Mutation burden and other molecular markers of prognosis in the QUASAR2 clinical trial of colorectal cancer treated with curative intent - Domingo, E; Camps, C; Kaisaki, P; Parsons, M; Mouradov, D; Pentony, M; Makino, S; Palmieri, M; Ward, R; Hawkins, N; Gibbs, P; Askautrud, H; et al. (Elsevier, 2018)
Background Several relatively large studies have assessed molecular indicators of colorectal cancer (CRC) prognosis, but most analyses have been restricted to a handful of markers. Methods In stage II/III CRCs from the QUASAR2 clinical trial and from an Australian community-based series, we assessed gene panels for somatic driver mutations and overall mutation burden. We determined molecular pathways of tumorigenesis, and analysed associations with treatment response and prognosis. Findings In QUASAR2 (N=511), TP53, KRAS, BRAF and GNAS mutations were independently associated with shorter relapse-free survival, whereas total somatic mutation burden was associated with longer survival, even after excluding mismatch repair-deficient (MSI+) and POLE-mutant tumours. We successfully validated these associations in the Australian sample set (N=296). In an extended analysis of 1,752 QUASAR2 and Australian CRCs for which KRAS, BRAF and MSI status was available, we found that KRAS and BRAF mutations were specifically associated with poor prognosis in MSI- cancers. This association was not present in MSI+ cancers, and MSI+ tumours with KRAS or BRAF mutation actually had better prognosis than MSI- cancers that were wildtype for KRAS or BRAF. New rare molecular pathways were also uncovered: mutations in the genes NF1 and NRAS from the MAP kinase pathway co-occurred, mutations in TP53 and ATM appeared to be alternative ways of inactivating the DNA damage response pathway. Interpretation A multi-gene panel has identified two previously unreported prognostic associations in CRC involving both TP53 mutation and total mutation burden, and confirmed associations with KRAS and BRAF. We conclude that even a modest-sized gene panel can provide important information for use in clinical practice and out-perform MSI-based models.