TY - JOUR
T1 - Genomic–transcriptomic evolution in lung cancer and metastasis
AU - TRACERx Consortium
AU - Martínez-Ruiz, Carlos
AU - Black, James R.M.
AU - Puttick, Clare
AU - Hill, Mark S.
AU - Demeulemeester, Jonas
AU - Larose Cadieux, Elizabeth
AU - Thol, Kerstin
AU - Jones, Thomas P.
AU - Veeriah, Selvaraju
AU - Naceur-Lombardelli, Cristina
AU - Toncheva, Antonia
AU - Prymas, Paulina
AU - Rowan, Andrew
AU - Ward, Sophia
AU - Cubitt, Laura
AU - Athanasopoulou, Foteini
AU - Pich, Oriol
AU - Karasaki, Takahiro
AU - Moore, David A.
AU - Salgado, Roberto
AU - Colliver, Emma
AU - Castignani, Carla
AU - Dietzen, Michelle
AU - Huebner, Ariana
AU - Al Bakir, Maise
AU - Tanić, Miljana
AU - Watkins, Thomas B.K.
AU - Lim, Emilia L.
AU - Al-Rashed, Ali M.
AU - Lang, Danny
AU - Clements, James
AU - Cook, Daniel E.
AU - Rosenthal, Rachel
AU - Wilson, Gareth A.
AU - Frankell, Alexander M.
AU - de Carné Trécesson, Sophie
AU - East, Philip
AU - Kanu, Nnennaya
AU - Litchfield, Kevin
AU - Birkbak, Nicolai J.
AU - Hackshaw, Allan
AU - Beck, Stephan
AU - Van Loo, Peter
AU - Jamal-Hanjani, Mariam
AU - McGranahan, Nicholas
AU - Swanton, Charles
AU - Bakir, Maise Al
AU - Van Loo, Peter
AU - Pan, Xiaoxi
AU - Yuan, Yinyin
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4/20
Y1 - 2023/4/20
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85152867441&partnerID=8YFLogxK
U2 - 10.1038/s41586-023-05706-4
DO - 10.1038/s41586-023-05706-4
M3 - Article
C2 - 37046093
AN - SCOPUS:85152867441
SN - 0028-0836
VL - 616
SP - 543
EP - 552
JO - Nature
JF - Nature
IS - 7957
ER -