Browsing Clinical Studies by author "Heide, Timon"
Now showing items 1-4 of 4
-
Circulating tumour DNA sequencing to determine therapeutic response and identify tumour heterogeneity in patients with paediatric solid tumours.
Stankunaite, R; George, SL; Gallagher, L; Jamal, S; Shaikh, R; et al. (ELSEVIER SCI LTD, 2021-12-18)OBJECTIVE: Clinical diagnostic sequencing of circulating tumour DNA (ctDNA) is well advanced for adult patients, but application to paediatric cancer patients lags behind. METHODS: To address this, we have developed a ... -
Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial.
Khan, KH; Cunningham, D; Werner, B; Vlachogiannis, G; Spiteri, I; et al. (AMER ASSOC CANCER RESEARCH, 2018-08-30)Sequential profiling of plasma cell-free DNA (cfDNA) holds immense promise for early detection of patient progression. However, how to exploit the predictive power of cfDNA as a liquid biopsy in the clinic remains unclear. ... -
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 ...