Background Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for
Endometrial carcinoma (EC) is traditionally diagnosed by a histopathological assessment of an endometrial biopsy, leaving up to 30% of patients undiagnosed due to technical failure or an inadequate amount of tissue. The aim of the current study is to assess whether mutational analysis of cervical cytology or pipelle endometrial biopsies improves the diagnostic accuracy of traditional histopathological diagnosis of EC. This prospective multicentre cohort study included patients surgically treated for EC or a benign gynaecological condition (control group). A Pap brush sample, cervicovaginal self‐sample, pipelle endometrial biopsy and surgical specimen of either the EC or normal endometrium were obtained. A targeted next‐generation sequencing panel was used to analyse these samples for mutations in eight genes. Sensitivity, specificity and predictive values were calculated. Fifty‐nine EC patients and 31 control patients were included. In these patients, traditional histopathological diagnosis by pipelle had a sensitivity of 79% and a specificity of 100%. For EC patients, 97% of surgical specimens contained at least one mutation. Mutational analysis of Pap brush samples, self‐samples and pipelle endometrial biopsies yielded a sensitivity of 78, 67 and 96% with a specificity of 97, 97 and 94%, respectively. Combining one of these three methods with histopathological pipelle endometrial biopsy evaluations yielded a sensitivity of 96, 93 and 96%, respectively. Our study has shown that mutational analysis of either cervical cytology or pipelle endometrial biopsies improves diagnosis of EC. Prospective validation will support implementation in clinical practice.
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 nextgeneration 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 < 0.001) and ovarian MD cases (p < 0.001). Classification according to the TCGA identified four groups with different clinical outcomes. TP53 mutations and extra-uteroovarian 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.Additional Supporting Information may be found in the online version of this article.
Endometrial carcinomas are histologically classified as endometrioid, assumed to originate from hyperplastic endometrium, or non-endometrioid carcinomas, assumed to originate from atrophic endometrium. However, both on a histological and a molecular level there are indications that there are more carcinoma types and carcinogenetic pathways. This study aims to analyze endometrial carcinogenesis on a molecular level. The presence of known KRAS, PIK3CA, AKT1, CTNNB1, BRAF, EGFR and NRAS mutations was studied in proliferative, atrophic and hyperplastic endometrium, endometrioid and serous carcinomas, and the endometrium next to these carcinomas, using single molecule Molecular Inversion Probes. Mutations were found in 9 (15%) of the 62 non atypical, and in 6 (18%) of the 34 atypical hyperplasia cases. In comparison, mutations were found in 1 (3%) of the simple, and 8 (30%) of the 27 complex hyperplasia cases. In 12/22 (55%) endometrioid carcinomas, a mutation was found. The KRAS gene was most often mutated in carcinomas next to hyperplastic endometrium, whereas PIK3CA and CTNNB1 mutations were found in endometrioid carcinomas with adjacent atrophic endometrium. Complex hyperplasia rather than atypical hyperplasia appears to be the most important lesion in the carcinogenesis of endometrioid carcinomas, and KRAS, PIK3CA and CTNNB1 mutations appear to play an important role in this process. Carcinogenesis of endometrioid carcinomas next to hyperplasia seems to be different to that of those next to atrophia. The value of these findings in managing endometrial hyperplasia and carcinoma should be studied.
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