Genomic rearrangements are a major source of evolutionary divergence in eukaryotic genomes, a cause of genetic diseases and a hallmark of tumor cell progression, yet the mechanisms underlying their occurrence and evolutionary fixation are poorly understood. Statistical associations between breakpoints and specific genomic features suggest that genomes may contain elusive “fragile regions” with a higher propensity for breakage. Here, we use ancestral genome reconstructions to demonstrate a near-perfect correlation between gene density and evolutionary rearrangement breakpoints. Simulations based on functional features in the human genome show that this pattern is best explained as the outcome of DNA breaks that occur in open chromatin regions coming into 3D contact in the nucleus. Our model explains how rearrangements reorganize the order of genes in an evolutionary neutral fashion and provides a basis for understanding the susceptibility of “fragile regions” to breakage.
Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant 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 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, 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 may be preferable for clinical applications.
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