Genomic-transcriptomic evolution in lung cancer and metastasis

Carlos Martínez-Ruiz, James R.M. Black, Clare Puttick, Mark S. Hill, Jonas Demeulemeester, Elizabeth Larose Cadieux, Kerstin Thol, Thomas P. Jones, Selvaraju Veeriah, Cristina Naceur-Lombardelli, Antonia Toncheva, Paulina Prymas, Andrew Rowan, Sophia Ward, Laura Cubitt, Foteini Athanasopoulou, Oriol Pich, Takahiro Karasaki, David A. Moore, Roberto SalgadoEmma Colliver, Carla Castignani, Michelle Dietzen, Ariana Huebner, Maise Al Bakir, Miljana Tanić, Thomas B.K. Watkins, Emilia L. Lim, Ali M. Al-Rashed, Danny Lang, James Clements, Daniel E. Cook, Rachel Rosenthal, Gareth A. Wilson, Alexander M. Frankell, Sophie de Carné Trécesson, Philip East, Nnennaya Kanu, Kevin Litchfield, Nicolai J. Birkbak, Allan Hackshaw, Stephan Beck, Peter Van Loo, Mariam Jamal-Hanjani, Charles Swanton, Nicholas McGranahan, TRACERx Consortium

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

5 Citations (Scopus)


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 study2,3. 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.

Original languageEnglish
Pages (from-to)543-552
Number of pages10
Publication statusPublished - Apr 2023


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