L1CAM Expression is Related to Non-Endometrioid Histology, and Prognostic for Poor Outcome in Endometrioid Endometrial Carcinoma

The majority of endometrial carcinomas are classified as Type I endometrioid endometrial carcinomas (EECs) and have a good prognosis. Type II non-endometrioid endometrial carcinomas (NEECs) have a significant worse outcome. Yet, 20 % of the EECs are associated with an unexplained poor outcome. The aim of this study was to determine if L1CAM expression, a recently reported biomarker for aggressive tumor behavior in endometrial carcinoma, was associated with clinicopathological features of EECs. A total of 103 patients diagnosed as EEC at the Radboud University Medical Centre, based on the pathology report were selected. L1CAM status of these tumors was determined, and histologic slides were reviewed by two expert pathologists. L1CAM-positivity was observed in 17 % (18/103). Review of the diagnostic slides revealed that 11 out of these 18 L1CAM-positive tumors (61 %) contained a serous- or mixed carcinoma component that was not initially mentioned in the pathology report. L1CAM-expression was associated with advanced age, poor tumor grade, and lymphovascular space invasion. A worse five year progression free survival rate was observed for patients with L1CAM-positive tumors (55.6 % for the L1CAM-positive group, compared to 83.3 % for the L1CAM-negative group P = 0.01). L1CAM expression carries prognostic value for histologically classified EEC and supports the identification of tumors with a NEEC component. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12253-016-0047-8) contains supplementary material, which is available to authorized users

Identification of global inhibitors of cellular glycosylation

Small molecule inhibitors of glycosylation enzymes are valuable tools for dissecting glycan functions and potential drug candidates. Screening for inhibitors of glycosyltransferases are mainly performed by in vitro enzyme assays with difficulties moving candidates to cells and animals. Here, we circumvent this by employing a cell-based screening assay using glycoengineered cells expressing tailored reporter glycoproteins. We focused on GalNAc-type O-glycosylation and selected the GalNAc-T11 isoenzyme that selectively glycosylates endocytic low-density lipoprotein receptor (LDLR)-related proteins as targets. Our screen of a limited small molecule compound library did not identify selective inhibitors of GalNAc-T11, however, we identify two compounds that broadly inhibited Golgi-localized glycosylation processes. These compounds mediate the reversible fragmentation of the Golgi system without affecting secretion. We demonstrate how these inhibitors can be used to manipulate glycosylation in cells to induce expression of truncated O-glycans and augment binding of cancer-specific Tn-glycoprotein antibodies and to inhibit expression of heparan sulfate and binding and infection of SARS-CoV-2

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

Targeted Desialylation Overcomes Glyco-Immune Checkpoints and Potentiates the Anticancer Immune Response in Vivo

Currently approved immune checkpoint inhibitor (ICI) therapies targeting the PD-1 and CTLA-4 receptor pathways are powerful treatment options for certain cancers; however, the majority of patients across cancer types still fail to respond. Addressing alternative pathways that mediate immune suppression could enhance ICI efficacy. One such mechanism is the increase in sialic acid-containing proteins and lipids (sialoglycans) in malignancy, which recently has been shown to inhibit immune cell activation through multiple mechanisms including Siglec receptor binding, and therefore represents a targetable glyco-immune checkpoint. Here, we report the design of a trastuzumab- sialidase conjugate that potently and selectively strips diverse sialoglycans from breast cancer cells in vivo. In a syngeneic orthotopic HER2+ breast cancer model, targeted desialylation delayed tumor growth and enhanced immune cell infiltration and activation, leading to prolonged survival of mice with trastuzumab-resistant breast cancer. Thus, antibody-sialidase conjugates represent a promising modality for cancer immune therapy.</div

Improved preoperative risk stratification with CA-125 in low-grade endometrial cancer: A multicenter prospective cohort study

Objectives: The global obesity epidemic has great impact on the prevalence of low-grade endometrial carcinoma. The preoperative tumor serum marker cancer antigen 125 (CA-125) might contribute to improved identification of high-risk patients within this group. The study aimed to investigate the prognostic value of CA-125 in relation to established preoperative prognosticators, with a focus on identifying patients with poor outcome in low-grade endometrial carcinoma (EC) patients. Methods: Prospective multicenter cohort study including all consecutive patients surgically treated for endometrial carcinoma in nine collaborating hospitals from September 2011 until December 2013. All preoperative histopathological diagnoses were reviewed in a blinded manner. Associations between CA-125 and clinicopathological features were determined. Univariable and multivariable analysis by Cox regression were used. Separate analyses were performed for preoperatively designated low-grade and high-grade endometrial carcinoma patients. Results: A total of 333 patients were analyzed. CA-125 was associated with poor prognostic features including advanced International Federation of Gynecology and Obstetrics (FIGO) stage. In multivariable analysis, age, preoperative tumor and CA-125 were significantly associated with disease-free survival (DFS); preoperative grade, tumor type, FIGO and CA-125 were significantly associated with disease-specific survival (DSS). Low-grade EC patients with elevated CA-125 revealed a DFS of 80.6% and DSS of 87.1%, compared to 92.1% and 97.2% in low-grade EC patients with normal CA-125. Conclusion: Preoperative elevated CA-125 was associated with poor prognostic features and independently associated with DFS and DSS. Particularly patients with low-grade EC and elevated CA-125 represent a group with poor outcome and should be considered as high-risk endometrial carcinoma

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