Abstract
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.
Highlights
IntroductionThe study of intra-tumour genetic heterogeneity (for short: heterogeneity) is a major focus of cancer genomics research [1,2,3,4,5,6,7,8,9,10,11,12] due to its potential to provide prognostic information [13,14,15] and to explain mechanisms of drug resistance [16,17,18,19]
In many cancers, such as high-grade serous ovarian cancer (HGSOC), most of this heterogeneity is not reflected in point mutations but in genomic rearrangements and endoreduplications that lead to aberrant copy-number profiles [20,21]
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
Summary
The study of intra-tumour genetic heterogeneity (for short: heterogeneity) is a major focus of cancer genomics research [1,2,3,4,5,6,7,8,9,10,11,12] due to its potential to provide prognostic information [13,14,15] and to explain mechanisms of drug resistance [16,17,18,19]. Quantifying tumour heterogeneity and understanding its aetiology crucially depends on our ability to accurately reconstruct the evolutionary history of cancer cells within each patient In many cancers, such as high-grade serous ovarian cancer (HGSOC), most of this heterogeneity is not reflected in point mutations but in genomic rearrangements and endoreduplications that lead to aberrant copy-number profiles [20,21]. In these cases tree inference is hindered by unknown phasing of parental alleles and horizontal dependencies between adjacent genomic loci. While simplifying the computational problem, this approach discards potentially informative data
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