise the largest subset of IntFil genes. Whereas the first keratin gene appeared in sponge, and three genes in arthropods, more rapid increases in keratin genes occurred in lungfish and amphibian genomes, concomitant with land animal-sea animal divergence (~ 440 to 410 million years ago). Human, mouse and zebrafish genomes contain 18, 17 and 24 non-keratin IntFil genes, respectively. Human has 27 of 28 type I "acidic" keratin genes clustered at chromosome (Chr) 17q21.2, and all 26 type II "basic" keratin genes clustered at Chr 12q13.13. Mouse has 27 of 28 type I keratin genes clustered on Chr 11, and all 26 type II clustered on Chr 15. Zebrafish has 18 type I keratin genes scattered on five chromosomes, and 3 type II keratin genes on two chromosomes. Types I and II keratin clusters-reflecting evolutionary blooms of keratin genes along one chromosomal segment-are found in all land animal genomes examined, but not fishes; such rapid gene expansions likely reflect sudden requirements for many novel paralogous proteins having divergent functions to enhance species survival following sea-to-land transition. Using data from the Genotype-Tissue Expression (GTEx) project, tissue-specific keratin expression throughout the human body was reconstructed. Clustering of gene expression patterns revealed similarities in tissue-specific expression patterns for previously described "keratin pairs" (i.e., KRT1/KRT10, KRT8/KRT18, KRT5/KRT14, KRT6/KRT16 and KRT6/KRT17 proteins). The ClinVar database currently lists 26 human disease-causing variants within the various domains of keratin proteins.

Two Novel Ceramide-Like Molecules and miR-5100 Levels as Biomarkers Improve Prediction of Prostate Cancer in Gray-Zone PSA

Reliable liquid biopsy-based tools able to accurately discriminate prostate cancer (PCa) from benign prostatic hyperplasia (BPH), when PSA is within the “gray zone” (PSA 4–10), are still urgent. We analyzed plasma samples from a cohort of 102 consecutively recruited patients with PSA levels between 4 and 16 ng/ml, using the SANIST-Cloud Ion Mobility Metabolomic Mass Spectrometry platform, combined with the analysis of a panel of circulating microRNAs (miR). By coupling CIMS ion mobility technology with SANIST, we were able to reveal three new structures among the most differentially expressed metabolites in PCa vs. BPH. In particular, two were classified as polyunsaturated ceramide ester-like and one as polysaturated glycerol ester-like. Penalized logistic regression was applied to build a model to predict PCa, using six circulating miR, seven circulating metabolites, and demographic/clinical variables, as covariates. Four circulating metabolites, miR-5100, and age were selected by the model, and the corresponding prediction score gave an AUC of 0.76 (C.I. = 0.66–0.85). At a specified cut-off, no high-risk tumor was misclassified, and 22 out of 53 BPH were correctly identified, reducing by 40% the false positives of PSA. We developed and applied a novel, minimally invasive, liquid biopsy-based powerful tool to characterize novel metabolites and identified new potential non-invasive biomarkers to better predict PCa, when PSA is uninformative as a tool for precision medicine in genitourinary cancers.

Myosin 1C isoform A is a novel candidate diagnostic marker for prostate cancer

Early diagnosis of prostate cancer is a challenging issue due to the lack of specific markers. Therefore, a sensitive diagnostic marker that is expressed or upregulated exclusively in prostate cancer cells would facilitate diagnostic procedures and ensure a better outcome. We evaluated the expression of myosin 1C isoform A in 5 prostate cell lines, 41 prostate cancer cases, and 11 benign hyperplasias. We analyzed the expression of 12 surface molecules on prostate cancer cells by flow cytometry and analyzed whether high or low myosin 1C isoform A expression could be attributed to a distinct phenotype of prostate cancer cells. Median myosin 1C isoform A expression in prostate cancer samples and cancer cell lines was 2 orders of magnitude higher than in benign prostate hyperplasia. Based on isoform A expression, we could also distinguish clinical stage 2 from clinical stage 3. Among cell lines, PC-3 cells with the highest myosin 1C isoform A level had diminished numbers of CD10/CD13-positive cells and increased numbers of CD29 (integrin β1), CD38, CD54 (ICAM1) positive cells. The surface phenotype of clinical samples was similar to prostate cancer cell lines with high isoform A expression and could be described as CD10-/CD13- with heterogeneous expression of other markers. Both for cell lines and cancer specimens we observed the strong correlation of high myosin 1C isoform A mRNA expression and elevated levels of CD29 and CD54, suggesting a more adhesive phenotype for cells with high isoform A expression. Compared to normal tissue, prostate cancer samples had also reduced numbers of CD24- and CD38-positive cells. Our data suggest that a high level of myosin 1C isoform A is a specific marker both for prostate cancer cells and prostate cancer cell lines. High expression of isoform A is associated with less activated (CD24/CD38 low) and more adhesive (CD29/CD54 high) surface phenotype compared to benign prostate tissue.

Using machine learning method to identify MYLK as a novel marker to predict biochemical recurrence in prostate cancer

Aim: This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negative surgical margin. Materials & methods: The Cancer Genome Atlas database, Gene Expression Omnibus database and Cancer Cell Line Encyclopedia database were employed. The ensemble support vector machine-recursive feature elimination method was performed to select crucial gene for BCR. Results: We identified MYLK as a novel and independent biomarker for BCR in The Cancer Genome Atlas training cohort and confirmed in four independent Gene Expression Omnibus validation cohorts. Multi-omic analysis suggested that MYLK was a DNA methylation-driven gene. Additionally, MYLK had significant positive correlations with immune infiltrations. Conclusion: MYLK was identified and validated as a novel, robust and independent biomarker for BCR in prostate cancer.

A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients

Background

Biochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.

Methods

A comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.

Results

A total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.

Conclusions

The proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.

A 10-gene signature as a predictor of biochemical recurrence after radical prostatectomy in patients with prostate cancer and a Gleason score ≥7

The time and speed of biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) is highly variable. Stratification methods based on TNM staging and Gleason score (GS) do not allow the identification of patients at risk of BCR following RP. Therefore, the aim of the present study was to identify molecular signatures that can predict BCR risk effectively and facilitate treatment-related decisions for patients with PCa. RNA sequencing data and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Oncomine databases. Bioinformatics analysis was performed to identify differentially expressed genes in patients with GS=6 and GS ≥7. Cox regression models were used to determine the PCa signature (PCasig) and a clinical nomogram for the prediction of BCR. The performance of nomograms was assessed using time-dependent receiver operating characteristic curves and the concordance index (C-index). A PCasig comprising 10 genes, including SEMG2, KCNJ16, TFAP2B, SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11, was significantly associated with BCR, which was identified in TCGA cohort [hazard ratio (HR), 5.18; 95% CI, 3.241-8.272; C-index, 0.777] and validated in the Oncomine cohort (HR, 2.78; 95% CI, 1.39-5.54; C-index, 0.66). The expression levels of SEMG2, KCNJ16 and TFAP2B were downregulated in patients with GS ≥7. The expression levels of SYCE1, KCNU1, AFP, GUCY1B2, GRIA4, NXPH1 and SOX11 were upregulated in patients with GS ≥7. The clinical nomogram was constructed based on the GS and pathologic T stage (HR, 4.15; 95% CI, 1.39-5.54; C-index, 0.713). The addition of the PCasig to the clinical nomogram significantly improved prognostic value (HR, 7.25; 95% CI, 4.54-11.56; C-index, 0.782) with an net reclassification improvement of 75.3% (95% CI, 46.8-104.6%). Furthermore, the endogenous expression of each gene in the PCasig was measured in five PCa cell lines and in normal prostate cells, and these genes exhibited different expression levels relative to one another. In conclusion, an PCasig was identified by mining TCGA and successfully validated in an Oncomine cohort. This PCasig was an independent prognostic factor with a greater prognostic value for all patients regardless of GS than traditional clinical variables, which can improve the performance of clinical nomograms in predicting BCR of patients with GS ≥7.

Identification of novel oncogenic events occurring early in prostate carcinogenesis using purified autologous malignant and non-malignant prostate epithelial cells

OBJECTIVE

To interrogate enriched prostate cancer cells and autologous non-malignant prostate epithelial cells from men with localized prostate cancer, in order to identify early oncogenic pathways.

PATIENTS AND METHODS

We collected malignant and matched non-malignant prostatectomy samples from men with adenocarcinoma involving two or more contiguous areas in only one lobe of the prostate. Tissue samples from both lobes were subjected to digestion and single-cell suspensions were prepared. Epithelial cell adhesion molecule-positive cells from cancerous and contralateral non-malignant (control) samples were isolated using magnetic beads, ensuring uniform populations were obtained for each donor. Unbiased RNA sequencing analysis was used to measure gene expression and for detection of transcribed mutations or splice variants that were over- or under-represented in malignant prostate epithelial cells relative to autologous control prostate epithelial cells.

RESULTS

From five patient samples we identified 17 genes that were altered in prostate cancer epithelial cells, with 82% of genes being downregulated. Three genes, TDRD1, ANGTL4, and CLDN3, were consistently upregulated in malignant tissue. Malignant cells from three of the five patients showed evidence of upregulated ERG signalling, however, only one of these contained a TMPRSS2-ERG rearrangement. We did not identify mutations, gene rearrangements, or splice variants that were consistent amongst the patients.

CONCLUSIONS

Events occurring early in prostate cancer oncogenesis in these samples were characterized by a predominant downregulation of gene expression along with upregulation of TDRD1, ANGTL4 and CLDN3. No consistent mutations or splice variants were observed, but upregulation of ERG signalling was seen both in the presence and absence of the classic TMPRSS2-ERG rearrangement.

Proteomics Analysis of Formalin Fixed Paraffin Embedded Tissues in the Investigation of Prostate Cancer

Prostate cancer (PCa) is one of the leading causes of death in men worldwide. The molecular features, associated with the onset and progression of the disease, are under vigorous investigation. Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources for large-scale studies; however, their application in proteomics is limited due to protein cross-linking. In this study, the adjustment of a protocol for the proteomic analysis of FFPE tissues was performed which was followed by a pilot application on FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data for the investigation of PCa. For the optimization, FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization methods (beads, sonication, boiling) and buffers (SDS based and urea-thiourea based). The proteome extraction efficacy was then evaluated based on protein identifications and reproducibility using SDS electrophoresis and high resolution LC-MS/MS analysis. Comparison between the FFPE and matched fresh frozen (FF) tissues, using an optimized protocol involving protein extraction with an SDS-based buffer following beads homogenization and boiling, showed a substantial overlap in protein identifications with a strong correlation in relative abundances (r_s = 0.819, p < 0.001). Next, FFPE tissues (3 sections, 15 μm each per sample) from 10 patients with PCa corresponding to tumor (GS = 6 or GS ≥ 8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis. Proteins significantly deregulated between PCa GS ≥ 8 and PCa GS = 6 represented extracellular matrix organization, gluconeogenesis, and phosphorylation pathways. Proteins deregulated between cancerous and adjacent benign tissues, reflected increased translation, peptide synthesis, and protein metabolism in the former, which is consistent with the literature. In conclusion, the results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material.

Prognostic Value of Biochemical Recurrence Following Treatment with Curative Intent for Prostate Cancer: A Systematic Review

CONTEXT

In men with prostate cancer (PCa) treated with curative intent, controversy exists regarding the impact of biochemical recurrence (BCR) on oncological outcomes.

OBJECTIVE

To perform a systematic review of the existing literature on BCR after treatment with curative intent for nonmetastatic PCa. Objective 1 is to investigate whether oncological outcomes differ between patients with or without BCR. Objective 2 is to study which clinical factors and tumor features in patients with BCR have an independent prognostic impact on oncological outcomes.

EVIDENCE ACQUISITION

Medline, Medline In-Process, Embase, and the Cochrane Central Register of Controlled Trials were searched. For objective 1, prospective and retrospective studies comparing survival outcomes of patients with or without BCR following radical prostatectomy (RP) or radical radiotherapy (RT) were included. For objective 2, all studies with at least 100 participants and reporting on prognostic patient and tumor characteristics in patients with BCR were included. Risk-of-bias and confounding assessments were performed according to the Quality in Prognosis Studies tool. Both a narrative synthesis and a meta-analysis were undertaken.

EVIDENCE SYNTHESIS

Overall, 77 studies were included for analysis, of which 14 addressed objective 1, recruiting 20 406 patients. Objective 2 was addressed by 71 studies with 29 057, 11 301, and 4272 patients undergoing RP, RT, and a mixed population (mix of patients undergoing RP or RT as primary treatment), respectively. There was a low risk of bias for study participation, confounders, and statistical analysis. For most studies, attrition bias, and prognostic and outcome measurements were not clearly reported. BCR was associated with worse survival rates, mainly in patients with short prostate-specific antigen doubling time (PSA-DT) and a high final Gleason score after RP, or a short interval to biochemical failure (IBF) after RT and a high biopsy Gleason score.

CONCLUSIONS

BCR has an impact on survival, but this effect appears to be limited to a subgroup of patients with specific clinical risk factors. Short PSA-DT and a high final Gleason score after RP, and a short IBF after RT and a high biopsy Gleason score are the main factors that have a negative impact on survival. These factors may form the basis of new BCR risk stratification (European Association of Urology BCR Risk Groups), which needs to be validated formally.

PATIENT SUMMARY

This review looks at the risk of death in men who shows rising prostate-specific antigen (PSA) in the blood test performed after curative surgery or radiotherapy. For many men, rising PSA does not mean that they are at a high risk of death from prostate cancer in the longer term. Men with PSA that rises shortly after they were treated with radiotherapy or rapidly rising PSA after surgery and a high tumor grade for both treatment modalities are at the highest risk of death. These factors may form the basis of new risk stratification (European Association of Urology biochemical recurrence Risk Groups), which needs to be validated formally.

Specific and reliable detection of Myosin 1C isoform A by RTqPCR in prostate cancer cells

Background

Prostate cancer (PC) diagnostics and treatment often present a challenging task due to cancer subtype heterogeneity and differential disease progression in patient subgroups. Hence, the critical issue is finding a reliable and sensitive diagnostic and prognostic PC marker, especially for cases of biopsies with low percentages of cancer cells. Isoform A of myosin 1C was shown to be expressed in PC cells and responsible for their invasive properties, however, its feasibility for diagnostic purposes remains to be elucidated.

Methods

To verify the role of myosin 1C isoform A mRNA expression as a putative prostate cancer marker we performed RT qPCR normalized by three reference genes (GAPDH, YWHAZ, HPRT1) on PC3, RWPE-1, LNCaP and 22Rv1 cell lines. Myosin 1C isoform A detection specificity was confirmed by immunofluorescence staining, cancer and non-cancer prostate cell lines were immunophenotyped by flow cytometry.

Results

Median normalized mRNA expression level of myosin 1C isoform A in PC cells (PC3 and 22Rv1) is two orders of magnitude higher compared to RWPE-1 cells, which functionally correspond to benign prostate cells. Myosin 1C isoform A expression allows PC cell detection even at a dilution ratio of 1:1000 cancer to non-cancer cells. At the protein level, the mean fluorescence intensity of myosin 1C isoform A staining in PC3 nuclei was only twice as high as in RWPE-1, while the immunophenotypes of both cell lines were similar (CD44+/CD90-/CD133-/CD57-/CD24+-).

Conclusions

We report a distinct difference in myosin 1C isoform A mRNA levels in malignant (PC3) and benign (RWPE-1) prostate cell lines and suggest a combination of three reference genes for accurate data normalization. For the first time we provide an immunophenotype comparison of RWPE-1 and PC3 cells and demonstrate that RT qPCR analysis of MYO 1C A using appropriate reference genes is sufficient for PC detection even in low-abundance cancer specimens.

Fifteen-gene expression based model predicts the survival of clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is the major renal cell carcinoma subtype, but its postsurgical prognosis varies among individual patients.We used gene expression, machine learning (random forest variable hunting), and Cox regression analysis to develop a risk score model based on 15 genes to predict survival of patients with ccRCC in the The Cancer Genome Atlas dataset (N = 533). We validated this model in another cohort, and analyzed correlations between risk score and other clinical indicators.Patients in the high-risk group had significantly worse overall survival (OS) than did those in the low-risk group (P = 5.6e-16); recurrence-free survival showed a similar pattern. This result was reproducible in another dataset, E-MTAB-1980 (N = 101, P = .00029). We evaluated correlations between risk score and other clinical indicators. Risk was independent of age and sex, but was significantly associated with hemoglobin level, primary tumor size, and grade. Radiation therapy also had no effect on the prognostic value of the risk score. Cox multivariate regression showed risk score to be an important indicator for ccRCC prognosis. We plotted a nomogram for 3-year OS to facilitate use of risk score and other indicators.The risk score model based on expression of the 15 selected genes can predict survival of patients with ccRCC.

Identification of genes that predict the biochemical recurrence of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancer types in men. Biochemical recurrence continues to occur in a large proportion of patients after radical prostatectomy. Thus, prognostic biomarkers are required to determine which treatment is suitable. In the present study, RNA-sequencing gene expression data from The Cancer Genome Atlas was used in order to develop a risk-score staging system based on the expression of eight genes. Cox multivariate regression was used to predict the outcome of patients with PCa. The biomedical recurrence-free survival of patients with low-risk scores was significantly longer compared with patients with high-risk scores (P=5×10⁻⁷). This result was further validated using another dataset, GSE70769, from the National Center for Biotechnology Information. The prognostic values of other clinical information and risk scores were evaluated for 5-year biochemical recurrence. The prognostic value of the risk score was determined using an area under curve value of 0.819, predicting the 5-year biochemical recurrence of patients with PCa. The risk score was identified to be significantly associated with primary tumor stage (P<0.01), Gleason score (P<0.01), and lymph node invasion (P<0.05), but was independent of age. Cox multivariate regression revealed that the risk score was an indicator for prediction of biochemical recurrence. Thus, the risk score is a valuable and robust indicator for predicting the biochemical recurrence of patients with PCa.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer

Therapeutic resistance is a central problem in clinical oncology. We have developed a systematic genome-wide computational methodology to allow prioritization of patients with favorable and poor therapeutic response. Our method, which integrates DNA methylation and mRNA expression data, uncovered a panel of 5 differentially methylated sites, which explain expression changes in their site-harboring genes, and demonstrated their ability to predict primary resistance to androgen-deprivation therapy (ADT) in the TCGA prostate cancer patient cohort (hazard ratio = 4.37). Furthermore, this panel was able to accurately predict response to ADT across independent prostate cancer cohorts and demonstrated that it was not affected by Gleason, age, or therapy subtypes. We propose that this panel could be utilized to prioritize patients who would benefit from ADT and patients at risk of resistance that should be offered an alternative regimen. Such approach holds a long-term objective to build an adaptable accurate platform for precision therapeutics.

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Integrative DNA methylation and mRNA expression analysis discovers a panel of markers of treatment resistance.

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This panel can predict patients with predisposition to resistance and those who would benefit from the therapy.

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Our approach is applicable to a wide range of therapeutic regimens.

Therapeutic resistance is an emerging clinical problem, with detrimental implications in oncology. Here, we propose a computational approach that integrates genomic and epigenomic data to prioritize patients at risk of treatment resistance. We have integrated DNA methylation and mRNA expression patient profiles, which defined a comprehensive panel of markers of therapeutic response. We have demonstrated that this panel predicts patients with predisposition to resistance and those who would benefit from the therapy. Even though driven by a critical need to investigate resistance to androgen-deprivation therapy in prostate cancer, our approch is applicable to a wide range of therapeutic regimens.

Filamin C promotes lymphatic invasion and lymphatic metastasis and increases cell motility by regulating Rho GTPase in esophageal squamous cell carcinoma

To establish treatments to improve the prognosis of cancer patients, it is necessary to find new targets to control metastasis. We found that expression of FilaminC (FLNC), a member of the actin binding and cross-linking filamin protein family is correlated with lymphatic invasion and lymphatic metastasis in esophageal squamous cell carcinoma (ESCC) by increasing cell motility through activation of Rho GTPase.

Immunohistochemistry analysis showed that FLNC expression in ESCC is associated with lymphatic invasion, metastasis, and prognosis. FLNC knockdown in esophageal cancer cell lines decreased cell migration in wound healing and transwell migration assays, and invasion in transwell migration assays. Furthermore, FLNC knockdown reduced the amount of activated Rac-1 (GTP-Rac1) and activated Cdc42 (GTP-Cdc42). Our results suggest that FLNC expression is a useful biomarker of ESCC metastatic tendency and that inhibiting FLNC function may be useful to control the metastasis of ESCC.

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

BACKGROUND

Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.

METHODS

Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.

RESULTS

A pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis.

CONCLUSION

We show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting.

Molecular analyses of prostate tumors for diagnosis of malignancy on fine-needle aspiration biopsies

Prostate cancer (PCa) is a common cancer and remains the second-leading cause of cancer-associated mortality in men, but diagnosis of PCa remains a main clinical challenge. To investigate the involvement of differentially expressing genes in PCa with deregulated pathways to allow earlier diagnosis of the disease, transcriptomic analyses of differential expression genes in fine-needle aspiration (FNA) biopsies helped to discriminate PCa from benign prostatic hyperplasia (BPH). We identified 255 genes that were deregulated in prostate tumors compared with BPH tissues. qRT-PCR was conducted to examine the expression levels of the four genes in FNA biopsies and confirmed that ITGBL1 was significantly up-regulated and HOXA7, KRT15 and TGM4 were down-regulated in the PCa compared to the BPH, with a sensitivity of 87.1% and a specificity of 87.8%; the area under the receiver operating characteristic curve was estimated at 0.94, which was significantly improved compared with PSA alone (AUC = 0.82). Moreover, the increased expression of ITGBL1 correlated with total cholesterol, triglyceride and PSA. Our results demonstrated that transcriptomic analyses in FNA biopsies could facilitate rapid identification of potential targets for therapy and diagnosis of PCa.

Database-augmented Mass Spectrometry Analysis of Exosomes Identifies Claudin 3 as a Putative Prostate Cancer Biomarker

In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer.Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n = 58; 42 with Gleason score 6-7, 16 with Gleason score ≥8) and metastatic prostate cancer (n = 11) compared with patients with benign prostatic hyperplasia (n = 15) and healthy individuals (n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia (p = 0.012) and Gleason 6-7 tumors (p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA.By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities.

Identification of Filamin-A and -B as potential biomarkers for prostate cancer

Aim:

A novel strategy for prostate cancer (PrCa) biomarker discovery is described.

Materials & methods:

In vitro perturbation biology, proteomics and Bayesian causal analysis identified biomarkers that were validated in in vitro models and clinical specimens.

Results:

Filamin-B (FLNB) and Keratin-19 were identified as biomarkers. Filamin-A (FLNA) was found to be causally linked to FLNB. Characterization of the biomarkers in a panel of cells revealed differential mRNA expression and regulation. Moreover, FLNA and FLNB were detected in the conditioned media of cells. Last, in patients without PrCa, FLNA and FLNB blood levels were positively correlated, while in patients with adenocarcinoma the relationship is dysregulated.

Conclusion:

These data support the strategy and the potential use of the biomarkers for PrCa.

The goal of this study was to use a novel strategy that combines biological outputs with Bayesian network learning to identify potential biomarkers for prostate cancer (PrCa). This methodology identified two proteins, filamin B and keratin-19, as potential biomarkers for PrCa. The network map also identified a direct linkage between filamin B and filamin A, which is a protein that has previously been identified as playing a role in PrCa etiology. The identified proteins were then validated by examining their levels in a panel of PrCa cell lines and in human plasma samples.

Proteomics analysis of vesicles isolated from plasma and urine of prostate cancer patients using a multiplex, aptamer-based protein array

Proteomics analysis of biofluid-derived vesicles holds enormous potential for discovering non-invasive disease markers. Obtaining vesicles of sufficient quality and quantity for profiling studies has, however, been a major problem, as samples are often replete with co-isolated material that can interfere with the identification of genuine low abundance, vesicle components. Here, we used a combination of ultracentrifugation and size-exclusion chromatography to isolate and analyse vesicles of plasma or urine origin. We describe a sample-handling workflow that gives reproducible, quality vesicle isolations sufficient for subsequent protein profiling. Using a semi-quantitative aptamer-based protein array, we identified around 1,000 proteins, of which almost 400 were present at comparable quantities in plasma versus urine vesicles. Significant differences were, however, apparent with elements like HSP90, integrin αVβ5 and Contactin-1 more prevalent in urinary vesicles, while hepatocyte growth factor activator, prostate-specific antigen–antichymotrypsin complex and many others were more abundant in plasma vesicles. This was also applied to a small set of specimens collected from men with metastatic prostate cancer, highlighting several proteins with the potential to indicate treatment refractory disease. The study provides a practical platform for furthering protein profiling of vesicles in prostate cancer, and, hopefully, many other disease scenarios.

Prostate cancer cells specifically reorganize epithelial cell-fibroblast communication through proteoglycan and junction pathways

Microenvironment and stromal fibroblasts are able to inhibit tumor cell proliferation both through secreted signaling molecules and direct cell-cell interactions but molecular mechanisms of these effects remain unclear. In this study, we investigated a role of cell-cell contact-related molecules (protein ECM components, proteoglycans (PGs) and junction-related molecules) in intercellular communications between the human TERT immortalized fibroblasts (BjTERT fibroblasts) and normal (PNT2) or cancer (LNCaP, PC3, DU145) prostate epithelial cells. It was shown that BjTERT-PNT2 cell coculture resulted in significant decrease of both BjTERT and PNT2 proliferation rates and reorganization of transcriptional activity of cell-cell contact-related genes in both cell types. Immunocytochemical staining revealed redistribution of DCN and LUM in PNT2 cells and significant increase of SDC1 at the intercellular contact zones between BjTERT and PNT2 cells, suggesting active involvement of the PGs in cell-cell contacts and contact inhibition of cell proliferation. Unlike to PNT2 cells, PC3 cells did not respond to BjTERT in terms of PGs expression, moderately increased transcriptional activity of junctions-related genes (especially tight junction) and failed to establish PC3-BjTERT contacts. At the same time, PC3 cells significantly down-regulated junctions-related genes (especially focal adhesions and adherens junctions) in BjTERT fibroblasts resulting in visible preference for homotypic PC3-PC3 over heterotypic PC3-BjTERT contacts and autonomous growth of PC3 clones. Taken together, the results demonstrate that an instructing role of fibroblasts to normal prostate epithelial cells is revoked by cancer cells through deregulation of proteoglycans and junction molecules expression and overall disorganization of fibroblast-cancer cell communication.

Bioinformatics analysis of differentially expressed proteins in prostate cancer based on proteomics data

We mined the literature for proteomics data to examine the occurrence and metastasis of prostate cancer (PCa) through a bioinformatics analysis. We divided the differentially expressed proteins (DEPs) into two groups: the group consisting of PCa and benign tissues (P&b) and the group presenting both high and low PCa metastatic tendencies (H&L). In the P&b group, we found 320 DEPs, 20 of which were reported more than three times, and DES was the most commonly reported. Among these DEPs, the expression levels of FGG, GSN, SERPINC1, TPM1, and TUBB4B have not yet been correlated with PCa. In the H&L group, we identified 353 DEPs, 13 of which were reported more than three times. Among these DEPs, MDH2 and MYH9 have not yet been correlated with PCa metastasis. We further confirmed that DES was differentially expressed between 30 cancer and 30 benign tissues. In addition, DEPs associated with protein transport, regulation of actin cytoskeleton, and the extracellular matrix (ECM)–receptor interaction pathway were prevalent in the H&L group and have not yet been studied in detail in this context. Proteins related to homeostasis, the wound-healing response, focal adhesions, and the complement and coagulation pathways were overrepresented in both groups. Our findings suggest that the repeatedly reported DEPs in the two groups may function as potential biomarkers for detecting PCa and predicting its aggressiveness. Furthermore, the implicated biological processes and signaling pathways may help elucidate the molecular mechanisms of PCa carcinogenesis and metastasis and provide new targets for clinical treatment.

The mdx Mutation in the 129/Sv Background Results in a Milder Phenotype: Transcriptome Comparative Analysis Searching for the Protective Factors

The mdx mouse is a good genetic and molecular murine model for Duchenne Muscular Dystrophy (DMD), a progressive and devastating muscle disease. However, this model is inappropriate for testing new therapies due to its mild phenotype. Here, we transferred the mdx mutation to the 129/Sv strain with the aim to create a more severe model for DMD. Unexpectedly, functional analysis of the first three generations of mdx¹²⁹ showed a progressive amelioration of the phenotype, associated to less connective tissue replacement, and more regeneration than the original mdx^C57BL. Transcriptome comparative analysis was performed to identify what is protecting this new model from the dystrophic characteristics. The mdx^C57BL presents three times more differentially expressed genes (DEGs) than the mdx¹²⁹ (371 and 137 DEGs respectively). However, both models present more overexpressed genes than underexpressed, indicating that the dystrophic and regenerative alterations are associated with the activation rather than repression of genes. As to functional categories, the DEGs of both mdx models showed a predominance of immune system genes. Excluding this category, the mdx¹²⁹ model showed a decreased participation of the endo/exocytic pathway and homeostasis categories, and an increased participation of the extracellular matrix and enzymatic activity categories. Spp1 gene overexpression was the most significant DEG exclusively expressed in the mdx¹²⁹ strain. This was confirmed through relative mRNA analysis and osteopontin protein quantification. The amount of the 66 kDa band of the protein, representing the post-translational product of the gene, was about 4,8 times higher on western blotting. Spp1 is a known DMD prognostic biomarker, and our data indicate that its upregulation can benefit phenotype. Modeling the expression of the DEGs involved in the mdx mutation with a benign course should be tested as a possible therapeutic target for the dystrophic process.

Involvement of Kallikrein-Related Peptidases in Normal and Pathologic Processes

Human kallikrein-related peptidases (KLKs) are a subgroup of serine proteases that participate in proteolytic pathways and control protein levels in normal physiology as well as in several pathological conditions. Their complex network of stimulatory and inhibitory interactions may induce inflammatory and immune responses and contribute to the neoplastic phenotype through the regulation of several cellular processes, such as proliferation, survival, migration, and invasion. This family of proteases, which includes one of the most useful cancer biomarkers, kallikrein-related peptidase 3 or PSA, also has a protective effect against cancer promoting apoptosis or counteracting angiogenesis and cell proliferation. Therefore, they represent attractive therapeutic targets and may have important applications in clinical oncology. Despite being intensively studied, many gaps in our knowledge on several molecular aspects of KLK functions still exist. This review aims to summarize recent data on their involvement in different processes related to health and disease, in particular those directly or indirectly linked to the neoplastic process.

Filamin C, a dysregulated protein in cancer revealed by label-free quantitative proteomic analyses of human gastric cancer cells

Gastric cancer (GC) is the fourth and fifth most common cancer in men and women, respectively. We identified 2,750 proteins at false discovery rates of 1.3% (protein) and 0.03% (spectrum) by comparing the proteomic profiles of three GC and a normal gastric cell lines. Nine proteins were significantly dysregulated in all three GC cell lines, including filamin C, a muscle-specific filamin and a large actin-cross-linking protein. Downregulation of filamin C in GC cell lines and tissues were verified using quantitative PCR and immunohistochemistry. Data-mining using public microarray datasets shown that filamin C was significantly reduced in many human primary and metastasis cancers. Transient expression or silencing of filamin C affected the proliferation and colony formation of cancer cells. Silencing of endogenous filamin C enhanced cancer cell migration and invasion, whereas ectopic expression of filamin C had opposing effects. Silencing of filamin C increased the expression of matrix metallopeptidase 2 and improved the metastasis of prostate cancer in a zebrafish model. High filamin C associated with better prognosis of prostate cancer, leukemia and breast cancer patients. These findings establish a functional role of filamin C in human cancers and these data will be valuable for further study of its mechanisms.

Systematic Identification of MicroRNAs That Impact on Proliferation of Prostate Cancer Cells and Display Changed Expression in Tumor Tissue

BACKGROUND

Systematic approaches to functionally identify key players in microRNA (miRNA)-target networks regulating prostate cancer (PCa) proliferation are still missing.

OBJECTIVE

To comprehensively map miRNA regulation of genes relevant for PCa proliferation through phenotypic screening and tumor expression data.

DESIGN, SETTING, AND PARTICIPANTS

Gain-of-function screening with 1129 miRNA molecules was performed in five PCa cell lines, measuring proliferation, viability, and apoptosis. These results were integrated with changes in miRNA expression from two cohorts of human PCa (188 tumors in total). For resulting miRNAs, the predicted targets were collected and analyzed for patterns with gene set enrichment analysis, and for their association with biochemical recurrence free survival.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Rank product statistical analysis was used to evaluate miRNA effects in phenotypic screening and for expression differences in the prostate tumor cohorts. Expression data were analyzed using the significance analysis of microarrays (SAM) method and the patient material was subjected to Kaplan-Meier statistics.

RESULTS AND LIMITATIONS

Functional screening identified 25 miRNAs increasing and 48 miRNAs decreasing cell viability. Data integration resulted in 14 miRNAs, with aberrant expression and effect on proliferation. These miRNAs are predicted to regulate >3700 genes, of which 28 were found up-regulated and 127 down-regulated in PCa compared with benign tissue. Seven genes, FLNC, MSRB3, PARVA, PCDH7, PRNP, RAB34, and SORBS1, showed an inverse association to their predicted miRNA, and were identified to significantly correlate with biochemical recurrence free survival in PCa patients.

CONCLUSIONS

A systematic in vitro screening approach combined with in vivo expression and gene set enrichment analysis provide unbiased means for revealing novel miRNA-target links, possibly driving the oncogenic processes in PCa.

PATIENT SUMMARY

This study identified novel regulatory molecules, which impact on PCa proliferation and are aberrantly expressed in clinical tumors. Thus, our study reveals regulatory nodes with potential for therapy.

SANIST: a rapid mass spectrometric SACI/ESI data acquisition and elaboration platform for verifying potential candidate biomarkers

RATIONALE

Surface-Activated Chemical Ionization/Electrospray Ionization mass spectrometry (SACI/ESI-MS) is a technique with high sensitivity and low noise that allows accurate biomarker discovery studies. We developed a dedicated SACI/ESI software, named SANIST, for both biomarker fingerprint data acquisition and as a diagnostic tool, using prostate cancer (PCa) as the disease of interest.

METHODS

Liquid chromatography (LC)/SACI/ESI-MS technology was employed to detect a potential biomarker panel for PCa disease prediction. Serum from patients with histologically confirmed or negative prostate biopsies for PCa was employed. The biomarker data (m/z or Thompson value, retention time and extraction mass chromatogram peak area) were stored in an ascii database. SANIST software allowed identification of potential biomarkers. A Bayesian scoring algorithm developed in house allowed sample separation based on comparison with samples in the database.

RESULTS

Biomarker candidates from the carnitine family were detected at significantly lower levels in patients showing histologically confirmed PCa. Using these biomarkers, the SANIST scoring algorithm allowed separation of patients with PCa from biopsy negative subjects with high accuracy and sensitivity.

CONCLUSIONS

SANIST was able to rapidly identify and perform a preliminary evaluation of the potential diagnostic efficiency of potential biomarkers for PCa.

sE-cadherin serves as a diagnostic and predictive parameter in prostate cancer patients

BACKGROUND

Measurement of prostate-specific antigen (PSA) advanced the diagnostic and prognostic potential for prostate cancer (PCa). However, due to PSA's lack of specificity, novel biomarkers are needed to improve risk assessment and ensure optimal personalized therapy. A set of protein molecules as potential biomarkers was therefore evaluated in serum of PCa patients.

METHODS

Serum samples from patients undergoing radical prostatectomy (RPE) for biopsy-proven PCa without neoadjuvant treatment were compared to serum samples from healthy subjects. Preliminary screening of 119 proteins in 10 PCa patients and 10 controls was carried out by the Proteome Profiler Antibody Array. Those markers showing distinct differences between patients and controls were then further evaluated by ELISA in the serum of 165 PCa patients and 19 controls. Uni- and multivariate as well as correlation analysis were performed to test the capability of these molecules to detect disease and predict pathological outcome.

RESULTS

Screening showed that soluble (s)E-cadherin, E-selectin, MMP2, MMP9, TIMP1, TIMP2, Galectin and Clusterin warranted further evaluation. sE-Cadherin, TIMP1, Galectin and Clusterin were significantly over- and MMP9 under-expressed in PCa compared to controls. The concentration of sE-cadherin, MMP2 and Clusterin correlated negatively and that of MMP9 and TIMP1 positively with the Gleason Sum at prostatectomy. Only sE-cadherin significantly correlated with the highest Gleason pattern. Compared to serum PSA, sE-cadherin provided an independent and better matching predictive ability for discriminating PCas with an upgrade at RPE and aggressive tumors with a Gleason Sum ≥7.

CONCLUSIONS

sE-cadherin performed most favorably from a large panel of serum proteins in terms of diagnostic and predictive potential in curatively treatable PCa. sE-cadherin merits further investigation as a biomarker for PCa.

Cancer Biomarkers: A Status Quo

At present, there are a growing number of biomolecules under investigation to understand their potential role as cancer biomarker for diagnostic, prognostic and therapeutic purposes. Intriguingly, the state of art on cancer biomarkers research shows interesting and promising results together to clamorous failures. Also from a clinical point of view, there are contradictory results on routine clinical use of the present cancer biomarkers. Some patients may be simply monitored in their course by a periodic blood sample, but sometimes this monitoring show dramatic limits. A lot of patients show serious and extensive relapses without significant change in serum concentrations of biomarkers tested. Often the physician who should utilize these biomarkers does not entirely know their limits and the total potential applications as well and sometimes this knowledge is influenced by economical and marketing strategies. This limited and "polluted" knowledge may have dramatic consequences for patient. A critical approach towards old and new cancer biomarkers should foster a deepened and useful understanding of the diagnostic and prognostic index of these fundamental parameters of laboratory medicine and in the same time can facilitate the research of new and more sensitive-specific signals of the cancer cell proliferation.

A PSA-guided approach for a better diagnosis of prostatic adenocarcinoma based on MALDI profiling and peptide identification

BACKGROUND

Prostate cancer (PCa) is the second cause of mortality in men worldwide. The prostate-specific antigen (PSA) test is routinely adopted in diagnosis; nevertheless more reliable biomarkers are continuously under investigation by monitoring the release of molecules into the bloodstream. The serum protein profiles appear to provide cancer-specific fingerprints that help to discriminate patients (especially with low PSA level) from controls, improving the performance of existing clinical tests.

METHODS

Samples from healthy controls and PCa patients with low (≤4 ng/mL) and high PSA (>4 ng/mL) levels were analyzed by MALDI profiling, and by a multi fractionation approach coupled to ESI-MS for peaks identification.

RESULTS

MALDI profiling achieved to detect 10 and 14 changed peaks (p-value <0.05), respectively, in PCa patients with low and high PSA versus controls. In particular, a peak identified as C3f fragment, resulted overexpressed in low PSA PCa patients.

CONCLUSIONS

PSA test, coupled to MALDI profiling, is able to detect changes, specifically related to PCa, in low molecular weight protein range. Furthermore, for the first time in prostate cancer research, the identification and quantification of the small peptide C3f appears to be relevant for the detection of false negatives, providing an additive diagnostic power to PSA (p<0.01) and suggesting its use in clinical tests.

Novel tools for prostate cancer prognosis, diagnosis, and follow-up

Prostate-specific antigen (PSA) is the main diagnostic tool when it comes to prostate cancer but it possesses serious limitations. Therefore, there is an urgent need for more sensitive and specific biomarkers for prostate cancer prognosis and patient follow-up. Recent advances led to the discovery of many novel diagnostic/prognostic techniques and provided us with many worthwhile candidates. This paper briefly reviews the most promising biomarkers with respect to their implementation in screening, early detection, diagnostic confirmation, prognosis, and prediction of therapeutic response or monitoring disease and recurrence; and their use as possible therapeutic targets. This review also examines the possible future directions in the field of prostate cancer marker research.