Proteomic analysis of dunning prostate cancer cell lines with variable metastatic potential using SELDI-TOF.

BACKGROUND: Surface enhanced laser desorption and ionization-time-of-flight (SELDI-TOF) is an evolving proteomic technology for improving biomarker discovery that allows for rapid and sensitive analysis of complex protein mixtures generated from body fluids, cells, and/or tissues. SELDI--based profiling identifies unique, differentially expressed proteins relating to specific cancer-related disease states. We utilized SELDI-TOF following pre-processing with molecular separation and chemical fractionation of cell membrane extracts from three Dunning rat prostate cancer cell lines of varying metastatic potential to search novel proteins that are differentially expressed. METHODS: Dunning rat cell sublines of variable (%) metastatic potential; G (0%), AT-1 (20%), and Mat-Ly-Lu (100%) were cultured in two different laboratories. Cell lysis was performed in a homogenation buffer (320 mM sucrose/50 mM Tris/0.5 mM PSMF) using Dounce homogenation. After centrifugation, the membrane pellet was washed 2x and then solublized in 2% CHAPS/8 M urea. This sample was further processed using positive pressure molecular ultrafiltration at 30 kDa or precipitation with 50% ammonium sulfate. Next, each sample was applied to an IMAC3-Ni ProteinChip (Ciphergen Biosystems, Freemont, CA) and analyzed using Ciphergen's Protein Biology System with protein peak analysis software. RESULTS: SELDI-TOF analysis differentiated the three Dunning rat cell sublines based upon protein concentration normalized profiles between 5,000 and 20,000 Da. The preparations from the three cells lines showed clear differences when the extracts from the metastatic sublines (AT-1 and MLL) were compared to the benign subline (G) for proteins with molecular weights of 9 kDa (decrease), 12 kDa (significant decrease), 14 kDa (decrease), and 17 kDa (significant gain). After pre-processing extracts with ammonium sulfate and molecular ultrafiltration, the molecular profile changes from one subline to the next became more apparent. Our results were reproducible using multiple runs including from Dunning cells cultured in a separate laboratory, and using different lots of SELDI ProteinChips. CONCLUSIONS: The application of SELDI-TOF to a series of Dunning rat prostate cancer cell lines illustrated apparent changes in protein profiles among the three cell lines with known differences in metastatic biologic activity. SELDI-TOF identified four reproducible changes in protein expression in the AT1 and MLL metastatic cell sublines. Three of the expression changes were manifested as decreases, but one protein (17 kDa) was over-expressed in the AT1 and MLL cell lines. Emphasis will be placed on the isolation, purification, and characterization of the 17 kDa over-expressed protein and its potential role in PCa metastasis.

Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium.

OBJECTIVES: We previously presented nomograms combining preoperative serum prostate-specific antigen (PSA), clinical (TNM) stage, and biopsy Gleason score to provide the likelihood of various final pathologic stages at radical retropubic prostatectomy. The data for the original nomograms were collected from men treated between 1982 and 1996. During the past 10 years, the stage at presentation has shifted, with more men presenting with Stage T1c, Gleason score 5 to 6, and serum PSA levels less than 10.0 ng/mL. In this work, we update the "Partin Tables" with a more contemporary cohort of men treated since 1994 and with revised PSA and Gleason categories. METHODS: Multinomial log-linear regression analysis was used to estimate the likelihood of organ-confined disease, extraprostatic extension, seminal vesicle or lymph nodal status from the preoperative PSA stratified as 0 to 2.5, 2.6 to 4.0, 4.1 to 6.0, 6.1 to 10.0, and greater than 10 ng/mL, clinical (AJCC-TNM, 1992) stage (T1c, T2a, T2b, or T2c), and biopsy Gleason score stratified as 2 to 4, 5 to 6, 3 + 4 = 7, 4 + 3 = 7, or 8 to 10 among 5079 men treated with prostatectomy (without neoadjuvant therapy) between 1994 and 2000 at Johns Hopkins Hospital. The average age was 58 years. RESULTS: In this cohort, more than 60% had T1c, more than 75% had Gleason score of 6, more than 70% had PSA greater than 2.5 and less than 10.0 ng/mL, and more than 60% had organ-confined disease. Nomograms of the robust estimated likelihoods and 95% confidence intervals were developed from 1000 bootstrap analyses. The probability of organ-confined disease improved across the groups, and further stratification of the Gleason score and PSA level allowed better differentiation of individual patients. CONCLUSIONS: These updated "Partin Tables" were generated to reflect the trends in presentation and pathologic stage for men newly diagnosed with clinically localized prostate cancer at our institution. Clinicians can use these nomograms to counsel individual patients and help them make important decisions regarding their disease.

Molecular and immunohistochemical staging of men with seminal vesicle invasion and negative pelvic lymph nodes at radical prostatectomy.

BACKGROUND: Patients with seminal vesicle invasion (SVI) at radical retropubic prostatectomy (RRP) have a poor prognosis. Routine microscopic examination of pelvic lymph nodes (LNs) can miss small metastases and, thereby, confuse tumor staging and clinical decision-making. The authors used immunohistochemical and molecular methods to examine archival paraffin-embedded LNs of men who had undergone RRP for clinically localized prostate carcinoma and who had tumors demonstrating SVI and negative LNs at surgery. METHODS: Between June 1982 and June 1997, 2151 consecutive men underwent RRP for clinically localized prostate carcinoma. Of these, 109 (5.1%) tumors had SVI with negative LNs. The actuarial likelihood of having a tumor that was undetectable by testing prostate-specific antigen (PSA) 5 and 10 years after surgery was 45% and 29%, respectively, for men with isolated SVI. Archival LN specimens were available for 102 men who had isolated SVI. Reverse transcription polymerase chain reaction (RT-PCR) was performed for PSA and prostate-specific membrane antigen (PSMA). All specimens were examined concurrently by immunohistochemistry (IHC). RESULTS: Careful reevaluation of pelvic LNs demonstrated metastases in 9 (8.8%) men originally classified as metastasis-free. Reevaluation by hematoxylin and eosin (H&E) staining identified three previously unrecognized cases of LN metastases. IHC identified six cases, three of which were missed by H&E. RT-PCR identified four cases, three of which were not revealed by other methods. CONCLUSIONS: The poor prognosis of patients with SVI does not seem due to occult LN metastases. The low yield of unsuspected foci of prostate carcinoma in the LNs of men with SVI and negative LNs by routine staging does not justify IHC or molecular examination to find occult carcinoma.

Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy.

OBJECTIVES: To use pathologic, morphometric, DNA ploidy, and clinical data to develop and test a genetically engineered neural network (GENN) for the prediction of biochemical (prostate-specific antigen [PSA]) progression after radical prostatectomy in a select group of men with clinically localized prostate cancer. METHODS: Two hundred fourteen men who underwent anatomic radical retropubic prostatectomy for clinically localized prostate cancer were selected on the basis of adequate follow-up, pathologic criteria indicating an intermediate risk of progression, and availability of archival tissue. The median age was 58.9 years (range 40 to 87). Men with Gleason score 5 to 7 and clinical Stage T1b-T2c tumors were included. Follow-up was a median of 9.5 years. Three GENNs were developed using pathologic findings (Gleason score, extraprostatic extension, surgical margin status), age, quantitative nuclear grade (QNG), and DNA ploidy. These networks were developed using three randomly selected training (n = 136) and testing (n = 35) sets. Different variable subsets were compared for the ability to maximize prediction of progression. Both standard logistic regression and Cox regression analyses were used concurrently to calculate progression risk. RESULTS: Biochemical (PSA) progression occurred in 84 men (40%), with a median time to progression of 48 months (range 1 to 168). GENN models were trained using inputs consisting of (a) pathologic features and patient age; (b) QNG and DNA ploidy; and (c) all variables combined. These GENN models achieved an average accuracy of 74.4%, 63.1 %, and 73.5%, respectively, for the prediction of progression in the training sets. In the testing sets, the three GENN models had an accuracy of 74.3%, 80.0%, and 78.1%, respectively. CONCLUSIONS: The GENN models developed show promise in predicting progression in select groups of men after radical prostatectomy. Neural networks using QNG and DNA ploidy as input variables performed as well as networks using Gleason score and staging information. All GENN models were superior to logistic regression modeling and to Cox regression analysis in prediction of PSA progression. The development of models using improved input variables and imaging systems in larger, well-characterized patient groups with long-term follow-up is ongoing.