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Phylogenetic Quantification of Intra-tumour Heterogeneity

Overview of attention for article published in PLoS Computational Biology, April 2014
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Title
Phylogenetic Quantification of Intra-tumour Heterogeneity
Published in
PLoS Computational Biology, April 2014
DOI 10.1371/journal.pcbi.1003535
Pubmed ID
Authors

Roland F. Schwarz, Anne Trinh, Botond Sipos, James D. Brenton, Nick Goldman, Florian Markowetz

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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Geographical breakdown

Country Count As %
United States 6 2%
Germany 3 1%
Netherlands 1 <1%
Norway 1 <1%
Italy 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Australia 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 229 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 29%
Researcher 54 22%
Student > Master 28 11%
Student > Bachelor 22 9%
Other 12 5%
Other 27 11%
Unknown 31 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 32%
Biochemistry, Genetics and Molecular Biology 56 23%
Medicine and Dentistry 27 11%
Computer Science 22 9%
Mathematics 8 3%
Other 18 7%
Unknown 36 15%