Now showing items 1-7 of 7

    • Detecting repeated cancer evolution in human tumours from multi-region sequencing data 

      Caravagna, G; Giarratano, Y; Ramazzotti, D; Graham, T; Sanguinetti, G; Sottoriva, A (2017-06-27)
      Carcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for ...
    • EGFR amplification and outcome in a randomised phase III trial of chemotherapy alone or chemotherapy plus panitumumab for advanced gastro-oesophageal cancers. 

      Smyth, EC; Vlachogiannis, G; Hedayat, S; Harbery, A; Hulkki-Wilson, S; Salati, M; Kouvelakis, K; Fernandez-Mateos, J; Cresswell, GD; Fontana, E; Seidlitz, T; Peckitt, C; Hahne, JC; Lampis, A; Begum, R; Watkins, D; Rao, S; Starling, N; Waddell, T; Okines, A; Crosby, T; Mansoor, W; Wadsley, J; Middleton, G; Fassan, M; Wotherspoon, A; Braconi, C; Chau, I; Vivanco, I; Sottoriva, A; Stange, DE; Cunningham, D; Valeri, N (2020-11-16)
      Objective Epidermal growth factor receptor (EGFR) inhibition may be effective in biomarker-selected populations of advanced gastro-oesophageal adenocarcinoma (aGEA) patients. Here, we tested the association between outcome ...
    • Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. 

      Acar, A; Nichol, D; Fernandez-Mateos, J; Cresswell, GD; Barozzi, I; Hong, SP; Trahearn, N; Spiteri, I; Stubbs, M; Burke, R; Stewart, A; Caravagna, G; Werner, B; Vlachogiannis, G; Maley, CC; Magnani, L; Valeri, N; Banerji, U; Sottoriva, A (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; Heide, T; Mateos, JF; Vatsiou, A; Lampis, A; Damavandi, MD; Lote, H; Huntingford, IS; Hedayat, S; Chau, I; Tunariu, N; Mentrasti, G; Trevisani, F; Rao, S; Anandappa, G; Watkins, D; Starling, N; Thomas, J; Peckitt, C; Khan, N; Rugge, M; Begum, R; Hezelova, B; Bryant, A; Jones, T; Proszek, P; Fassan, M; Hahne, JC; Hubank, M; Braconi, C; Sottoriva, A; Valeri, N (2018-10)
      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; Khan, K; Lampis, A; Eason, K; Huntingford, I; Burke, R; Rata, M; Koh, D-M; Tunariu, N; Collins, D; Hulkki-Wilson, S; Ragulan, C; Spiteri, I; Moorcraft, SY; Chau, I; Rao, S; Watkins, D; Fotiadis, N; Bali, M; Darvish-Damavandi, M; Lote, H; Eltahir, Z; Smyth, EC; Begum, R; Clarke, PA; Hahne, JC; Dowsett, M; de Bono, J; Workman, P; Sadanandam, A; Fassan, M; Sansom, OJ; Eccles, S; Starling, N; Braconi, C; Sottoriva, A; Robinson, SP; Cunningham, D; Valeri, N (2018-02)
      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 ...
    • 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; Caravagna, G; Graham, TA; Sottoriva, A (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; Chkhaidze, K; Cross, W; Cresswell, GD; Werner, B; Acar, A; Chesler, L; Barnes, CP; Sanguinetti, G; Graham, TA; Sottoriva, A (2020-09-02)
      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 ...