Geographic and age-related variations in mutational processes in colorectal cancer

Abstract

Colorectal cancer incidence rates vary geographically and have changed over time. Notably, in the past two decades, the incidence of early-onset colorectal cancer, affecting individuals under the age of 50 years, has doubled in many countries. The reasons for this increase are unknown. Here, we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown etiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia, and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals diagnosed before age 40 than in those over 70, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that early-life mutagenic exposure to colibactin-producing bacteria may contribute to the rising incidence of early-onset colorectal cancer.

Fig. 1.. Geographic, clinical, and molecular characterization of the Mutographs colorectal cancer cohort.

a, Geographic distribution of the 981 patients across four continents and 11 countries, with an indication of the total number of cases as well as the percentage of early-onset (eo) cases below 50 years of age. Countries were colored according to their age-standardized incidence rates (ASR) per 100,000 individuals. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the authors or their institutions concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. b, Tumor subsite distribution of the cohort across the colorectum, with an indication of the total number of cases and the percentage of early-onset cases. Different subsites were colored according to the percentage of early-onset cases. Additional 2 cases had unspecified subsites. c, Scatter plots indicating the distribution of molecular subgroups across the sequenced tumors according to the total number of single base substitutions (SBS) and small insertions and deletions (indels; ID), as well as the percentage of genome aberrated (PGA). Cases for which tumor purity was insufficient to determine an accurate copy number profile or without large copy number alterations (65/981) were excluded from the SBS - PGA panel. d, Box plots indicating the distribution of SBS and ID across early-onset (under 50 years; purple) and late-onset (50 years or older; green) microsatellite stable (MSS) colorectal tumors. Statistically significant differences were evaluated using multivariable linear regression models adjusted by sex, country, tumor subsite, and tumor purity. The line within the box is plotted at the median, while the upper and lower ends indicate the 25^th and 75^th percentiles. Whiskers show 1.5 × interquartile range, and values outside it are shown as individual data points. e-g, Average mutational profiles of early-onset and late-onset MSS colorectal tumors for SBS (SBS-288 mutational context; e), ID (ID-83 mutational context; f), and copy number alterations (CN-68 mutational context; g).

Fig. 2.. Geographic variation of mutational signatures in microsatellite stable colorectal cancers.

a, Dot plot indicating the variation of mutational signature prevalence in specific countries compared to all others. Statistically significant enrichments were evaluated using multivariable logistic regression models adjusted by age of diagnosis, sex, tumor subsite, and tumor purity. Firth’s bias-reduced logistic regressions were used for regression presenting complete or quasi-complete separation. Data points were colored according to the odds ratio (OR) of the enrichment, with their size representing statistical significance. P-values were adjusted for multiple comparisons based on the total number of mutational signatures considered per variant type and the total number of countries assessed, and reported as q-values. Q-values<0.05 were considered statistically significant and marked in red. b-c, Geographic distribution of the ID_J (b) and SBS_F (c) mutational signatures. Countries were colored based on the signature prevalence. d, Volcano plots indicating the association of mutational signature activities with the age-standardized incidence rates. Statistically significant associations were evaluated using multivariable linear regression models adjusted by age of diagnosis, sex, tumor subsite, and tumor purity. P-values were adjusted for multiple comparisons based on the total number of mutational signatures considered per variant type and reported as q-values. Horizontal lines marking statistically significant thresholds were included at 0.05 (dashed orange line) and 0.01 q-values (dashed red line). e-f, Scatter plots indicating the association of the mutations attributed to the SBS88 and ID18 mutational signatures with the age-standardized incidence rates (ASR) across countries for colorectal cancer (e), and independently for colon and rectal cancers (f). Data points were colored based on signature prevalence, with their size indicating the total number of cases per country. Statistically significant associations were evaluated using the sample-level multivariable linear regression models used in d (e), and similar multivariable linear regression models adjusted by age of diagnosis, sex, and tumor purity (f).

Fig. 3.. Variation of mutational signatures with age of onset in microsatellite stable colorectal cancers.

a, Volcano plots indicating the enrichment of mutational signature prevalence in early-onset and late-onset cases. Statistically significant enrichments were evaluated using multivariable logistic regression models for age of onset categorized in two subgroups (early-onset, <50 years of age; and late-onset, ≥50) and adjusted by sex, country, tumor subsite, and tumor purity. Firth’s bias-reduced logistic regressions were used for regression presenting complete or quasi-complete separation. P-values were adjusted for multiple comparisons based on the total number of mutational signatures considered per variant type and reported as q-values. Horizontal lines marking statistically significant thresholds were included at 0.05 (dashed orange line) and 0.01 q-values (dashed red line). b, Line plots indicating mutational signature prevalence trend across ages of onset, using five different age groups. Signatures significantly enriched in early-onset or late-onset cases (as shown in a) were colored in purple and green, respective, whereas signatures not varying significantly with age were colored in grey. c, Bar plots indicating mutational signature prevalence across age groups, with indication of the total number of cases where signatures were detected. Statistically significant trends were evaluated using multivariable logistic regression models for age categorized in five subgroups (0–39, 40–49, 50–59, 60–69, ≥70) and adjusted by sex, country, tumor subsite, and tumor purity. Firth’s bias-reduced logistic regressions were used for regressions presenting complete or quasi-complete separation. d-e, Box plots indicating the variation in age of onset according to the presence of colibactin mutational signatures (either SBS88, ID18, or both) in all microsatellite stable cases (d) and across tumor subsites (e). Statistically significant differences were evaluated using multivariable linear regression models adjusted by sex, country, tumor purity, and tumor subsite (only for the analysis of all cases, d). The line within the box is plotted at the median, while the upper and lower ends indicate the 25^th and 75^th percentiles. Whiskers show 1.5 × interquartile range, and values outside it are shown as individual data points.

Fig. 4.. Colibactin mutagenesis as an early event in microsatellite stable colorectal cancer evolution.

a, Box plots indicating the fold-change of the relative contribution per sample of each signature between early clonal and late clonal single base substitutions (SBS, left) and small insertions and deletions (ID, right). SBS signatures that generated early clonal SBSs in fewer than 50 samples and also generated late clonal SBSs in fewer than 50 samples were excluded from the analysis. Similarly, ID signatures that generated early clonal IDs in fewer than 20 samples and late clonal IDs in fewer than 20 samples were also excluded. Signatures were sorted by their median fold-change. The line within the box is plotted at the median, while the upper and lower ends indicate the 25^th and 75^th percentiles. Whiskers show 1.5 × interquartile range, and values outside it are shown as individual data points. b, Bar plot indicating the lack of concordance between colibactin exposure status determined by the presence of colibactin-induced mutational signatures SBS88 or ID18, and the microbiome pks status. Statistical significance was evaluated using a multivariable Firth’s bias-reduced logistic regression model (due to quasi-complete separation) adjusted by age of diagnosis, sex, country, tumor subsite, and tumor purity. c-d, Distribution of the age of onset (c) and cases across age groups (d) based on the detection of colibactin-positive samples using genomic and microbiome status. The genomic status is defined by the presence of SBS88 or ID18 signatures, while the microbiome status (pks) is determined by coverage of at least half of the pks island, and suggests ongoing or active pks+ bacterial infection. Statistical significance was evaluated using a multivariable linear regression model adjusted by sex, country, tumor subsite, and tumor purity.

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

a, Bar plot indicating the prevalence of driver mutations affecting the 48 bioinformatically detected driver genes in microsatellite stable colorectal cancers. Genes were colored 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, Box plots indicating the distribution of total driver mutations across early-onset (under 50 years of age; purple) and late-onset (50 or over; green) tumors. Statistical significance was evaluated using a multivariable linear regression model adjusted by sex, country, tumor subsite, and tumor purity. The line within the box is plotted at the median, while the upper and lower ends indicate the 25^th and 75^th percentiles. Whiskers show 1.5 × interquartile range, and values outside it are shown as individual data points. c, Volcano plot indicating the 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, tumor subsite, and tumor purity. Firth’s bias-reduced logistic regressions were used for regressions presenting complete or quasi-complete separation. P-values were adjusted for multiple comparisons based on the total number of cancer driver genes considered and reported as q-values. Horizontal lines marking statistically significant thresholds were included at 0.05 (dashed orange line) and 0.01 q-values (dashed red line). d, Line plot indicating the prevalence of driver mutations in cancer driver genes across ages of onset, using five different age groups. Cancer driver genes significantly enriched in late-onset cases (as shown in c) were colored in green, whereas genes not varying significantly with age of onset were colored in grey. e, Bar plots indicating the proportion and number of driver mutations probabilistically assigned to colibactin-induced and other mutational signatures, including single base substitutions (e) and small insertions and deletions (indels; f). Driver mutations were divided into different groups, including the APC c.835–8A>G splicing-associated driver mutation, other APC driver mutations, TP53 driver mutations, and driver mutations affecting other cancer driver genes.