ARPC2 is a subunit of the Arp2/3 complex, which is essential for lamellipodia, invadopodia and filopodia, and ARPC2 has been identified as a migrastatic target molecule. To identify ARPC2 inhibitors, we generated an ARPC2 knockout DLD‐1 human colon cancer cell line using the clustered regularly interspaced short palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9) system and explored gene signature‐based strategies, such as a connectivity map (CMap) using the gene expression profiling data of ARPC2 knockout and knockdown cells. From the CMap‐based drug discovery strategy, we identified pimozide (a clinically used antipsychotic drug) as a migrastatic drug and ARPC2 inhibitor. Pimozide inhibited the migration and invasion of various cancer cells. Through drug affinity responsive target stability (DARTS) analysis and cellular thermal shift assay (CETSA), it was confirmed that pimozide directly binds to ARPC2. Pimozide increased the lag phase of Arp2/3 complex‐dependent actin polymerization and inhibited the vinculin‐mediated recruitment of ARPC2 to focal adhesions in cancer cells. To validate the likely binding of pimozide to ARPC2, mutant cells, including ARPC2F225A, ARPC2F247A and ARPC2Y250F cells, were prepared using ARPC2 knockout cells prepared by gene‐editing technology. Pimozide strongly inhibited the migration of mutant cells because the mutated ARPC2 likely has a larger binding pocket than the wild‐type ARPC2. Therefore, pimozide is a potential ARPC2 inhibitor, and ARPC2 is a new molecular target. Taken together, the results of the present study provide new insights into the molecular mechanism and target that are responsible for the antitumor and antimetastatic activity of pimozide.

No abstract

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. ‘dada2’ performs trimming of the high-throughput sequencing data. ‘QuasR’ and ‘mosaics’ perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, ‘ChIPseeker’ performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.

Objectives.Thyroid cancer is the most common endocrine tumor, with rapidly increasing incidence worldwide. However, its transcriptomic characteristics associated with immunological signatures, driver fusion, and recurrence markers are still unclear. We aimed to investigate the transcriptomic characteristics of advanced papillary thyroid cancer patients.MethodsThis study included 282 papillary thyroid cancer tumor samples, and 155 normal samples from Chungnam National University Hospital and Seoul National University Hospital. Transcriptomic quantification was determined by high-throughput RNA sequencing. We investigated the association of clinical parameters and molecular signatures using RNA sequencing. We validated the predictive biomarker using The Cancer Genome Atlas (TCGA) database.ResultsBy comparing differentially expressed genes, gene sets, and pathways in papillary thyroid cancer than normal adjacent of tumor tissue, we found increased immune signaling associated with cytokine or T-cell and decreased thyroid hormone synthetic pathways. In addition, patients with recurrence represented increased CD8+ T-cell signature and Th1 cells signature, respectively. Interestingly, we found differentially overexpressed genes related to immune-escape signaling such as CTLA4, IDO1, LAG3, and PDCD1 in advanced PTC with low thyroid differentiation score (TDS). Fusion analysis showed that the PI3K and MAPK signaling pathways were regulated differently according to RET fusion partner genes (CCDC6 and NCOA4). Finally, we identified HOXD9 as a novel molecular biomarker that predicts the recurrence of thyroid cancer in addition to known risk factors (tumor size, lymph node metastasis, and extrathyroidal extension).ConclusionWe identified a high association with immune-escape signaling in the immune-hot group with aggressive clinical factors of Korean thyroid cancer patients. Moreover, RET fusion differentially regulated PI3K and MAPK signaling depending on the partner gene of RET, and HOXD9 was found to be a recurrence marker for advanced PTC patients.