bioRxivImmunology
KALRN Mutations Promote Anti-tumor Immunity and Immunotherapy Response in Cancer
Abstract
BackgroundKALRN (kalirin RhoGEF kinase) is mutated in a wide range of cancers. Nevertheless, the association between KALRN mutations and the pathogenesis of cancer remains unexplored. The identification of biomarkers for cancer immunotherapy response is important considering that immunotherapies show beneficial effects only in a subset of cancer patients. MethodsWe explored the correlation between KALRN mutations and anti-tumor immunity in 10 cancer cohorts from The Cancer Genome Atlas (TCGA) program by the bioinformatics approach. Moreover, we verified the findings from bioinformatics analysis by in vitro experiments. Furthermore, we explored the correlation between KALRN mutations and immunotherapy response in four cancer cohorts receiving immune checkpoint blockade therapy. ResultsWe found that anti-tumor immune signatures were stronger in KALRN-mutated than in KALRN-wildtype cancers. Moreover, KALRN mutations correlated with increased tumor mutation burden and the microsatellite instability or DNA damage repair deficiency genomic properties which may explain the elevated anti-tumor immunity in KALRN-mutated cancers. Furthermore, we found that PD-L1 expression was significantly upregulated in KALRN-mutated versus KALRN-wildtype cancers. The enhanced anti-tumor immune signatures and PD-L1 expression in KALRN-mutated cancers may favor the response to immune checkpoint blockade therapy in this cancer subtype, as evidenced in four cancer cohorts receiving anti-PD-1/PD-L1/CTLA-4 immunotherapy. We further revealed that the significant association between KALRN mutations and increased anti-tumor immunity was attributed to that KALRN mutations compromised the function of KALRN target Rho GTPases on regulating DNA damage repair pathways. ConclusionsThe KALRN mutation is a useful biomarker for predicting the response to immunotherapy in cancer patients.
- organism: human
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- approach: biophysical modeling
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