Fig. 5: Variation of driver mutations with age of onset and association with colibactin mutagenesis in MSS colorectal cancers.

a, Prevalence of driver mutations affecting the 48 detected driver genes. Genes were coloured according to their status as known cancer driver genes for colorectal cancer, known cancer driver genes for other cancer types or newly detected cancer driver genes. b, Distribution of total driver mutations across early-onset and late-onset tumours. Statistical significance was evaluated using a multivariable linear regression model adjusted by sex, country, tumour subsite and tumour purity. In box plots, the horizontal line indicates the median, the upper and lower ends of the box indicate the 25th and 75th percentiles. Whiskers show 1.5 × the interquartile range, and values outside the whiskers are shown as individual data points. c, Enrichment of driver mutations in cancer driver genes in early-onset and late-onset cases. Statistically significant enrichments were evaluated using multivariable logistic regression models adjusted by sex, country, tumour subsite and tumour purity. Firth’s bias-reduced logistic regressions were used for regressions presenting complete or quasi-complete separation. P values were adjusted for multiple comparisons using the Benjamini–Hochberg method based on the total number of cancer driver genes and reported as q values. Dashed lines indicate q values of 0.05 (orange) and 0.01 (red). d, Prevalence of driver mutations in cancer driver genes across ages of onset. Cancer driver genes significantly enriched in late-onset cases (as shown in c) were coloured in green. e,f, Proportion of driver mutations probabilistically assigned to colibactin-induced and other SBS (e) and ID (f) signatures. Driver mutations were divided into different groups, including APC c.835-8A>G splicing-associated driver mutation, as well as driver mutations affecting APC, TP53 and other cancer driver genes.