Increased KRAS G12C Prevalence, High Tumor Mutational Burden, and Specific Mutational Signatures Are Associated With MUTYH Mutations: A Pan-Cancer Analysis

Abstract

The aim of this study was to determine the pan-cancer landscape of MUTYH alterations and the relationship between MUTYH mutations and potentially actionable biomarkers such as specific genomic alterations, tumor mutational burden, and mutational signatures. We used a large pan-cancer comprehensive genomic dataset from patients profiled (tissue next generation sequencing) during routine clinical care. Overall, 2.8% of 229 120 solid tumors had MUTYH alterations, of which 55% were predicted germline. Thirty tumor types had a 2% or greater MUTYH mutation rate. MUTYH-altered versus -WT cancers had significantly higher tumor mutational burden and more frequent alterations in KRAS G12C, but not in KRAS in general; these observations were statistically significant, especially in colorectal cancers. Across cancers, PD-L1 expression levels (immunohistochemistry) were not associated with MUTYH alteration status. In silico computation demonstrated that MUTYH mutational signatures are associated with higher levels of hydrophobicity (which may reflect higher immunogenicity of neoantigens) relative to several other signature types such as microsatellite instability. Survival of patients with MUTYH-altered versus -WT tumors was similar. In conclusion, comprehensive genomic profiling suggests that several features of MUTYH-altered cancers may be pharmacologically targetable. Drugs such as sotorasib (targeting KRAS G12C) and immune checkpoint inhibitors, targeting the increased mutational load and higher neo-antigen hydrophobicity/immunogenicity merit investigation in MUTYH-mutated malignancies.

Keywords: KRAS G12C; MUTYH mutations; genomic alterations; tumor mutational burden.

Figure 1.

Pan-cancer prevalence of MUTYH alterations. (A) Bar plots presenting the prevalence of MUTYH alterations in a pan-cancer cohort comprising 229 120 cases. The color of the bar corresponds to the type of alteration—prevalence of predicted pathogenic alterations and variants of unknown significance (left), breakdown of monoallelic versus biallelic versus unknown MUTYH alterations (middle), as well as prevalence of all predicted germline MUTYH alterations (right). (B) Lollipop plot of all predicted pathogenic MUTYH alterations. Short variants comprising single nucleotide alterations and insertions/deletions identified along the transcript are shown.

Figure 2.

Association of MUTYH alterations and immunotherapy-related biomarkers. (A) Boxplot of the distribution of TMB across different tumor types based on the MUTYH alteration status (MUTYH, gMUTYH, wild type WT). Comparisons of the TMB distribution was performed against MUTYH-WT cases using a Wilcoxon test. Statistically significant differences are displayed (P-value thresholds: .0001: ****, .001: ***, .01: **, .05: *). The tumor types are ordered based on the difference in median TMB between MUTYH-altered and WT groups; the top 15 tumor types with at least 500 total samples and at least 15 altered samples are shown. (B) Breakdown of PD-L1 status (negative, 1%-49%, 50%+) across different tumor types based on the MUTYH alteration status. The 15 tumor cohorts with the largest number of samples annotated for PD-L1 status are shown. (C) Statistical analysis to test for association between the presence of MUTYH alterations and immunotherapy-associated biomarkers using a Fisher’s exact test (P ≤ .05: statistically significant, ns: not significant).

Figure 3.

Examination of MUTYH-altered colorectal cancers (see also Supplementary Tables S3-S6). (A) Distribution of TMB based on the MUTYH alteration status (MUTYH, gMUTYH, wild type WT) in our cohort of colorectal cancer. TMB is assessed in the following bins: <10, 10-20, ≥20 mutations/megabase. (B) Statistical analysis to test for association between the presence of MUTYH alterations and immunotherapy-associated biomarkers as well as KRAS alterations using a Fisher’s exact test (P ≤ .05: statistically significant, ns: not significant). (C) Prevalence of MUTYH alterations based on microsatellite instability status. (D) Prevalence of KRAS G12C mutations in MUTYH-altered and wildtype cases, in microsatellite stable and instable CRCs. (E) Prevalence of KRAS G12C mutations in MUTYH-altered and wildtype cases, in other tumor types.

Figure 4.

Examination of mutational signatures associated with MUTYH. (A) Prevalence of mutational signatures SBS 18 and 36 in MUTYH-altered and wildtype colorectal cancer cases. (B) Relative number of alterations attributed to different mutational signatures based on the MUTYH-alteration status in colorectal cancer. (C) Iterative simulation of weighted hydrophobicity for MSI (SBS6, 14, 15, 20, 21, 26, and 44), MUTYH (SBS18 and 36), and APOBEC (SBS2 and 13) mutational signatures as performed in Boichard et al..

Figure 5.

Overall survival analysis comparing patients with altered (n = 169) and unaltered (n = 10634) MUTYH variant status from the TCGA pan-cancer dataset within cBioPortal shows no statistically significant relationship between the gene alteration and survival of patients, irrespective of origin of tumor (log-rank test P-value .22).