How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma. Issue 8 (14th December 2017) - Droop, Alastair; Bruns, Alexander; Tanner, Georgette; Rippaus, Nora; Morton, Ruth; Harrison, Sally; King, Henry; Ashton, Katherine; Syed, Khaja; Jenkinson, Michael D.; Brodbelt, Andrew; Chakrabarty, Aruna; et al.
Abstract : Many traits of cancer progression (e.g., development of metastases or resistance to therapy) are facilitated by tumour evolution: Darwinian selection of subclones with distinct genotypes or phenotypes that enable such progression. Characterising these subclones provide an opportunity to develop drugs to better target their specific properties but requires the accurate identification of somatic mutations shared across multiple spatiotemporal tumours from the same patient. Current best practices for calling somatic mutations are optimised for single samples, and risk being too conservative to identify shared mutations with low prevalence in some samples. We reasoned that datasets from multiple matched tumours can be used for mutual validation and thus propose an adapted two‐stage approach: (1) low‐stringency mutation calling to identify mutations shared across samples irrespective of the weight of evidence in a single sample; (2) high‐stringency mutation calling to further characterise mutations present in a single sample. We applied our approach to three‐independent cohorts of paired primary and recurrent glioblastoma tumours, two of which have previously been analysed using existing approaches, and found that it significantly increased the amount of biologically relevant shared somatic mutations identified. We also found that duplicate removal was detrimental when identifying shared somatic mutations. Our approach is also applicable when multiple datasets e.g. DNA and RNA are available for the same tumour. Abstract : What's new? Tumor evolution, in which selection favors the survival of genetically and phenotypically distinct subclones, is a key feature of cancer progression. Within a single patient, such subclones may be shared across multiple tumors and carry somatic mutations that facilitate cancerous driver events. Here, an adapted two‐stage approach to mutation calling was used to identify shared somatic mutations across multiple matched tumors from the same patient. The approach showed high sensitivity in the detection of shared genetic variants in analyses of paired primary and recurrent glioblastoma samples, suggesting that it could potentially improve the capture of biologically relevant somatic mutations.