Team:Worldshaper-Shanghai/Proof Of Concept
Proof of Concept
Introduction
Studies shows that the use of PSA testing remains controversial, approximately 75% of men who have undergone prostate biopsies have negative results. Moreover, since prostate sampling is random and localized, the initial prostate biopsy also has a false-negative rate of 20% to 30%. Because these uncertainties existence, many patients with high PSA level may suffer repeated biopsy procedures (up to 4 times in bad cases )[1-3]. These situations not only have a negative clinical impact on patients, but also increase medical costs.
We believe that higher accuracy must be one of the advantages of the new prostate cancer diagnosis method, and it can limit the number of unnecessary biopsies and reduce the healthcare costs of individuals or national healthcare system.
Our research analyzes the accuracy and reliability of the project based on current achievements. This set of analysis method will also be used in the further analysis of the improved products in the future:
1. Standard curve and sensitivity analysis
In order to determine the effective range of PCA3 copy number in urine, we need to make a standard curve of PCA3 detection in synthetic urine. By mixing PCA3 molecules with different copy numbers into the synthetic urine, the fluorescence intensity corresponding to different copies of PCA3 can be measured, and the fitting curve reflecting numerical law can be obtained.
Figure 1. The fluorescence values produced by different PCA3 mRNA copies
As represented in the plate (Figure 1), the red color of the solution increases generally from the left side to the right side, just as the copies of PCA3 mRNA also increases from 0 to 2000. From bare eyes, it seems to reveal that the red color and the RNA copies have a positive correlation. By analyzing the data gained from BioTek Multi-Mode Microplate Reader, a more elaborate interpretation can be obtained. The result of fluorescence from different copies of PCA3 revealed a one-to-one correlation between copies of RNA and the value of fluorescence. Along with the increment of PCA3 copies is the increment of fluorescence, accompanied by a rising gradient.
Figure 2 . The parametric regression model between PCA3 copies and fluorescence values
By using the 4-parametric regression model to fit a curve of fluorescence and PCA3 copies, we obtained the above graph(Figure 2). The curve possesses an “S” shape. Between a copy number of 0 to 50, a relatively flat curve is observed, where the value of fluorescence increases slowly along with the increase of PCA3 copies. Then, after entering the phrase between 50 to 1000 copies, the fluorescence values increases quite sharply until they gradually show the tendency of leveling at 1000 to 2000 copies. The fluorescence value increased linearly in the range of 200-800 copies, indicating that the amount of PCA3 mRNA in this range is the effective identification range of the product.
2 . ROC curve and reliability analysis
The reliability of the detection includes sensitivity and specificity. Thus, we use the ROC curve (receiver operating characteristic curve) to prove the reliability, which can help researchers to analyze the clinical accuracy of diagnostic tests simply and intuitively.
We selected the value of 300 copies in the linear rising region of the standard curve as the amount of PCA3 mRNA for the reliability test. Then 300 copies of PCA3 was randomly added to 96 urine samples, the final reaction solution was added to 96 well plates for fluorescence determination ( Figure 3).
Figure 3. The detection results of samples
Plotting data on the graph and linking them can obtain us an empirical ROC curve to analyze the accuracy of clinical trials. ROC curves possess an x-axis of true positive rate or sensitivity, and the y-axis represents false positive rate or specificity. Sensitivity and specificity have an inverse correlation, and a trade-off must take place for each of these points on the graph. The values of points indicate the percentage of true and false positivity, and therefore evaluates the reliability of our method. The AUC (area under the curve) value can be used to describe the effectiveness of our diagnostic method. Using the R program, the AUC value is calculated to be 82.4%, which means that our diagnosis method is quite effective (Figure 4).
Figure 4. The ROC Curve for product evaluating
References
[1] Aubry W , Lieberthal R , Willis A , et al. Budget Impact Model: Epigenetic Assay Can Help Avoid Unnecessary Repeated Prostate Biopsies and Reduce Healthcare Spending[J]. American Health and Drug Benefits, 2013, 6(1):15-24.
[2] Stewart G, Van Neste L, Delvenne P, et al. Clinical utility of a multiplexed epigenetic gene assay to detect cancer in histopathologically negative prostate biopsies: results of the multicenter MATLOC study. J Urol. (2012), doi: 10.1016/j.juro.2012.08.219.
[3] Resnick MJ, Lee DJ, Magerfleisch L, et al. Repeat prostate biopsy and the incremental risk of clinically insignificant prostate cancer. Urology. 2011; 77: 548–552
Worldshaper-Shanghai 2020
New non-invasive technique for early stage prostate cancer diagnosis