Phylogenetic Quantification of Intra-tumour Heterogeneity

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.

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Cited by papers we hold 10

• Single-cell genomic variation induced by mutational processes in cancer · 2022
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• Parsimonious Clone Tree Integration in cancer · 2022
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• MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution · 2022
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• Eleven grand challenges in single-cell data science · 2020
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• Reconstructing metastatic seeding patterns of human cancers · 2017
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• Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations · 2017
  Corrected
• OncoNEM: inferring tumor evolution from single-cell sequencing data · 2016
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• Ancestral Reconstruction · 2016
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• Spatial and Temporal Heterogeneity in High-Grade Serous Ovarian Cancer: A Phylogenetic Analysis · 2015
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• Cancer Evolution: Mathematical Models and Computational Inference · 2014
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