Targeting Monounsaturated Fatty Acid Metabolism for Radiosensitization of KRAS Mutant 3D Lung Cancer Models

Mol Cancer Ther. 2025 Jan 21. doi: 10.1158/1535-7163.MCT-24-0213. Online ahead of print.

Abstract

Mutations in the KRAS oncogene can mediate resistance to radiation. KRAS mutation (mut) driven tumors have been reported to express cancer stem cell (CSC)-like features and may harbor metabolic liabilities through which CSC-associated radioresistance can be overcome. We established a radiation/drug screening approach that relies on the growth of 3D spheres under anchorage-independent and lipid-limiting culture conditions, which promote stemness and lipogenesis. In this format, we screened 32 KRASmut-enriched lung cancer models. As predicted from published data, CB-839, a glutaminase inhibitor, displayed the highest degree of radiosensitization in KRASmut models with LKB1 co-mutations. Radiosensitization by inhibition of stearoyl-CoA desaturase-1, SCD1, displayed a similar genotype preference though the data also implicated KEAP1 co-mutation and SCD1 expression as potential predictors of radiosensitization. In an isogenic model, KRASmut cells were characterized by increased SCD1 expression and a higher ratio of monounsaturated fatty acids (MUFA) to saturated fatty acids. Accordingly, pharmacological inhibition or depletion of SCD1 radiosensitized isogenic KRASmut but not wild-type cells. The radiosensitizing effect was notably small, especially compared to several DNA repair inhibitors. As an alternative strategy to targeting MUFA metabolism, adding polyunsaturated FAs (PUFA) phenocopied some aspects of SCD1 inhibition, suppressed tumor growth in vivo, and opposed the CSC-like phenotype of KRASmut cells. In conclusion, we report a 3D screening approach that recapitulates clinically relevant features of KRASmut tumors and can be leveraged for therapeutic targeting of metabolic vulnerabilities. Our data highlight pronounced inter-tumoral heterogeneity in radiation/drug responses and the complexity of underlying genomic dependencies.