Response of Degarelix treatment in human prostate cancer monitored by HR-MAS <Superscript>1</Superscript>H NMR spectroscopy

Introduction

The androgen receptor (AR) is the master regulator of prostate cancer cell metabolism. Degarelix is a novel gonadotrophin-releasing hormone blocker, used to decrease serum androgen levels in order to treat advanced human prostate cancer. Little is known of the rapid metabolic response of the human prostate cancer tissue samples to the decreased androgen levels.

Objectives

To investigate the metabolic responses in benign and cancerous tissue samples from patients after treatment with Degarelix by using HRMAS ¹H NMR spectroscopy.

Methods

Using non-destructive HR-MAS ¹H NMR spectroscopy we analysed the metabolic changes induced by decreased AR signalling in human prostate cancer tissue samples. Absolute concentrations of the metabolites alanine, lactate, glutamine, glutamate, citrate, choline compounds [t-choline = choline + phosphocholine (PC) + glycerophosphocholine (GPC)], creatine compounds [t-creatine = creatine (Cr) + phosphocreatine (PCr)], taurine, myo-inositol and polyamines were measured in benign prostate tissue samples (n = 10), in prostate cancer specimens from untreated patients (n = 7) and prostate cancer specimens from patients treated with Degarelix (n = 6).

Results

Lactate, alanine and t-choline concentrations were significantly elevated in high-grade prostate cancer samples when compared to benign samples in untreated patients. Decreased androgen levels resulted in significant decreases of lactate and t-choline concentrations in human prostate cancer biopsies.

Conclusions

The reduced concentrations of lactate and t-choline metabolites due to Degarelix could in principle be monitored by in vivo ¹H MRS, which suggests that it would be possible to monitor the effects of physical or chemical castration in patients by that non-invasive method.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Male breast cancer: thirteen years experience of a single center

Background

This retrospective study analysed the epidemiological, clinical, and therapeutic profiles of breast cancer in males.

Methods

We report our experience at the Hospital of the University of Baskent, where 20 cases of male breast cancer were observed and treated between 1995–2008.

Results

Median age at presentation was 66,7 ± 10,9 years. Average follow-up was 63 ± 18,5 months. The main presenting symptom was a mass in 65% of cases (13 patients). Ýnvasive ductal carcinoma was the most frequent pathologic type (70% of cases).

Conclusion

Male breast cancer patients have an incidence of prostate cancer higher than would be predicted in the general population. Cause of men have a higher rate of ER positivity the responses with hormonal agents are good.

     

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

Introduction

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

Objectives

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

Methods

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using ¹H nuclear magnetic resonance (¹H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

Results

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

Conclusion

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

     

A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer

Background

Over 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.

Methods

In the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n?=?106), endometrial cancer (n?=?74), benign ovarian tumors (n?=?150) and healthy population controls (n?=?399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n?=?280), endometrial cancer (n?=?228), women with benign ovarian tumors (n?=?76) and healthy controls (n?=?57).

Results

In the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III–IV with a sensitivity of 0.88 and specificity of 0.92 (AUC?=?0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p?=?9.56e?22). Also, population controls could be distinguished from ovarian cancer stage III–IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC?=?0.89).

Conclusion

The PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.

Funding

The Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.

     

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

Background

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Methods

We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.

Results

Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.

Conclusion

A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

     

Reproducibility of non-fasting plasma metabolomics measurements across processing delays

Introduction

Processing delays after blood collection is a common pre-analytical condition in large epidemiologic studies. It is critical to evaluate the suitability of blood samples with processing delays for metabolomics analysis as it is a potential source of variation that could attenuate associations between metabolites and disease outcomes.

Objectives

We aimed to evaluate the reproducibility of metabolites over extended processing delays up to 48 h. We also aimed to test the reproducibility of the metabolomics platform.

Methods

Blood samples were collected from 18 healthy volunteers. Blood was stored in the refrigerator and processed for plasma at 0, 15, 30, and 48 h after collection. Plasma samples were metabolically profiled using an untargeted, ultrahigh performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) platform. Reproducibility of 1012 metabolites over processing delays and reproducibility of the platform were determined by intraclass correlation coefficients (ICCs) with variance components estimated from mixed-effects models.

Results

The majority of metabolites (approximately 70% of 1012) were highly reproducible (ICCs?≥?0.75) over 15-, 30- or 48-h processing delays. Nucleotides, energy-related metabolites, peptides, and carbohydrates were most affected by processing delays. The platform was highly reproducible with a median technical ICC of 0.84 (interquartile range 0.68–0.93).

Conclusion

Most metabolites measured by the UPLC–MS/MS platform show acceptable reproducibility up to 48-h processing delays. Metabolites of certain pathways need to be interpreted cautiously in relation to outcomes in epidemiologic studies with prolonged processing delays.

     

High-throughput and direct measurement of androgen levels using turbulent flow chromatography liquid chromatography-triple quadrupole mass spectrometry (TFC-LC-TQMS) to discover chemicals that modulate dihydrotestosterone production in human prostate cancer cells

Objectives

To develop a high-throughput screening system to measure the conversion of testosterone to dihydrotestosterone (DHT) in cultured human prostate cancer cells using turbulent flow chromatography liquid chromatography-triple quadrupole mass spectrometry (TFC-LC-TQMS).

Results

After optimizing the cell reaction system, this method demonstrated a screening capability of 103 samples, including 78 single compounds and 25 extracts, in less than 12 h without manual sample preparation. Consequently, fucoxanthin, phenethyl caffeate, and Curcuma longa L. extract were validated as bioactive chemicals that inhibited DHT production in cultured DU145 cells. In addition, naringenin boosted DHT production in DU145 cells.

Conclusion

The method can facilitate the discovery of bioactive chemicals that modulate the DHT production, and four phytochemicals are potential candidates of nutraceuticals to adjust DHT levels in male hormonal dysfunction.

     

Metabolic fingerprints of human primary endothelial and fibroblast cells

Introduction

Human primary cells originating from different locations within the body could differ greatly in their metabolic phenotypes, influencing both how they act during physiological/pathological processes and how susceptible/resistant they are to a variety of disease risk factors. A novel way to monitor cellular metabolism is through cell energetics assays, so we explored this approach with human primary cell types, as models of sclerotic disorders.

Objectives

In order to better understand pathophysiological processes at the cellular level, our goals were to measure metabolic pathway activities of endothelial cells and fibroblasts, and determine their metabolic phenotype profiles.

Methods

Biolog Phenotype MicroArray? technology was used for the first time to characterize metabolic phenotypes of diverse primary cells. These colorimetric assays enable detection of utilization of 367 specific biochemical substrates by human endothelial cells from the coronary artery (HCAEC), umbilical vein (HUVEC) and normal, healthy lung fibroblasts (NHLF).

Results

Adenosine, inosine, d-mannose and dextrin were strongly utilized by all three cell types, comparable to glucose. Substrates metabolized solely by HCAEC were mannan, pectin, gelatin and prevalently tricarballylic acid. HUVEC did not show any uniquely metabolized substrates whereas NHLF exhibited strong utilization of sugars and carboxylic acids along with amino acids and peptides.

Conclusion

Taken together, we show for the first time that this simple energetics assay platform enables metabolic characterization of primary cells and that each of the three human cell types examined gives a unique and distinguishable profile.

     

The co-regulators SRC-1 and SMRT are involved in interleukin-6-induced androgen receptor activation

Background

The androgen receptor (AR) can be stimulated by interleukin-6 (IL-6) in the absence of androgens to induce prostate cancer progression. The purpose of this study was to investigate whether the co-activator steroid receptor coactivator-1 (SRC-1) and co-repressor silencing mediator for retinoid and thyroid hormone receptors (SMRT) are involved in IL-6-induced AR activation.

Methods

The effects of IL-6 on LNCaP cell proliferation were monitored using real-time cell analysis (RTCA) iCELLigence system. The impacts of IL-6 on the association of the AR with SRC-1 and SMRT were investigated using the mammalian two-hybrid assay.

Results

IL-6 increased the proliferation of LNCaP cells with maximal induction at 50 ng/mL. The AR-SRC-1interaction was enhanced by IL-6, with maximal induction at the concentration of 50 ng/mL (P<0.05). IL-6 decreased theAR-SMRT interaction and a marked reduction was detected at 50 ng/mL (P<0.05).

Conclusions

IL-6 enhances LNCaP cells proliferation, which suggests that IL-6 might cause AR-positive prostate cancer growth through activation of the AR. The mechanism of IL-6-inducedARactivation is mediated through enhancing AR-SRC-1 interaction and inhibiting AR-SMRT interaction. We have shown a significant role for SRC-1 and SMRT in modulating IL-6-induced AR transactivation.

     

Evaluation of insulin-like growth factor-I in postmenopausal women with breast cancer treated with raloxifene

Background

The objective of this study was to evaluate serum IGF-I levels in postmenopausal women with breast cancer treated primarily with raloxifene.

Methods

Twenty-two postmenopausal patients with operable, stage I or II, estrogen receptor-positive carcinomas participated in this study. Following confirmation of diagnosis, the patients received 60 mg of raloxifene for 28 days prior to definitive surgery. Blood samples were collected for evaluation of serum IGF-I levels prior to initiating medication and following a 28-day treatment course. Student's t-test for paired samples was used in the statistical analysis. Significance was established at p < 0.05.

Results

Mean serum IGF-I levels pre- and post-raloxifene treatment were 143.7 ± 9.7 ng/ml and 94.8 ± 7.6 ng/ml, respectively. This reduction in serum IGF-I levels following treatment with raloxifene was statistically significant (p < 0.001).

Conclusion

Raloxifene significantly reduced serum IGF-I levels in postmenopausal women with breast cancer.

     

Best serum biomarker combination for ovarian cancer classification

Background

Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.

Methods

In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.

Results

The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.

Conclusions

We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

     

High salt and fat intake,inflammation, and risk of cancer

Background

Inflammatory conditions are involved in the pathophysiology of cancer. Recent findings have revealed that excessive salt and fat intake is involved in the development of severe inflammatory reactions.

Methods

literature search was performed on various online databases (PubMed, Scopus, and Google Scholar) regarding the roles of high salt and fat intake in the induction of inflammatory reactions and their roles in the etiopathogenesis of cancer.

Results

The results indicate that high salt and fat intake can induce severe inflammatory conditions. However, various inflammatory conditions have been strongly linked to the development of cancer. Hence, high salt and fat intake might be involved in the pathogenesis of cancer progression via putative mechanisms related to inflammatory reactions.

Conclusion

Reducing salt and fat intake may decrease the risk of cancer.

     

NF-κB potentiates tumor growth by suppressing a novel target LPTS

Background

Chronic inflammation is causally linked to the carcinogenesis and progression of most solid tumors. LPTS is a well-identified tumor suppressor by inhibiting telomerase activity and cancer cell growth. However, whether and how LPTS is regulated by inflammation signaling is still incompletely elucidated.

Methods

Real-time PCR and western blotting were used to determine the expression of p65 and LPTS. Reporter gene assay, electrophoretic mobility shift assay and chromatin immunoprecipitation were performed to decipher the regulatory mechanism between p65 and LPTS. Cell counting kit-8 assays and xenograt models were used to detect p65-LPTS-regulated cancer cell growth in vitro and in vivo, respectively.

Results

Here we for the first time demonstrated that NF-κB could inhibit LPTS expression in the mRNA and protein levels in multiple cancer cells (e.g. cervical cancer and colon cancer cells). Mechanistically, NF-κB p65 could bind to two consensus response elements locating at ?1143/?1136 and ?888/?881 in the promoter region of human LPTS gene according to EMSA and ChIP assays. Mutation of those two binding sites rescued p65-suppressed LPTS promoter activity. Functionally, NF-κB regulated LPTS-dependent cell growth of cervical and colon cancers in vitro and in xenograft models. In translation studies, we verified that increased p65 expression was associated with decreased LPTS level in multiple solid cancers.

Conclusions

Taken together, we revealed that NF-κB p65 potentiated tumor growth via suppressing a novel target LPTS. Modulation of NF-κB-LPTS axis represented a potential strategy for treatment of those inflammation-associated malignancies.

     

Multiplex PCR analysis of clusters of unexplained viral respiratory tract infection in Cambodia

Background

Fevers of unknown origin constitute a substantial disease burden in Southeast Asia. In majority of the cases, the cause of acute febrile illness is not identified.

Methods

We used MassTag PCR, a multiplex assay platform, to test for the presence of 15 viral respiratory agents from 85 patients with unexplained respiratory illness representing six disease clusters that occurred in Cambodia between 2009 and 2012.

Results

We detected a virus in 37 (44%) of the cases. Human rhinovirus, the virus detected most frequently, was found in both children and adults. The viruses most frequently detected in children and adults, respectively, were respiratory syncytial virus and enterovirus 68. Sequence analysis indicated that two distinct clades of enterovirus 68 were circulating during this time period.

Conclusions

This is the first report of enterovirus 68 in Cambodia and contributes to the appreciation of this virus as an important respiratory pathogen.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Akt1 inhibition promotes breast cancer metastasis through EGFR-mediated β-catenin nuclear accumulation

Background

Knockdown of Akt1 promotes Epithelial-to-Mesenchymal Transition in breast cancer cells. However, the mechanisms are not completely understood.

Methods

Western blotting, immunofluorescence, luciferase assay, real time PCR, ELISA and Matrigel invasion assay were used to investigate how Akt1 inhibition promotes breast cancer cell invasion in vitro. Mouse model of lung metastasis was used to measure in vivo efficacy of Akt inhibitor MK2206 and its combination with Gefitinib.

Results

Knockdown of Akt1 stimulated β-catenin nuclear accumulation, resulting in breast cancer cell invasion. β-catenin nuclear accumulation induced by Akt1 inhibition depended on the prolonged activation of EGFR signaling pathway in breast cancer cells. Mechanistic experiments documented that knockdown of Akt1 inactivates PIKfyve via dephosphorylating of PIKfyve at Ser³¹⁸ site, resulting in a decreased degradation of EGFR signaling pathway. Inhibition of Akt1 using MK2206 could induce an increase in the expression of EGFR and β-catenin in breast cancer cells. In addition, MK2206 at a low dosage enhance breast cancer metastasis in a mouse model of lung metastasis, while an inhibitor of EGFR tyrosine kinase Gefitinib could potentially suppress breast cancer metastasis induced by Akt1 inhibition.

Conclusion

EGFR-mediated β-catenin nuclear accumulation is critical for Akt1 inhibition-induced breast cancer metastasis.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.