Screening for aggressive prostate cancer: A single-center experience using the 4Kscore and multiparametric MRI for the detection of Gleason 7 or higher prostate cancer.

84 Background: While non-invasive biomarkers and multi-parametric MRI (mpMRI) are routinely used for prostate cancer detection, few studies have assessed their performance together. We evaluated the performance of mpMRI and the 4Kscore for the detection of significant prostate cancer. Methods: We identified a consecutive series of men who underwent an mpMRI and 4Kscore for evaluation of prostate cancer at the University of Miami. We selected those who underwent a biopsy of the prostate. The primary outcome was the presence of Gleason 7 or higher cancer on biopsy. The 4Kscore was modeled as a continuous variable, but also categorized into low ( < 7.5%), intermediate (7.5-20%), and high ( > 20) risk scores. The mpMRI was categorized as being either negative or positive for a suspicion of cancer. We used logistic regression and Decision Curve Analysis to report the discrimination and clinical utility of using mpMRI and the 4Kscore for prostate cancer detection. Finally, we modeled the probability of harboring a Gleason 7 or higher prostate cancer based on various categories of the 4Kscore and mpMRI. Results: Among 235 men who underwent a 4Kscore and mpMRI, only 112 (52%) were referred for a biopsy, allowing a significant proportion of men to avoid a biopsy. Among those who had a biopsy, the AUC for using the 4Kscore and mpMRI together [0.81(0.72-0.90)] was superior to using the 4Kscore [0.71(0.61-0.81);p = 0.004] and mpMRI [0.74(0.65-0.83);p = 0.02] alone. Similarly, decision curve analysis revealed the highest net benefit for using both tests together, compared to either test alone. Finally, we found that having a positive mpMRI was a predictor of aggressive cancer in the higher two 4Kscores, but not in the lowest category, suggesting that men with a low 4Kscore may not benefit from getting an mpMRI, most likely due to the low likelihood of cancer and having a positive mpMRI. Conclusions: The 4Kscore and mpMRI provides independent, but complementary, information to enhance the prediction of aggressive prostate cancer. Prospective trials are required to confirm these findings.

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1415: A Novel Serological Marker, Prostate Secretory Protein-94 (PSP94), Enhances Prostate Cancer Detection and Identifies Patients with Aggressive Prostate Cancer Better than PSA or free: Total PSA

The Journal of Urology ◽

10.1016/s0022-5347(18)35549-6 ◽

2005 ◽

Vol 173 (4S) ◽

pp. 383-384

Author(s):

Robert K. Nam ◽

Jonathan Reeves ◽

Helene Delude ◽

John Trachtenberg ◽

Michael A.S. Jewett ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Detection ◽

Secretory Protein ◽

Serological Marker ◽

Aggressive Prostate Cancer ◽

Prostate Cancer Detection ◽

Total Psa ◽

Better Than

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The Association of the Long Prostate Cancer Expressed PDE4D Transcripts to Poor Patient Outcome Depends on the Tumour’s TMPRSS2-ERG Fusion Status

Prostate Cancer ◽

10.1155/2019/8107807 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Dianne van Strijp ◽

Christiane de Witz ◽

Birthe Heitkötter ◽

Sebastian Huss ◽

Martin Bögemann ◽

...

Keyword(s):

Prostate Cancer ◽

Patient Outcome ◽

Logistic Regression ◽

Risk Model ◽

Curve Analysis ◽

Net Benefit ◽

Decision Curve Analysis ◽

Kaplan Meier ◽

Biological Outcomes ◽

Prognostic Power

Objectives. To investigate the added value of assessing transcripts for the long cAMP phosphodiesterase-4D (PDE4D) isoforms, PDE4D5 and PDE4D9, regarding the prognostic power of the ‘CAPRA & PDE4D7’ combination risk model to predict longitudinal postsurgical biological outcomes in prostate cancer.Patients and Methods. RNA was extracted from both biopsy punches of resected tumours (606 patients; RP cohort) and diagnostic needle biopsies (168 patients; DB cohort). RT-qPCR was performed in order to determine PDE4D5, PDE4D7, and PDE4D9 transcript scores in both study cohorts. By RNA sequencing, we determined the TMPRSS2-ERG fusion status of each tumour sample in the RP cohort. Kaplan-Meier survival analyses were then applied to correlate the PDE4D5, PDE4D7 and PDE4D9 scores with postsurgical patient outcomes. Logistic regression was then used to combine the clinical CAPRA score with PDE4D5, PDE4D7, and PDE4D9 scores in order to build a ‘CAPRA & PDE4D5/7/9’ regression model. ROC and decision curve analysis was used to estimate the net benefit of the ‘CAPRA & PDE4D5/7/9’ risk model.Results. Kaplan-Meier survival analysis, on the RP cohort, revealed a significant association of the PDE4D7 score with postsurgical biochemical recurrence (BCR) in the presence of the TMPRSS2-ERG gene rearrangement (logrank p<0.0001), compared to the absence of this gene fusion event (logrank p=0.08). In contrast, the PDE4D5 score was only significantly associated with BCR in TMPRSS2-ERG fusion negative tumours (logrank p<0.0001 vs. logrank p=0.4 for TMPRSS2-ERG+ tumours). This was similar for the PDE4D9 score although less pronounced compared to that of the PDE4D5 score (TMPRSS2ERG- logrank p<0.0001 vs. TMPRSS2ERG+ logrank p<0.005). In order to predict BCR after primary treatment, we undertook ROC analysis of the logistic regression combination model of the CAPRA score with the PDE4D5, PDE4D7, and PDE4D9 scores. For the DB cohort, this demonstrated significant differences in the AUC between the CAPRA and the PDE4D5/7/9 regression model vs. the CAPRA and PDE4D7 risk model (AUC 0.87 vs. 0.82; p=0.049) vs. the CAPRA score alone (AUC 0.87 vs. 0.77; p=0.005). The CAPRA and PDE4D5/7/9 risk model stratified 19.2% patients of the DB cohort to either ‘no risk of biochemical relapse’ (NPV 100%) or the ‘start of any secondary treatment (NPV 100%)’, over a follow-up period of up to 15 years. Decision curve analysis presented a clear, net benefit for the use of the novel CAPRA & PDE4D5/7/9 risk model compared to the clinical CAPRA score alone or the CAPRA and PDE4D7 model across all decision thresholds.Conclusion. Association of the long PDE4D5, PDE4D7, and PDE4D9 transcript scores to prostate cancer patient outcome, after primary intervention, varies in opposite directions depending on the TMPRSS2-ERG genomic fusion background of the tumour. Adding transcript scores for the long PDE4D isoforms, PDE4D5 and PDE4D9, to our previously presented combination risk model of the combined ‘CAPRA & PDE4D7’ score, in order to generate the CAPRA and PDE4D5/7/9 score, significantly improves the prognostic power of the model in predicting postsurgical biological outcomes in prostate cancer patients.

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Aggressive Prostate Cancer Detection with an 8-Protein Biomarker Panel

ECS Meeting Abstracts ◽

10.1149/ma2019-01/44/2114 ◽

2019 ◽

Keyword(s):

Prostate Cancer ◽

Cancer Detection ◽

Protein Biomarker ◽

Aggressive Prostate Cancer ◽

Prostate Cancer Detection ◽

Biomarker Panel

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A multivariate index analyte (MIA) assay for differentiating aggressive from nonaggressive prostate cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.e588 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. e588-e588

Author(s):

Douglas Campbell ◽

Vicki Velonas ◽

Julie T Soon ◽

Sandra Wissmueller ◽

David Gillatt ◽

...

Keyword(s):

Prostate Cancer ◽

Gleason Score ◽

Cancer Detection ◽

The Other ◽

P Value ◽

Serum Samples ◽

Protein Marker ◽

Aggressive Prostate Cancer ◽

Prostate Cancer Detection ◽

Aggressive Cancer

e588 Background: Biomarkers that can assist clinicians and patients to proceed when PSA and /or DRE are equivocal. Such biomarkers should establish both sensitivity and specificity for prostate cancer detection in order to improve go-forward decisions to perform prostate biopsy. Following the successful use of a three-protein marker panel to increase the specificity of prostate cancer detection1 we have now used the same technology to examine whether an MIA assay can assist in differentiating aggressive from non-aggressive cancer in prostate cancer patients. Methods: Samples from patients with either aggressive prostate cancer or non- aggressive prostate cancer were obtained from two sources. The cohort criteria comprised of serum samples where blood was drawn from patients with adenocarcinoma and a PSA greater than or equal to 2ng/mL. All men were Caucasian with the exception of 3 who were African American. Non-aggressive prostate cancer was defined as having a Gleason score of 6 (n = 35) and aggressive prostate cancer was characterized as Gleason score 7 and above (n = 69). Biomarker levels were determined using a plate based ELISA for GPC-12 and a bead-based MIA assay for the other markers. Results: By using biostatistical analysis (Simplicity Bio, Switzerland) two models were identified that were able to differentiate between aggressive and non-aggressive prostate cancer. One consisted of a combination of 5 analytes and the other used 6 analytes. Model 1 containing PSA and GPC-1 plus 4 analytes produced a combined sensitivity of 81% and specificity of 78% (AUC 0.81). The second model comprising of GPC-1 with an additional 4 analytes achieved a sensitivity of 72% with a specificity of 76% (AUC 0.76). Both models had a p value of less than 0.05. By itself PSA was a poor predictor of prostate cancer with a sensitivity of 58% and specificity of 43% (AUC 0.55). Conclusions: The analytes identified by the two statistical models demonstrate potential utility for using the combined markers as a new means of differentiating aggressive prostate cancer from non-aggressive cancer. An additional study to further validate these models is currently being constructed.

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