Tumors are made of evolving and heterogeneous populations of cells which arise from succes- sive appearance and expansion of subclonal populations, following acquisitions of mutations
conferring them a selective advantage. Those subclonal populations can be sensitive or re- sistant to different treatments, and provide information about tumor aetiology and future
evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 14 computational methods to estimate tumor heterogeneity on 1,758 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types. We observe significant differences
between the estimates produced by different methods, and identify several likely confound- ing factors in heterogeneity assessment for the different methods. We further show that
the prognostic value of tumor heterogeneity for survival prediction is limited, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in
cancer prognosis, should be considered with caution. Other approaches to assess intra- tumoral heterogeneity such as those based on multiple samples or single-cell sequencing may be preferable for clinical applications.