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How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma
Authors:Alastair Droop  Alexander Bruns  Georgette Tanner  Nora Rippaus  Ruth Morton  Sally Harrison  Henry King  Katherine Ashton  Khaja Syed  Michael D. Jenkinson  Andrew Brodbelt  Aruna Chakrabarty  Azzam Ismail  Susan Short  Lucy F. Stead
Affiliation:1. MRC Medical Bioinformatics Centre, University of Leeds, Leeds, United Kingdom;2. Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom;3. Lancashire Teaching Hospitals NHS Trust, Royal Preston Hospital, Preston, United Kingdom;4. Walton Centre NHS Trust, Liverpool, United Kingdom;5. Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom;6. Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, United Kingdom
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.
Keywords:somatic mutation  variant calling  intratumour heterogeneity  spatiotemporal  duplicates  tumour evolution
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