Prostate Cancer Diagnosis in the Clinic Using an 8-Protein Biomarker Panel.

The inability to distinguish aggressive from indolent prostate cancer is a longstanding clinical problem. Prostate specific antigen (PSA) tests and digital rectal exams cannot differentiate these forms. Because only ∼10% of diagnosed prostate cancer cases are aggressive, existing practice often results in overtreatment including unnecessary surgeries that degrade patients' quality of life. Here, we describe a fast microfluidic immunoarray optimized to determine 8-proteins simultaneously in 5 µL of blood serum for prostate cancer diagnostics. Using polymeric horseradish peroxidase (poly-HRP, 400 HRPs) labels to provide large signal amplification and limits of detection in the sub-fg mL-1 range, a protocol was devised for the optimization of the fast, accurate assays of 100-fold diluted serum samples. Analysis of 130 prostate cancer patient serum samples revealed that some members of the protein panel can distinguish aggressive from indolent cancers. Logistic regression was used to identify a subset of the panel, combining biomarker proteins ETS-related gene protein (ERG), insulin-like growth factor-1 (IGF-1), pigment epithelial-derived factor (PEDF), and serum monocyte differentiation antigen (CD-14) to predict whether a given patient should be referred for biopsy, which gave a much better predictive accuracy than PSA alone. This represents the first prostate cancer blood test that can predict which patients will have a high biopsy Gleason score, a standard pathology score used to grade tumors.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection.

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

Troponin T correlates with MRI results in neonatal encephalopathy.

AIM: Troponin is a sensitive marker of asphyxia in term infants mirroring the myocardial injury sustained in global hypoxia-ischaemia. In addition, troponin is a sensitive marker of severity of stroke in adults and neonatal encephalopathy (NE). We aimed to examine the relationship between troponin T in infants with perinatal asphyxia and brain injury on MRI and correlate with neurodevelopmental outcome. METHODS: Serum troponin was sampled in infants requiring resuscitation at birth and/or neonatal encephalopathy in a tertiary referral neonatal centre. Birth history, clinical parameters, neuroimaging and developmental outcome (Bayley Scores of Infant Development [BSID] III) were evaluated. RESULTS: Infants with perinatal asphyxia (n = 54) had serum troponin T measured and 27 required therapeutic hypothermia. Troponin T levels on days 1 and 2 were predictive of need for TH, development of seizures and grade II/III NE (AUC = 0.7; P-values < .001), troponin T levels on days 1, 2 and 3 were highly significant predictors of mortality (AUC = 0.99, P-values .005). The cut-off values of troponin T for best prediction of mortality were 0.84, 0.63 and 0.58 ng/mL on days 1, 2 and 3, respectively. Troponin T on day 3 of life was predictive of injury in the combined area of basal ganglia/watershed on MRI (AUC 0.70; P-value = .045). CONCLUSION: Infants with brain injury on neuroimaging following perinatal asphyxia had significantly elevated serum troponin, and troponin also correlated with developmental scores at 2 years. Further studies combining troponin and MRI may assist in the classification of neonatal brain injury to define aetiology, prognosis and response to treatment.

A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort.

BACKGROUND: Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy. METHODS: Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created. RESULTS: IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis. CONCLUSION: The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling.

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients' serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

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.

Role of serum response factor expression in prostate cancer biochemical recurrence.

BACKGROUND: Up to a third of prostate cancer patients fail curative treatment strategies such as surgery and radiation therapy in the form of biochemical recurrence (BCR) which can be predictive of poor outcome. Recent clinical trials have shown that men experiencing BCR might benefit from earlier intervention post-radical prostatectomy (RP). Therefore, there is an urgent need to identify earlier prognostic biomarkers which will guide clinicians in making accurate diagnosis and timely decisions on the next appropriate treatment. The objective of this study was to evaluate Serum Response Factor (SRF) protein expression following RP and to investigate its association with BCR. MATERIALS AND METHODS: SRF nuclear expression was evaluated by immunohistochemistry (IHC) in TMAs across three international radical prostatectomy cohorts for a total of 615 patients. Log-rank test and Kaplan-Meier analyses were used for BCR comparisons. Stepwise backwards elimination proportional hazard regression analysis was used to explore the significance of SRF in predicting BCR in the context of other clinical pathological variables. Area under the curve (AUC) values were generated by simulating repeated random sub-samples. RESULTS: Analysis of the immunohistochemical staining of benign versus cancer cores showed higher expression of nuclear SRF protein expression in cancer cores compared with benign for all the three TMAs analysed (P < 0.001, n = 615). Kaplan-Meier curves of the three TMAs combined showed that patients with higher SRF nuclear expression had a shorter time to BCR compared with patients with lower SRF expression (P < 0.001, n = 215). Together with pathological T stage T3, SRF was identified as a predictor of BCR using stepwise backwards elimination proportional hazard regression analysis (P = 0.0521). Moreover ROC curves and AUC values showed that SRF was better than T stage in predicting BCR at year 3 and 5 following radical prostatectomy, the combination of SRF and T stage had a higher AUC value than the two taken separately. CONCLUSIONS: SRF assessment by IHC following RP could be useful in guiding clinicians to better identify patients for appropriate follow-up and timely treatment.

Investigating the role of the IGF axis as a predictor of biochemical recurrence in prostate cancer patients post-surgery.

BACKGROUND: Between 20% and 35% of prostate cancer (PCa) patients who undergo treatment with curative intent (ie, surgery or radiation therapy) for localized disease will experience biochemical recurrence (BCR). Alterations in the insulin-like growth factor (IGF) axis and PTEN expression have been implicated in the development and progression of several human tumors including PCa. We examined the expression of the insulin receptor (INSR), IGF-1 receptor (IGF-1R), PTEN, and AKT in radical prostatectomy tissue of patients who developed BCR post-surgery. METHODS: Tissue microarrays (TMA) of 130 patients post-radical prostatectomy (65 = BCR, 65 = non-BCR) were stained by immunohistochemistry for INSR, IGF-1R, PTEN, and AKT using optimized antibody protocols. INSR, IGF1-R, PTEN, and AKT expression between benign and cancerous tissue, and different Gleason grades was assessed. Kaplan-Meier survival curves were used to examine the relationship between proteins expression and BCR. RESULTS: INSR (P < 0.001), IGF-1R (P < 0.001), and AKT (P < 0.05) expression was significantly increased and PTEN (P < 0.001) was significantly decreased in cancerous versus benign tissue. There was no significant difference in INSR, IGF-1R, or AKT expression in the cancerous tissue of non-BCR versus BCR patients (P = 0.149, P = 0.990, P = 0.399, respectively). There was a significant decrease in PTEN expression in the malignant tissue of BCR versus non-BCR patients (P = 0.011). Combinational analysis of the tissue proteins identified a combination of decreased PTEN and increased AKT or increased INSR was associated with worst outcome. We found that in each case, our hypothesized worst group was most likely to experience BCR and this was significant for combinations of PTEN+INSR and PTEN+AKT but not PTEN+IGF-1R (P = 0.023, P = 0.028, P = 0.078, respectively). CONCLUSIONS: Low PTEN is associated with BCR and this association is strongly modified by high INSR and high AKT expression. Measurement of these proteins could help inform appropriate patient selection for postoperative adjuvant therapy and prevent BCR.

Serial cytokine alterations and abnormal neuroimaging in newborn infants with encephalopathy.

AIM: Inflammatory cytokines may play a role in the final common pathway in the pathogenesis of hypoxic-ischaemic injury in experimental models. We aimed to profile the systemic pro-and anti-inflammatory response over the first week of life in term infants at risk of neonatal encephalopathy. METHOD: In a tertiary referral university neonatal intensive care unit, serial blood samples were analysed from 41 term infants (requiring resuscitation at birth) in this prospective observational pilot study. Serum levels of 10 pro-and anti-inflammatory cytokines were evaluated including interleukin(IL)-1α, IL-1ß, IL-6, IL-8, IL-10, tumour necrosis factor(TNF)-α, interferon (IFN)-γ, vascular endothelial growth factor (VEGF), granulocyte/colony-stimulating factor (G-CSF) and granulocyte macrophage/colony-stimulating factor (GM-CSF). RESULTS: Infants with neonatal encephalopathy and abnormal neuroimaging (n = 15) had significantly elevated granulocyte macrophage/colony-stimulating factor at 0-24 h and interleukin-8, interleukin-6 and interleukin-10 at 24-48 hour. Tumour necrosis factor-α and vascular endothelial growth factor levels were lower at 72-96 hour (p < 0.05). Significantly elevated levels of interleukin-10 were associated with mortality. CONCLUSION: Serum cytokine changes and innate immune dysregulation in the first week of life may be indicators of outcome in neonatal encephalopathy but require validation in larger studies.

Improving multivariable prostate cancer risk assessment using the Prostate Health Index.

OBJECTIVES: To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment. PATIENTS AND METHODS: Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men's clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models. RESULTS: The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities. CONCLUSION: A logical and standardized approach to the use of clinical risk factors can allow more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

Folate-targeted amphiphilic cyclodextrin.siRNA nanoparticles for prostate cancer therapy exhibit PSMA mediated uptake, therapeutic gene silencing in vitro and prolonged circulation in vivo.

In this study, a folate targeted cyclodextrin (CD) nanoparticle was prepared by co-formulating CD.siRNA complexes with DSPE-PEG5000-folate to target the prostate specific membrane antigen (PSMA). Targeted formulations showed increased uptake, relative to untargeted controls, in two prostate cancer cell lines expressing PSMA (VCaP and LNCaP). Competitive uptake studies, using excess folate, significantly reduced uptake of targeted nanoparticles in PSMA positive cell lines (P<0.001). Relative to untreated controls, folate-targeted nanoparticles significantly reduced the levels of RelA mRNA in VCaP and LNCaP cells by 44% and 22% respectively (P<0.001). In contrast there was no significant reduction in RelA mRNA in these cell lines by untargeted complexes. Pharmacokinetic (PK) data indicated that the incorporation of PEG into the formulation increased the circulation time of siRNA 8-fold. This study highlights the ability of incorporating a folate ligand into CD.siRNA nanoparticles to allow for targeted delivery of siRNA to prostate cancer cells via the PSMA.

Relationship between serum response factor and androgen receptor in prostate cancer.

BACKGROUND: Serum response factor (SRF) is an important transcription factor in castrate-resistant prostate cancer (CRPC). Since CRPC is associated with androgen receptor (AR) hypersensitivity, we investigated the relationship between SRF and AR. MATERIALS AND METHODS: Transcriptional activity was assessed by luciferase assay. Cell proliferation was measured by MTT and flow cytometry. Protein expression in patients was assessed by immunohistochemistry. RESULTS: To investigate AR involvement in SRF response to androgen, AR expression was down-regulated using siRNA. This resulted in the abrogation of SRF induction post-DHT. Moreover, DHT stimulation failed to induce SRF transcriptional activity in AR-negative PC346 DCC cells, which was only restored following AR over-expression. Next, SRF expression was down-regulated by siRNA, resulting in AR increased transcriptional activity in castrate-resistant LNCaP Abl cells but not in the parental LNCaP. This negative feedback loop in the resistant cells was confirmed by immunohistochemistry which showed a negative correlation between AR and SRF expression in CRPC bone metastases and a positive correlation in androgen-naïve prostatectomies. Cell proliferation was next assessed following SRF inhibition, demonstrating that SRF inhibition is more effective than AR inhibition in castrate-resistant cells. CONCLUSION: Our data support SRF as a promising therapeutic target in combination with current treatments.

The analysis of serum response factor expression in bone and soft tissue prostate cancer metastases.

BACKGROUND: Castration-resistant prostate cancer (CRPC) represents a challenge to treat with no effective treatment options available. We recently identified serum response factor (SRF) as a key transcription factor in an in vitro model of castration resistance where we showed that SRF inhibition resulted in reduced cellular proliferation. We also demonstrated an association between SRF protein expression and CRPC in a cohort of castrate-resistant transurethral resections of the prostate (TURPS). The mechanisms regulating the growth of CRPC bone and visceral metastases have not been explored in depth due to the paucity of patient-related material available for analysis. In this study, we aim to evaluate SRF protein expression in prostate cancer (PCa) metastases, which has not previously been reported. METHODS AND RESULTS: We evaluated the nuclear tissue expression profile of SRF by immunohistochemistry in 151 metastatic sites from 42 patients who died of advanced PCa. No relationship between SRF nuclear expression and the site of metastasis was observed (P = 0.824). However, a negative association between SRF nuclear expression in bone metastases and survival from (a) diagnosis with PCa (P = 0.005) and (b) diagnosis with CRPC (P = 0.029) was seen. These results demonstrate that SRF nuclear expression in bone metastases is associated with survival, with patients with the shortest survival showing high SRF nuclear expression and patients with the longest survival having low SRF nuclear expression. CONCLUSION: Our study indicates that SRF is a key factor determining patients' survival in metastatic CRPC and therefore may represent a promising target for future therapies.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer.

BACKGROUND: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. OBJECTIVE: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. DESIGN, SETTING, AND PARTICIPANTS: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. RESULTS AND LIMITATIONS: The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. CONCLUSIONS: The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. PATIENT SUMMARY: We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

The identification and internal validation of a preoperative serum biomarker panel to determine extracapsular extension in patients with prostate cancer.

BACKGROUND: Accurate preoperative staging of prostate cancer (PCa) is important but current diagnostic methods cannot accurately determine extracapsular extension (ECE), resulting in the possible triage of patients towards a less appropriate arm of therapy. This has consequences to patient care and better methods of preoperatively determining ECE are required. METHODS: We followed a biomarker development pathway and compared the preoperative serum expressions of VEGF-D, PEDF, IGF-I, IGFBP3, and CD14 in patients from the Irish Prostate Cancer Research Consortium (PCRC) with radical prostatectomy determined ECE against patients with nonECE. RESULTS: The expression measurements of five proteins were fitted into a logistic regression model and backwards variable elimination methods were applied which resulted in a model with IGFBP3 and CD14 as the best combination biomarker panel. This panel was tested in an independent cohort of patients using an optimized multiplex electrochemiluminescence assay. Receiver operating characteristic curves were generated and the areas under the curve (AUC) were calculated as an estimation of prediction accuracy. The biomarker panel was validated with an AUC of 76.6%, and a sensitivity and specificity of 80% and 75% was obtained. CONCLUSIONS: This is the first internally validated, preoperative serum biomarker panel that identifies ECE in patients with Gleason score 7 PCa with AUC 76.6%. The panel surpasses the routinely used diagnostic standards in accuracy and may help to improve preoperative cancer staging, better inform treatment options, and improve the referral patterns of patients with urgently treatable cancers towards more appropriate arms of therapy.

EuroSCORE and neutrophil adhesion molecules predict outcome post-cardiac surgery.

BACKGROUND: This study tested the hypothesis that surgical stress and the host response to this trauma trigger an inflammatory cascade in which the neutrophil plays a central role. We hypothesised that pre-operative neutrophil migratory responses will correlate with post-operative clinical outcome in our shock model of open-heart surgery patients. We also tested the hypothesis that surface expression of adhesion molecules involved in the migratory process - CD11b, CD47 and CD99 - could be used to predict outcome. We believe that combining neutrophil migratory response, CD11b, CD47 and CD99 with the logistic European System for Cardiac Operative Risk Evaluation (EuroSCORE) will strengthen the power of the EuroSCORE not only in predicting post-operative mortality but also other clinical endpoints. MATERIALS AND METHODS: Neutrophils were isolated pre-operatively from n = 31 patients undergoing open-heart surgery and allowed to migrate across endothelial monolayers in response to N-formyl-methionine-leucine-phenylalanine (fMLP). Isolated neutrophils were also assessed for surface expression of CD11b, CD47 and CD99 in response to fMLP by flow cytometry. Post-operative clinical parameters collected included days 1-5 white cell count and creatinine levels as well as intensive care unit (ICU) and post-operative hospital stay. RESULTS: Pre-operative surface expression of CD99 and CD47 correlates with post-operative creatinine levels (P < 0·05), a measurement of renal injury. We also show that while the logistic EuroSCORE alone can be used as a predictor of ICU stay, when combined with pre-operative CD99 surface expression, it improves its AUC value (0·794). CONCLUSION: Immunological markers, specifically the ability of the neutrophil to migrate, combined with the logistic EuroSCORE lead to improved sensitivity and specificity to predict patient outcome.

Soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) in neonatal sepsis.

BACKGROUND: Efficient and accurate diagnosis of neonatal sepsis is challenging. The potential impact for a reduction in morbidity and mortality as well as antibiotic usage has stimulated the ongoing search for biomarkers of early sepsis. The objective of this pilot study was to quantify the levels of sTREM-1 and correlate with blood cultures and inflammatory markers in neonates evaluated for sepsis. METHODS: Neonates with suspected sepsis were enrolled (n = 83; Preterm n = 35; Term n = 48). Routine bloods for sepsis evaluation were included and plasma sTREM-1 levels were quantified by ELISA. RESULTS: Term and preterm neonates (n = 83; Preterm n = 35; Term n = 48) were enrolled and 16 neonates had positive blood cultures (preterm n = 15; term n = 1). sTREM-1 levels were not significantly different in infants with culture-positive or culture-negative sepsis (356 ± 218 pg/mL and 385 ± 254 pg/mL respectively). The immature-to-total granulocyte (I/T) ratio showed a significant positive correlation with sTREM-1 in the preterm group with positive blood cultures. Additionally, sTREM-1 showed a positive correlation with CRP in the preterm group with negative blood cultures. CONCLUSIONS: sTREM-1 was associated with traditional markers of inflammation (I/T ratio and CRP). However, in this cohort sTREM-1 did not improve the early detection of neonatal culture-positive sepsis.

Benefits of heat treatment to the protease packed neutrophil for proteome analysis: halting protein degradation.

Neutrophils, cells of the innate immune system, contain an array of proteases and reactive oxygen species-generating enzymes that assist in controlling the invasion of bacteria and pathogens. The high content of intracellular proteolytic enzymes makes them difficult cells to work with as they can degrade proteins of potential interest. Here, we describe the benefits of heat treatment of neutrophils in reducing protein degradation for subsequent proteome analysis. Neutrophils isolated from four healthy volunteers were each divided into three aliquots and subjected to different preparation methods for 2-DE: (i) Heat treatment, (ii) resuspension in NP40 lysis buffer and (iii) resuspension in standard 2-DE lysis buffer. Representative spots found to be statistically significant between groups (p<0.01) were excised and identified by LC-MS/MS, three of which were validated by immunoblotting. Heat-treated samples contained proteins in the high-molecular-weight range that were absent from NP40-treated samples. Moreover, NP40-treated samples showed an increase in spot number and volume at lower molecular weights suggestive of protein degradation. Incorporating heat treatment into sample preparation resulted in the identification of proteins that may not have previously been detected due to sample degradation, thus leading to a more comprehensive 2-DE map of the human neutrophil proteome.

Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer.

In recent years, Prostate Specific Antigen (PSA) testing is widespread and has been associated with deceased mortality rates; however, this testing has raised concerns of overdiagnosis and overtreatment. It is clear that additional biomarkers are required. To identify these biomarkers, we have undertaken proteomics and metabolomics expression profiles of serum samples from BPH, Gleason score 5 and 7 using two-dimensional difference in gel electrophoresis (2D-DIGE) and nuclear magnetic resonance spectroscopy (NMR). Panels of serum protein biomarkers were identified by applying Random Forests to the 2D-DIGE data. The evaluation of selected biomarker panels has shown that they can provide higher prediction accuracy than the current diagnostic standard. With careful validation of these serum biomarker panels, these panels may potentially help to reduce unnecessary invasive diagnostic procedures and more accurately direct the urologist to curative surgery.