Clinical utility of genetic variants of glutamate carboxypeptidase II in predicting breast cancer and prostate cancer risk

Abstract

In view of documented evidence showing glutamate carboxypeptidase II (GCPII) inhibitors as promising anti-cancer agents, certain variants of GCPII modulate breast and prostate cancer risk, and we developed an artificial neural network (ANN) model of GCPII variants and ascertained the risk associated with eight genetic variants of GCPII. In parallel, an in silico model was developed to substantiate the ANN simulations. The ANN model with modified sigmoid function was used for disease prediction, whereas the hyperbolic tangent function was used to predict folate hydrolase 1 (FOLH1) and prostate specific membrane antigen (PSMA) expression. PyMOL models of GCPII variants were developed, and their affinity toward the folylpolyglutamate (FPG) ligand was tested using glide score analysis. Of the eight genetic variants of GCPII, p.P160S alone conferred protection against both of the cancers. This variant exhibited higher affinity toward FPG compared with wild GCPII (-2.06 vs. -1.69); and positive correlation was observed between the P160S variant and circulating folate (r = 0.60). The ANN model for disease prediction showed significant predictability associated with GCPII variants toward breast cancer (area under the curve (AUC): 1.00) and prostate cancer (AUC: 0.97), with clear distinguishing ability of healthy controls (AUC: 0.96). The ANN models for the expression of FOLH1 and PSMA explained 60.5% and 86.7% of the variability, respectively. Thus, GCPII variants are potential contributors of risk toward breast cancer and prostate cancer. Risk modulation appeared to be mediated through changes in the expression of FOLH1 and PSMA.

Keywords: Artificial neural network; Breast cancer; Folate hydrolase 1; Glutamate carboxypeptidase II; Prostate cancer; Prostate specific membrane antigen.