Rare mutation-dominant compound EGFR-positive NSCLC is associated with enriched kinase domain-resided variants of uncertain significance and poor clinical outcomes.

BACKGROUND

Compound epidermal growth factor receptor (EGFR) mutations are less responsive to tyrosine kinase inhibitors (TKIs) than single EGFR mutations in non-small cell lung cancer (NSCLC). However, the detailed clinical characteristics and prognosis of various compound EGFR mutations remain to be elucidated.

METHODS

We retrospectively studied the next-generation sequencing (NGS) data of treatment-naïve tumors from 1025 NSCLC patients with compound EGFR mutations, which were sub-categorized into different combinations of common mutations (19-Del and EGFR exon 21 p.L858R), rare mutations, and variants of uncertain significance (VUSs). Prognosis and drug resistance to first-line TKIs were analyzed in 174 and 95 patients, respectively.

RESULTS

Compound EGFR mutations were enriched with EGFR exon 21 p.L858R and rare mutations, but not 19-Del (P < 0.001). The common + rare and rare + rare subtypes had fewer concurrent mutations in the PI3K pathway (P = 0.032), while the rare + rare and common + VUSs subtypes showed increased association with smoking- and temozolomide-related mutational signatures, respectively (P < 0.001). The rare mutation-dominant subtypes (rare + VUSs and rare + rare) had the worst clinical outcomes to first-line TKIs (P < 0.001), which was further confirmed using an external cohort (P = 0.0066). VUSs in the rare + VUSs subtype selectively reside in the EGFR kinase domain (P < 0.001), implying these tumors might select additional mutations to disrupt the regulation/function of the kinase domain.

CONCLUSIONS

Different subtypes of compound EGFR mutations displayed distinct clinical features and genetic architectures, and rare mutation-dominant compound EGFR mutations were associated with enriched kinase domain-resided VUSs and poor clinical outcomes. Our findings help better understand the oncogenesis of compound EGFR mutations and forecast prognostic outcomes of personalized treatments.