Studies on genetic profilingdemonstrate its potential utility for classifying tumours, leading to a 'genetic-staging' system for predicting disease outcomes. A precise prediction of individual disease outcome is important to identify patients who have a high risk of disease recurrence, and to tailor treatments to the individual patient. Genes, however, are not the sole determinants of disease outcomes. Non-genetic factors have roles in many stages of tumourigenesis, and the simultaneous use of genetic-staging and clinical risk factors may therefore improve the prediction of disease ouctome. In this paper, we aim to quantify the prognostic value of genetic-staging from gene expressions by using mixture model-based clustering methods. We also investigate via the use of logistric regression whether a more accurate prediction of disease outcome can be obtained by using geneticstaging in conjunction with clinical risk factors. The proposed method is illustrated using a real example of breast cancer data. It shows that genetic-staging provides significant additional prognostic information when it is obtained by applying sophisticated modelbsed clustering method for the identification of marker-genes that are relevant to predict disease outcomes.