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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 ...
Evolutionary dynamics of neoantigens in growing tumors.
(NATURE PORTFOLIO, 2020-10-01)
Cancers accumulate mutations that lead to neoantigens, novel peptides that elicit an immune response, and consequently undergo evolutionary selection. Here we establish how negative selection shapes the clonality of ...
Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome.
(BMC, 2018-05-31)
BACKGROUND: Natural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic ...
Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors.
(NATURE PORTFOLIO, 2023-03-01)
In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous ...
Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes.
(NATURE PORTFOLIO, 2017-10-13)
Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell ...