Yutaka Osuga

Does polycystic ovary syndrome independently affect oncologic and reproductive outcomes in patients with endometrial cancer receiving fertility-sparing treatment?

Cancers associated with extraovarian endometriosis at less common/rare sites: A nationwide survey in Japan

AbstractAimEndometriosis mostly affects the ovary but can also be present outside of the ovary including the pelvic peritoneum, intestine, urinary tract and lung. In case of ovarian endometriotic cyst, an increased risk of ovarian cancer, especially of clear cell and endometrioid histology, has been reported. However, because of the rarity, cancer occurrence from endometriosis at less common sites/rare sites is poorly understood.MethodsWe conducted a nationwide survey on the less common/rare site endometriosis in 3539 authorized facilities in Japan. We requested to complete a case report form for each case, including information on the history of endometriosis, treatment for endometriosis, type of surgery, involved site(s) of cancer and endometriosis, histology of cancer, chemotherapy and outcome.ResultsOut of 1397 confirmed cases of less common/rare site endometriosis, 11 cases of rare site endometriosis‐associated cancer (RSEAC) were reported: seven of them were associated with intestinal endometriosis, three were associated with urinary tract endometriosis and one was associated with umbilical endometriosis. Interestingly, the histology was endometrioid in seven (64%) cases, and serous, seromucinous borderline, clear cell and mucinous in one case each (10%), differing from the case of ovarian endometriosis‐associated cancer, in which clear cell carcinoma are more common.ConclusionOur nationwide survey on RSEAC has revealed that: (i) the incidence of malignant transformation may be lower than ovarian endometriosis, (ii) malignant transformation from endometriosis outside the abdominal cavity may be extremely rare and (iii) the histology of RSEAC is predominantly endometrioid type, suggesting an association of a hormonal effect.

Survival and reproductive outcomes after fertility‐sparing surgery performed for borderline epithelial ovarian tumor in Japanese adolescents and young adults: Results of a retrospective nationwide study

AbstractObjectiveEpithelial borderline ovarian tumor (BOT) frequently occurs in young women. Because progression‐free survival, overall survival, and reproductive function are important outcomes, BOT is often treated by fertility‐sparing surgery (FSS). We conducted a Japan‐wide study to understand post‐FSS prognosis in relation to clinical characteristics and types of FSS performed.MethodsWe analyzed clinical and outcome data pertaining to 531 adolescent and young adult (AYA) patients (aged 15–39 years) who underwent FSS for BOT between 2009 and 2013.ResultsMedian (range) age was 30 (15–39) years, and median observation time was 70 (2–120) months. The disease was of FIGO stage I in 492 (93%) patients. Histopathologically, tumors were of the mucinous (n = 372, 70%), serous (n = 120, 23%), seromucinous (n = 23, 4%), and other (n = 16, 3%) types. Five‐year overall survival was 99.5% among patients with stage I and 100% among those with stage II–IV. Five‐year progression‐free survival was 96.7% and 69.3%, respectively. Multivariate analysis in cases of stage I showed a positive peritoneal cytology to be a significant risk factor for recurrence (HR, 5.199; p = 0.0188). The post‐FSS pregnancy rate was relatively low for patients aged ≥30 years (OR, 0.868; 95% CI, 1.16–3.00; p = 0.0090).ConclusionPost‐FFS outcomes in terms of overall and progression‐free survival are favorable, especially for AYA patients with stage I BOT. However, the relapse rate is high for patients with FIGO stage II–IV and for those with stage I but a positive peritoneal cytology. A long‐term prospective observation is needed before reproductive outcomes can be fully established.

Recurrent cervical cancer with PD‐L1 amplification treated with nivolumab: A case enrolled in the BELIEVE trial

AbstractPatients with cervical cancer benefiting from immune checkpoint inhibitors (ICIs) are limited. Recently, PD‐L1 amplification has been attracted attention as a reliable marker of ICIs. A 47‐year‐old woman with stage IIB cervical cancer experienced disease progression during postoperative adjuvant chemotherapy. Cancer genomic profiling revealed that the tumor was microsatellite stable with PD‐L1 amplification, therefore, nivolumab was administered by enrolling in the BELIEVE trial. Despite nivolumab treatment, remarkable disease progression was observed. At the beginning of nivolumab treatment, the patient already had multiple liver metastases with severe systemic inflammation as indicated by a high neutrophil‐to‐lymphocyte ratio (NLR), both of which are negative predictive markers for ICI. Despite the presence of PD‐L1 amplification, nivolumab was ineffective in cancer progression, which may be attributable to the presence of liver metastasis and high NLR. ICI is recommended to be administered at an early stage of cancer treatment to enhance its effectiveness.

Downregulation of HLA Class I Expression through HLA-A DNA Methylation Is Associated with Reduced CD8+ T-cell Infiltration in Cervical Cancer

Abstract Human leukocyte antigen class I (HLA-I) is central to tumor immune recognition, but its regulatory mechanisms in cervical cancer remain poorly understood. This study aimed to elucidate the impact of HLA-I regulatory mechanisms on CD8+ T-cell infiltration and identify distinct histotype-specific immune escape strategies across cervical cancer subtypes. Using 98 cervical cancer cases, including squamous cell carcinoma (SCC; n = 53), adenocarcinoma (n = 32), gastric-type adenocarcinoma (GAS; n = 5), small cell carcinoma (Small, n = 4), and mixed histologic types (n = 4), we examined the relationship between CD8+ T-cell infiltration patterns (categorized as infiltrated, excluded, or absent) and HLA-I expression, HLA-A DNA methylation, and HLA-I loss of heterozygosity (LOH). CD8+ T-cell infiltration patterns varied significantly by histologic subtype (P < 0.0001). SCC showed the highest frequency of the infiltrated pattern (73.6%), whereas GAS and Small predominantly displayed an absent pattern. Reduced CD8+ T-cell infiltration correlated with poor survival (P < 0.0001). HLA-I expression mirrored these trends being highest in SCC and lowest in Small and GAS. HLA-A DNA methylation emerged as a key driver of HLA-I downregulation, leading to reduced CD8+ infiltration (P < 0.05). In SCC, both HLA-A methylation and HLA-I LOH contributed to immune evasion; cases lacking these alterations exhibited the highest CD8+ T-cell infiltration levels (P < 0.01). This study identifies distinct HLA-I regulatory mechanisms in cervical cancer, highlighting HLA-A methylation—and particularly HLA-I LOH in SCC—as key drivers of immune evasion. These findings provide a foundation for developing predictive biomarkers and suggest that targeting these specific HLA-I regulatory mechanisms could enhance immunotherapy efficacy.

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.