Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study

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 lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients.Methods and findings: Within the European Network for Individualized Treatment of Endometrial Cancer (ENI-TEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with Conclusions: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.</div

Gene Promoter Methylation in Endometrial Carcinogenesis

Up to 60% of untreated atypical hyperplastic endometrium will develop into endometrial carcinoma (EC), and for those who underwent a hysterectomy a coexisting EC is found in up to 50%. Gene promoter methylation might be related to the EC development. The aim of this study is to determine changes in gene promoter profiles in normal endometrium, atypical hyperplasia (AH) and EC in relation to K-Ras mutations. A retrospective study was conducted in patients diagnosed with endometrial hyperplasia with and without subsequent EC. Promoter methylation of APC, hMLh1, O6-MGMT, P14, P16, RASSF1, RUNX3 was analysed on pre-operative biopsies, and correlated to the final histological diagnosis, and related to the presence of K-Ras mutations. In the study cohort (n=98), differences in promoter methylation were observed for hMLH1, O6-MGMT, and P16. Promoter methylation of hMLH1 and O6-MGMT gradually increased from histologically normal endometrium to AH to EC; 27.3, 36.4% and 38.0% for hMLH1 and 8.3%, 18.2% and 31.4% for O6-MGMT, respectively. P16 promoter methylation was significantly different in AH (7.7%) compared to EC (38%). K-Ras mutations were observed in 12.1% of AH, and in 19.6% of EC cases. No association of K-Ras mutation with promoter methylation of any of the tested genes was found. In conclusion,hMLH1 and O6-MGMT promoter methylation are frequently present in AH, and thus considered to be early events in the carcinogenesis of EC, whereas P16 promoter methylation was mainly present in EC, and not in precursor lesions supporting a late event in the carcinogenesis

Limited independent prognostic value of MMP-14 and MMP-2 expression in ovarian cancer

Contains fulltext : 165697.pdf (publisher's version ) (Open Access)BACKGROUND: In cancer, various MMPs play a role in progression and metastasis and their overexpression generally indicates a poor prognosis. MMP-14 is the main activator of MMP-2 and both molecules play a role in normal ovarian follicular development. Earlier reports indicated a prognostic value for both MMP-14 and MMP-2 in ovarian cancer. This study was designed to determine the prognostic value of MMP-14 and MMP-2 expression in ovarian cancer with data on long-term follow-up. METHODS: Tumor samples of 94 consecutive ovarian cancer patients from one regional laboratory were evaluated. Clinical and survival data were collected and related to known prognostic factors, as well as to the expression of MMP-14 and MMP-2 as determined by semi-quantitative immunohistochemistry. RESULTS: Epithelial MMP-14 expression correlated with stromal MMP-14 expression (rho = .47, p < .01) and epithelial MMP-2 expression was found to correlate with both MMP-14 epithelial and stromal expression (rho = -.28, p < .01 respectively rho = -.21, p < .05). In univariable analysis of 64 advanced-staged tumours, no MMP parameter was significant for progression-free or overall survival. In multivariable analysis for PFS, stromal MMP-14 expression and epithelial MMP-2 expression remained in the model. For overall survival, no MMP parameter showed significance. CONCLUSIONS: We confirmed the correlation between epithelial and stromal MMP-14 expression and between epithelial MMP-2 and both epithelial and stromal MMP-14 expression. In this study with long-term follow-up, the independent prognostic value of MMP-14 and MMP-2 expression in ovarian cancer is limited to a role in PFS for stromal MMP-14 expression and epithelial MMP-2 expression

Preoperative identification of synchronous ovarian and endometrial cancers: the importance of appropriate workup

Item does not contain fulltextOBJECTIVE: For treatment of patients with both endometrial and ovarian cancer, it is important to discriminate between 2 primary tumors and metastatic disease. Currently, criteria are based on postoperative findings. The aim of this study was to determine whether clinical parameters can discriminate between these groups preoperatively and whether a practical guideline could improve appropriate workup and treatment. METHODS: A total of 45 patients with a diagnosis of both endometrium and ovarian cancer between 1998 and 2009 and were included for analysis. Clinical and pathological data were obtained, and initial CA-125 was registered; patients had a diagnosis of 2 primary tumors or tumors with metastasis. All patients were reclassified according to workup and treatment. RESULTS: Patients with synchronous primary tumors were significantly younger, presented more often with abnormal uterine bleeding, and had a lower initial CA-125 than both metastatic groups (P < 0.05). With age and CA-125 included in a polytomic logistic regression model, 83.3% of diagnoses could be classified correctly. In 15 of 17 patients presented with adnexal mass, workup was incomplete owing to lack on information of the endometrial status. In patients presenting with abnormal uterine bleeding, 13 of 21 patients had an incomplete workup leading to staging laparotomy secondary to initial surgical treatment in 2 patients. CONCLUSIONS: Patients with synchronous endometrial and ovarian cancers are young, often present with abnormal uterine bleeding and have a low initial CA-125. Adequate workup with attention to both ovarian and endometrial status, especially in young patients with a wish to preserve fertility, is important to make the right decision for treatment

Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer

Objective: Pre-operative immunohistochemical (IHC) biomarkers are not incorporated in endometrial cancer (EC) risk classification. We aim to investigate the added prognostic relevance of IHC biomarkers to the ESMO-ESGO-ESTRO risk classification and lymph node (LN) status in EC. Methods: Retrospective multicenter study within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), analyzing pre-operative IHC expression of p53, L1 cell-adhesion molecule (L1CAM), estrogen receptor (ER) and progesterone receptor (PR), and relate to ESMO-ESGO-ESTRO risk groups, LN status and outcome. Results: A total of 763 EC patients were included with a median follow-up of 5.5-years. Abnormal IHC expression was present for p53 in 112 (14.7%), L1CAM in 79 (10.4%), ER- in 76 (10.0%), and PR- in 138 (18.1%) patients. Abnormal expression of p53/L1CAM/ER/PR was significantly related with higher risk classification groups, and combined associated with the worst outcome within the ‘high and advanced/metastatic’ risk group. In multivariate analysis p53-abn, ER/PR- and ESMO-ESGO-ESTRO ‘high and advanced/metastatic’ were independently associated with reduced disease-specific survival (DSS). Patients with abnormal IHC expression and lymph node metastasis (LNM) had the worst outcome. Patients with LNM and normal IHC expression had comparable outcome with patients without LNM and abnormal IHC expression. Conclusion: The use of pre-operative IHC biomarkers has important prognostic relevance in addition to the ESMO-ESGO-ESTRO risk classification and in addition to LN status. For daily clinical practice, p53/L1CAM/ER/PR expression could serve as indicator for surgical staging and refine selective adjuvant treatment by incorporation into the ESMO-ESGO-ESTRO risk classification

Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: a development and validation study

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 lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. Methods and findings: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. Conclusions: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic