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Reply to 'Neutral tumor evolution?'
(2018-12)
Modeling evolutionary dynamics of epigenetic mutations in hierarchically organized tumors.
(PUBLIC LIBRARY SCIENCE, 2011-05-05)
The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult ...
Resolving genetic heterogeneity in cancer.
(NATURE PUBLISHING GROUP, 2019-07-01)
To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and ...
Measuring single cell divisions in human tissues from multi-region sequencing data.
(NATURE PUBLISHING GROUP, 2020-02-25)
Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in ...
Quantification of subclonal selection in cancer from bulk sequencing data.
(NATURE PUBLISHING GROUP, 2018-05-28)
Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational ...
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
(PUBLIC LIBRARY SCIENCE, 2019-07-29)
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth ...
Measuring Clonal Evolution in Cancer with Genomics.
(ANNUAL REVIEWS, 2019-08-31)
Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal ...
Subclonal reconstruction of tumors by using machine learning and population genetics.
(NATURE PUBLISHING GROUP, 2020-09-01)
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations ...