Johanna M. A. Pijnenborg

Usefulness of microRNA detection in the diagnostics of endometrial cancer

AbstractIntroductionMicroRNAs (miRNAs) are noncoding RNAs that regulate gene expression and contribute to the development of cancer. They have been shown to be stable in tissue samples and may be promising diagnostic biomarkers for endometrial cancer.Material and methodsA retrospective cohort study of women diagnosed with endometrial cancer between January 2017 and December 2017 was performed at the Royal Cornwall Hospital. Archived formalin‐fixed paraffin‐embedded samples were obtained from patients with endometrial cancer and healthy women. MicroRNA was isolated and quantitative real‐time polymerase chain reaction was used to detect expression levels of miRNAs.ResultsA total of 76 women were included: 36 endometrial cancer patients, 40 healthy controls. A distinct panel of miR‐200a, miR‐200b, miR‐200c, miR‐205, and miR‐182 showed an area under the curve of 0.958, sensitivity 92%, specificity 89%, positive predictive value of 89% (95% CI 82%–94%) and negative predictive value of 91% (95% CI 85%–96%) in diagnosing endometrial cancer. High miR‐182 expression levels were significantly related to high‐grade endometrioid tumors compared with low‐grade tumors.ConclusionsWe demonstrated high diagnostic accuracy of miRNA for detecting endometrial cancer. In addition, miRNA contributed to an improvement in distinguishing between high‐grade and low‐grade endometrioid tumors.

Implementation of a Personalized Risk Model for Lymph Node Metastasis in Endometrial Carcinoma: Healthcare Providers' Perspectives on Use, Barriers, and Facilitators

ABSTRACT Background The ENDORISK model estimates the risk of lymph node metastases (LNM) in endometrial carcinoma (EC) patients using preoperative clinical variables and biomarkers. This qualitative study investigated healthcare providers' (HCP) perspectives on the use of the model and barriers and facilitators for clinical implementation. Methods Eight focus group interviews were performed among HCPs. A semi‐structured interview guide was used based on the Grol and Wensing implementation model. Results Focus groups included gynecologists, residents of gynecology, pathologists, radiation oncologists, and a nurse specialist ( n  = 41). ENDORISK was deemed supportive for counseling of patients and shared decision‐making for optimal surgical and adjuvant treatment. Barriers for implementation were difficulty in explaining the model and risk percentages to patients, differences in preoperative diagnostic tools used per hospital, and use of the model with the sentinel node procedure. Facilitators were a clear guideline for using the model with a predefined risk cutoff and making the model easily understandable for patients. A 10% risk cutoff was considered clinically relevant for lymph node assessment. Conclusion HCP found ENDORISK use in clinical practice supportive for patient counseling. Future implementation should focus on a user‐friendly interface, a cohesive guideline, and training to aid efficient use and counseling of patients.

Improving pre-operative binary grading: relevance of p53 and PR expression in grade 2 endometrioid endometrial carcinoma

This study aimed to evaluate the association between pre-operative progesterone receptor (PR) and p53 expression and prognosis in pre-operative grade 2 endometrioid endometrial carcinoma compared with grade 1 and grade 3 carcinomas. Three European endometrial carcinoma cohort studies were included. Patients with pre-operative grade 2 endometrioid carcinoma and known pre-operative PR and p53 status were included (n = 400), as were patients with pre-operative grade 1 (n = 602) or grade 3 (n = 148) endometrioid carcinomas. Kaplan-Meier and Cox regression analyses were performed to analyze disease-specific and disease-free survival. Patients with pre-operative grade 2 endometrial carcinoma and wild-type p53 plus PR-positive expression showed a similar 7-year disease-specific survival to grade 1 endometrial carcinoma patients (95.8% vs 97.5%, p = .13), while the 7-year disease-specific survival of patients with grade 2 endometrial carcinoma with p53 aberrant and/or negative PR expression (83.5%) was significantly lower (p < .001). The combination of these markers was an independent prognostic factor in multivariate Cox regression analyses. The prognostic impact of pre-operative p53 and PR expression in patients with grade 2 endometrioid endometrial carcinoma supports a modified binary grading system in which grade 2 patients should be pre-operatively classified as low- or high-grade depending on p53 and PR expression.

The cutoff for estrogen and progesterone receptor expression in endometrial cancer revisited: a European Network for Individualized Treatment of Endometrial Cancer collaboration study

There is no consensus on the cutoff for positivity of estrogen receptor (ER) and progesterone receptor (PR) in endometrial cancer (EC). Therefore, we determined the cutoff value for ER and PR expression with the strongest prognostic impact on the outcome. Immunohistochemical expression of ER and PR was scored as a percentage of positive EC cell nuclei. Cutoff values were related to disease-specific survival (DSS) and disease-free survival (DFS) using sensitivity, specificity, and multivariable regression analysis. The results were validated in an independent cohort. The study cohort (n = 527) included 82% of grade 1-2 and 18% of grade 3 EC. Specificity for DSS and DFS was highest for the cutoff values of 1-30%. Sensitivity was highest for the cutoff values of 80-90%. ER and PR expression were independent markers for DSS at cutoff values of 10% and 80%. Consequently, three subgroups with distinct clinical outcomes were identified: 0-10% of ER/PR expression with, unfavorable outcome (5-year DSS = 75.9-83.3%); 20-80% of ER/PR expression with, intermediate outcome (5-year DSS = 93.0-93.9%); and 90-100% of ER/PR expression with, favorable outcome (5-year DSS = 97.8-100%). The association between ER/PR subgroups and outcomes was confirmed in the validation cohort (n = 265). We propose classification of ER and PR expression based on a high-risk (0-10%), intermediate-risk (20-80%), and low-risk (90-100%) group.

The effect of progestin therapy in advanced and recurrent endometrial cancer: A systematic review and meta‐analysis

AbstractBackgroundFifteen percent of patients with endometrial cancer (EC) have advanced stage disease or develop a recurrence. Progestins have been applied as systemic treatment for decades, but there is limited evidence on response prediction with biomarkers and toxicity.ObjectivesTo review the response and toxicity of progestin therapy and stratify response to progesterone receptor (PR) expression and tumour grade.Search strategyWe used the search terms ‘Endometrial cancer’, ‘Progestins’, ‘Disease progression’, ‘Recurrence’ and related terms in Pubmed, Embase and Cochrane databases.Selection criteriaStudies on patients with advanced stage or recurrent EC treated with progestin monotherapy were included. Studies on adjuvant therapy, with fewer than ten cases and with sarcoma histology were excluded.Data collection and analysisEvaluation for bias was performed with the Revised Cochrane RoB2 tool for randomised studies and the ROBINS‐I tool for non‐randomised studies. A random effects meta‐analysis was performed with the overall response rate (ORR), clinical benefit rate and toxicity as primary outcome measures.Main resultsTwenty‐six studies (1639 patients) were included. The ORR of progestin therapy was 30% (95% CI 25–36), the clinical benefit rate was 52% (95% CI 42–61). In PR‐positive EC, the ORR was 55%, compared with 12% in PR‐negative disease (risk difference 43%, 95% CI 15–71). Severe toxicity occurred in 6.5%.ConclusionsProgestin therapy is a viable treatment option in patients with advanced stage and recurrent EC with low toxicity and high ORR in PR‐positive disease. The role of PR expression in relation to progression‐free survival and overall survival is unclear.

L1CAM expression as a predictor of platinum response in high‐risk endometrial carcinoma

AbstractFor high‐risk endometrial cancer (EC) patients, adjuvant chemotherapy is recommended to improve outcome. Yet, predictive biomarkers for response to platinum‐based chemotherapy (Pt‐aCT) are currently lacking. We tested expression of L1 cell‐adhesion molecule (L1CAM), a well‐recognised marker of poor prognosis in EC, in tumour samples from high‐risk EC patients, to explore its role as a predictive marker of Pt‐aCT response. L1CAM expression was determined using RT‐qPCR and immunohistochemistry in a cohort of high‐risk EC patients treated with Pt‐aCT and validated in a multicentric independent cohort. The association between L1CAM and clinicopathologic features and L1CAM additive value in predicting platinum response were determined. The effect of L1CAM gene silencing on response to carboplatin was functionally tested on primary L1CAM‐expressing cells. Increased L1CAM expression at both genetic and protein level correlated with high‐grade, non‐endometrioid histology and poor response to platinum treatment. A predictive model adding L1CAM to prognostic clinical variables significantly improved platinum response prediction (C‐index 78.1%, P = .012). In multivariate survival analysis, L1CAM expression was significantly associated with poor outcome (HR: 2.03, P = .019), potentially through an indirect effect, mediated by its influence on response to chemotherapy. In vitro, inhibition of L1CAM significantly increased cell sensitivity to carboplatin, supporting a mechanistic link between L1CAM expression and response to platinum in EC cells. In conclusion, we have demonstrated the role of L1CAM in the prediction of response to Pt‐aCT in two independent cohorts of high‐risk EC patients. L1CAM is a promising candidate biomarker to optimise decision making in high‐risk patients who are eligible for Pt‐aCT.

Molecular profiling identifies synchronous endometrial and ovarian cancers as metastatic endometrial cancer with favorable clinical outcome

Synchronous primary endometrial and ovarian cancers (SEOs) represent 10% of all endometrial and ovarian cancers and are assumed to develop as independent entities. We investigated the clonal relationship between endometrial and ovarian carcinomas in a large cohort classified as SEOs or metastatic disease (MD). The molecular profiles were compared to The Cancer Genome Atlas (TCGA) data to explore primary origin. Subsequently, the molecular profiles were correlated with clinical outcome. To this extent, a retrospective multicenter study was performed comparing patients with SEOs (n = 50), endometrial cancer with synchronous ovarian metastasis (n = 19) and ovarian cancer with synchronous endometrial metastasis (n = 20). Targeted next‐generation sequencing was used, and a clonality index was calculated. Subsequently, cases were classified as POLE mutated, mismatch repair deficient (MMR‐D), TP53‐wild‐type or TP53‐mutated. In 92% of SEOs (46/50), the endometrial and concurrent ovarian carcinoma shared at least one somatic mutation, with a clonality index above 0.95, supporting a clonal origin. The SEO molecular profiles showed striking similarities with the TCGA endometrial carcinoma set. SEOs behaved distinctly different from metastatic disease, with a superior outcome compared to endometrial MD cases (p &lt; 0.001) and ovarian MD cases (p &lt; 0.001). Classification according to the TCGA identified four groups with different clinical outcomes. TP53 mutations and extra‐utero‐ovarian disease were independent predictors for poor clinical outcome. Concluding, SEOs were clonally related in an overwhelming majority of cases and showed a favorable prognosis. Their molecular profile implied a primary endometrial origin. TP53 mutation and extra‐utero‐ovarian disease were independent predictors for outcome, and may impact adjuvant systemic treatment planning.

Predictive value of estrogen and progesterone receptors in endometrial hyperplasia and cancer

Somatic Mutation Profiling in Premalignant Lesions of Vulvar Squamous Cell Carcinoma

Vulvar squamous cell carcinoma (VSCC) originates from the progression of either a high-grade squamous intraepithelial lesion (HSIL) or differentiated-type vulvar intraepithelial neoplasia (dVIN), often in a background of lichen sclerosus (LS). The mechanisms leading to the progression of these premalignant lesions to VSCC are elusive. This study aims to identify pathogenic mutations implicated in VSCC development. Using next-generation sequencing, 38 HSIL, 19 dVIN, 20 LS, of which 10 were solitary lesions and 10 with adjacent VSCC, and 10 VSCC adjacent to LS, were screened for hotspot mutations in 50 genes covered by the Ion AmpliSeq Cancer Hotspot Panel v2 Kit (Thermo Fisher Scientific). Pathogenic mutations of TP53 were the most common genetic alterations identified in 53% and 24% of dVIN and HSIL cases, respectively, followed by CDKN2A (p16) mutated in 42% and 0% of dVIN and HSIL, respectively. Seven (70%) and three (30%) of 10 cases of VSCC associated with LS carried TP53 and CDKN2A mutations, respectively, whereas neither solitary LS nor LS associated with VSCC cases harbored mutations in these genes. It appears that TP53 mutations are early events during VSCC carcinogenesis, being present in both HSIL and dVIN lesions. Our preliminary data do not support a genetic background for the notion of LS as the VSCC premalignant lesion.

Impact of patient characteristics on primary treatment approach of endometrial cancer: a population-based study in the Netherlands

The incidence of endometrial cancer is rising worldwide, with increasing numbers of elderly patients with substantial comorbidity. We aimed to provide insight into the primary treatment strategies, trends, and outcomes for patients with endometrial cancer in the Netherlands across different periods, and to evaluate the influence of patient characteristics on treatment approaches within these periods. All patients diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage I to III endometrial cancer between 1995 and 2022 were identified in the Netherlands Cancer Registry. Data on histology, FIGO stage, primary treatment, Charlson comorbidity index, body mass index, and outcome were collected and analyzed. Patients were evaluated in 6 5-year cohorts to observe possible trends in treatment choices and outcomes over time, based on patient and tumor characteristics. A total of 43,443 patients were included. Most patients were diagnosed with endometrioid histology (89%, n = 38,751) and early-stage disease (FIGO I-II, 87%, n = 38,077). An increase in endometrial cancer incidence and survival was observed over time, along with a shift toward more non-endometrioid endometrial cases. A primary surgical approach was performed in 95% (n = 41,107) of patients. Among those receiving non-surgical management, curative treatment was reported in only 4% (n = 88). Most patients unfit for surgery either received no treatment (n = 1053, 2%) or were treated with hormonal therapy (n = 840, 2%). Over time, we observed increased use of hormonal therapy as a non-surgical treatment. Patients who did not undergo surgery were older, more often had a body mass index >30 kg/m Incidence and survival in patients with endometrial cancer increased between 1999 and 2022. Patients unfit for surgery most often received no treatment or palliative hormonal therapy. Curative radiotherapy is not often used in patients unfit for surgery and should be considered for this group of patients.

ENDORISK-2: A personalized Bayesian network for preoperative risk stratification in endometrial cancer, integrating molecular classification and preoperative myometrial invasion assessment

ENDORISK is a Bayesian network that can assist in preoperative risk estimation of lymph node metastasis (LNM) risk in endometrial cancer (EC) with consistent performance in external validations. To reflect state of the art care, ENDORISK was optimized by integrating molecular classification and preoperative assessment of myometrial invasion (MI). Variables for POLE, MSI, and preoperative assessment of MI, either by expert transvaginal ultrasound or pelvic magnetic resonance imaging (MRI), were added to develop ENDORISK-2. The p53 biomarker, part of the molecular classification, was already included in ENDORISK. External validation of ENDORISK-2 for LNM prediction was performed in two independent cohorts from: Brno (CZ), (n = 581) and Tübingen (DE), (n = 247). ENDORISK-2 yielded AUCs of 0·85 (95 % CI 0·80-0·90) (CZ) and 0·86 (95 % CI 0·77-0·96) (DE) for predicting LNM. In patients with low-grade histology, 83 % (CZ) and 89 % (DE) were estimated having less than 10 % risk of LNM, with false negative rates (FNR) of 4·3 % (CZ) and 2·2 % (DE). The previously defined set of minimally required variables, i.e.: preoperative tumor grade, three of the four immunohistochemical (IHC) markers, and one clinical marker, could be interchanged with the new variables, with comparable validation metrics, including AUC values of 0·79-0·87 for LNM prediction. Incorporation of molecular data and preoperative MI improved the flexibility of ENDORISK with comparable diagnostic accuracy for estimating LNM as when based on low-cost immunohistochemical biomarkers. In addition, the high diagnostic accuracy in patients with low-grade EC demonstrates how ENDORISK-2 could aid clinicians in identifying patients in whom surgical lymph node assessment may safely be omitted. These results underline its power for clinical use in both high and low resource countries.

Federated causal discovery with missing data in a multicentric study on endometrial cancer

Establishing causal dependencies is crucial in applied domains, such as medicine and healthcare, where decision-making must be explainable. In these settings, small sample sizes and missing data call for federated approaches to maximise the amount of information we can use. We propose a novel federated causal discovery algorithm capable of pooling information from multiple sources with heterogeneous missing data to learn a graph representing cause-effect relationships. In particular, we learn a causal graph on a centralised server while taking into account both prior knowledge and missingness mechanism specific to each client. We applied the proposed algorithm to synthetic data and real-world data from a multicentric study on endometrial cancer, validating the obtained causal graph through quantitative analyses and a clinical literature review. Our approach learns an accurate model despite data missing not-at-random.

Redefining the Position of Hormonal Therapy in Endometrial Cancer in the Era of Molecular Classification