The immunohistochemical molecular risk classification in endometrial cancer: A pragmatic and high-reproducibility method

Comparison of PD-L1, VISTA, LAG-3, and GAL-3 Expressions and Their Relationships to Mismatch Repair Protein and p53 Expression in 529 Cases of Endometrial Carcinoma

The aim of this study is to evaluate the expressions of programmed death-ligand 1 (PD-L1), V-domain Ig suppressor of T-cell activation (VISTA), lymphocyte activation gene-3 (LAG-3), and galectin-3 (GAL-3), in mismatch repair-deficient (MMRd)/MMR-proficient and abnormal p53 expressing endometrial carcinomas and their relationship with clinical-histopathological features. Patients who underwent surgery for endometrial carcinoma between January 2008 and December 2018 were included in the study. Immunohistochemical analysis of MLH1, PMS2, MSH2, MSH6, p53, PD-L1, VISTA, LAG-3, and GAL-3 was performed on the tissue samples of microarray. A total of 529 patients were included. MMRd and p53-mutant tumors accounted for 31.5% and 11.5% of cases, respectively. PD-L1 and LAG-3 expressions in the MMRd and p53-mutant groups were higher than in the MMR-proficient group (P < 0.001). GAL-3 expression in the MMR-proficient group was statistically higher than in the MMRd and p53-mutant groups (P < 0.001). Mean age, grade, International Federation of Gynecology and Obstetrics stage, lymphovascular invasion, and lymph node metastasis were significantly higher in the p53-mutant group (P < 0.001). In the group with PD-L1 expression, nonendometrioid histologic type, tumor grade, and lymphovascular invasion were significantly higher (P < 0.001). Tumor grade, lymphovascular invasion, lymph node metastasis, and microcystic, elongated and fragmented pattern of invasion were significantly higher in the group with high VISTA expression (P < 0.05). Tumor grade was significantly higher in the group with LAG-3 expression (P < 0.001). Immunohistochemically determined subgroups and PD-L1, VISTA, LAG-3, and GAL-3 expression levels may be useful indicators of molecular features, and clinical outcomes also may have important implications for the development of targeted therapies in endometrial carcinoma.

The new 2023 FIGO staging system for endometrial cancer: what is different from the previous 2009 FIGO staging system?

OBJECTIVE

The International Federation of Gynecology and Obstetrics committee modified the endometrial cancer (EC) staging system based on the histopathological feature and molecular profile. The aim is to evaluate the clinical implications of the new 2023 system compared with the previous 2009 system.

METHODS

We retrospectively identified 161 patients with EC who underwent primary surgical treatment between 2014 and 2018 at Seoul National University Hospital. The droplet-digital polymerase chain reaction for POLE mutations and immunohistochemistry for MLH1, PMS2, MS2, MSH6, and p53 were performed using tissues from formalin-fixed, paraffin-embedded blocks. All patients were categorized according to the 2009 and 2023 staging systems.

RESULTS

The median follow-up period was 62.9 months (range, 0.3-110.9), and the median age was 57.2 years old (range, 28.0-85.9). The 5-year progression-free survival (PFS) for the 2023 system with molecular classification was 80.3% for stage I, 75.2% for stage II, 61.2% for stage III, and 22.2% for stage IV (p<0.001). Patients with the 2009 stage I and II disease were restaged using the 2023 system. In contrast, patients with stage III and IV disease were fixed in the 2009 and 2023 systems. Molecular classification downstaged 10 patients (71.4%) to IAmPOLEmut and upstaged 6 patients (37.5%) to IICmp53abn. The 2023 system with molecular classification was associated with PFS and overall survival (p<0.001 and p=0.038).

CONCLUSION

The 2023 staging system for EC subdivided stages I and II compared to the 2009 system. The 2023 system with molecular classification is a good predictor of survival.

Critical Overview of Serous Endometrial Intraepithelial Cancer Treatment: Systematic Review of Adjuvant Options

SEIC is a non-invasive lesion of the endometrial epithelium considered to be the precursor to uterine serous carcinoma (USC) and is just as aggressive as USC. Currently, there are no reliable data about the behavior and prognosis of SEIC; therefore, the therapeutic management approach is not clear. Method: A systematic search of the Pubmed, Scopus and Embase databases was conducted, following the recommendations in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Of the 296 studies that matched the search criteria, only 9 met the inclusion criteria, covering a total of 81 patients. The main disease-presenting pattern was AUB (abnormal uterine bleeding). In 31 cases, SEIC was associated with extrauterine disease. All patients underwent hysterectomy and salpingo-oophorectomy, while only 15 of the 81 patients received adjuvant treatments. In the patients receiving adjuvant therapy, the RR was 42.67%, the DFS was 35.71% and the OS was 57.13%. In patients subjected to follow-up alone, the RR was only 28.78%, the DFS was 59.1% and the OS was 66.6%. Conclusions: The presence of an extrauterine disease significantly worsens outcomes, regardless of adjuvant treatment. In cases of disease confined to the uterine mucosa alone, the prognosis is good and follow-up allows a good control of the disease; however, adjuvant therapy could further increase survival rates and reduce relapse rates.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Back to the future: The impact of oestrogen receptor profile in the era of molecular endometrial cancer classification

PURPOSE

The aim of this study is to evaluate the impact of the oestrogen receptor (ER) profile on oncologic outcomes in the new endometrial cancer (EC) risk classification.

METHODS

Immunohistochemistry (IHC) analyses were performed in a retrospectively reviewed large series of ECs to assess the presence/absence of oestrogen receptors (ER0\1+ or ER2+\3+) and other molecular factors (i.e. p53 mutation, p53mut; and mismatch repair mutational status, MMRd (mismatch repair deficient) versus MMRp (mismatch repair proficient)), histopathologic and clinical outcomes. ER status was correlated with molecular, histologic, clinical and prognostic data.

RESULTS

891 EC patients were included in the study (211 ER0\1+ and 680 ER2+\3+). The ER0\1+ phenotype was associated with an unfavourable clinicopathological profile (i.e. grading, histotype, lymphovascular space invasion (LVSI), stages, etc.). Simple regression showed that risk class, p53mut, and ER0/1+ impacted on both disease-free survival (DFS) and overall survival (OS) (p < 0.05). In the ER0/1+ population, p53mut no longer influenced DFS and OS (p > 0.05). In multiple regression, age, high and advanced/metastatic risk classes influenced survival outcomes (p < 0.05), but lost significance in the ER0/1+ population (p > 0.05). ER-positivity retained a remarkable prognostic impact even after stratification of the population according to the European Society of Gynaecological Oncology, the European Society for Radiotherapy and Oncology, and the European Society of Pathology (ESGO/ESTRO/ESP) 2021 risk classes and molecular classification. ER0/1+ intermediate, high-intermediate, high and advanced risk versus ER2+/3+ intermediate, high-intermediate, high and advanced risk classes showed statistically different OS and DFS (p< 0.001). ER0/1+ status was associated with a worse prognosis when associated with MMRp, MMRd and p53mut compared to the same molecular classes associated with ER2+/3 (p < 0.001).

CONCLUSIONS

We demonstrated that ER status has a significant impact on oncologic outcomes, regardless of risk class and p53/MMR status. Based on our results, we recommend the inclusion of ER assessment in featured EC risk classification system.

Estrogen/Progesterone Receptor Loss, CTNNB1 and KRAS Mutations Are Associated With Local Recurrence or Distant Metastasis in Low-Grade Endometrial Endometrioid Carcinoma

A subset of endometrial endometrioid carcinomas (EECs) with low-grade histology recur with poor outcomes. Published evidence suggests that poor outcomes may be associated with loss of expression of ER-alpha (ER-α) as well as with β-Catenin-1 ( CTNNB1 ) and Kirsten rat sarcoma viral oncogene homolog ( KRAS ) mutations. This study reports on institutional experience with the incidence of recurrence in low-grade EEC and their association with CTNNB1 and KRAS mutations as well as estrogen/progesterone receptor (ER/PR) expression. Forty-eight (8.5%) out of 568 cases of low-grade EEC with biopsy-proven recurrence were identified; and were analyzed by immunohistochemistry for ER, PR, p53, MMR protein, and mutation analysis for exon 3 of the CTNNB1 and exon 2 of KRAS in relation to recurrence type, local or distant metastasis/recurrence. Twenty-three patients (4%) developed local, and 25 patients (4.4%) developed distant metastases/recurrence. Decreased expression or loss of ER/PR was found in 17/44 (38.6%) patients with recurrence. Eighty-four percent of patients with low-grade EEC and local recurrence had CTNNB1 mutations. Seventy-three percent of patients with distant metastasis/recurrence had KRAS mutations. The association of these mutations with the type of recurrence was statistically significant for both. Five cases with the morphology of low-grade EEC were reclassified as mesonephric-like carcinoma and were universally characterized by distant metastasis/recurrence, loss of ER/PR expression, large tumor size, absence of CTNNB1 mutations, and the presence of KRAS mutations. In low-grade EEC, CTNNB1 and KRAS mutations are associated with local recurrence and distant metastasis/recurrence, respectively, suggesting that these 2 different progression types may be conditioned by tumor genotype. ER/PR immunohistochemistry may be helpful in identifying poor performers in low-grade EEC. Furthermore, identification of the decreased expression or loss of ER/PR in tumors with low-grade histology should prompt consideration of mesonephric-like carcinoma, which is a more aggressive tumor than the low-grade EEC. KRAS mutations were associated with distant metastasis/recurrence in tumors with and without mesonephric-like phenotype.

Lynch Syndrome and Gynecologic Tumors: Incidence, Prophylaxis, and Management of Patients with Cancer

This review provides a comprehensive update on recent evidence regarding gynecologic tumors associated with Lynch Syndrome (LS). Endometrial cancer (EC) and ovarian cancer (OC) are the first and second most common gynecologic malignancies in developed countries, respectively, and LS is estimated to be the hereditary cause in 3% of both EC and OC. Despite the increasing evidence on LS-related tumors, few studies have analyzed the outcomes of LS-related EC and OC stratified by mutational variant. This review aims to provide a comprehensive overview of the literature and comparison between updated international guidelines, to help outline a shared pathway for the diagnosis, prevention, and management of LS. Through the widespread adoption of the immunohistochemistry-based Universal Screening, LS diagnosis and identification of mutational variants could be standardized and recognized by international guidelines as a feasible, reproducible, and cost-effective method. Furthermore, the development of a better understanding of LS and its mutational variants will support our ability to better tailor EC and OC management in terms of prophylactic surgery and systemic treatment in the light of the promising results shown by immunotherapy.

Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness

PURPOSE

to investigate the preoperative role of ML-based classification using conventional ¹⁸F-FDG PET parameters and clinical data in predicting features of EC aggressiveness.

METHODS

retrospective study, including 123 EC patients who underwent ¹⁸F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities.

RESULTS

on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression.

CONCLUSIONS

ML-based classification using conventional ¹⁸F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.

Integration of Molecular Data in the Prognostic Stratification and Management of Endometrial Carcinoma

In the last years, the TCGA-based molecular classifier have been progressively integrated in the management of endometrial carcinoma. While molecular assays are increasingly available across pathology laboratories, the additional costs will expectedly be compensated by a reduction in overtreatments and a prevention of recurrences. The additional time might be shortened by assessing molecular markers on biopsy specimens. Retrospective data suggest that the molecular classifier will have a major impact of on the risk stratification, with many patients having their risk class down- or upstaged based on POLE mutations or p53 abnormalities, respectively. However, there are still several issues to be resolved, such as the prognostic value of the TCGA classifier in each FIGO stage, or the type of adjuvant treatment most suitable for each molecular group. Other issues regard the prognostic stratification of the mismatch repair-deficient and "no specific molecular profile" groups, which currently follows the same criteria; however, the former seems to be prognostically consistent regardless of FIGO grade and histotype, whereas the latter appears highly heterogeneous. Numerous clinical, histological, immunohistochemical and molecular markers have been proposed to refine the TCGA classification, but their prognostic value is still undefined. Hopefully, prospective data collected in the next years will help resolving these issues.

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study

Objective

This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC).

Methods

Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC.

Results

The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC.

Conclusion

FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.