bioRxivGenomicsPreprint
Immune evasion impacts the selective landscape of driver genes during tumorigenesis
Lucie E. GourmetiD, Andrea SottorivaiD, Maria SecrieriD, Luis ZapataiD
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Abstract
Carcinogenesis is an evolutionary process fueled by the interplay of somatic mutations and the local microenvironment. In recent years, hundreds of cancer related genes have been discovered using cancer cohorts. However, these cohorts are heterogenous mixtures of different molecular phenotypes, which hampers the identification of driver genes associated to a specific cancer hallmark or microenvironment. Here, we compared the landscape of positively selected somatic mutations in immune-escaped (escape+) versus non-escaped (escape-) tumors. We applied the ratio of non-synonymous to synonymous mutations (dN/dS) to 9896 individuals from 31 primary tumor tissues from the Cancer Genome Atlas (TCGA) separated by escape status. Altogether, we found 85 driver genes, including 27 and 16 novel driver genes in escape- and escape+ tumors, respectively. Overall, driver dN/dS of escape+ tumors (dN/dS=1.23) was significantly lower and closer to neutrality than driver dN/dS of escape-tumors (dN/dS=1.62), suggesting a relaxation of positive selection in driver genes, a relaxation of negative selection on immunogenic driver sites, or a combination of both fueled by immune escape. We also found that the proportion of unique sites mutated in escape+ tumors is almost double than in escape-tumors, and that immune evasion allows for a more diverse repertoire of mutational signatures. We also identified that strong immunoediting in the absence of escape leads to a better overall survival in tumors enriched by an inflamed phenotype. Ultimately, our findings reveal differences in the evolutionary strategies used by cancer cells to establish tumorigenesis and highlight the need for better patient stratification to develop tailored treatments based on molecular targets.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- approach: statistical model
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