A novel morphology-based risk stratification model for stage I uterine leiomyosarcoma: an analysis of 203 cases

MG132 induces cell type‑specific anticancer effects in uterine leiomyosarcoma cell lines

Uterine leiomyosarcoma (Ut‑LMS) is a rare and aggressive malignant tumor with limited therapeutic options. Therefore, exploration of novel treatment strategies is necessary. MG132 is a potent proteasome inhibitor that has shown promising potential in cancer therapy by inducing apoptosis through disruption of protein homeostasis. Despite its promising applications in various cancers, its effects on Ut‑LMS remains largely unexplored. Therefore, the present study investigated the effects of MG132 on Ut‑LMS cell lines (SK‑LMS‑1, SK‑UT‑1 and SK‑UT‑1B) in terms of cytotoxicity, apoptosis induction, cell cycle progression, autophagy and reactive oxygen species (ROS) production. Treatment with MG132 (0‑2 µM for 24 h) induced a dose‑dependent reduction in cell viability across all three cell lines, and the lactate dehydrogenase release assays confirmed membrane damage. Moreover, apoptosis induction was assessed using annexin V and 7‑AAD staining, which revealed dose‑dependent apoptosis in all three cell lines. Western blot analysis revealed increased cleaved poly‑adenosine diphosphate ribose polymerase and caspase‑3 levels, thereby indicating activation of the apoptotic pathway in response to MG132 treatment. MG132 also induced G2/M phase arrest in SK‑LMS‑1 and SK‑UT‑1 cells and altered the expression of cell cycle regulatory proteins, such as p21, p27 and p53. Furthermore, MG132 promoted autophagy in all three cell lines by increasing light chain 3 II levels. ROS levels remained unchanged in SK‑LMS‑1 cells but increased in SK‑UT‑1B and SK‑UT‑1 cells. Furthermore, the ROS scavenger N‑acetylcysteine effectively reduced MG132‑induced apoptosis in SK‑UT‑1 cells. These findings highlight the cytotoxicity of MG132 in Ut‑LMS cells, emphasize its potential as a therapeutic agent for Ut‑LMS, provide insights into its mechanisms of action, and suggest possible strategies for improving treatment efficacy.

Leiomyosarcoma Therapeutic Approaches and Future Directions

Leiomyosarcomas (LMS) are mesenchymal malignancies with smooth muscle differentiation that most commonly arise in the uterus, retroperitoneum, and extremity. Clinical course is variable, and management differs by site of origin. Recently completed and ongoing clinical trials are optimizing LMS management, and multidisciplinary care by an expert team is essential to navigate the nuances and provide optimal care. Advances in molecular characterization have improved diagnosis and identification of novel prognostic factors and systemic therapies. This article provides an overview of LMS presentation, diagnosis, molecular features, and clinical management, and highlights advancements in LMS biology and treatment.

Risk Stratification of Uterine Smooth Muscle Tumors: The Role of Morphology, Immunohistochemistry, and Molecular Testing

Uterine smooth muscle neoplasms are a biologically and clinically heterogeneous group of tumors. Morphology is the cornerstone of pathologic diagnosis of these tumors, and most are readily classified as benign or malignant on the basis of routine histologic examination. However, rare subsets-including intravenous leiomyomatosis, benign metastasizing leiomyoma, and disseminated peritoneal leiomyomatosis-have a capacity for extrauterine spread despite benign cytomorphology. A further subset of uterine smooth muscle neoplasms, termed "smooth muscle tumor of uncertain malignant potential (STUMP)," are not readily classified as benign or malignant and carry an intermediate prognosis. STUMP is a protean category, whose precise definition is subject to disagreement among experts. The risk profiles of different STUMP morphotypes remain largely unresolved. Finally, multiple morphology-based systems for risk stratification of uterine leiomyosarcoma have been proposed, though none is widely adopted. Immunohistochemical and molecular prognostic markers for both STUMP and leiomyosarcoma remain in the early phases of adoption in routine diagnostic practice.

Uterine Smooth Muscle Tumors: An Overview

Uterine smooth muscle tumors are a heterogeneous group of mesenchymal neoplasms with multiple histologic variants and distinct biological behaviors. Pathologic classification (benign, uncertain malignant potential, malignant) relies on the evaluation of mitotic index, necrosis, and degree of cytologic atypia, with different thresholds based on each subtype. Immunohistochemistry and other ancillary studies may be necessary to establish the diagnosis in a subset of cases, given the morphologic overlap with other mesenchymal neoplasms, including low-grade and high-grade endometrial stromal tumors, inflammatory myofibroblastic tumors, and PEComa. Recent advances in molecular diagnostics have refined the classification of smooth muscle tumors, but most cases are diagnosed purely on histologic grounds.

Macroscopic examination of gynaecological specimens: a critial and often underemphasised aspect of pathological reporting

Pathological examination of surgical specimens and compilation of a surgical pathology report comprises a series of events which includes macroscopic examination and tissue sampling, either complete or selected. This step is critical but often overlooked in the literature and not given the attention it deserves. In this review, we discuss the macroscopic examination and grossing of gynaecological pathology specimens, with reference to national and international protocols. We provide guidance as to the degree of sampling necessary in different scenarios and stress that a common-sense approach is necessary with flexibility in the degree of sampling depending on a variety of factors.

Primary Ovarian Leiomyosarcoma Is a Very Rare Entity: A Narrative Review of the Literature

BACKGROUND

Primary ovarian leiomyosarcoma is a very rare malignancy characterized by unclear management and poor survival. We reviewed all the cases of primary ovarian leiomyosarcoma to identify prognostic factors and the best treatment.

METHODS

We collected and analyzed the articles published in the English literature regarding primary ovarian leiomyosarcoma from January 1951 to September 2022, using PubMed research. Clinical and pathological characteristics, different treatments and outcomes were analyzed.

RESULTS

113 cases of primary ovarian leiomyosarcoma were included. Most patients received surgical resection, associated with lymphadenectomy in 12.5% of cases. About 40% of patients received chemotherapy. Follow-up information was available for 100/113 (88.5%) patients. Stage and mitotic count were confirmed to affect survival, and lymphadenectomy and chemotherapy were associated with a better survival rate. A total of 43.4% of patients relapsed, and their mean disease-free survival was 12.5 months.

CONCLUSIONS

Primary ovarian leiomyosarcomas are more common in women in their 50s (mean age 53 years). Most of them are at an early stage at presentation. Advanced stage and mitotic count showed a detrimental effect on survival. Surgical excision associated with lymphadenectomy and chemotherapy are associated with increased survival. An international registry could help collect clear and reliable data to standardize the diagnosis and treatment.

Loss of dystrophin is common in uterine leiomyosarcoma: a potential biomarker for clinical application

Uterine leiomyosarcoma (LMS) is a deadly disease with high rates of recurrence and a poor prognosis. Its tumorigenesis remains largely unknown, and no specific biomarkers can be used for the differential diagnosis of LMS from other mimics. Recent whole-genome studies revealed a loss of dystrophin is common in LMS, especially in uterine LMS. To investigate the expression pattern of dystrophin expression across different types of uterine smooth muscle tumors, immunohistochemistry was performed, including usual-type leiomyoma, fumarate hydratase-deficient leiomyoma, leiomyoma with bizarre nuclei, conventional LMS, and normal myometrium for this study. To further evaluate the genomic change in dystrophin gene region, whole-genome sequencing in 10 LMS cases were analyzed. Dystrophin expression was detected in 94% (45/48) of myometrium, 97% (34/35) of usual-type leiomyoma, 84% (26/31) of fumarate hydratase-deficient leiomyoma, 60% (12/20) of leiomyoma with bizarre nuclei, and 18% (6/34) of LMS. Loss of dystrophin expression was significantly different between benign and malignant tumors (LMS cases counted as malignant only) (p < 0.01). Of note, copy number loss in the dystrophin genomic region was found in all 10 cases of LMS. Additionally, patients with dystrophin-positive LMS tend to have a better overall survival than patients with dystrophin-negative LMS.

Data Set for Reporting of Uterine Malignant and Potentially Malignant Mesenchymal Tumors: Recommendations From the International Collaboration on Cancer Reporting (ICCR)

The International Collaboration on Cancer Reporting (ICCR) seeks to produce standardized, evidence-based protocols for the reporting of tumors with the aim of ensuring that all cancer reports generated worldwide will be of similar high quality and record the same elements. Herein, we describe the development of the data set for the reporting of uterine malignant and potentially malignant mesenchymal tumors by a panel of expert pathologists and a single clinician and provide the commentary and rationale for the inclusion of core and noncore elements. This data set, which incorporates the recent updates from the 5th edition of the World Health Organization Classification of Female Genital Tumors, addresses several subjects of debate including which mesenchymal tumors should be graded, how to document extent of invasion, mitotic counts, and the role of ancillary testing in tumor diagnosis and patient management. The inclusion of elements is evidence-based or based on consensus of the expert panel with clinical relevance being the guiding standard.

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, with both a high malignant potential and poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance to leiomyosarcoma, often being accompanied by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three biomarkers (i.e., mitosis count, necrosis, and nuclear atypia). Among these biomarkers, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.