Estimation of rearrangement phylogeny for cancer genomes

Abstract

Cancer genomes are complex, carrying thousands of somatic mutations including base substitutions, insertions and deletions, rearrangements, and copy number changes that have been acquired over decades. Recently, technologies have been introduced that allow generation of high-resolution, comprehensive catalogs of somatic alterations in cancer genomes. However, analyses of these data sets generally do not indicate the order in which mutations have occurred, or the resulting karyotype. Here, we introduce a mathematical framework that begins to address this problem. By using samples with accurate data sets, we can reconstruct relatively complex temporal sequences of rearrangements and provide an assembly of genomic segments into digital karyotypes. For cancer genes mutated in rearranged regions, this information can provide a chronological examination of the selective events that have taken place.[Supplemental material is available for this article.]

Lechromothripsis

“…Le chromothripsis pourrait ne pas être un événement unique et isolé, mais faire partie d'un mécanisme plus complexe et dynamique de gestion de l'instabilité génomique. Des expériences de simulation du chromothripsis dans des cellules en culture sont en cours [16], et des modèles in silico sont à l'étude pour mieux caractériser les chromothripsis et mieux comprendre les mécanismes qui pilotent leur apparition [49]. D'un point de vue clinique, l'application des nouvelles techniques de séquençage à haut débit de l'ADN pour l'analyse des remaniements chromosomiques constitutionnels va sans doute révéler l'importance du chromothripsis dans l'émergence de nombreux désordres congénitaux, alors qu'en cancérologie, le lien découvert entre le chromothripsis et le dysfonctionnement du gène TP53 pourrait ouvrir de nouvelles voies thérapeu-tiques.…”

Identification of complex genomic rearrangements in cancers using CouGaR

“…If the underlying normal genome violates this assumption, then our copy count estimates in the tumor genome will be proportionately off. Secondly, we do not use the allelic ratios in WGS data to stabilize copy count estimates across tumor adjacencies (Greenman et al 2012). Furthermore, we do not resolve ambiguities presented by the overlap of circular contigs.…”

“…Greenman et al developed a method that combines allelic copy counts from a SNP array with discordant pairs from WGS into an allelic graph (Greenman et al 2012). By matching allelic copy counts across breakpoints and solving an integer program to minimize the total number of predicted rearrangements, they were able to detect parsimonious CGR structures.…”

nFuse: Discovery of complex genomic rearrangements in cancer using high-throughput sequencing

“…In a recent study, Greenman et al (2011) propose methods for reconstructing 'digital karyotypes' from copy number and breakpoint predictions. Their method requires precise breakpoint predictions and could not guarantee a unique solution for a reasonably complex genome.…”