Browsing ICR Divisions by author "Chkhaidze, Ketevan"
Now showing items 1-6 of 6
-
Evolutionary dynamics of residual disease in human glioblastoma.
Spiteri, I; Caravagna, G; Cresswell, GD; Vatsiou, A; Nichol, D; et al. (OXFORD UNIV PRESS, 2019-03-01)BACKGROUND: Glioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery and chemoradiation provide limited benefit. Even when a good treatment response is obtained, recurrence ... -
Measuring single cell divisions in human tissues from multi-region sequencing data.
Werner, B; Case, J; Williams, MJ; Chkhaidze, K; Temko, D; et al. (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 ... -
Measuring tumour evolution at the genetic and epigenetic level in individual patients from cancer genomic data
Sottoriva, A; Chkhaidze, K (Institute of Cancer Research (University Of London), 2020-01-31)High-throughput genomic data from cancers uncovered high intra and inter-tumour heterogeneity and subclonal architecture of cancer cell populations. If we consider cells as asexually reproducing individuals, we can apply ... -
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
Chkhaidze, K; Heide, T; Werner, B; Williams, MJ; Huang, W; et al. (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 ... -
Subclonal reconstruction of tumors by using machine learning and population genetics.
Caravagna, G; Heide, T; Williams, MJ; Zapata, L; Nichol, D; et al. (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 ... -
The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer.
Barry, P; Vatsiou, A; Spiteri, I; Nichol, D; Cresswell, GD; et al. (SPRINGER, 2018-02-01)Purpose: The most significant prognostic factor in early breast cancer is lymph node involvement. This stage between localized and systemic disease is key to understanding breast cancer progression; however, our knowledge ...