Mutations in KMT2C , BCOR and KDM5C Predict Response to Immune Checkpoint Blockade Therapy in Non-Small Cell Lung Cancer.
Dingxie LiuJonathan BenzaquenLuc G T MorrisVéronique HofmanPaul HofmanPublished in: Cancers (2022)
Efficient predictive biomarkers are urgently needed to identify non-small cell lung cancer (NSCLC) patients who could benefit from immune checkpoint blockade (ICB) therapy. Since chromatin remodeling is required for DNA repair process, we asked whether mutations in chromatin remodeling genes could increase tumor mutational burden (TMB) and predict response to ICB therapy in NSCLC. Analysis of seven ICB-treated NSCLC cohorts revealed that mutations of three chromatin remodeling-related genes, including KMT2C , BCOR and KDM5C , were significantly associated with ICB response, and combined mutations of these three genes further enhance this association. NSCLC patients with KMT2C/BCOR/KDM5C mutations had comparable clinical outcomes to TMB-high patients in terms of objective response rate, durable clinical benefit and overall survival. Although KMT2C/BCOR/KDM5C mutations were positively correlated with TMB levels in NSCLC, the association of this mutation with better ICB response was independent of tumor TMB and programmed death-ligand 1 (PD-L1) level, and combination of KMT2C/BCOR/KDM5C mutations with TMB or PD-L1 further improve the prediction of ICB response in NSCLC patients. Cancer Genome Atlas (TCGA) pan-cancer analysis suggested that the association of KMT2C/BCOR/KDM5C mutations with ICB response observed here might not result from DNA repair defects. In conclusion, our data indicate that KMT2C/BCOR/KDM5C mutation has the potential to serve as a predictive biomarker, alone or combined with PD-L1 expression or TMB, for ICB therapy in NSCLC.
Keyphrases
- small cell lung cancer
- dna repair
- dna damage
- advanced non small cell lung cancer
- genome wide
- ejection fraction
- gene expression
- end stage renal disease
- newly diagnosed
- clear cell
- prognostic factors
- single cell
- risk assessment
- dna methylation
- machine learning
- papillary thyroid
- bone marrow
- squamous cell carcinoma
- dna damage response
- patient reported outcomes
- risk factors
- electronic health record
- epidermal growth factor receptor
- big data
- deep learning
- tyrosine kinase