Browsing Cancer Therapeutics by author "Sottoriva, Andrea"
Now showing items 1-10 of 10
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Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer.
Lote, H; Spiteri, I; Ermini, L; Vatsiou, A; Roy, A; et al. (2017-06)Background Patients often ask oncologists how long a cancer has been present before causing symptoms or spreading to other organs. The evolutionary trajectory of cancers can be defined using phylogenetic approaches but ... -
Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer.
Lote, H; Spiteri, I; Ermini, L; Vatsiou, A; Roy, A; et al. (OXFORD UNIV PRESS, 2017-02-23)BACKGROUND: Patients often ask oncologists how long a cancer has been present before causing symptoms or spreading to other organs. The evolutionary trajectory of cancers can be defined using phylogenetic approaches but ... -
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 ... -
Detecting repeated cancer evolution from multi-region tumor sequencing data.
Caravagna, G; Giarratano, Y; Ramazzotti, D; Tomlinson, I; Graham, TA; et al. (NATURE PUBLISHING GROUP, 2018-08-31)Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal ... -
Exploiting evolutionary steering to induce collateral drug sensitivity in cancer.
Acar, A; Nichol, D; Fernandez-Mateos, J; Cresswell, GD; Barozzi, I; et al. (NATURE PORTFOLIO, 2020-04-21)Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another ... -
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. ... -
Patient-derived organoids model treatment response of metastatic gastrointestinal cancers.
Vlachogiannis, G; Hedayat, S; Vatsiou, A; Jamin, Y; Fernández-Mateos, J; et al. (AMER ASSOC ADVANCEMENT SCIENCE, 2018-02-23)Patient-derived organoids (PDOs) have recently emerged as robust preclinical models; however, their potential to predict clinical outcomes in patients has remained unclear. We report on a living biobank of PDOs from ... -
Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma.
Tari, H; Kessler, K; Trahearn, N; Werner, B; Vinci, M; et al. (CELL PRESS, 2022-08-30)Diffuse midline gliomas (DMGs) are highly aggressive, incurable childhood brain tumors. They present a clinical challenge due to many factors, including heterogeneity and diffuse infiltration, complicating disease management. ... -
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 ...