Genomic–transcriptomic evolution in lung cancer and metastasis


  • C. Martínez-Ruiz
  • J.R.M. Black
  • C. Puttick
  • M.S. Hill
  • J. Demeulemeester
  • E. Larose Cadieux
  • K. Thol
  • T.P. Jones
  • S. Veeriah
  • C. Naceur-Lombardelli
  • A. Toncheva
  • P. Prymas
  • A. Rowan
  • S. Ward
  • L. Cubitt
  • F. Athanasopoulou
  • O. Pich
  • T. Karasaki
  • D.A. Moore
  • R. Salgado
  • E. Colliver
  • C. Castignani
  • M. Dietzen
  • A. Huebner
  • M. Al Bakir
  • M. Tanić
  • T.B.K. Watkins
  • E.L. Lim
  • A.M. Al-Rashed
  • D. Lang
  • J. Clements
  • D.E. Cook
  • R. Rosenthal
  • G. A. Wilson
  • A.M. Frankell
  • S. de Carné Trécesson
  • P. East
  • N. Kanu
  • K. Litchfield
  • N.J. Birkbak
  • A. Hackshaw
  • S. Beck
  • P. Van Loo
  • M. Jamal-Hanjani
  • N. McGranahan
  • C. Swanton


  • Nature


  • Nature 616 (7957): 543-552


  • Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic–transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary–metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis.