How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma |
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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 |
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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 |
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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. |
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Keywords: | somatic mutation variant calling intratumour heterogeneity spatiotemporal duplicates tumour evolution |
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