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

Molecular markers to predict prognosis and guide therapy in endometrial cancer

Background: Endometrial cancer is the most common gynecological malignancy in the Western world. The disease occurs in the epithelial lining of the uterus, called the endometrium. Although prognosis is good and most of the patients are diagnosed at an early stage, 15-20 % of patients experience recurrence. An accurate risk-stratification is lacking and as incidence is increasing due to the increased prevalence of obesity and extended life-expectancy, biomarkers for improved risk-stratification are needed. Main objective: The main objective was to define biomarkers to better identify high-risk patients from low-risk patients in order to individualize therapy and targeted treatment. Materials and methods: A prospectively and population-based series was collected and includes endometrial hyperplasias, primary tumors and metastases (Paper I-IV). Immunohistochemical staining was used for evaluation of HSF1, MSH6, PD-L1 and PD-1 (Paper I, III and IV). ELISA was performed for determination of plasma GDF-15 (Paper II). RNA microarray data were used for evaluation of mRNA levels (Paper I, III and IV). Results: High expression of HSF1 associated with aggressive disease and poor survival in endometrial cancer. Protein level of HSF1 increased from primary tumors to metastasis. We found HSF1 to be an independent prognostic marker within ER-positive patients, a patient group with a presumed favourable prognosis. Gene expression analyses identified HSP90 inhibitors for targeted therapy (Paper I). High plasma levels of GDF-15 associated with aggressive disease characteristics and poor prognosis, also in low-risk patients. GDF-15 can indicate recurrence during follow-up and was an independent marker for recurrence. We validated the role of GDF-15 as an independent marker for lymph node metastasis (Paper II). PD-L1 and PD-1 are frequently expressed in endometrial cancer, 59% and 63%, respectively (Paper III). Expression was similar across MSS and MSI tumors. PD-L1 and PD-1 have no impact on survival, nor when stratified for MSI. In corresponding metastatic lesions, expression was discordant and intra-variable compared to primary tumors. High protein level of MSH6 identified aggressive endometrial cancer, also in low-risk patients (Paper IV). The prognostic value of MSH6 was validated both in curettage and hysterectomy specimen. MSH6 has independent prognostic impact preoperatively adjusted for age, histological risk-classification and hormone receptor status in the whole patient cohort. Also in a subgroup of patients with a putative low-risk disease, MSH6 demonstrated independent prognostic impact adjusted for age and hormone receptor status (Paper IV). Conclusion: High expression of HSF1, GDF-15 and MSH6 predicts aggressive disease and poor survival (Paper I, II and IV). GDF-15 is an independent predictor of recurrent disease and lymph node metastasis (Paper II). PD-L1 and PD-1 are frequently expressed and expression pattern is similar across MSS and MSI tumors. Expression in corresponding metastatic lesions is discordant and intra-variable (Paper III)

Plasma growth differentiation factor-15 is an independent marker for aggressive disease in endometrial cancer

Objective: Better biomarkers are needed in order to identify patients with endometrial carcinoma at risk of recurrence and who may profit from a more aggressive treatment regimen. Our objective was to explore the applicability of plasma growth differentiation factor 15 (GDF-15) as a marker for recurrent disease, as well as a marker for poor prognosis and lymph node metastases. Methods: EDTA-blood samples were obtained from 235 patients with endometrial cancer before primary surgery. For 36 of these patients, matching blood samples were collected at time of recurrence. Blood samples were also collected from 78 patients with endometrial hyperplasia. Plasma GDF-15 was measured by an enzyme-linked immunosorbent assay (ELISA). Preoperative pelvic MRI scans for 141 patients were investigated in parallel for imaging variables. Results: Preoperative plasma level of GDF-15 was significantly higher for patients who experienced recurrence (1780 ng/L; 95% CI; 518–9475 ng/L) than for patients who did not develop recurrent disease (1236 ng/L; 95% CI; 307–7030 ng/L) (p<0.001). Plasma levels of GDF-15 at recurrence (2818 ng/L, 95% CI 2088–3548 ng/L) were significantly higher than plasma levels of GDF-15 measured at time of primary diagnosis (1857 ng/L, 95% CI; 1317–2398 ng/L) (p = 0.001). High plasma level GDF-15 independently predicts recurrent disease (OR = 3.14; 95% CI 2.10–4.76) and lymph node metastases (OR = 2.64; 95% CI 1.52–4.61). Patients with high plasma level of GDF-15 had significantly larger tumor volume (p = 0.008). Conclusion: Elevated plasma level of GDF-15 is associated with aggressive disease and lymph node metastasis in endometrial carcinoma. GDF-15 may be helpful in indicating recurrent disease

Mismatch repair markers in preoperative and operative endometrial cancer samples; expression concordance and prognostic value

Background The endometrial cancer mismatch repair (MMR) deficient subgroup is defined by loss of MSH6, MSH2, PMS2 or MLH1. We compare MMR status in paired preoperative and operative samples and investigate the prognostic impact of differential MMR protein expression levels. Methods Tumour lesions from 1058 endometrial cancer patients were immunohistochemically stained for MSH6, MSH2, PMS2 and MLH1. MMR protein expression was evaluated as loss or intact to determine MMR status, or by staining index to evaluate the prognostic potential of differential expression. Gene expression data from a local (n = 235) and the TCGA (n = 524) endometrial cancer cohorts was used for validation. Results We identified a substantial agreement in MMR status between paired curettage and hysterectomy samples. Individual high expression of all four MMR markers associated with non-endometrioid subtype, and high MSH6 or MSH2 strongly associated with several aggressive disease characteristics including high tumour grade and FIGO stage, and for MSH6, with lymph node metastasis. In multivariate Cox analysis, MSH6 remained an independent prognostic marker, also within the endometrioid low-grade subgroup (P < 0.001). Conclusion We demonstrate that in addition to determine MMR status, MMR protein expression levels, particularly MSH6, may add prognostic information in endometrial cancer.publishedVersio

High degree of heterogeneity of PD-L1 and PD-1 from primary to metastatic endometrial cancer

Objective PD-L1 and PD-1 are predictive markers for immunotherapy and increasingly relevant in endometrial cancer. The reported fraction of positive primary tumors has been inconsistent. We investigated the expression of PD-L1 and PD-1 in primary tumors, also stratified by MSI. As immunotherapy is foremost relevant for metastatic disease, PD-L1 and PD-1 expression was also assessed in corresponding metastatic lesions. Methods PD-L1 and PD-1 was investigated in a prospective, population based endometrial cancer cohort of 700 patients with corresponding metastatic lesions from 68 and 74 patients respectively. Fresh tissue was used for gene expression analysis. Results In primary tumors, PD-L1 and PD-1 are expressed in 59% and 63%, respectively, but with no impact on survival, nor when stratified for MSS and MSI. Expression patterns of PD-L1 and PD-1 are similar in MSI and MSS tumors. Available metastatic lesions show heterogeneous expression of PD-L1 and PD-1. In gene expression analysis several genes related to immunological activity, including CD274 (encoding for PD-L1), were upregulated in PD-1 positive tumors. Conclusion PD-L1 and PD-1 are frequently expressed in endometrial cancer and expression patterns are similar across MSS and MSI tumors. Expression in corresponding metastatic lesions is discordant compared to primary tumors. These findings are in particular relevant for treatment decisions in advanced and recurrent disease

Maintained survival outcome after reducing lymphadenectomy rates and optimizing adjuvant treatment in endometrial cancer

Objective: Main controversies in endometrial cancer treatment include the role of lymphadenectomy and optimal adjuvant treatment. We assessed clinical outcome in a population-based endometrial cancer cohort in relation to changes in treatment management over two decades. Methods: All consenting endometrial cancer patients receiving primary treatment at Haukeland University Hospital from 2001 to 2019 were included (n = 1308). Clinicopathological variables were evaluated for year-to-year changes. Clinical outcome before and after discontinuing adjuvant radiotherapy and individualizing extent of lymphadenectomy was analyzed. Results: The rate of lymphadenectomy was reduced from 78% in 2001–2012 to 53% in 2013–2019. The rate of patients with verified lymph node metastases was maintained (9% vs 8%, p = 0.58) and FIGO stage I patients who did not undergo lymphadenectomy had stable 3-year recurrence-free survival (88% vs 90%, p = 0.67). Adjuvant chemotherapy for completely resected FIGO stage III patients increased from 27% to 97% from 2001 to 2009 to 2010–2019, while adjuvant radiotherapy declined from 57% to 0% (p < 0.001). These patients had improved 5-year overall- and recurrence-free survival; 0.49 [95% CI: 0.37–0.65] in 2001–2009 compared to 0.61 [0.45–0.83] in 2010–2019, p = 0.04 and 0.51 [0.39–0.68] to 0.71 [0.60–0.85], p = 0.03, respectively. For stage I, II and IV, survival rates were unchanged. Conclusions: Our study demonstrates that preoperative stratification by imaging and histological assessments permits a reduction in lymphadenectomy to around 50%, and is achievable without an increase in recurrences at 3 years. In addition, our findings support that adjuvant chemotherapy alone performs equally to adjuvant radiotherapy with regard to survival, and is likely superior in advanced stage patients

A 10-gene prognostic signature points to LIMCH1 and HLA-DQB1 as important players in aggressive cervical cancer disease

Background: Advanced cervical cancer carries a particularly poor prognosis, and few treatment options exist. Identification of effective molecular markers is vital to improve the individualisation of treatment. We investigated transcriptional data from cervical carcinomas related to patient survival and recurrence to identify potential molecular drivers for aggressive disease. Methods: Primary tumour RNA-sequencing profiles from 20 patients with recurrence and 53 patients with cured disease were compared. Protein levels and prognostic impact for selected markers were identified by immunohistochemistry in a population-based patient cohort. Results: Comparison of tumours relative to recurrence status revealed 121 differentially expressed genes. From this gene set, a 10-gene signature with high prognostic significance (p = 0.001) was identified and validated in an independent patient cohort (p = 0.004). Protein levels of two signature genes, HLA-DQB1 (n = 389) and LIMCH1 (LIM and calponin homology domain 1) (n = 410), were independent predictors of survival (hazard ratio 2.50, p = 0.007 for HLA-DQB1 and 3.19, p = 0.007 for LIMCH1) when adjusting for established prognostic markers. HLA-DQB1 protein expression associated with programmed death ligand 1 positivity (p < 0.001). In gene set enrichment analyses, HLA-DQB1high tumours associated with immune activation and response to interferon-γ (IFN-γ). Conclusions: This study revealed a 10-gene signature with high prognostic power in cervical cancer. HLA-DQB1 and LIMCH1 are potential biomarkers guiding cervical cancer treatment

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