Gene duplication is associated with gene diversification and potential neofunctionalization in lung cancer evolution.

Tumors evolve through a process of selection on somatic mutations, driving cell division and tissue growth through aberrations in cell-cycle control. In non-small-cell lung cancer (NSCLC), genome instability occurs early in tumor growth, resulting in pronounced intratumor heterogeneity, including changes in gene copy number, and whole-genome doubling (WGD) in ∼75% of tumors. Gene duplication, genetic drift, and selection mediate functional diversification during evolution. In this study, we seek to identify the diversification and potential gene neofunctionalization of lung tumors in the TRACERx cohort. We develop a novel computational protocol to identify preduplication and postduplication mutations predicted to affect protein function. Mutations are analyzed using paralogs grouped into functional families with highly similar functions, identifying 355 functional impact events (FIEs) through their proximity and clustering near to functional sites. The use of functional family paralogs to map mutations to protein structures from the PDB helps predict putative rare driver events in lung tumors. By extending the analysis with high-quality structural models from AlphaFold using The Encyclopedia of Domains (TED), we find a significant increase in the diversity of both genes and functional families with postduplication FIEs in lung adenocarcinomas, including some metabolic enzymes with the potential to be neofunctional. The postduplication diversification of driver genes and functions may indicate selection for somatic copy number changes in lung tumors and an increased scope for tumor adaptations.