Inhibition of protein arginine methyltransferase 6 activates interferon signaling and induces the apoptosis of endometrial cancer cells via histone modification.

Histone modification, a major epigenetic mechanism regulating gene expression through chromatin remodeling, introduces dynamic changes in chromatin architecture. Protein arginine methyltransferase 6 (PRMT6) is overexpressed in various types of cancer, including prostate, lung and endometrial cancer (EC). Epigenome regulates the expression of endogenous retrovirus (ERV), which activates interferon signaling related to cancer. The antitumor effects of PRMT6 inhibition and the role of PRMT6 in EC were investigated, using epigenome multi­omics analysis, including an assay for chromatin immunoprecipitation sequencing (ChIP­seq) and RNA sequencing (RNA­seq). The expression of PRMT6 in EC was analyzed using reverse transcription­quantitative polymerase chain reaction (RT­qPCR) and immunohistochemistry (IHC). The prognostic impact of PRMT6 expression was evaluated using IHC. The effects of PRMT6­knockdown (KD) were investigated using cell viability and apoptosis assays, as well as its effects on the epigenome, using ChIP­seq of H3K27ac antibodies and RNA­seq. Finally, the downstream targets identified by multi­omics analysis were evaluated. PRMT6 was overexpressed in EC and associated with a poor prognosis. PRMT6­KD induced histone hypomethylation, while suppressing cell growth and apoptosis. ChIP­seq revealed that PRMT6 regulated genomic regions related to interferons and apoptosis through histone modifications. The RNA­seq data demonstrated altered interferon­related pathways and increased expression of tumor suppressor genes, including NK6 homeobox 1 and phosphoinositide­3­kinase regulatory subunit 1, following PRMT6­KD. RT­qPCR revealed that eight ERV genes which activated interferon signaling were upregulated by PRMT6­KD. The data of the present study suggested that PRMT6 inhibition induced apoptosis through interferon signaling activated by ERV. PRMT6 regulated tumor suppressor genes and may be a novel therapeutic target, to the best of our knowledge, in EC.

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma.

OBJECTIVE: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. METHODS: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. RESULTS: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. CONCLUSION: Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Identification of target cells of human papillomavirus 18 using squamocolumnar junction organoids.

Human papillomavirus 18 (HPV18) is a highly malignant HPV genotype among high-risk HPVs, characterized by the difficulty of detecting it in precancerous lesions and its high prevalence in adenocarcinomas. The cellular targets and molecular mechanisms underlying its infection remain unclear. In this study, we aimed to identify the cells targeted by HPV18 and elucidate the molecular mechanisms underlying HPV18 replication. Initially, we established a lentiviral vector (HPV18LCR-GFP vector) containing the HPV18 long control region promoter located upstream of EGFP. Subsequently, HPV18LCR-GFP vectors were transduced into patient-derived squamocolumnar junction organoids, and the presence of GFP-positive cells was evaluated. Single-cell RNA sequencing of GFP-positive and GFP-negative cells was conducted. Differentially expressed gene analysis revealed that 169 and 484 genes were significantly upregulated in GFP-positive and GFP-negative cells, respectively. Pathway analysis showed that pathways associated with cell cycle and viral carcinogenesis were upregulated in GFP-positive cells, whereas keratinization and mitophagy/autophagy-related pathways were upregulated in GFP-negative cells. siRNA-mediated luciferase reporter assay and HPV18 genome replication assay validated that, among the upregulated genes, ADNP, FHL2, and NPM3 were significantly associated with the activation of the HPV18 early promoter and maintenance of the HPV18 genome. Among them, NPM3 showed substantially higher expression in HPV-related cervical adenocarcinomas than in squamous cell carcinomas, and NPM3 knockdown of HPV18-infected cells downregulated stem cell-related genes. Our new experimental model allows us to identify novel genes involved in HPV18 early promoter activities. These molecules might serve as therapeutic targets in HPV18-infected cervical lesions.

Evolution of a surgical system using deep learning in minimally invasive surgery (Review).

Recently, artificial intelligence (AI) has been applied in various fields due to the development of new learning methods, such as deep learning, and the marked progress in computational processing speed. AI is also being applied in the medical field for medical image recognition and omics analysis of genomes and other data. Recently, AI applications for videos of minimally invasive surgeries have also advanced, and studies on such applications are increasing. In the present review, studies that focused on the following topics were selected: i) Organ and anatomy identification, ii) instrument identification, iii) procedure and surgical phase recognition, iv) surgery-time prediction, v) identification of an appropriate incision line, and vi) surgical education. The development of autonomous surgical robots is also progressing, with the Smart Tissue Autonomous Robot (STAR) and RAVEN systems being the most reported developments. STAR, in particular, is currently being used in laparoscopic imaging to recognize the surgical site from laparoscopic images and is in the process of establishing an automated suturing system, albeit in animal experiments. The present review examined the possibility of fully autonomous surgical robots in the future.

Application of organoid culture from HPV18-positive small cell carcinoma of the uterine cervix for precision medicine.

BACKGROUND: Small cell carcinoma of the uterine cervix (SCCC) is a rare and highly malignant human papillomavirus (HPV)-associated cancer in which human genes related to the integration site can serve as a target for precision medicine. The aim of our study was to establish a workflow for precision medicine of HPV-associated cancer using patient-derived organoid. METHODS: Organoid was established from the biopsy of a patient diagnosed with HPV18-positive SCCC. Therapeutic targets were identified by whole exome sequencing (WES) and RNA-seq analysis. Drug sensitivity testing was performed using organoids and organoid-derived mouse xenograft model. RESULTS: WES revealed that both the original tumor and organoid had 19 somatic variants in common, including the KRAS p.G12D pathogenic variant. Meanwhile, RNA-seq revealed that HPV18 was integrated into chromosome 8 at 8q24.21 with increased expression of the proto-oncogene MYC. Drug sensitivity testing revealed that a KRAS pathway inhibitor exerted strong anti-cancer effects on the SCCC organoid compared to a MYC inhibitor, which were also confirmed in the xenograft model. CONCLUSION: In this study, we confirmed two strategies for identifying therapeutic targets of HPV-derived SCCC, WES for identifying pathogenic variants and RNA sequencing for identifying HPV integration sites. Organoid culture is an effective tool for unveiling the oncogenic process of rare tumors and can be a breakthrough for the development of precision medicine for patients with HPV-positive SCCC.

Cells with stem-like properties are associated with the development of HPV18-positive cervical cancer.

The cellular origins of cervical cancer and the histological differentiation of human papillomavirus (HPV)-infected cells remain unexplained. To gain new insights into the carcinogenesis and histological differentiation of HPV-associated cervical cancer, we focused on cervical cancer with mixed histological types. We conducted genomic and transcriptomic analyses of cervical cancers with mixed histological types. The commonality of the cellular origins of these cancers was inferred using phylogenetic analysis and by assessing the HPV integration sites. Carcinogenesis was estimated by analyzing human gene expression profiles in different histological types. Among 42 cervical cancers with known HPV types, mixed histological types were detected in four cases, and three of them were HPV18-positive. Phylogenetic analysis of these three cases revealed that the different histological types had a common cell of origin. Moreover, the HPV-derived transcriptome and HPV integration sites were common among different histological types, suggesting that HPV integration could occur before differentiation into each histological type. Human gene expression profiles indicated that HPV18-positive cancer retained immunologically cold components with stem cell properties. Mixed cervical cancer has a common cellular origin among different histological types, and progenitor cells with stem-like properties may be associated with the development of HPV18-positive cervical cancer.

The Histone Methyltransferase SETD8 Regulates the Expression of Tumor Suppressor Genes via H4K20 Methylation and the p53 Signaling Pathway in Endometrial Cancer Cells.

The histone methyltransferase SET domain-containing protein 8 (SETD8), which methylates histone H4 lysine 20 (H4K20) and non-histone proteins such as p53, plays key roles in human carcinogenesis. Our aim was to determine the involvement of SETD8 in endometrial cancer and its therapeutic potential and identify the downstream genes regulated by SETD8 via H4K20 methylation and the p53 signaling pathway. We examined the expression profile of SETD8 and evaluated whether SETD8 plays a critical role in the proliferation of endometrial cancer cells using small interfering RNAs (siRNAs). We identified the prognostically important genes regulated by SETD8 via H4K20 methylation and p53 signaling using chromatin immunoprecipitation sequencing, RNA sequencing, and machine learning. We confirmed that SETD8 expression was elevated in endometrial cancer tissues. Our in vitro results suggest that the suppression of SETD8 using siRNA or a selective inhibitor attenuated cell proliferation and promoted the apoptosis of endometrial cancer cells. In these cells, SETD8 regulates genes via H4K20 methylation and the p53 signaling pathway. We also identified the prognostically important genes related to apoptosis, such as those encoding KIAA1324 and TP73, in endometrial cancer. SETD8 is an important gene for carcinogenesis and progression of endometrial cancer via H4K20 methylation.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.

Histone arginine methyltransferase CARM1 selective inhibitor TP-064 induces apoptosis in endometrial cancer.

Histone modification is the key epigenetic mechanism that regulates gene expression. Coactivator-associated arginine methyltransferase 1 (CARM1) is an arginine methyltransferase that catalyzes dimethylation of histone H3 (H3R17) at arginine 17. Lately, it has been suggested that CARM1 is associated with human carcinogenesis, and the CARM1-selective inhibitor, TP-064, has been shown to be a potential therapeutic agent for multiple myeloma. However, the physiological significance of CARM1 in endometrial cancer remains unclear. Therefore, we aimed to explore the role of CARM1 and the effect of TP-064 in endometrial cancer. To this end, we analyzed CARM1 expression in endometrial cancer using quantitative real-time polymerase chain reaction and examined the antitumor mechanism with CARM1 knockdown endometrial cancer cells. Moreover, we evaluated the therapeutic capability of TP-064 in endometrial cancer cells. CARM1 was remarkably overexpressed in 52 endometrial cancer tissues compared to normal endometrial tissues. The growth of CARM1 knockdown endometrial cancer cells was suppressed and CARM1 knockdown induced apoptosis. TP-064 also inhibited endometrial cancer cell growth and declined the number of endometrial cancer cell colonies. These data suggest that CARM1 may be a powerful therapeutic target for endometrial cancer.

Application of artificial intelligence in gynecologic malignancies: A review.

With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.

Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presents an artificial-intelligence-based system to detect the regions affected by endometrial cancer automatically from hysteroscopic images. In this study, 177 patients (60 with normal endometrium, 21 with uterine myoma, 60 with endometrial polyp, 15 with atypical endometrial hyperplasia, and 21 with endometrial cancer) with a history of hysteroscopy were recruited. Machine-learning techniques based on three popular deep neural network models were employed, and a continuity-analysis method was developed to enhance the accuracy of cancer diagnosis. Finally, we investigated if the accuracy could be improved by combining all the trained models. The results reveal that the diagnosis accuracy was approximately 80% (78.91-80.93%) when using the standard method, and it increased to 89% (83.94-89.13%) and exceeded 90% (i.e., 90.29%) when employing the proposed continuity analysis and combining the three neural networks, respectively. The corresponding sensitivity and specificity equaled 91.66% and 89.36%, respectively. These findings demonstrate the proposed method to be sufficient to facilitate timely diagnosis of endometrial cancer in the near future.

Targeting Epigenetic Regulators for Endometrial Cancer Therapy: Its Molecular Biology and Potential Clinical Applications.

Endometrial cancer is one of the most frequently diagnosed gynecological malignancies worldwide. However, its prognosis in advanced stages is poor, and there are only few available treatment options when it recurs. Epigenetic changes in gene function, such as DNA methylation, histone modification, and non-coding RNA, have been studied for the last two decades. Epigenetic dysregulation is often reported in the development and progression of various cancers. Recently, epigenetic changes in endometrial cancer have also been discussed. In this review, we give the main points of the role of DNA methylation and histone modification in endometrial cancer, the diagnostic tools to determine these modifications, and inhibitors targeting epigenetic regulators that are currently in preclinical studies and clinical trials.

Desensitization strategy for hypersensitivity reactions to carboplatin in five patients with gynecological cancer.

AIM: Carboplatin is a key drug for gynecologic cancers. However, hypersensitivity reactions (HSR) are major adverse effects that might necessitate carboplatin discontinuation. Desensitization is an effective method in patients who developed initial HSR and further required carboplatin treatment. Here, we aimed to evaluate our experience with the use of the carboplatin desensitization protocol in five patients at the University of Tokyo Hospital. METHODS: We established a four-step, 5-h desensitization protocol for our hospital. Observational and retrospective analyses were performed. Additionally, we have shared the patients' clinical information with the emergency department to ensure the safety of this protocol. RESULTS: Five patients with recurrent gynecological cancer were treated using this protocol. Four of the five patients were treated effectively and 28 of 29 desensitization protocols were completed successfully. In one patient, we switched to olaparib successfully after two courses of our protocol. One patient who developed grade 4 HSR during initial carboplatin administration developed grade 2 HSR and we discontinued the protocol. CONCLUSION: The carboplatin desensitization protocol is very efficient. The outcome of our protocol was on a par with other protocols. To the best of our knowledge, this is the first study to indicate that switching to olaparib can be considered a suitable option in patients who develop HSR to carboplatin.