MED12 exerts an emerging role in actin-mediated cytokinesis via LIMK2/cofilin pathway in NSCLC.

BACKGROUND: Mediator complex subunit 12 (MED12) is an essential hub for transcriptional regulation, in which mutations and overexpression were reported to be associated with several kinds of malignancies. Nevertheless, the role of MED12 in non-small cell lung cancer (NSCLC) remains to be elucidated. METHODS: MED12 mutation was detected by Next-generation sequencing. The expression of MED12 in 179 human NSCLC tissue samples and 73 corresponding adjacent normal lung tissue samples was measured by immunohistochemistry (IHC). CRISPR-Cas9 was used to knock out MED12 in PC9 and SPC-A1 cells. MED12 rescued stable cell lines were generated by lentivirus infection. We traced cell division process by live cell imaging. The molecular mechanism of aborted cytokinesis resulted by MED12 knockout was investigated by RNA-seq. Effects of MED12 deletion on the proliferation of NSCLC cells were determined by MTT assay and Colony-formation assay in vitro and xenograft tumor model in nude mouse. Cell senescence was measured by SA-ß-gal staining. RESULTS: In our study, no MED12 exon mutation was detected in NSCLC samples, whereas we found that MED12 was overexpressed in human NSCLC tissues, which positively correlated with the tumor volume and adversely affected patient survival. Furthermore, knockout MED12 in NSCLC cell lines resulted in cytokinesis failure, displayed a multinuclear phenotype, and disposed to senescence, and become non-viable. Lack of MED12 decreased the proliferative potential of NSCLC cells and limited the tumor growth in vivo. Mechanism investigations revealed that MED12 knockout activated LIMK2, caused aberrant actin cytoskeleton remodeling, and disrupted the abscission of intercellular bridge, which led to the cytokinesis failure. Reconstitution of exogenous MED12 restored actin dynamics, normal cytokinesis and cell proliferation capacity in MED12 knockout cells. CONCLUSIONS: These results revealed a novel role of MED12 as an important regulator for maintaining accurate cytokinesis and survival in NSCLC cells, which may offer a therapeutic strategy to control tumor growth for NSCLC patients especially those highly expressed MED12.

Construction and validation of an immunoediting-based optimized neoantigen load (ioTNL) model to predict the response and prognosis of immune checkpoint therapy in various cancers.

BACKGROUND: Only a minority of patients clinically benefit from immune checkpoint therapy. Tumor clones with neoantigens have immunogenicity; therefore, they are eliminated by T-cell-mediated immune editing. Identifying neoantigen clones with the ability to induce immune elimination may better predict the clinical outcome of immunotherapy. METHODS: We developed ioTNL model, which indicates the immunoediting-based optimized tumor neoantigen load, by identifying tumor clones that could induce immune elimination. Data of more than two hundred patients from our patient pool and previously reported studies who underwent anti-PD-(L)1 therapy were collected to validate the prediction performance of ioTNL model. Clonal architectures, immune editing scores and ioTNL scores were identified. The association between the response as well as prognosis and the ioTNL were evaluated. Panel sequencing of genes from 2,469 patients within 20 cancer types was performed to profile the landscape of immunoediting. RESULTS: As expected, the ioTNL score could predict the response in patients who underwent immune checkpoint inhibitor (ICI) immunotherapy for various cancers, including non-small cell lung cancer (NSCLC; p = 0.0066), skin cutaneous melanoma (SKCM; p = 0.026) and nasopharyngeal carcinoma (NPC; p = 0.0025). Patients with a high ioTNL score demonstrated longer survival than those with a low score. We verified the ioTNL on our cohort through panel sequencing and found that the ioTNL was associated with the response (p = 0.025) and prognosis (p = 0.00082) in anti-PD-(L)1 monotherapy. In addition, we found that the immune editing score correlated with the tumor mutation burden (TMB) and the objective response rate of immunotherapy. CONCLUSIONS: Identifying neoantigen clones with the ability to induce immune elimination would better predict the efficacy of immunotherapy. We have proved that the reliable method of ioTNL can be applied to whole-exome sequencing (WES) and panel data and would have a broad application in precision diagnosis in immunotherapy.

A Novel Computational Framework for Predicting the Survival of Cancer Patients With PD-1/PD-L1 Checkpoint Blockade Therapy.

Background: Immune checkpoint inhibitors (ICIs) induce durable responses, but only a minority of patients achieve clinical benefits. The development of gene expression profiling of tumor transcriptomes has enabled identifying prognostic gene expression signatures and patient selection with targeted therapies. Methods: Immune exclusion score (IES) was built by elastic net-penalized Cox proportional hazards (PHs) model in the discovery cohort and validated via four independent cohorts. The survival differences between the two groups were compared using Kaplan-Meier analysis. Both GO and KEGG analyses were performed for functional annotation. CIBERSORTx was also performed to estimate the relative proportion of immune-cell types. Results: A fifteen-genes immune exclusion score (IES) was developed in the discovery cohort of 65 patients treated with anti-PD-(L)1 therapy. The ROC efficiencies of 1- and 3- year prognosis were 0.842 and 0.82, respectively. Patients with low IES showed a longer PFS (p=0.003) and better response rate (ORR: 43.8% vs 18.2%, p=0.03). We found that patients with low IES enriched with high expression of immune eliminated cell genes, such as CD8+ T cells, CD4+ T cells, NK cells and B cells. IES was positively correlated with other immune exclusion signatures. Furthermore, IES was successfully validated in four independent cohorts (Riaz's SKCM, Liu's SKCM, Nathanson's SKCM and Braun's ccRCC, n = 367). IES was also negatively correlated with T cell-inflamed signature and independent of TMB. Conclusions: This novel IES model encompassing immune-related biomarkers might serve as a promising tool for the prognostic prediction of immunotherapy.

Single-cell exome sequencing reveals multiple subclones in metastatic colorectal carcinoma.

BACKGROUND: Colorectal cancer (CRC) is a major cancer type whose mechanism of metastasis remains elusive. METHODS: In this study, we characterised the evolutionary pattern of metastatic CRC (mCRC) by analysing bulk and single-cell exome sequencing data of primary and metastatic tumours from 7 CRC patients with liver metastases. Here, 7 CRC patients were analysed by bulk whole-exome sequencing (WES); 4 of these were also analysed using single-cell sequencing. RESULTS: Despite low genomic divergence between paired primary and metastatic cancers in the bulk data, single-cell WES (scWES) data revealed rare mutations and defined two separate cell populations, indicative of the diverse evolutionary trajectories between primary and metastatic tumour cells. We further identified 24 metastatic cell-specific-mutated genes and validated their functions in cell migration capacity. CONCLUSIONS: In summary, scWES revealed rare mutations that failed to be detected by bulk WES. These rare mutations better define the distinct genomic profiles of primary and metastatic tumour cell clones.

Integrated bioinformatics analysis to identify 15 hub genes in breast cancer.

The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the 'limma' package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.