Characterisation of the main PSA glycoforms in aggressive prostate cancer.

Serum levels of prostate specific antigen (PSA) are commonly used for prostate cancer (PCa) detection. However, their lack of specificity to distinguish benign prostate pathologies from PCa, or indolent from aggressive PCa have prompted the study of new non-invasive PCa biomarkers. Aberrant glycosylation is involved in neoplastic progression and specific changes in PSA glycosylation pattern, as the reduction in the percentage of α2,6-sialic acid (SA) are associated with PCa aggressiveness. In this study, we have characterised the main sialylated PSA glycoforms from blood serum of aggressive PCa patients and have compared with those of standard PSA from healthy individuals' seminal plasma. PSA was immunoprecipitated and α2,6-SA were separated from α2,3-SA glycoforms using SNA affinity chromatography. PSA N-glycans were released, labelled and analysed by hydrophilic interaction liquid chromatography combined with exoglycosidase digestions. The results showed that blood serum PSA sialylated glycoforms containing GalNAc residues were largely increased in aggressive PCa patients, whereas the disialylated core fucosylated biantennary structures with α2,6-SA, which are the major PSA glycoforms in standard PSA from healthy individuals, were markedly reduced in aggressive PCa. The identification of these main PSA glycoforms altered in aggressive PCa opens the way to design specific strategies to target them, which will be useful to improve PCa risk stratification.

PSA Kinetics as Prognostic Markers of Overall Survival in Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Abiraterone Acetate.

BACKGROUND: Abiraterone acetate (AA) is widely used in the treatment of patients with metastatic castration-resistant prostate cancer (mCRPC). However, a significant percentage of patients will still progress, highlighting the need to identify patients more likely to benefit from AA. Parameters linked to prostate-specific antigen (PSA) kinetics are promising prognostic markers. We have examined clinical and PSA-related factors potentially associated with overall survival (OS) in patients treated with AA. METHODS: Between 2011 and 2014, 104 patients with mCRPC treated with AA after progression to docetaxel at centers of the Catalan Institute of Oncology were included in this retrospective study. Patients were assessed monthly. Baseline characteristics and variables related to PSA kinetics were included in univariate and multivariate analyses of OS. RESULTS: Median OS was 16.4 months (range 12.4-20.6) for all patients. The univariate analysis identified the following baseline characteristics as significantly associated with OS: ECOG PS, location of metastases, time between starting androgen deprivation therapy and starting AA, time between stopping docetaxel treatment and starting AA, neutrophil-lymphocyte ratio (NLR), alkaline phosphatase levels, and PSA levels. Factors related to PSA kinetics associated with longer OS were PSA response >50%, early PSA response (>30% decline at four weeks), PSA decline >50% at week 12, PSA nadir <2.4ng/mL, time to PSA nadir >140 days, the combination of PSA nadir and time to PSA nadir, and low end-of-treatment PSA levels. The multivariate analysis identified ECOG PS (HR 37.46; p<0.001), NLR (HR 3.7; p<0.001), early PSA response (HR 1.22; p=0.002), and time to PSA nadir (HR 0.39; p=0.002) as independent prognostic markers. CONCLUSION: Our results indicate an association between PSA kinetics, especially early PSA response, and outcome to AA after progression to docetaxel. Taken together with other factors, lack of an early PSA response could identify patients who are unlikely to benefit from AA and who could be closely monitored with a view to offering alternative therapies.

Erratum: Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes: Erratum.

[This corrects the article DOI: 10.7150/thno.15226.].

Comparative Study of Blood-Based Biomarkers, α2,3-Sialic Acid PSA and PHI, for High-Risk Prostate Cancer Detection.

Prostate Specific Antigen (PSA) is the most commonly used serum marker for prostate cancer (PCa), although it is not specific and sensitive enough to allow the differential diagnosis of the more aggressive tumors. For that, new diagnostic methods are being developed, such as PCA-3, PSA isoforms that have resulted in the 4K score or the Prostate Health Index (PHI), and PSA glycoforms. In the present study, we have compared the PHI with our recently developed PSA glycoform assay, based on the determination of the α2,3-sialic acid percentage of serum PSA (% α2,3-SA), in a cohort of 79 patients, which include 50 PCa of different grades and 29 benign prostate hyperplasia (BPH) patients. The % α2,3-SA could distinguish high-risk PCa patients from the rest of patients better than the PHI (area under the curve (AUC) of 0.971 vs. 0.840), although the PHI correlated better with the Gleason score than the % α2,3-SA. The combination of both markers increased the AUC up to 0.985 resulting in 100% sensitivity and 94.7% specificity to differentiate high-risk PCa from the other low and intermediate-risk PCa and BPH patients. These results suggest that both serum markers complement each other and offer an improved diagnostic tool to identify high-risk PCa, which is an important requirement for guiding treatment decisions.

Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes.

New markers based on PSA isoforms have recently been developed to improve prostate cancer (PCa) diagnosis. However, novel approaches are still required to differentiate aggressive from non-aggressive PCa to improve decision making for patients. PSA glycoforms have been shown to be differentially expressed in PCa. In particular, changes in the extent of core fucosylation and sialylation of PSA N-glycans in PCa patients compared to healthy controls or BPH patients have been reported. The objective of this study was to determine these specific glycan structures in serum PSA to analyze their potential value as markers for discriminating between BPH and PCa of different aggressiveness. In the present work, we have established two methodologies to analyze the core fucosylation and the sialic acid linkage of PSA N-glycans in serum samples from BPH (29) and PCa (44) patients with different degrees of aggressiveness. We detected a significant decrease in the core fucose and an increase in the α2,3-sialic acid percentage of PSA in high-risk PCa that differentiated BPH and low-risk PCa from high-risk PCa patients. In particular, a cut-off value of 0.86 of the PSA core fucose ratio, could distinguish high-risk PCa patients from BPH with 90% sensitivity and 95% specificity, with an AUC of 0.94. In the case of the α2,3-sialic acid percentage of PSA, the cut-off value of 30% discriminated between high-risk PCa and the group of BPH, low-, and intermediate-risk PCa with a sensitivity and specificity of 85.7% and 95.5%, respectively, with an AUC of 0.97. The latter marker exhibited high performance in differentiating between aggressive and non-aggressive PCa and has the potential for translational application in the clinic.

A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images.

Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91 ± 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67 ± 0.02 s.

A spline-based non-linear diffeomorphism for multimodal prostate registration.

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980±0.004, average 95% Hausdorff distance of 1.63±0.48 mm and mean target registration and target localization errors of 1.60±1.17 mm and 0.15±0.12 mm respectively.

Usefulness of prebiopsy multifunctional and morphologic MRI combined with free-to-total prostate-specific antigen ratio in the detection of prostate cancer.

OBJECTIVE: The purpose of the study was to assess the predictive value for prostate cancer of MRI using morphologic (T2-weighted imaging [T2WI]) and functional (MR spectroscopy [MRS], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI) sequences and the free-to-total prostate-specific antigen (PSA) ratio, alone and combined. MATERIALS AND METHODS: This retrospective study included 70 patients (PSA level, > 4 ng/mL; free-to-total PSA ratio, < 20%) who underwent endorectal 1.5-T MRI before biopsy. We graded the likelihood of cancer on a 5-point scale. Imaging data were compared with histologic results on biopsy or prostatectomy. Accuracies were estimated from the area under receiver operating characteristic using the hemiprostate as the unit of analysis. A p value less than 0.05 denoted statistical significance. RESULTS: The model combining all variables was more accurate than each variable alone (95.2% vs 73.5% for T2WI, 76.0% for MRS, 81.8% for DWI, 75.6% for DCE-MRI, and 78.8% for free-to-total PSA ratio). The complete model had accuracy similar to that of combining two imaging variables with free-to-total PSA ratio, especially free-to-total PSA ratio, T2WI, and DWI (94.0%); and free-to-total PSA ratio, DWI, and MRS (93.8%); with negative predictive values of 91.0% and 89.5%, respectively. The best models combining two imaging variables (MRS and DWI, 85.8%; T2WI and DWI, 84.8%) had accuracy that was similar to that of the combination of all imaging variables (87.3%) and higher than that of the best individual imaging variable (DWI, 81.8%), but lower than that of the complete model. CONCLUSION: The combination of at least one functional technique with free-to-total PSA ratio is more accurate than combining only imaging variables in cancer detection. The use of more than two imaging variables does not increase the detection rate. Functional MRI has the potential to help avoid a large number of negative biopsies.

Glycan characterization of PSA 2-DE subforms from serum and seminal plasma.

Prostate-specific antigen (PSA) two-dimensional electrophoresis (2-DE) subforms (F1-F5) have been described to be altered in prostate cancer (PCa) compared to benign prostatic hyperplasia (BPH). To understand their molecular differences, characterization of these subforms from PCa serum and seminal plasma, namely, at the glycan level, was performed. PSA 2-DE subforms from two serum PCa samples and seminal plasma were analyzed by N-glycan sequencing using high-performance liquid chromatography (HPLC) combined with exoglycosidase array digestions and by mass spectrometry. F1, F2, and F3 subforms showed the same N-glycan pattern, which contained higher levels of sialic acid than the F4 subform, whereas the F5 subform was unglycosylated. When comparing PSA subforms from PCa with seminal plasma, a decrease in sialylation was observed. Furthermore, the analysis of F3, the more abundant PSA subform, showed a higher proportion of alpha 2-3 sialic acid and a decrease in core fucosylated glycans in the PCa sample. These N-glycan changes in PCa PSA subforms highlight the importance of glycosylation as an indicator of PCa disease.

Differential percentage of serum prostate-specific antigen subforms suggests a new way to improve prostate cancer diagnosis.

BACKGROUND: Prostate-specific antigen (PSA) is the tumor marker currently used for prostate cancer (PCa) screening and diagnosis. However, its use is controversial as serum PSA levels are also increased in other non-malignant prostatic diseases such as benign prostatic hyperplasia (BPH). PSA sialic acid content is altered in tumor situation and modifies PSA's isoelectric point (pI). Our goal has been to evaluate serum PSA subforms from PCa and BPH patients by two-dimensional electrophoresis (2-DE) and to investigate whether they could be used to improve PCa diagnosis. METHODS: PSA from 20 PCa and 20 BPH patients' sera was subjected to a four-step method to obtain serum PSA 2-DE subforms from free PSA (fPSA) plus PSA released from the complex with alpha-1-antichymotrypsin. Relative percentages of PSA spots were quantified and subjected to statistical analysis. RESULTS: Five PSA subforms (F1, F2, F3, F4, and F5) of different pI were obtained. Relative percentages of F3 (%F3) and F4 (%F4) were different between PCa and BPH groups. %F3 decreased in cancers and this decrease correlated with the cancer stage, while F4 behaved oppositely. These observations were also found when only focusing on the patients within the low total PSA (tPSA) range 2-20 ng/ml. CONCLUSIONS: %F3 showed a tendency of higher sensitivity and specificity than the currently used tPSA and %fPSA tests. Therefore, %F3 measurement should be investigated in a larger cohort of patients to study whether it could be introduced to improve PCa diagnosis.

Peripheral zone prostate cancer in patients with elevated PSA levels and low free-to-total PSA ratio: detection with MR imaging and MR spectroscopy.

PURPOSE: To retrospectively assess the value of endorectal magnetic resonance (MR) imaging and MR spectroscopy combined with the free-to-total prostate-specific antigen (PSA) ratio for detecting prostate cancer in men with elevated PSA levels. MATERIALS AND METHODS: The institutional review board approved the study, and all patients provided informed written consent. Endorectal MR imaging and MR spectroscopy were performed in 54 patients with PSA levels greater than 3 ng/mL but less than 15 ng/mL and free-to-total PSA ratio of less than 20%, followed by sextant biopsy in the peripheral zone. For each patient, MR imaging and MR spectroscopic findings, PSA level, and free-to-total PSA ratio were analyzed and compared with biopsy results and/or histopathologic tumor maps with regard to a sextant-modified distribution. The likelihood of cancer in each sextant according to MR and MR spectroscopic findings was graded independently on a scale of 1 (benign) to 5 (malignant). Detection accuracy and a multivariate logistic regression analysis were used to determine the most accurate combination of imaging, and clinical tests were used to detect prostate cancer according to the area under the receiver operating characteristic curve (AUC). RESULTS: The model incorporating MR imaging, MR spectroscopy, and free-to-total PSA ratio (AUC = 97.5%) was significantly more accurate in predicting prostate cancer than models using MR imaging alone (AUC = 85.1%; P = .007), MR spectroscopy alone (AUC = 87.2%; P = .041), or MR imaging and free-to-total PSA ratio combined (AUC = 90.8%; P = .038). CONCLUSION: MR and MR spectroscopy combined with free-to-total PSA ratio improves the predictive value for prostate cancer detection.