Moreover, it is difficult to distinguish the few disease-causing mutations from the dozens, often hundreds of mutations observed in a tumour. Indeed, two patients usually do not share the same set of mutations and can even have none in common. However, the statistical analysis of these mutation profiles remains challenging. With the advent of next-generation sequencing technologies (NGS) in the last decade, thousands of tumours have been sequenced and their mutation profiles determined. The transition from a normal cell to a cancer cell is driven by genetic alterations, such as mutations, that induce uncontrolled cell proliferation. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.
Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events.
Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward.
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