Phylogenetic Quantification of Intra-tumour Heterogeneity

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data. Author Summary Cancer is a disease of random mutation and selection within the cellular genomes of an organism. As a result, when advanced disease is diagnosed, the cells comprising the tumour show a great amount of variability on the genomic level, a phenomenon termed intra-tumour genetic heterogeneity. Heterogeneity is thought to be one of the main reasons why tumors become resistant to therapy, and thus hinders personalised medicine approaches. If we want to understand tumour heterogeneity and its connection to resistance development we need to quantify it, which implies reconstructing the evolutionary history of cancer within the patient. Unfortunately, so far, methods for accurate reconstructions of these particular evolutionary trees and for quantification of heterogeneity have been missing. We here present MEDICC, a method that uses a minimum evolution criterion to compare cancer genomes based on genomic profiles of DNA content (copy-number profiles). It enables accurate reconstruction of the history of the disease and quantifies heterogeneity. It is specifically designed to deal with diploid human genomes, in that it disentangles genomic events on both parental alleles and includes a variety of accompanying algorithms to test for shapes of the evolutionary trees as well as the rate at which the cancer evolves.