Karim Saba

Prognostic Role of Preoperative Serum Lipid Levels in Patients Undergoing Radical Prostatectomy for Clinically Localized Prostate Cancer

BACKGROUND. The prognostic role of preoperative serum lipid levels in patients undergoing radical prostatectomy (RP) for clinically localized prostate cancer (PCa) is unclear. The aim of the present study was to investigate preoperative serum lipid levels in patients with clinically localized PCa undergoing RP and their association with clinicopathological features and oncological outcome.

External Evaluation of a Novel Prostate Cancer Risk Calculator (ProstateCheck) Based on Data from the Swiss Arm of the ERSPC

PURPOSE We externally validated a novel prostate cancer risk calculator based on data from the Swiss arm of the ERSPC and assessed whether the risk calculator (ProstateCheck) is superior to the PCPT-RC and SWOP-RC in an independent Swiss cohort. MATERIALS AND METHODS Data from all men who underwent prostate biopsy at an academic tertiary care center between 2004 and 2012 were retrospectively analyzed. The probability of having any prostate cancer or high grade prostate cancer (Gleason score 7 or greater) on prostate biopsy was calculated using the ProstateCheck. Risk calculator performance was assessed using calibration and discrimination, and additionally compared with the PCPT-RC and SWOP-RC by decision curve analyses. RESULTS Of 1,615 men 401 (25%) were diagnosed with any prostate cancer and 196 (12%) with high grade prostate cancer. Our analyses of the ProstateCheck-RC revealed good calibration in the low risk range (0 to 0.4) and moderate overestimation in the higher risk range (0.4 to 1) for any and high grade prostate cancer. The AUC for the discrimination of any prostate cancer and high grade prostate cancer was 0.69 and 0.72, respectively, which was slightly but significantly higher compared to the PCPT-RC (0.66 and 0.69, respectively) and SWOP-RC (0.64 and 0.70, respectively). Decision analysis, taking into account the harms of transrectal ultrasound measurement of prostate volume, showed little benefit for ProstateCheck-RC, with properties inferior to those of the PCPT-RC and SWOP-RC. CONCLUSIONS Our independent external evaluation revealed moderate performance of the ProstateCheck-RC. Its clinical benefit is limited, and inferior to that of the PCPT-RC and SWOP-RC.

Prevalence and causes of abnormal PSA recovery

Abstract Background: Prostate-specific antigen (PSA) test is of paramount importance as a diagnostic tool for the detection and monitoring of patients with prostate cancer. In the presence of interfering factors such as heterophilic antibodies or anti-PSA antibodies the PSA test can yield significantly falsified results. The prevalence of these factors is unknown. Methods: We determined the recovery of PSA concentrations diluting patient samples with a standard serum of known PSA concentration. Based on the frequency distribution of recoveries in a pre-study on 268 samples, samples with recoveries <80% or >120% were defined as suspect, re-tested and further characterized to identify the cause of interference. Results: A total of 1158 consecutive serum samples were analyzed. Four samples (0.3%) showed reproducibly disturbed recoveries of 10%, 68%, 166% and 4441%. In three samples heterophilic antibodies were identified as the probable cause, in the fourth anti-PSA-autoantibodies. The very low recovery caused by the latter interference was confirmed in serum, as well as heparin- and EDTA plasma of blood samples obtained 6 months later. Analysis by eight different immunoassays showed recoveries ranging between <10% and 80%. In a follow-up study of 212 random plasma samples we found seven samples with autoantibodies against PSA which however did not show any disturbed PSA recovery. Conclusions: About 0.3% of PSA determinations by the electrochemiluminescence assay (ECLIA) of Roche diagnostics are disturbed by heterophilic or anti-PSA autoantibodies. Although they are rare, these interferences can cause relevant misinterpretations of a PSA test result.

A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients

Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies.

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Application of pressure cycling technology and Sequential Windowed Acquisition of all THeoretical mass spectrometry allows quantifying the degree of intra-tumor heterogeneity of protein expression in prostate tumors. The data show that protein intra-tumor heterogeneity, if not characterized, may distort protein biomarker suitability in tumor tissues. It remains unclear to what extent tumor heterogeneity impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of prostate tissues using pressure cycling technology and Sequential Windowed Acquisition of all THeoretical fragment ion mass spectrometry. We quantified 6,873 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied depending on proteins and tissue types. Benign tissues exhibited more complex ITH patterns than malignant tissues. Spatial variability of 10 prostate biomarkers was validated by immunohistochemistry in an independent cohort (n = 83) using tissue microarrays. Prostate-specific antigen was preferentially variable in benign prostatic hyperplasia, whereas growth/differentiation factor 15 substantially varied in prostate adenocarcinomas. Furthermore, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery: it suggests that recent technological progress should be exploited to quantify and account for spatial proteome variation to complement biomarker identification and utilization.

391 How accurate is the PSA test? A prevalence study of disturbed PSA values in a tertiary referral hospital

INTRODUCTION AND OBJECTIVES: Prostate cancers (PC) associated with overt metastases at diagnosis (stage M1) or that progress to this stage are lethal. Although pathologic features of most M1 cases are indistinguishable from high-grade, non-metastatic PC (stage M0), profound disparities in outcome are apparent. More than 70% of M1 patients progress to castration-resistant prostate cancer (CRPC) and death within 5 years compared to 100% survival of highgrade M0 cases. Improved tools that detect features of lethal high-grade disease in prostate needle biopsies (PNBXs) will enable early identification of patients at greatest risk of metastatic progression. We have applied novel quantitative imaging (QI) technology and validated machine learning software tools to extract histomorphometric features from PNBX specimens that distinguish M1 and M0 cancers. This 00computer vision00 approach can be applied to routine diagnostic biopsies with the goal of detecting lethal PC as early as possible and improving patient stratification for entry into clinical trials. METHODS: We created a diverse, annotated biorepository with detailed clinical information corresponding to PNBX tissue from 427 patients with high-grade M0 or M1 PC. Retrospective analysis was used to assemble cohorts matched for age, race, and Gleason grade that were either M0 (n1⁄48) or M1 (n1⁄410). Archival PNBX slides stained with hematoxylin and eosin were quality checked and digitally scanned at 40X magnification. Fifteen image tiles were collected from each slide and two classifiers were constructed to assign each image tile to the M0 or M1 groups. QI analysis was used to investigate a set of features, including Fractal Dimensions (FD), Lacunarity (LA) [1st classifier], and Nuclear Features (NF) [2nd classifier] in M0 and M1 PC. RESULTS: Approximately 332 image tiles from the M0-RRP (n1⁄4122) and M1-ADT (n1⁄4210) cases were converted to nuclear masks. Using LA and FD tests, a trained classifier achieved 70% accuracy in distinguishing images from M1 and M0 cases, in which LA features were significantly different. We also identified 63 NFs differentially expressed in the M0 and M1 cases after random selection of 30% of nuclei and calculation of 1,500 NFs using partial least squares method. With this method, a classifier trained with the selected NFs to characterize nuclear intensity, texture, size, and shape achieved an accuracy of 75.5% in M0-RRP cases and 74.8% in M1-ADT cases. CONCLUSIONS: This innovative cohort of patients at the extremes of high-grade PC provides the opportunity to generate and test the performance of novel QI algorithms.