Evolutionary Analysis of TCGA Data Using Over- and Under- Mutated Genes Identify Key Molecular Pathways and Cellular Functions in Lung Cancer Subtypes

Abstract

We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation rate. Driver mutations, which increase fitness (and proliferation), are observed more frequently than expected. Non-synonymous mutations in essential genes reduce fitness and are eliminated by natural selection resulting in lower prevalence than expected. We apply this “evolutionary triage” principle to TCGA data from EGFR-mutant, KRAS-mutant, and NEK (non-EGFR/KRAS) lung adenocarcinomas. We find frequent overlap of evolutionarily selected non-synonymous gene mutations among the subtypes suggesting enrichment for adaptations to common local tissue selection forces. Overlap of conserved genes in the LUAD subtypes is rare suggesting negative evolutionary selection is strongly dependent on initiating mutational events during carcinogenesis. Highly expressed genes are more likely to be conserved and significant changes in expression (>20% increased/decreased) are common in genes with evolutionarily selected mutations but not in conserved genes. EGFR-mut cancers have fewer average mutations (89) than KRAS-mut (228) and NEK (313). Subtype-specific variation in conserved and mutated genes identify critical molecular components in cell signaling, extracellular matrix remodeling, and membrane transporters. These findings demonstrate subtype-specific patterns of co-adaptations between the defining driver mutation and somatically conserved genes as well as novel insights into epigenetic versus genetic contributions to cancer evolution.

Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways

“…The initial dataset consisted of 17,112 genes. The mutational but not expression component of the dataset has been previously investigated [ 9 , 10 ]. Of these, 15,487 genes met our expression-level criterion for inclusion.…”

“…We note that our methodology for identifying evolutionary selection of gene mutations assumes a roughly equal probability for mutations in all base pairs and, therefore, differs from prior studies that find variation in mutation rates related to gene expression and chromosomal location [ 16 ]. However, as discussed in prior publications [ 9 , 10 ], variation in the observed mutation rates of individual genes resulting from decreased fitness with loss of the cell due to evolutionary selection could be interpreted as the result of intra-genomic variation in mutation rates. For example, Monroe et al [ 17 ], observed that, in Arabidopsis thaliana “genes subject to stronger purifying selection have a lower mutation rate.” In our hypothesis, important regions of the genome are observed to be mutated less often [ 18 ] because such mutations reduce fitness and proliferation and, therefore, not transmitted across generations.…”

“…In prior studies [ 9 , 10 ], we found cancer cells appear to adapt to a relatively fixed number of intracellular and extracellular barriers to unconstrained proliferation [ 10 ]. Driver genes function by simultaneously overcoming many barriers through their interactomes.…”