Abstract 4352: Prediction of prostate cancer progression with biomarkers and tissue morphometry changes
Authors: Zhu Guangjing , Lee George , Davis Christine , Kagohara Luciane Tsukamoto , Epstein Jonathan I. , Landis Patricia , Carter H. Ballantine , Madabhushi Anant , Veltri Robert W. 
Published: August 2015

Introduction:

The progression of prostate cancer (PCa) involves both tissue morphometric changes and critical molecular alterations (such as cancer-associated markers). We have studied 80 men that underwent radical prostatectomy (RP) to evaluate this dual approach to discriminate Gleason score and progression. Next, we applied our approach at the diagnostic biopsy of men that chose active surveillance (AS) as an option for the management of their PCa, to predict the likelihood for re-classification of their disease to immediate treatment.

Materials & Methods:

Two tissue microarrays (TMAs) with 80 RP PCa cases, stratified by Gleason scores were used first. Next, a total of 27 favorable and 24 unfavorable AS biopsy PCa cases were evaluated. In both series we applied multiplex tissue immunoblotting (MTI) & quantitative nuclear morphometry studies. Data were first analyzed alone and then in combination using multivariate logistic regression (MLR) to predict the aggressive RP cases or AS biopsy reclassification status of the cancer. MTI was used to simultaneously detect 5∼6 biomarkers ((-5,-7)ProPSA, PCNA, RBM3, Her2/neu, & CACNA1D, etc) on a single 5 micron section. Proteins on the slide were transferred onto a series of 5∼6 P-film membranes and each membrane was probed with different primary antibody. The quantified FITC fluorescence signal of the biomarker were normalized to CY5 labelled total protein. For the nuclear morphometry analysis, quantification of the nuclear features were achieved either using ImagePro Premier 9.1 Software for the TMAs or the adaptive active contour scheme (AdACM) that uses nuclear shape, architectural and textural features extracted from AS biopsies.

RESULTS:

Using MLR, to differentiate aggressive PCa (Gleason score 4+3 & &gt; = 8) from less aggressive PCa (Gleason score 3+3 & 3+4) on the TMAs of RP cases, our biomarkers model generated an ROC-AUC of 0.8 with accuracy of 71.43%, while morphometry model generate an ROC-AUC of 0.92 with accuracy of 82.50

%. When combined, it improved to an ROC-AUC of 0.96 and accuracy of 87.01%. Additionally, MLR was used for differentiation unfavorable biopsy cases that requires reclassification due to upgraded Gleason score, increased tumor volume, and/or PSA/PSAD during monitoring and need definitive treatment. On the contrary, favorable cases are very low risk (VLR) PCa biopsies that are not reclassified and can continue monitoring. The biomarkers model produced an ROC-AUC of 0.71 with an accuracy of 73.91% while morphometry model produces an ROC-AUC of 0.84 and accuracy of 74.51%. When combined, the new model produces an ROC-AUC of 0.88 and accuracy of 80.43%.

CONCLUSIONS:

Our method combining tissue morphometry with biomarkers demonstrated its translational clinical relevance since it can predict PCa aggressiveness in men that have undergone RP. Also, this approach predicts AS PCa cases requiring reclassification and immediate treatment at biopsy.

Citation Format: Guangjing Zhu, George Lee, Christine Davis, Luciane Tsukamoto Kagohara, Jonathan I. Epstein, Patricia Landis, H. Ballantine Carter, Anant Madabhushi, Robert W. Veltri. Prediction of prostate cancer progression with biomarkers and tissue morphometry changes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4352. doi:10.1158/1538-7445.AM2015-4352