Prediction of adenocarcinoma development using game theory

Recent research shows that gene expression changes appear to correlate well with the progression of many types of cancers. Using changes in gene expression as a basis, this paper proposes a data-driven 2-player game-theoretic model to predict the risk of adenocarcinoma based on Nash equilibrium. A key innovation in this work is the pay-off function which is a weighted composite of the expression of a cohort of tumor-suppressor genes (as one player) and an analogous cohort of oncogenes (as the other player). Another novelty of the model is its ability to predict the risk that a healthy sample will develop adenocarcinoma, if its associated gene expression is comparable to that of early-stage tumor samples. The model is validated using two of the largest publicly available adenocarcinoma datasets. The results show that i) the model is able to distinguish between healthy and cancerous samples with an accuracy of 93%, and ii) 95% of the healthy samples said to be at risk had gene expressions comparable to those of samples with stage I or stage II tumors, thereby predicting the imminent onset of adenocarcinoma.