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.

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.

Correlation of integrated ERG/PTEN assessment with biochemical recurrence in prostate cancer.

INTRODUCTION: Prostate cancer is a heterogeneous disease, with a complex molecular landscape that evolves throughout disease progression. Common alterations in genes such as ERG and PTEN have been attributed to worse prognosis. This study aimed to further examine the clinical relevance of PTEN and ERG expression in a cohort of patients with prostate cancer post radical prostatectomy. METHODS: Tissue microarrays were constructed from 132 patients with prostate cancer from the Irish Prostate Cancer Research Consortium and University Hospital of Orebro, Sweden. Patients were divided into three groups - Group 1: biochemical recurrence, Group 2: no biochemical recurrence and Group 3: immediate progression after surgery. PTEN and ERG immunohistochemical analysis was performed and the association between expression levels and clinical parameters were compared. RESULTS: Pathological stage pT3 tumours were more common at borderline significantly higher levels amongst patients who biochemically recurred when compared to patients who did not recur after radical prostatectomy (p = 0.05). ERG and PTEN expression levels were compared separately and concurrently across all three patient groups. Lack of ERG expression was strongly associated with immediate progression after surgery (p = 0.029). Loss of/low PTEN trended towards an association with immediate progression, however this was not statistically significant (p = 0.066). CONCLUSION: In this study, negative ERG expression was strongly associated with immediate biochemical progression after radical prostatectomy. Moreover, a trend towards a relationship between aberrant PTEN expression and progression was observed. Additional studies with long-term follow up data may provide further clinical insight into the genomic heterogeneity in this population.

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy.

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients' outcomes and quality of life.

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.

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.

Inhibition of Serum Response Factor Improves Response to Enzalutamide in Prostate Cancer.

Castrate-resistant prostate cancer (CRPC) is challenging to treat with the androgen receptor (AR), the main target and key focus of resistance. Understanding the mechanisms of AR interaction with co-regulators will identify new therapeutic targets to overcome AR resistance mechanisms. We previously identified the serum response factor (SRF) as a lead target in an in vitro model of CRPC and showed that SRF expression in tissues of CRPC patients was associated with shorter survival. Here, we tested SRF inhibition in vitro and in vivo to assess SRF as a potential target in CRPC. Inhibition of SRF with the small-molecule inhibitor CCG1423 resulted in enhanced response to enzalutamide in vitro and reduced tumour volume of LuCaP 35CR, a CRPC patient-derived xenograft model. Nuclear localisation of AR post-CCG1423 was significantly decreased and was associated with decreased α-tubulin acetylation in vitro and decreased prostate specific antigen (PSA) levels in vivo. SRF immunoreactivity was tested in metastatic tissues from CRPC patients to investigate its role in enzalutamide response. Kaplan-Meier curves showed that high SRF expression was associated with shorter response to enzalutamide. Our study supports the use of SRF inhibitors to improve response to enzalutamide.

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.

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.

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.

The transcription factor CUX1 negatively regulates invasion in castrate resistant prostate cancer.

Metastatic prostate cancer is treated with androgen ablation therapy but progress to castrate resistant prostate cancer (CRPC). This study aimed to investigate the role of CUX1 in CRPC using clinical samples and in vitro models. CUX1 expression was increased in androgen-independent cells compared to androgen-sensitive cells. The multi-isoform nature of CUX1 makes it difficult to assay in tissue microarrays as there is no epitope able to distinguish the many isoforms for immunohistochemistry. Using surrogate markers, we found no differential expression between castrate resistant and local hormone naïve tissue. However, differences have been demonstrated at the transcript level. In androgen-sensitive cells, migration, but not invasion, increased following CUX1 knockdown. Conversely, in androgen-independent cells, invasion was increased. This observed difference in invasion capacity is not E-cadherin mediated, as CUX1 knockdown increases the expression of E-cadherin in both cell lines with no inter-cell line difference. Cells expressed different ratios of p110/p200 isoforms depending on androgen status and cathepsin L was only detectable in androgen-sensitive cells. MMP3 is upregulated in the androgen-independent cells. Rather than a simple presence or absence of CUX1, the relative balance of CUX1 isoforms and their interplay may be a significant factor in the functional role of CUX1 in CRPC.

Pathology in Irish medical education.

Pathology is the study of disease and is an important component in medical education. However, with medical curriculum reform, its role and contribution to medical courses is under potential threat. We surveyed the status of pathology in all six Irish medical schools. Information was received from five direct undergraduate and four graduate entry programmes. Pathology was recognisable as a core subject in all but one of the medical schools, was generally taught in years two or three, and the greatest contact hours were for histopathology (44-102 hours). Lectures were the most common teaching modality, and all used single best or extended matching answer multiple-choice questions as part of assessments. Currently, pathology is very visible in Irish medical education but needs to remain relevant with the move to theme and case-based teaching. There is heavy reliance on lectures and on non-academic/full-time hospital staff to deliver teaching, which may not be sustainable.

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.

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.

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.

MARCKS promotes invasion and is associated with biochemical recurrence in prostate cancer.

BACKGROUND: Overtreatment of low-grade prostate cancer is a recognised problem for clinicians and patients. However, under-treatment runs the risk of missing the opportunity for cure in those who could benefit. Identification of new biomarkers of disease progression, including metastases, is required to better stratify and appropriately treat these patients. The ability to predict if prostate cancer will recur is an important clinical question that would impact treatment options for patients. Studies in other cancers have associated MARCKS with metastasis. METHODS: Tissue microarrays of local prostatectomy samples from a cohort of biochemical recurrent and non-biochemical recurrent tumours were assayed for MARCKS protein expression. Prostate cancer cell lines were transfected with siRNA targeting MARCKS or a control and functional endpoints of migration, invasion, proliferation, viability and apoptosis were measured. Actin was visualised by fluorescent microscopy and evidence of a cadherin switch and activation of the AKT pathway were assayed. RESULTS: MARCKS was upregulated in biochemical recurrent patients compared to non-biochemical recurrent. Knockdown of MARCKS reduced migration and invasion of prostate cancer cells, reduced MMP9 mRNA expression, as well as decreasing cell spreading and increased cell:cell adhesion in prostate cancer cell colonies. Knockdown of MARCKS had no effect on proliferation, viability or apoptosis of the prostate cancer cells. CONCLUSIONS: In conclusion, MARCKS promotes migration and invasion and is associated with biochemical recurrence in localised prostate cancer tumours. The mechanisms by which this occurs have yet to be fully elucidated but lack of a cadherin switch indicates it is not via epithelial-to-mesenchymal transition. Actin rearrangement indicates that MARCKS promotes invasion through regulating the architecture of the cell.

Advances in Precision Medicine: Tailoring Individualized Therapies.

The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs.

Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer.

While progression from normal prostatic epithelium to invasive cancer is driven by molecular alterations, tumor cells and cells in the cancer microenvironment are co-dependent and co-evolve. Few human studies to date have focused on stroma. Here, we performed gene expression profiling of laser capture microdissected normal non-neoplastic prostate epithelial tissue and compared it to non-transformed and neoplastic low-grade and high-grade prostate epithelial tissue from radical prostatectomies, each with its immediately surrounding stroma. Whereas benign epithelium in prostates with and without tumor were similar in gene expression space, stroma away from tumor was significantly different from that in prostates without cancer. A stromal gene signature reflecting bone remodeling and immune-related pathways was upregulated in high compared to low-Gleason grade cases. In validation data, the signature discriminated cases that developed metastasis from those that did not. These data suggest that the microenvironment may influence prostate cancer initiation, maintenance, and metastatic progression.Stromal cells contribute to tumor development but the mechanisms regulating this process are still unclear. Here the authors analyze gene expression profiles in the prostate and show that stromal gene signature changes ahead of the epithelial gene signature as prostate cancer initiates and progresses.

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.