Title: Are Thousands of Samples Really Needed to Generate Robust Gene-List for Prediction of Cancer Outcome?,
Abstract: The prediction of cancer prognosis and metastatic potential immediately after
the initial diagnoses is a major challenge in current clinical research. The
relevance of such a signature is clear, as it will free many patients from the
agony and toxic side-effects associated with the adjuvant chemotherapy
automatically and sometimes carelessly subscribed to them. Motivated by this
issue, Ein-Dor (2006) and Zuk (2007) presented a Bayesian model which leads to
the following conclusion: Thousands of samples are needed to generate a robust
gene list for predicting outcome. This conclusion is based on existence of some
statistical assumptions. The current work raises doubts over this determination
by showing that: (1) These assumptions are not consistent with additional
assumptions such as sparsity and Gaussianity. (2) The empirical Bayes
methodology which was suggested in order to test the relevant assumptions
doesn't detect severe violations of the model assumptions and consequently an
overestimation of the required sample size might be incurred.