Clinicopathological significance of multiple molecular features in undifferentiated and dedifferentiated endometrial carcinomas

Unsolved Issues in the Integrated Histo-Molecular Classification of Endometrial Carcinoma and Therapeutic Implications

Endometrial carcinoma (EC) is the most frequent gynecological cancer, with an increasing incidence and mortality in recent times. The last decade has represented a true revolution with the development of the integrated histo-molecular classification of EC, which allows for the stratification of patients with morphologically indistinguishable disease into groups with different prognoses. Particularly, the POLE-mutated subgroup exhibits outstanding survival. Nevertheless, the indiscriminate application of molecular classification appears premature. Its prognostic significance has been proven mainly in endometrioid EC, the most common histotype, but it has yet to be convincingly confirmed in the other minor histotypes, which indeed account for a relevant proportion of EC mortality. Moreover, its daily use both requires a mindful pathologist who is able to correctly evaluate and unambiguously report immunohistochemical staining used as a surrogated diagnostic tool and is hampered by the unavailability of POLE mutation analysis. Further molecular characterization of ECs is needed to allow for the identification of better-tailored therapies in different settings, as well as the safe avoidance of surgery for fertility preservation. Hopefully, the numerous ongoing clinical trials in the adjuvant and metastatic settings of EC will likely produce evidence to refine the histo-molecular classification and therapeutic guidelines. Our review aims to retrace the origin and evolution of the molecular classification for EC, reveal its strengths and limitations, show clinical relevance, and uncover the desired future developments.

Analysis of Prognostic Factors and Cancer-Specific Survival in Patients with Undifferentiated and Dedifferentiated Endometrial Carcinoma Undergoing Various Postoperative Adjuvant Therapies

Purpose

To investigate prognostic factors affecting cancer-specific survival (CSS) and to analyze the survival outcomes of patients with undifferentiated and dedifferentiated endometrial carcinoma (UDEC) who underwent various postoperative adjuvant therapies.

Methods

The independent risk factors affecting CSS were studied using univariate and multivariate Cox regression analysis, and CSS in the presence of various postoperative treatments was evaluated using Kaplan-Meier method based on the cohort with pathologically confirmed UDEC from the Surveillance, Epidemiology, and End Results (SEER) database. Meanwhile, the study included 18 cases with UDEC in our center and explored their molecular characteristics and prognosis.

Results

Between 2000 and 2019, a total of 443 patients were included from the SEER database. The median CSS duration was 14 months, with corresponding 3- and 5-year CSS rates of 45.9% and 44.0%, respectively. Factors such as pTNM stage, surgical resection of primary lesion, and chemoradiation independently influenced CSS. Postoperative chemotherapy alone improved CSS in patients with initial tumor spread beyond the uterus (pT3 and pT4), or lymph node (LN) invasion, or distant metastases. Additionally, postoperative radiotherapy enhanced CSS in patients who had undergone postoperative chemotherapy, those with primary tumors progressing to stage pT3, and those with LN involvement but without distant metastases. Of the 18 patients diagnosed at our center, with a median follow-up of 15.5 months, one experienced relapse and two succumbed to UDEC, who exhibited aberrant p53 expression in immunohistochemical staining.

Conclusion

Postoperative chemotherapy and radiotherapy are beneficial for UDEC patients with tumors extending beyond the uterus or involving lymph nodes.

Clinicopathological characteristics of multiple-classifier endometrial cancers: a cohort study and systematic review

BACKGROUND

Endometrial cancers with more than one molecular feature-POLE mutations (POLEmut), mismatch repair protein deficiency (MMRd), p53 abnormality (p53abn)-are called 'multiple classifiers'.

OBJECTIVE

To describe our cohort of multiple classifiers and to report the results of a review on their incidence and the techniques used to identify them.

METHODS

Multiple classifiers identified at the European Institute of Oncology, Milan, between April 2019 and Decmber 2022, were included. Clinicopathological, molecular characteristics, and oncologic outcomes were summarized and compared between single and multiple classifiers sharing common features. Studies on molecular classification of endometrial cancer were searched in the PubMed Database to collect data on the incidence of multiple classifiers and the techniques used for classification.

RESULTS

Among 422 patients, 48 (11.4%) were multiple classifiers: 15 (3.6%) POLEmut-p53abn, 2 (0.5%) POLEmut-MMRd, 28 (6.6%) MMRd-p53abn, and 3 (0.7%) POLEmut-MMRd-p53abn. MMRd-p53abn and MMRd differed in histotype (non-endometrioid: 14.8% vs 2.0%, p=0.006), grade (high-grade: 55.6% vs 22.2%, p=0.001), and MMR proteins expression, whereas they differed from p53abn in histotype (non-endometrioid: 14.8% vs 50.0%, p=0.006). POLEmut-p53abn and POLEmut differed only in grade (high-grade: 66.7% vs 22.7%, p=0.008), while they differed from p53abn in age (56.1 vs 66.7 years, p=0.003), stage (advanced: 6.7% vs 53.4%, p=0.001), and histotype (non-endometrioid: 6.7% vs 50.0%, p=0.002). Two (7.1%) patients with MMRd-p53abn, 4 (4.0%) with MMRd, and 25 (34.3%) with p53abn had a recurrence. No recurrences were observed in POLEmut-p53abn and POLEmut. TP53 sequencing allowed the detection of additional 7 (18.9%) multiple classifiers with normal p53 immunostaining. The incidence of multiple classifiers ranged from 1.8% to 9.8% in 10 published studies including >100 patients. When only p53 immunohistochemistry was performed, the highest incidence was 3.9%.

CONCLUSIONS

The characteristics of POLEmut-p53abn resembled those of POLEmut, whereas MMRd-p53abn appeared to be intermediate between MMRd and p53abn. The high proportion of multiple classifiers may be related to the methods used for molecular classification, which included both p53 immunohistochemistry and TP53 sequencing.

Dedifferentiated Endometrial Carcinoma: A Rare Aggressive Neoplasm-Clinical, Morphological and Immunohistochemical Features

Dedifferentiated endometrioid adenocarcinoma is characterised by the coexistence of an undifferentiated carcinoma and a low-grade endometrioid adenocarcinoma. The low-grade component in this subtype of endometrial carcinoma is Grade 1 or 2 according to the Federation of Gynaecology and Obstetrics (FIGO) grading system. The coexistence of low-grade endometrial carcinoma and solid undifferentiated carcinoma can cause diagnostic problems on histological examination. In fact, this combination can often be mistaken for a more common Grade 2 or Grade 3 endometrial carcinoma. Therefore, this subtype of uterine carcinoma can often go under-recognised. An accurate diagnosis of dedifferentiated endometrial carcinoma is mandatory because of its poorer prognosis compared to Grade 3 endometrial carcinoma, with a solid undifferentiated component that can amount to as much as 20% of the entire tumour. The aim of this review is to provide clinical, immunohistochemical, and molecular data to aid with making an accurate histological diagnosis and to establish whether there are any findings which could have an impact on the prognosis or therapeutic implications of this rare and aggressive uterine neoplasm.

"Surface epithelial slackening" pattern in endometrioid carcinoma: A morphological feature for differentiating the POLE mutation-subtype from the no specific molecular profile subtype

Endometrial cancers are classified into mismatch repair (MMR) deficient- (MMRd), p53 mutation- (p53mut), DNA polymerase epsilon (POLE) mutation (POLEmut), and no specific molecular profile (NSMP) subtypes according to The Cancer Genome Atlas (TCGA). The distinction between POLEmut and NSMP subtypes is made on the basis of molecular analysis because the specific histological and immunohistochemical features of these two subtypes are still unknown. In this study, we analyzed histological features by scoring the presence of a mucinous pool, giant cells, clear cells, keratinization, neutrophilic abscess, and surface proliferating pattern in 82 cases of endometrial cancers in which an integrative diagnosis was confirmed by immunohistochemistry and genomic profiles showing POLE mutations, tumor mutation burden, and microsatellite instability. In contrast to the hierarchical branching of micropapillary proliferation observed in serous carcinoma, POLEmut-subtype endometrioid carcinomas often showed a surface epithelial slackening (SES) pattern in the tumor cells facing the uterine surface. The POLEmut subtype exhibited higher scores for clear cells and SES patterns than the other three subtypes. The scores for giant cells, clear cells, and the SES pattern were significantly higher in the POLEmut subtype than in the NSMP subtype, suggesting that these morphometric parameters are useful for differentiating POLEmut- and NSMP-subtype endometrioid carcinomas, although genomic profiling is still necessary for a definite molecular diagnosis.

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.