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.

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.

Evaluation of prediction models for the staging of prostate cancer.

BACKGROUND: There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease. METHODS: A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study. RESULTS: 30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage. CONCLUSIONS: This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician's face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.

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.

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer.

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave-one-out cross-validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C-Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.