Practical approaches to proteomic biomarkers within prostate cancer radiotherapy trials

Evaluation of inflammatory cytokines and oxidative stress markers in prostate cancer patients undergoing curative radiotherapy

Prostate cancer is the second leading cause of cancer-related death in men. The present study was carried out to investigate the radiation response of serum cytokines and oxidative markers to find out if these novel biomarkers have significant applications regarding radiation outcome in prostate cancer patients. Significant elevations of prostatic specific antigen (PSA), asymmetric dimethyl arginine (ADMA) and nitric oxide (NO) were recorded in cancer prostate patients at the time of diagnosis compared to controls. Patients were subjected to radiotherapy post prostatectomy with a total dose of 66 Gy in 33 fractions (5 sessions/week) for 7 weeks. At the end of the seventh week post radiotherapy, ADMA levels were accentuated while the levels of PSA and NO were lower than before therapy. The level of inflammatory cytokines (interleukins IL-4, IL-5 and interferon-gamma) in post radiation therapy patients were significantly elevated compared to both controls and prostate cancer patients. A significant inverse correlation was observed in prostate cancer patients between ADMA and NO. Moreover, a significant inverse correlation in post radiation therapy patients was observed between IL-5 and PSA. These results are highly suggestive that there is a specific cytokine response in patients undergoing curative radiotherapy for prostate cancer.

Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel

Biomarkers are important for early detection of cancer, prognosis, response prediction, and detection of residual or relapsing disease. Special attention has been given to diagnostic markers for prostate cancer since it is thought that early detection and surgery might reduce prostate cancer-specific mortality. The use of prostate-specific antigen, PSA (KLK3), has been debated on the base of cohort studies that show that its use in preventive screenings only marginally influences mortality from prostate cancer. Many groups have identified alternative or additional markers, among which PCA3, in order to detect early prostate cancer through screening, to distinguish potentially lethal from indolent prostate cancers, and to guide the treatment decision. The large number of markers proposed has led us to the present study in which we analyze these indicators for their diagnostic and prognostic potential using publicly available genomic data. We identified 380 markers from literature analysis on 20,000 articles on prostate cancer markers. The most interesting ones appeared to be claudin 3 (CLDN3) and alpha-methysacyl-CoA racemase highly expressed in prostate cancer and filamin C (FLNC) and keratin 5 with highest expression in normal prostate tissue. None of the markers proposed can compete with PSA for tissue specificity. The indicators proposed generally show a great variability of expression in normal and tumor tissue or are expressed at similar levels in other tissues. Those proposed as prognostic markers distinguish cases with marginally different risk of progression and appear to have a clinically limited use. We used data sets sampling 152 prostate tissues, data sets with 281 prostate cancers analyzed by microarray analysis and a study of integrated genomics on 218 cases to develop a multigene score. A multivariate model that combines several indicators increases the discrimination power but does not add impressively to the information obtained from Gleason scoring. This analysis of 10 years of marker research suggests that diagnostic and prognostic testing is more difficult in prostate cancer than in other neoplasms and that we must continue to search for better candidates.

Vascular endothelial growth factor (VEGF) expression in locally advanced prostate cancer: secondary analysis of radiation therapy oncology group (RTOG) 8610

Background

Angiogenesis is a key element in solid-tumor growth, invasion, and metastasis. VEGF is among the most potent angiogenic factor thus far detected. The aim of the present study is to explore the potential of VEGF (also known as VEGF-A) as a prognostic and predictive biomarker among men with locally advanced prostate cancer.

Methods

The analysis was performed using patients enrolled on RTOG 8610, a phase III randomized control trial of radiation therapy alone (Arm 1) versus short-term neoadjuvant and concurrent androgen deprivation and radiation therapy (Arm 2) in men with locally advanced prostate carcinoma. Tissue samples were obtained from the RTOG tissue repository. Hematoxylin and eosin slides were reviewed, and paraffin blocks were immunohistochemically stained for VEGF expression and graded by Intensity score (0–3). Cox or Fine and Gray’s proportional hazards models were used.

Results

Sufficient pathologic material was available from 103 (23%) of the 456 analyzable patients enrolled in the RTOG 8610 study. There were no statistically significant differences in the pre-treatment characteristics between the patient groups with and without VEGF intensity data. Median follow-up for all surviving patients with VEGF intensity data is 12.2 years. Univariate and multivariate analyses demonstrated no statistically significant correlation between the intensity of VEGF expression and overall survival, distant metastasis, local progression, disease-free survival, or biochemical failure. VEGF expression was also not statistically significantly associated with any of the endpoints when analyzed by treatment arm.

Conclusions

This study revealed no statistically significant prognostic or predictive value of VEGF expression for locally advanced prostate cancer. This analysis is among one of the largest sample bases with long-term follow-up in a well-characterized patient population. There is an urgent need to establish multidisciplinary initiatives for coordinating further research in the area of human prostate cancer biomarkers.

Changes in apparent diffusion coefficient and T2 relaxation during radiotherapy for prostate cancer

PURPOSE

To evaluate regional and temporal changes in apparent diffusion coefficient (ADC) and T2 relaxation during radiation therapy (RT) in patients with low and intermediate risk localized prostate cancer.

MATERIALS AND METHODS

Seventeen patients enrolled on a prospective clinical trial where MRI was acquired every 2 weeks throughout eight weeks of image-guided prostate IMRT (78 Gy/39 fractions). ADC and T2 quantification used entire prostate, central gland, benign peripheral zone, and tumor-dense regions-of-interest, and mean values were evaluated for common response trends.

RESULTS

Overall, the RT responses were greater than volunteer measurement repeatability, and week 6 appeared to be an optimum time-point for early detection. RT effects on the entire prostate were best detected using ADC (5-7% by week 2, P < 0.0125), effects on peripheral zone were best detected using T2 (19% reduction at week 6; P = 0.004) and effects on tumors were best detected using ADC (14% elevation at week 6; P = 0.004).

CONCLUSION

ADC and T2 may be candidate biomarkers of early response to RT warranting further investigation against clinical outcomes.

Plasma osteopontin as a biomarker of prostate cancer aggression: relationship to risk category and treatment response

BACKGROUND

High plasma osteopontin (OPN) has been linked to tumour hypoxia, metastasis, and poor prognosis. This study aims to assess whether plasma osteopontin was a biomarker of increasing progression within prostate cancer (PCa) prognostic groups and whether it reflected treatment response to local and systemic therapies.

METHODS

Baseline OPN was determined in men with localised (n=199), locally recurrent (n=9) and castrate-resistant, metastatic PCa (CRPC-MET; n=37). Receiver-operating curves (ROC) were generated to describe the accuracy of OPN for distinguishing between localised risk groups or localised vs metastatic disease. We also measured OPN pre- and posttreatment, following radical prostatectomy, external beam radiotherapy (EBRT), androgen deprivation (AD) or taxane-based chemotherapy.

RESULTS

The CRPC-MET patients had increased baseline values (mean 219; 56-513 ng ml(-1); P<0.0001) compared with the localised, non-metastatic group (mean 72; 12-438 ng ml(-1)). The area under the ROC to differentiate localised vs metastatic disease was improved when OPN was added to prostate-specific antigen (PSA) (0.943-0.969). Osteopontin neither distinguished high-risk PCa from other localised PCa nor correlated with serum PSA at baseline. Osteopontin levels reduced in low-risk patients after radical prostatectomy (P=0.005) and in CRPC-MET patients after chemotherapy (P=0.027), but not after EBRT or AD.

CONCLUSION

Plasma OPN is as good as PSA at predicting treatment response in CRPC-MET patients after chemotherapy. Our data do not support the use of plasma OPN as a biomarker of increasing tumour burden within localised PCa.

Searching for the noninvasive biomarker holy grail: are urine proteomics the answer?

Recently, biobehavioral nursing scientists have focused their attention on the search for biomarkers or biological signatures to identify patients at risk for various health problems and poor disease outcomes. In response to the national impetus for biomarker discovery, the measurement of biological fluids and tissues has become increasingly sophisticated. Urine proteomics, in particular, may hold great promise for biobehavioral focused nursing scientists for examination of symptom-and syndrome-related research questions. Urine proteins are easily accessible secreted proteins that provide direct and indirect windows into bodily functions. Advances in proteomics and biomarker discovery provide new opportunities to conduct research studies with banked and fresh urine to benefit diagnosis, prognosis, and evaluation of outcomes in various disease populations. This article provides a review of proteomics and a rationale for utilizing urine proteomics in biobehavioral research. It addresses as well some of the challenges involved in data collection and sample preparation.

Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Targeted proteomic strategy for clinical biomarker discovery

The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high-throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two-stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.