Mutational drivers of a dysfunctional local immune response in resected non-small cell lung cancer (NSCLC) patients
Permanent lenke
https://hdl.handle.net/10037/29633Dato
2023-05-30Type
Master thesisMastergradsoppgave
Forfatter
Holmstad, PatrickSammendrag
Background:
Patients with KEAP1 and STK11 alterations have shown poor response to immunotherapy in non-small cell lung cancer (NSCLC) due to unknown underlying mechanisms. In a sub-study of the TNM-I trial (NCT03299478), we discovered that lung adenocarcinomas (LUAD) with concurrent KEAP1 and STK11 mutations exhibit predominantly non-inflamed immunological features, potentially contributing to immunotherapy resistance (PMID: 37100205). However, it is unclear whether single mutations or co-mutations drive this phenomenon.
Methods:
Among 215 patients (stage I-IIIA) who underwent genomic profiling, tumor tissue from 23 LUAD patients with STK11 and KEAP1 mutations were included in this thesis. NanoString gene expression analysis with the nCounter PanCancer IO 360™ Panel was performed and analyzed. Comparisons of gene expression and metagene changes were assessed across single versus co-mutations.
Results:
44% (n = 10) of the cohort had co-mutations, while 56% (n = 13) had a single mutation with either KEAP1 or STK11. In STK11 vs co-mutation, pathway analysis revealed up-regulation of genes associated with adaptive immunity. Specifically, B cells were generally upregulated (p-adj < 0.05) in STK11 altered cases. In KEAP1 vs co-mutation, matrix remodeling and metastasis pathways were highly enriched, with the highest fold changes for MMP7 and MMP9 (5.19, 3.34, respectively; p-adj < 0.05). Additionally, we found up-regulation of chemoresistant pathways in KEAP1 mutated patients (p-adj < 0.05). In STK11 vs KEAP1, NF-kappaB was the most altered pathway.
Conclusion:
KEAP1 mutation is the main driver of the non-inflamed phenotype in LUAD compared to STK11 mutation, and it contributes to a more aggressive disease through activation of metastatic pathways and chemoresistance features. These results need to be validated in larger datasets.
Forlag
UiT Norges arktiske universitetUiT The Arctic University of Norway
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