Computational Biological Modeling Identifies PD-(L)1 Immunotherapy Sensitivity Among Molecular Subgroups of...

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• KRAS-mutated non-small-cell lung cancer (NSCLC) is a heterogeneous disease.
• Comutations in KRAS-mutated NSCLC may influence response to PD-(L)1 immunotherapy.
• Identifying specific molecular subgroups is crucial for predicting immunotherapy efficacy.

Purpose of the Study:

• To leverage computational biological modeling (CBM) to analyze tumor genomic data.
• To identify PD-(L)1 immunotherapy sensitivity across different molecular subgroups of KRAS-mutated NSCLC.

Main Methods:

• Retrospective analysis of genomic data from 776 patients with KRAS-mutated NSCLC.
• Molecular clustering using the genotype clustering frequency ranked method.
• CBM evaluation of PD-(L)1 immunotherapy sensitivity based on PD-L1 expression, dendritic cell infiltration, and immunosuppressive biomarkers.

Main Results:

• Eight molecular subgroups of KRAS-mutated NSCLC were identified.
• CBM predicted PD-(L)1 immunotherapy sensitivity in KRAS/TP53, KRAS/PIK3CA, and KRAS-alone subgroups.
• Resistance was predicted in KEAP1-containing subgroups; CBM-assessed survival correlated with actual survival (r=0.80, P<.001) in an exploratory cohort.

Conclusions:

• CBM successfully identified differential PD-(L)1 immunotherapy sensitivity among KRAS-mutated NSCLC molecular subgroups.
• These findings provide proof-of-concept for using computational modeling of tumor genomics to predict immunotherapy response.
• The study suggests potential mechanisms underlying PD-(L)1 immunotherapy sensitivity in NSCLC.