Association of clock-like mutational signature with immune checkpoint inhibitor outcome in patients with melanoma and NSCLC

Immune checkpoint inhibitor (ICI) therapy has achieved remarkable clinical benefit in melanoma and non-small cell lung cancer (NSCLC). Tumor mutational signatures are the fingerprints of endogenous and exogenous factors that have acted throughout tumorigenesis and heterogeneity; however, their association with immune response in ICI-treated samples remains unclear. Here, we leveraged whole-exome sequencing (WES)-based mutational profiles combined with clinicopathologic characteristics from melanoma and NSCLC datasets to examine whether tumor genomic features contribute to clinical benefit of ICI treatment. Mutational data acquired from targeted next-generation sequencing (NGS) assays (MSKIMPACT panels) were also employed for further corroboration. A mutational signature (known as age-related clock-like processing) characterized by enrichment of C>T mutations at NpCpG trinucleotides were identified to be associated with a worse prognosis and lower tumor mutation load (TML) in both WES and targeted NGS immunotherapy cohorts. We also analyzed gene transcriptomic profiles and identified immune regulation-related gene pathways that were significantly altered in samples with different clock-like signature grouping. Leucocyte subset analysis further revealed that clock-like signature was associated with the reduction of cytotoxic cell infiltration and elevation of regulatory T cells. Overall, our work re-annotated that the age-related clock-like signature was associated with worse prognosis and lower immune activity, offering opportunities to stratify patients into optimal immunotherapy plans based on genomic subtyping.

Automated summary

This study investigated the association between tumor genomic features and clinical benefit of ICI treatment, revealing that an age-related clock-like mutational signature is linked to worse prognosis and lower immune activity. The findings suggest opportunities to stratify patients into optimal immunotherapy plans based on genomic subtyping.