How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma



Droop, Alastair, Bruns, Alexander, Tanner, Georgette, Rippaus, Nora, Morton, Ruth, Harrison, Sally, King, Henry, Ashton, Katherine, Syed, Khaja, Jenkinson, Michael D ORCID: 0000-0003-4587-2139
et al (show 5 more authors) (2018) How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma. INTERNATIONAL JOURNAL OF CANCER, 142 (8). pp. 1620-1626.

[img] Text
2017_glioma technique.doc - Author Accepted Manuscript

Download (354kB)

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.

Item Type: Article
Uncontrolled Keywords: somatic mutation, variant calling, intratumour heterogeneity, spatiotemporal, duplicates, tumour evolution
Depositing User: Symplectic Admin
Date Deposited: 23 Feb 2018 14:28
Last Modified: 19 Jan 2023 06:39
DOI: 10.1002/ijc.31184
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3018340