To predict disease outcome: Clinical risk factors plus genetic-staging for cancer

Studies on genetic profiling demonstrate 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 genetic-staging 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 model-bsed clustering method for the identification of marker-genes that are relevant to predict disease outcomes. © 2010 pakistan journal of statistics.