Oral Presentation 29th Lorne Cancer Conference 2017

Tracking subclonal mutation frequencies throughout lymphoma development identifies cancer drivers in mouse models of lymphoma. (#4)

Philip Webster 1 , Joanna Dawes 1 , Hamlata Dewchand 1 , Kata Tacacs 1 , Barbara Iadarola 1 , Bruce Bolt 1 , Juan Caceres 2 , Jakub Kaczor 1 , Laurence Game 1 , Thomas Adejumo 1 , James Elliott 1 , Kikkeri Naresh 1 , Ge Tan 1 , Gopuraja Dharmalingam 1 , Alberto Paccanaro 2 , Anthony Uren 1
  1. Imperial college, London, GREATER LONDON, United Kingdom
  2. Department of Computer Science, Royal Holloway, University of London, London, Greater London, UK

Validating recurrent but rare cancer mutations as bona fide drivers remains a bottleneck in cancer research, particularly for non coding mutations. Here we use 700,000 insertion mutations from 355 murine leukemia virus (MuLV) driven lymphoid malignancies over a time course to determine the extent that subclonal mutations reflect verified cancer drivers. Additional correlation of mutations with genotypic and phenotypic features gives robust identification of known cancer genes independently of local variance in mutation density, and yields a high-resolution genome wide map of the selective forces surrounding cancer gene loci. Screening two BCL2 transgenic models revealed selection of known mature B lymphoma drivers and novel loci including costimulatory molecules and MHC loci. These results demonstrate that comparing background subclonal mutations from premalignant tissue with subclonal mutations of malignant tissue can greatly expand the statistical power required to identify non coding driver mutations.