Noncanonical role of NCOR1 as a facilitator of DNA mismatch repair and its deficiency sensitizes cancers to immune checkpoint blockade therapy
Tumor genomic heterogeneity significantly influences the efficacy of immune checkpoint blockade (ICB) therapy. To elucidate the underlying mechanisms, we pioneered the development of an ICB response score (IRS) based on machine learning analysis of microenvironmental features. Through a pan-cancer genomic screening, we identified the nuclear receptor corepressor NCOR1 as a key regulator, the tumors with genomic deficiency of which display a tumor infiltrating lymphocyte (TIL)-inflamed microenvironment. Genetic ablation of Ncor1 delays the tumorigenesis and metastasis in syngeneic and spontaneous tumor models in immunocompetent mice. Mechanistically, NCOR1 guards DNA mismatch repair by directly interacting with MSH2 and recruiting MutSa to chromatin for mismatch recognition, independent of its transcriptional regulatory activity coupling with histone deacetylase. Ncor1 depletion in hepatocellular carcinoma and melanoma cells promotes tumor immunogenicity by increasing tumor mutation burden (TMB), neoantigen load, and TIL infiltration and activation, thereby enhancing anti-PD-1 therapy efficacy. Our findings reveal that NCOR1 loss represents a novel mechanism underlying TMB-high tumors with intact MMR components, broadening the application of ICB. Overall design: RNA was extracted from mouse Hepa1-6 and B16 cell lines using the Quick-RNA column purification kit (Zymo, USA). RNA-seq libraries were constructed using the VAHTS mRNA-seq v2 Library Prep Kit (Illumina, USA) according to the manufacturer's protocol. The libraries were sequenced using Novaseq 6000 (Illumina, USA) at Bioguoke Corporation (Beijing, China). The quality of raw sequencing reads was evaluated using FastQC (v0.12.1). Adaptor sequences and low-quality score bases were trimmed using TrimGalore (v.0.6.7). These reads were then aligned to the mm10 reference mouse genomes using Bowtie2 (v.2.2.5). The fragments per kilobase of exon per million mapped reads (FPKM) and gene counts were computed using RSEM (v.1.2.8), and the differentially expressed genes (DEGs) were analyzed using the DESeq2 (v.1.37) in R package. The functional enrichment analysis of the DEGs was performed using the clusterProfiler (v.4.2.2) in R package, in which the gene ontology (GO) annotations and canonical pathways/gene sets (KEGG and Reactome) were obtained from MSigDB (www.broadinstitute.org/gsea/msigdb; v2022.1.Mm).