Epigenetic modification of α-N-acetylgalactosaminidase enhances cisplatin resistance in ovarian cancer.

Although cisplatin is one of the most effective antitumor drugs for ovarian cancer, the emergence of chemoresistance to cisplatin in over 80% of initially responsive patients is a major barrier to successful therapy. The precise mechanisms underlying the development of cisplatin resistance are not fully understood, but alteration of DNA methylation associated with aberrant gene silencing may play a role. To identify epigenetically regulated genes directly associated with ovarian cancer cisplatin resistance, we compared the expression and methylation profiles of cisplatin-sensitive and -resistant human ovarian cancer cell lines. We identified α-Nacetylgalactosaminidase (NAGA) as one of the key candidate genes for cisplatin drug response. Interestingly, in cisplatin-resistant cell lines, NAGA was significantly downregulated and hypermethylated at a promoter CpG site at position +251 relative to the transcriptional start site. Low NAGA expression in cisplatin-resistant cell lines was restored by treatment with a DNA demethylation agent, indicating transcriptional silencing by hyper-DNA methylation. Furthermore, overexpression of NAGA in cisplatin-resistant lines induced cytotoxicity in response to cisplatin, whereas depletion of NAGA expression increased cisplatin chemoresistance, suggesting an essential role of NAGA in sensitizing ovarian cells to cisplatin. These findings indicate that NAGA acts as a cisplatin sensitizer and its gene silencing by hypermethylation confers resistance to cisplatin in ovarian cancer. Therefore, we suggest NAGA may be a promising potential therapeutic target for improvement of sensitivity to cisplatin in ovarian cancer.

The full-length DNA sequence of Epstein Barr virus from a human gastric carcinoma cell line, SNU-719.

The consistent presence of Epstein-Barr virus (EBV) in malignant cells of EBV-associated gastric carcinoma (EBVaGC) suggests it plays an important role during the development of EBVaGC. However, the entire genomic sequence of EBV from EBVaGC has yet to be determined. This study first determined, annotated, and analyzed the full genomic sequence of EBV from the naturally infected gastric carcinoma cell line SNU-719 using next-generation sequencing and comparative analyses. In consistent with the notion that EBV sequence isolates better reflect their geographic area than tissue origin, the SNU-719 EBV (named as GC1) was categorized as an East Asian type I EBV. Compared with the prototype B95.8 sequence, SNU-719 EBV contained 1372 variations, with 937 and 435 within coding and non-coding regions, respectively. Of the 937 variations, 465 were non-synonymous changes, while 472 synonymous changes included partial internal deletions in the coding regions of LMP1 and gp350. The RNAseq transcriptome revealed that multiple BART transcripts comprised the majority of EBV RNA reads. The SNU-719 EBV expressed high levels of BART, LF3, BHLF1, and BNLF2. Evidence of RNA editing at multiple sites in the host chromosome was found; however, no evidence of genome integration was seen. The annotated SNU-719 EBV sequence will be a useful reference in future EBVaGC studies.

Dissemination and Symptoms of Monkeypox Virus Infection.

The monkeypox virus is endemic in Africa. However, the virus has spread worldwide since May 2022, and it has now been confirmed in 109 countries (as of September 22, 2022). Thus, an urgent investigation of the outbreak patterns of confirmed cases and evidence of viral infection is needed. This study investigated the spread of monkeypox in 109 countries, including 15 Asian-Pacific countries, by examining the number of cases, symptoms of infection, and the period between diagnosis and death.

TcellInflamedDetector: an R package to distinguish T cell inflamed tumor types from non-T cell inflamed tumor types.

A major issue in the use of immune checkpoint inhibitors is their lack of efficacy in many patients. Previous studies have reported that the T cell inflamed signature can help predict the response to immunotherapy. Thus, many studies have investigated mechanisms of immunotherapy resistance by defining the tumor microenvironment based on T cell inflamed and non-T cell inflamed subsets. Although methods of calculating T cell inflamed subsets have been developed, valid screening tools for distinguishing T cell inflamed from non-T cell inflamed subsets using gene expression data are still needed, since general researchers who are unfamiliar with the details of the equations can experience difficulties using extant scoring formulas to conduct analyses. Thus, we introduce TcellInflamedDetector, an R package for distinguishing T cell inflamed from non-T cell inflamed samples using cancer gene expression data via bulk RNA sequencing.

Analysis of the Rate of Confirmed COVID-19 Cases in Seoul and Factors Affecting It Using Big Data Analysis.

Coronavirus disease (COVID-19) is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and presents with mild to severe symptoms. Vaccines have been developed, but COVID-19 persists. Therefore, it is necessary to analyze big data at an early stage to establish an effective infection prevention strategy. To reduce SARS-CoV-2 infection, this study aimed to analyze the infection factors by region within Seoul, Korea and identify the major factors affecting the infection rate. For ease of data aggregation, the study was conducted after a data refinement operation that organized data in the same group into categories, and classified them in detail by specific keywords. Based on the results of this study, if preventive measures are established after identifying the representative infectious factors, periods, and routes of COVID-19 infection, the infection rate could be effectively reduced in the future.

Molecular landscape of osimertinib resistance in patients and patient-derived preclinical models.

INTRODUCTION: Osimertinib is a third-generation EGFR tyrosine kinase inhibitor (TKI) that is approved for the use of EGFR-mutant non-small cell lung cancer (NSCLC) patients. In this study, we investigated the acquired resistance mechanisms in NSCLC patients and patient-derived preclinical models. METHODS: Formalin-fixed paraffin-embedded tumor samples and plasma samples from 55 NSCLC patients who were treated with osimertinib were collected at baseline and at progressive disease (PD). Next-generation sequencing was performed in tumor and plasma samples using a 600-gene hybrid capture panel designed by AstraZeneca. Osimertinib-resistant cell lines and patient-derived xenografts and cells were generated and whole exome sequencing and RNA sequencing were performed. In vitro experiments were performed to functionally study the acquired mutations identified. RESULTS: A total of 55 patients and a total of 149 samples (57 tumor samples and 92 plasma samples) were analyzed, and among them 36 patients had matched pre- and post-treatment samples. EGFR C797S (14%) mutation was the most frequent EGFR-dependent mechanism identified in all available progression samples, followed by EGFR G824D (6%), V726M (3%), and V843I (3%). Matched pre- and post-treatment sample analysis revealed in-depth acquired mechanisms of resistance. EGFR C797S was still most frequent (11%) among EGFR-dependent mechanism, while among EGFR-independent mechanisms, PIK3CA, ALK, BRAF, EP300, KRAS, and RAF1 mutations were detected. Among Osimertinib-resistant cell lines and patient-derived models, we noted acquired mutations which were potentially targetable such as NRAS p.Q61K, in which resistance could be overcome with combination of osimertinib and trametinib. A patient-derived xenograft established from osimertinib-resistant patient revealed KRAS p.G12D mutation which could be overcome with combination of osimertinib, trametinib, and buparlisib. CONCLUSION: In this study, we explored the genetic profiles of osimertinib-resistant NSCLC patient samples using targeted deep sequencing. In vitro and in vivo models harboring osimertinib resistance revealed potential novel treatment strategies after osimertinib failure.

SKI-G-801, an AXL kinase inhibitor, blocks metastasis through inducing anti-tumor immune responses and potentiates anti-PD-1 therapy in mouse cancer models.

OBJECTIVES: AXL-mediated activation of aberrant tyrosine kinase drives various oncogenic processes and facilitates an immunosuppressive microenvironment. We evaluated the anti-tumor and anti-metastatic activities of SKI-G-801, a small-molecule inhibitor of AXL, alone and in combination with anti-PD-1 therapy. METHODS: In vitro pAXL inhibition by SKI-G-801 was performed in both human and mouse cancer cell lines. Immunocompetent mouse models of tumor were established to measure anti-metastatic potential of SKI-G-801. Furthermore, SKI-G-801, anti-PD-1 or their combination was administered as an adjuvant or neoadjuvant in the 4T1 tumor model to assess their potential for clinical application. RESULTS: SKI-G-801 robustly inhibited pAXL expression in various cell lines. SKI-G-801 alone or in combination with anti-PD-1 potently inhibited metastasis in B16F10 melanoma, CT26 colon and 4T1 breast models. SKI-G-801 inhibited the growth of B16F10 and 4T1 tumor-bearing mice but not immune-deficient mice. An antibody depletion assay revealed that CD8+ T cells significantly contributed to SKI-G-801-mediated survival. Anti-PD-1 and combination group were observed the increased CD8+Ki67+ and effector T cells and M1 macrophage and decreased M2 macrophage, and granulocytic myeloid-derived suppressor cell (G-MDSC) compared to the control group. The neoadjuvant combination of SKI-G-801 and anti-PD-1 therapy achieved superior survival benefits by inducing more profound T-cell responses in the 4T1 syngeneic mouse model. CONCLUSION: SKI-G-801 significantly suppressed tumor metastasis and growth by enhancing anti-tumor immune responses. Our results suggest that SKI-G-801 has the potential to overcome anti-PD-1 therapy resistance and allow more patients to benefit from anti-PD-1 therapy.

Brief Report: Heterogeneity of Acquired Resistance Mechanisms to Osimertinib and Savolitinib.

INTRODUCTION: MET amplification is a frequently observed mechanism of resistance to osimertinib, and coinhibition strategy of MET and EGFR revealed promising results in recent clinical trials. Nevertheless, acquired resistance mechanisms to combined EGFR and MET inhibition are poorly understood. In this study, we investigated the mechanisms of acquired resistance to osimertinib and savolitinib by using pretreatment and post-treatment tissue analysis. METHODS: Whole-exome sequencing was performed in EGFR-mutant, MET-amplified patients who received osimertinib and savolitinib using tissues obtained both before and after therapy. All patients achieved partial response or durable stable disease to osimertinib and savolitinib before developing acquired resistance. RESULTS: After progression on osimertinib and savolitinib, whole-exome analysis revealed MET-dependent mechanisms of resistance, such as acquired MET p.D1246H mutation, MET p.Y1230C mutation, and MET copy number gain. As for MET-independent mechanisms, development of ERBB2 mutation and amplification and copy number gains in amplifications in CCNE, CCND1, CDK6, and EGFR were observed. Patient 2 harbored an acquired PIK3CA p.H1047R mutation in which resistance could be overcome with combination of PI3K inhibitor and osimertinib in the patient-derived xenograft model. CONCLUSIONS: Our study reveals that acquired resistance to savolitinib plus osimertinib can occur from both MET-dependent and MET-independent mechanisms.

Modeling Clinical Responses to Targeted Therapies by Patient-Derived Organoids of Advanced Lung Adenocarcinoma.

PURPOSE: Patient-derived organoids (PDO) of lung cancer has been recently introduced, reflecting the genomic landscape of lung cancer. However, clinical relevance of advanced lung adenocarcinoma organoids remains unknown. Here, we examined the ability of PDOs to predict clinical responses to targeted therapies in individual patients and to identify effective anticancer therapies for novel molecular targets. EXPERIMENTAL DESIGN: Eighty-four organoids were established from patients with advanced lung adenocarcinoma. Formalin-fixed, paraffin-embedded tumor specimens from corresponding patients were analyzed by whole-exome sequencing (n = 12). Organoids were analyzed by whole-exome sequencing (n = 61) and RNA sequencing (n = 55). Responses to mono or combination targeted therapies were examined in organoids and organoid-derived xenografts. RESULTS: PDOs largely retained somatic alterations including driver mutations of matching patient tumors. PDOs were able to recapitulate progression-free survival and objective responses of patients with non-small cell lung cancer receiving clinically approved tyrosine kinase inhibitors. PDOs recapitulated activity of therapeutic strategies under clinical investigation. YUO-071 harboring an EGFR exon 19 deletion and a BRAF G464A mutation and the matching patient responded to dabrafenib/trametinib combination therapy. YUO-004 and YUO-050 harboring an EGFR L747P mutation was sensitive to afatinib, consistent with the response in the matching patient of YUO-050. Furthermore, we utilized organoids to identify effective therapies for novel molecular targets by demonstrating the efficacy of poziotinib against ERBB2 exon 20 insertions and pralsetinib against RET fusions. CONCLUSIONS: We demonstrated translational relevance of PDOs in advanced lung adenocarcinoma. PDOs are an important diagnostic tool, which can assist clinical decision making and accelerate development of therapeutic strategies.

Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer.

OBJECTIVE: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (PD-L1) level, are related to the anti-PD-1 response; however, none of these can independently serve as predictive biomarkers. Herein, we established a machine learning (ML)-based clinical decision support algorithm to predict the anti-PD-1 response by comprehensively combining the clinical information. MATERIALS AND METHODS: We collected clinical data, including patient characteristics, mutations and laboratory findings, from the electronic medical records of 142 patients with NSCLC treated with anti-PD-1 therapy; these were analysed for the clinical outcome as the discovery set. Nineteen clinically meaningful features were used in supervised ML algorithms, including LightGBM, XGBoost, multilayer neural network, ridge regression and linear discriminant analysis, to predict anti-PD-1 responses. Based on each ML algorithm's prediction performance, the optimal ML was selected and validated in an independent validation set of PD-1 inhibitor-treated patients. RESULTS: Several factors, including PD-L1 expression, tumour burden and neutrophil-to-lymphocyte ratio, could independently predict the anti-PD-1 response in the discovery set. ML platforms based on the LightGBM algorithm using 19 clinical features showed more significant prediction performance (area under the curve [AUC] 0.788) than on individual clinical features and traditional multivariate logistic regression (AUC 0.759). CONCLUSION: Collectively, our LightGBM algorithm offers a clinical decision support model to predict the anti-PD-1 response in patients with NSCLC.

3-Oxoacid CoA transferase 1 as a therapeutic target gene for cisplatin-resistant ovarian cancer.

Ovarian cancer (OC) is the second leading cause of mortality from gynecological malignancies and has the highest mortality rate worldwide. As it is commonly asymptomatic during the early stages of the disease, >70% of patients with OC are diagnosed at advanced stages with metastasis. Despite treatment methods, including optimal debulking surgery and chemotherapy with the platinum-based drug cisplatin, OC recurrence is often inevitable, with an overall 5-year survival rate of 45%, mostly due to the steady development of cisplatin resistance. To identify genes involved in cisplatin resistance, the present study determined the half-maximal inhibitory concentrations of eight different OC cell lines and classified them into two groups (sensitive and resistant). mRNA expression was analyzed with GeneChip Human Gene 1.0 ST Arrays, and DNA methylation profiles were evaluated with the HumanMethylation450 BeadChip. Using an integrated approach of analyzing gene expression levels and DNA methylation profiles simultaneously, 26 genes were selected that were differentially expressed and methylated between the resistant and sensitive groups. Among these 26 genes, 3-oxoacid CoA transferase 1 (OXCT1), which was demonstrated to be downregulated and hypermethylated at promoter CpGs in the cisplatin-resistant group compared with the cisplatin-sensitive group, was selected for further investigation. Treatment with a DNA methyltransferase inhibitor restored hypermethylation-mediated gene silencing of OXCT1 in the cisplatin-resistant group, but not in the cisplatin-sensitive group. Furthermore, overexpression of OXCT1 conferred sensitivity to cisplatin in OC cells. The results of the present study suggest that OXCT1 serves an important role in conferring cisplatin sensitivity, and may provide a potential therapeutic target for cisplatin chemotherapy in patients with recurrent OC.

Aberrant epigenetic regulation of GABRP associates with aggressive phenotype of ovarian cancer.

Metastasis is a major cause of therapeutic failure in ovarian cancer. To elucidate molecular mechanisms of ovarian cancer metastasis, we previously established a metastatic xenograft mouse model using human ovarian carcinoma SK-OV-3 cells. Using gene expression profiling, we found that γ-aminobutyric acid (GABA)A receptor π subunit (GABRP) expression was upregulated (>4-fold) in metastatic tissues from our xenograft mice compared with SK-OV-3 cells. Importantly, GABRP knockdown diminished the migration and invasion of SK-OV-3 cells, and reduced extracellular signal-regulated kinase (ERK) activation while overexpression of GABRP exhibited significantly increased cell migration, invasion and ERK activation. Moreover, treatment with the mitogen-activated protein kinase (MAPK)/ERK kinase (MEK) inhibitor U0126 similarly suppressed the migration and invasion of SK-OV-3 cells, implying that GABRP promotes these cellular behaviors by activating the MAPK/ERK pathway. Using genome-wide DNA methylation profiling, we identified hypomethylated CpG sites in the GABRP promoter in metastatic tissues from the xenograft mice compared with SK-OV-3 cells. Treatment with a DNA methyltransferase inhibitor demonstrated that methylation at -963 bp from the GABRP transcription start site (-963 CpG site) was critical for the epigenetic regulation of GABRP. Finally, we analyzed human ovarian cancer patient samples and showed DNA hypomethylation at the GABRP -963 CpG site in advanced stage, but not early-stage, primary tumors compared with their paired normal tissues. These findings suggest that GABRP enhances the aggressive phenotype of ovarian cancer cells, and that the DNA methylation status of the GABRP -963 CpG site may be useful for predicting the metastatic potential in ovarian cancer patients.

Aberrant Hypomethylation of Solute Carrier Family 6 Member 12 Promoter Induces Metastasis of Ovarian Cancer.

PURPOSE: Ovarian cancer (OC) is the most fatal of gynecological malignancies with a high rate of recurrence. We aimed to evaluate the expression of solute carrier family 6, member 12 (SLC6A12) and methylation of its promoter CpG sites in a xenograft mouse model of metastatic OC, and to investigate the regulatory mechanisms that promote aggressive properties during OC progression. MATERIALS AND METHODS: Expression of SLC6A12 mRNA was determined by reverse-transcription quantitative polymerase chain reaction (RT-qPCR), and DNA methylation status of its promoter CpGs was detected by quantitative methylation-specific PCR. The metastatic potential of SLC6A12 was evaluated by in vitro migration/invasion transwell assays. Gene expression and DNA methylation of SLC6A12 and clinical outcomes were further investigated from publicly available databases from curatedOvarianData and The Cancer Genome Atlas. RESULTS: SLC6A12 expression was 8.1-14.0-fold upregulated and its DNA methylation of promoter CpG sites was 41-62% decreased in tumor metastases. After treatment with DNA methyltransferase inhibitor and/or histone deacetylase inhibitor, the expression of SLC6A12 was profoundly enhanced (~8.0-fold), strongly supporting DNA methylation-dependent epigenetic regulation of SLC6A12. Overexpression of SLC6A12 led to increased migration and invasion of ovarian carcinoma cells in vitro, approximately 2.0-fold and 3.3-fold, respectively. The meta-analysis showed that high expression of SLC6A12 was significantly associated with poor overall survival [hazard ratio (HR)=1.07, p value=0.016] and that low DNA methylation levels of SLC6A12 at specific promoter CpG site negatively affected patient survival. CONCLUSION: Our findings provide novel evidence for the biological and clinical significance of SLC6A12 as a metastasis-promoting gene.

Genome-wide association and expression quantitative trait loci studies identify multiple susceptibility loci for thyroid cancer.

Thyroid cancer is the most common cancer in Korea. Several susceptibility loci of differentiated thyroid cancer (DTC) were identified by previous genome-wide association studies (GWASs) in Europeans only. Here we conducted a GWAS and a replication study in Koreans using a total of 1,085 DTC cases and 8,884 controls, and validated these results using expression quantitative trait loci (eQTL) analysis and clinical phenotypes. The most robust associations were observed in the NRG1 gene (rs6996585, P=1.08 × 10-10) and this SNP was also associated with NRG1 expression in thyroid tissues. In addition, we confirmed three previously reported loci (FOXE1, NKX2-1 and DIRC3) and identified seven novel susceptibility loci (VAV3, PCNXL2, INSR, MRSB3, FHIT, SEPT11 and SLC24A6) associated with DTC. Furthermore, we identified specific variants of DTC that have different effects according to cancer type or ethnicity. Our findings provide deeper insight into the genetic contribution to thyroid cancer in different populations.

Estimation of Prognostic Marker Genes by Public Microarray Data in Patients with Ovarian Serous Cystadenocarcinoma.

PURPOSE: Lymphatic invasion (LI) is regarded as a predictor of the aggressiveness of ovarian cancer (OC). However, LI is not always the major determinant of long-term patient survival. To establish proper diagnosis and treatment for OC, we analyzed differentially expressed genes (DEGs) for patients with serous epithelial OC, with or without LI, who did or did not survive for 5 years. MATERIALS AND METHODS: Gene expression data from 63 patients with OC and LI, and 35 patients with OC but without LI, were investigated using an Affymetrix Human Genome U133 Array and analyzed using The Cancer Genome Atlas (TCGA) database. Among these 98 patients, 16 survived for 5 years or more. DEGs were identified using the Bioconductor R package, and their functions were analyzed using the DAVID web tool. RESULTS: We found 55 significant DEGs (p<0.01) from the patients with LI and 20 highly significant DEGs (p<0.001) from those without it. Pathway analysis showed that DEGs associated with carbohydrate metabolism or with renal cell carcinoma pathways were enriched in the patients with and without LI, respectively. Using the top five prognostic marker genes, we generated survival scores that could be used to predict the 5-year survival of patients with OC without LI. CONCLUSION: The DEGs identified in this study could be used to elucidate the mechanism of tumor progression and to guide the prognosis and treatment of patients with serous OC but without LI.