PURPOSE KRAS-mutated ( KRASMUT) non–small-cell lung cancer (NSCLC) is emerging as a heterogeneous disease defined by comutations, which may confer differential benefit to PD-(L)1 immunotherapy. In this study, we leveraged computational biological modeling (CBM) of tumor genomic data to identify PD-(L)1 immunotherapy sensitivity among KRASMUT NSCLC molecular subgroups. MATERIALS AND METHODS In this multicohort retrospective analysis, the genotype clustering frequency ranked method was used for molecular clustering of tumor genomic data from 776 patients with KRASMUT NSCLC. These genomic data were input into the CBM, in which customized protein networks were characterized for each tumor. The CBM evaluated sensitivity to PD-(L)1 immunotherapy using three metrics: programmed death-ligand 1 expression, dendritic cell infiltration index (nine chemokine markers), and immunosuppressive biomarker expression index (14 markers). RESULTS Genotype clustering identified eight molecular subgroups and the CBM characterized their shared cancer pathway characteristics: KRAS MUT/ TP53 MUT, KRAS MUT/ CDKN2A/ B/ C MUT, KRAS MUT/ STK11 MUT, KRAS MUT/ KEAP1 MUT, KRAS MUT/ STK11 MUT/ KEAP1 MUT, KRAS MUT/ PIK3CA MUT, KRAS MUT/ ATM MUT, and KRAS MUT without comutation. CBM identified PD-(L)1 immunotherapy sensitivity in the KRAS MUT/ TP53 MUT, KRAS MUT/ PIK3CA MUT, and KRAS MUT alone subgroups and resistance in the KEAP1 MUT containing subgroups. There was insufficient genomic information to elucidate PD-(L)1 immunotherapy sensitivity by the CBM in the KRAS MUT/ CDKN2A/ B/ C MUT, KRAS MUT/ STK11 MUT, and KRAS MUT/ ATM MUT subgroups. In an exploratory clinical cohort of 34 patients with advanced KRASMUT NSCLC treated with PD-(L)1 immunotherapy, the CBM-assessed overall survival correlated well with actual overall survival ( r = 0.80, P < .001). CONCLUSION CBM identified distinct PD-(L)1 immunotherapy sensitivity among molecular subgroups of KRASMUT NSCLC, in line with previous literature. These data provide proof-of-concept that computational modeling of tumor genomics could be used to expand on hypotheses from clinical observations of patients receiving PD-(L)1 immunotherapy and suggest mechanisms that underlie PD-(L)1 immunotherapy sensitivity.
