Effect of the p53 P72R Polymorphism on Mutant TP53 Allele Selection in Human Cancer

Background TP53 mutations occur in more than 50% of cancers. We sought to determine the effect of the intragenic P72R SNP (rs1042522) on the oncogenic properties of mutant p53. Methods P72R allelic selection in tumors was determined from genotype calls and a Gaussian distributed mixture model. The SNP effect on mutant p53 was determined in p53-negative cancer cell lines. RNA-sequencing, chromatin immunoprecipitation, and survival analysis were performed to describe the SNP effect. All statistical tests were 2-sided. Results Among 409 patients with germline heterozygous P72R SNP who harbored somatic mutations in TP53, we observed a selection bias against missense TP53 mutants encoding the P72 SNP (P = 1.64 x 10-13). Exogenously expressed hotspot p53 mutants with the P72 SNP were negatively selected in cancer cells. Gene expression analyses showed the enrichment of p53 pathway genes and inflammatory genes in cancer cells transduced with mutants encoding P72 SNP. Immune gene signature is enriched in patients harboring missense TP53 mutations with homozygous P72 SNP. These patients have improved overall survival as compared to those with the R72 SNP (P = 0.04). Conclusion This is the largest study demonstrating a selection against the P72 SNP. Missense p53 mutants with the P72 SNP retain partial wild type tumor-suppressive functions, which may explain the selection bias against P72 SNP across cancer types. Ovarian cancer patients with the P72 SNP have a better prognosis than with the R72 SNP. Our study describes a previously unknown role through which the rs1042522 SNP modifies tumor suppressor activities of mutant p53 in patients.

Quantifying GC-biased gene conversion in great ape genomes using polymorphism-aware models

As multi-individual population-scale data is becoming available, more-complex modeling strategies are needed to quantify the genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We employ the new framework to a great ape dataset at we found patterns of allelic selection that match those of genome-wide GC-biased gene conversion (gBCG). In particular, we show that great apes have patterns of allelic selection that vary in intensity, a feature that we correlated with the great apes’ distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, promoting more polymorphisms in the base composition of great ape genomes. We further assess the impact of CG-bias in molecular analysis and we find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics.