The usefulness of phosphorylated-signal transduction and activators of transcription 3 in detecting prostate cancer from negative biopsies

Biomarkers in previous histologically negative prostate biopsies can be helpful in repeat biopsy decision-making processes

To evaluate whether the addition of biomarkers to traditional clinicopathological parameters may help to increase the accurate prediction of prostate re‐biopsy outcome. A training cohort with 98 patients and a validation cohort with 72 patients were retrospectively recruited into our study. Immunohistochemical analysis was used to evaluate the immunoreactivity of a group of biomarkers in the initial negative biopsy normal‐looking tissues of the training and validation cohorts. p‐STAT3, Mcm2, and/or MSR1 were selected out of 10 biomarkers to construct a biomarker index for predicting cancer and high‐grade prostate cancer (HGPCa) in the training cohort based on the stepwise logistic regression analysis; these biomarkers were then validated in the validation cohort. In the training cohort study, we found that the biomarker index was independently associated with the re‐biopsy outcomes of cancer and HGPCa. Moreover supplementing the biomarker index with traditional clinical‐pathological parameters can improve the area under the receiver operating characteristic curve of the model from 0.722 to 0.842 and from 0.735 to 0.842, respectively, for predicting cancer and HGPCa at re‐biopsy. In the decision‐making analysis, we found the model supplemented with the biomarker index can improve patients’ net benefit. The application of the model to clinical practice, at a 10% risk threshold, would reduce the number of biopsies by 34.7% while delaying the diagnosis of 7.8% cancers and would reduce the number of biopsies by 73.5% while delaying the diagnosis of 17.8% HGPCas. Taken together, supplementing the biomarker index with clinicopathological parameters may help urologists in re‐biopsy decision‐making processes.

Expression of tSTAT3, pSTAT3727 , and pSTAT3 705 in the epithelial cells of hormone-naïve prostate cancer

BACKGROUND

The signal transducer and activator of transcription 3 (STAT3) pathway is observed to be constitutively activated in several malignancies including prostate cancer (PCa). In the present study, we investigated the expression of total STAT3 (tSTAT3) and two forms of activated phosphorylated STAT3 (pSTAT3⁷²⁷ and pSTAT3⁷⁰⁵ ) in tissue microarrays (TMA) of two cohorts of localized hormone-naïve PCa patients and analyzed associations between the expression and disease outcome.

METHODS

The expression of tSTAT3, pSTAT3⁷²⁷ , and pSTAT3⁷⁰⁵ was scored in the nuclei and cytoplasm of prostatic gland epithelial cells in two TMAs of paraffin-embedded prostatic tissue. The TMAs consisted of tissue originated from hormone-naïve radical prostatectomy patients from two different sites: Malmö, Sweden (n = 300) and Dublin, Ireland (n = 99).

RESULTS

The nuclear expression levels of tSTAT3, pSTAT3⁷²⁷ , and pSTAT3⁷⁰⁵ in the epithelial cells of benign glands were significantly higher than in the cancerous glands. Cytoplasmic tSTAT3 levels were also higher in benign glands. Patients with low pSTAT3⁷²⁷ and pSTAT3⁷⁰⁵ levels in the cancerous glands showed reduced times to biochemical recurrence, compared with those with higher levels. No significant trends in nuclear nor in cytoplasmic tSTAT3 were observed in relation to biochemical recurrence in the Malmö cohort. Higher cytoplasmic tSTAT3 was associated with reduced time to biochemical recurrence in the Dublin cohort. Adding the tSTAT3 and pSTAT3 expression data to Gleason score or pathological T stage did not improve their prognostic values.

CONCLUSIONS

Low pSTAT3⁷²⁷ and pSTAT3⁷⁰⁵ expression in epithelial cells of cancerous prostatic glands in hormone-naïve PCa was associated with faster disease progression. However, pSTAT3 and tSTAT3 expression did not improve the prognostic value of Gleason score or pathological T stage and may not be a good biomarker in the early hormone naïve stages of PCa.

STAT3 and STAT5A are potential therapeutic targets in castration-resistant prostate cancer

Mechanisms of castration-resistant prostate cancer (CRPC) are not well understood, thus hindering rational-based drug design. Activation of STAT3/5A, key components of the JAK/STAT pathway, is implicated in aggressive PC, yet their clinical relevance in CRPC remains elusive. Here, we evaluated the possible role of STAT3/5A in CRPC using immunological, quantitative mRNA expression profiling, and pharmacological methods. We observed a strong nuclear immunoreactivity for STAT3 and STAT5A in 93% (n=14/15) and 80% (n=12/15) of CRPC cases, respectively, compared with benign prostatic hyperplasia (BPH). We demonstrated that PC cells express varying levels of STAT3 and STAT5A transcripts. In addition, we demonstrate that pimozide, a psychotropic drug and an indirect inhibitor of STAT5, attenuated PC cells growth, and induced apoptosis in a dose-dependent manner. Furthermore, our analysis of the PC public data revealed that the STAT3/5A genes were frequently amplified in metastatic CRPC. These findings suggest that STAT3/5A potentially serves as a predictive biomarker to evaluate the therapeutic efficacy of a cancer drug targeting the JAK/STAT pathway. Since the JAK/STAT and AR pathways are suggested to be functionally synergistic, inhibition of the JAK/STAT signaling alone or together with AR may lead to a novel treatment modality for patients with advanced PC.

Analysis of STAT3 post-translational modifications (PTMs) in human prostate cancer with different Gleason Score

Prostate Cancer (PCa) is a complex and heterogeneous disease. The androgen receptor (AR) and the signal transducer and activator of transcription 3 (STAT3) could be effective targets for PCa therapy. STAT3, a cytoplasmatic latent transcription factor, is a hub protein for several oncogenic signalling pathways and up-regulates the expression of numerous genes involved in tumor cell proliferation, angiogenesis, metastasis and cell survival. STAT3 activity can be modulated by several Post-Translational Modifications (PTMs) which reflect particular cell conditions and may be implicated in PCa development and progression. The aim of this work was to analyze STAT3 PTMs at different tumor stages and their relationship with STAT3 cellular functions. For this purpose, sixty-five prostatectomy, Formalin-fixed paraffin-embedded (FFPE) specimens, classified with different Gleason Scores, were subjected to immunoblotting, immunofluorescence staining and RT-PCR analysis. All experiments were carried out in matched non-neoplastic and neoplastic tissues. Data obtained showed different STAT3 PTMs profiles among the analyzed tumor grades which correlate with differences in the amount and distribution of specific STAT3 interactors as well as the expression of STAT3 target genes. These results highlight the importance of PTMs as an additional biomarker for the exactly evaluation of the PCa stage and the optimal treatment of this disease.

Uncovering influence links in molecular knowledge networks to streamline personalized medicine

OBJECTIVES

We developed Resource Description Framework (RDF)-induced InfluGrams (RIIG) - an informatics formalism to uncover complex relationships among biomarker proteins and biological pathways using the biomedical knowledge bases. We demonstrate an application of RIIG in morphoproteomics, a theranostic technique aimed at comprehensive analysis of protein circuitries to design effective therapeutic strategies in personalized medicine setting.

METHODS

RIIG uses an RDF "mashup" knowledge base that integrates publicly available pathway and protein data with ontologies. To mine for RDF-induced Influence Links, RIIG introduces notions of RDF relevancy and RDF collider, which mimic conditional independence and "explaining away" mechanism in probabilistic systems. Using these notions and constraint-based structure learning algorithms, the formalism generates the morphoproteomic diagrams, which we call InfluGrams, for further analysis by experts.

RESULTS

RIIG was able to recover up to 90% of predefined influence links in a simulated environment using synthetic data and outperformed a naïve Monte Carlo sampling of random links. In clinical cases of Acute Lymphoblastic Leukemia (ALL) and Mesenchymal Chondrosarcoma, a significant level of concordance between the RIIG-generated and expert-built morphoproteomic diagrams was observed. In a clinical case of Squamous Cell Carcinoma, RIIG allowed selection of alternative therapeutic targets, the validity of which was supported by a systematic literature review. We have also illustrated an ability of RIIG to discover novel influence links in the general case of the ALL.

CONCLUSIONS

Applications of the RIIG formalism demonstrated its potential to uncover patient-specific complex relationships among biological entities to find effective drug targets in a personalized medicine setting. We conclude that RIIG provides an effective means not only to streamline morphoproteomic studies, but also to bridge curated biomedical knowledge and causal reasoning with the clinical data in general.

Castration-resistant prostate cancer: potential targets and therapies

The treatment landscape for patients with castration-resistant prostate cancer (CRPC) is undergoing significant changes with the advent of new therapies and multidisciplinary efforts by scientists and clinicians. As activation of multiple molecular pathways in the neoplastic prostate makes it impossible for single-target drugs to be completely effective in treating CRPC, this has led to combination therapy strategy, where several molecules involved in tumor growth and disease progression are targeted by a therapeutic regimen. In the present review, we provide an update on the molecular pathways that play an important role in the pathogenesis of CRPC and discuss the current wave of new treatments to combat this lethal disease.