Yusuke Toyohara

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.

Clinical characteristics of bowel mucosal invasion in epithelial ovarian cancer

Abstract Aim Bowel mucosal invasion in epithelial ovarian cancer (EOC) is classified as stage IVB disease. However, the reason for this classification remains unclear, and the clinical outcomes of bowel mucosal invasion in EOC warrant further investigation. Therefore, we aimed to examine patients with EOC presenting with bowel mucosal invasion and evaluate the validity of the current classification. Methods We retrospectively reviewed data from patients with stage IVB EOC who presented with bowel mucosal invasion at our hospital between January 2015 and September 2023. Patients with bowel mucosal invasion and other factors associated with stage IVB EOC were excluded. The primary and secondary endpoints were progression‐free survival (PFS) and overall survival (OS), respectively. Results Among 226 patients diagnosed with stage IVB EOC, 22 (9.7%) exhibited bowel mucosal invasion and 13 (5.8%) were diagnosed with stage IVB EOC based solely on the presence of bowel mucosal invasion. The median follow‐up period was 40.5 months (range, 14.9–81.6 months). Primary debulking surgery was performed in nine patients (69.2%) and neoadjuvant chemotherapy‐interval debulking surgery in four (30.8%). Complete resection was achieved in all 13 patients without other stage IVB‐related factors. Among them, the 3‐year PFS and OS rates were 54.9% and 82.1%, respectively. Conclusion In cases of bowel mucosal invasion, complete resection appears feasible and may be associated with a more favorable prognosis compared with that of the overall stage IVB population. Therefore, bowel mucosal invasion alone may not represent a potential prognostic factor for stage IVB ovarian cancer.

The efficacy and safety of lenvatinib plus pembrolizumab in vulnerable patients with metastatic or recurrent endometrial cancer: a single institution experience

Effective management with second-line therapy with the lenvatinib + pembrolizumab regimen for patients with advanced endometrial cancer is necessary. This retrospective study enrolled patients with endometrial cancer treated with the lenvatinib + pembrolizumab regimen. We evaluated progression-free survival (PFS), overall survival (OS), safety for patients non-eligible for the KEYNOTE775 trial, aged ≥65 years, or with ECOG performance status 1-2. Forty-five patients were analyzed: 21 (47%) were aged ˃ 65 years, 16 (36%) had performance status 1-2, and 15 (33%) were non-eligible for KEYNOTE775 trial participation. Overall, the median PFS was 8.5 months (95% confidence interval [CI] 4.6-12.4), and the median OS was 15.6 months (95% CI 9.4-NA). Median PFS was significantly shorter in patients not eligible for KEYNOTE775 participation and with performance status 1-2. The median OS was significantly shorter in patients with performance status 1-2. Grade ˃3 adverse events (AEs) occurred in 78% of patients who received the lenvatinib + pembrolizumab regimen. AEs resulted in lenvatinib dose reductions in 35 patients (78%) and lenvatinib and pembrolizumab discontinuation in 3 (7%) and 5 (11%), respectively. The median time to the first lenvatinib dose reduction was 1.5 (0.92-2.3) months in all patients and was significantly shorter in patients aged >65 years. The current regimen has favorable efficacy and manageable safety with appropriate dose reduction of lenvatinib in the real world. However, the efficacy may be inferior in patients with performance status 1 or 2, heavily treated patients, and those with organ dysfunction. The current treatment status should reflect real-world data relative to the medical environment and management.

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

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. 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. 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. 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.