Aggressive Prostate Cancer Detection with an 8-Protein Biomarker Panel

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 84-84
Author(s):  
Vivek Venkatramani ◽  
Bruno Nahar ◽  
Tulay Koru-Sengul ◽  
Nachiketh Soodana-Prakash ◽  
Mark L. Gonzalgo ◽  
...  

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.


2017 ◽  
Vol 35 (6_suppl) ◽  
pp. e588-e588
Author(s):  
Douglas Campbell ◽  
Vicki Velonas ◽  
Julie T Soon ◽  
Sandra Wissmueller ◽  
David Gillatt ◽  
...  

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.


2012 ◽  
Vol 111 (7) ◽  
pp. 1031-1036 ◽  
Author(s):  
Juan Morote ◽  
Jordi Ropero ◽  
Jacques Planas ◽  
Juan M. Bastarós ◽  
Gueisy Delgado ◽  
...  

2006 ◽  
Vol 175 (4S) ◽  
pp. 487-487
Author(s):  
Stephen J. Freedland ◽  
Elizabeth A. Platz ◽  
Joseph C. Presti ◽  
William J. Aronson ◽  
Christopher L. Amling ◽  
...  

2006 ◽  
Vol 175 (4S) ◽  
pp. 476-477
Author(s):  
Freddie C. Hamdy ◽  
Joanne Howson ◽  
Athene Lane ◽  
Jenny L. Donovan ◽  
David E. Neal

2007 ◽  
Vol 177 (4S) ◽  
pp. 651-651
Author(s):  
Nicolas B. Delongchamps ◽  
Vishal Chandan ◽  
Richard Jones ◽  
Gregory Threatte ◽  
Mary Jumbelic ◽  
...  

2006 ◽  
Vol 175 (4S) ◽  
pp. 155-155
Author(s):  
Robert L. Grubb ◽  
David L. Levin ◽  
Paul F. Pinsky ◽  
Jerome Mabie ◽  
Thomas L. Riley ◽  
...  

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