Search

Background: Obesity is an important risk factor for the development of endometrial cancer (EC). Recent data showed that body fat distribution might be more relevant than Body Mass Index (BMI). High visceral fat percentage was shown to be an independent predictor for survival in EC, but mainly included grade 12 EC.Objective: To evaluate body fat distribution and its relation to outcome in high-grade endometrial cancer.Methods: Retrospective study in women diagnosed with high-grade EC between February 2006 and August 2017 at the Royal Cornwall Hospital who had abdominal CT-scan as part of routine diagnostic work-up. Subcutaneous abdominal fat volumes and visceral abdominal fat volumes were quantified based on CT-scan measurements, and visceral fat percentage calculated.Results: A total of 176 patients with high-grade EC were included. The median age was 70 years and median BMI was 29.4 kg/m(2). The majority of patients had non-endometrioid endometrial cancer (NEEC; 62 %). High visceral fat percentage was associated with poor overall- and disease-specific survival (p = 0.006 and p = 0.026 respectively) in NEEC patients, but not in high-grade endometrioid EC (EEC). The most frequent obesity comorbidities hypertension and diabetes mellitus were significantly associated with high BMI and high visceral fat percentage.Conclusion: In high-grade EC, high visceral fat percentage was an independent predictor of poor survival only in NEEC. The strong correlation between high visceral fat and obesity-related comorbidities might be reflective of an unhealthy macroenvironment. (C) 2020 Published by Elsevier B.V.

Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC. Purpose To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease. Study Type Retrospective. Population A total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30). Field Strength/Sequence Axial oblique T1‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection. Assessment Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area. Statistical Tests Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT) and validation (AUCV) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model. Results The whole‐tumor radiomic signatures yielded AUCT/AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P

BackgroundIn endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.MethodsProtein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.ResultsLNM was predicted with area under the curve 0.72–0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of bothFN1andCCND1messenger RNA was related to cancer invasion and mesenchymal phenotype.ConclusionsWe demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.

Background & aims: Visceral adipose tissue (VAT) volume is associated with common lifestyle diseases. Dietary quality, including food matrix and degree of carbohydrate cellularity, as well as the carbohydrate/fat ratio, may influence VAT volume. We aimed to determine the effects of isocaloric diets differing in either “cellularity”, a novel marker of dietary carbohydrate quality, or carbohydrate amount on visceral fat volume and anthropometric measures in adults with obesity. Methods: In a randomized controlled trial of 193 people with obesity/central adiposity, we compared changes in VAT volume after 6 and 12 months, measured by abdominal computed tomography, on three isocaloric eating patterns based on “acellular” carbohydrate sources (e.g., flour-based whole-grain products; comparator arm), “cellular” carbohydrate sources (minimally processed foods with intact cellular structures such as fruits, potatoes/tubers, and rice), or low-carbohydrate high-fat (LCHF) principles. Outcomes were compared by an intention-to-treat (ITT) analysis using constrained linear mixed-effects modelling (cLMM) providing baseline-adjusted change scores and proper missing data handling without imputation. Results: 78 and 57 participants completed 6 and 12 months, respectively, with similar intakes of energy (females: 1820−2060 kcal, males: 2480−2550 kcal) and protein (16–17 energy percent, E%) throughout the intervention, and only modest reductions in energy from baseline. Reported dietary intakes were 42–44, 41–42, and 11–15 E% carbohydrate and 36–38, 37–38, and 66–70 E% fat in the acellular, cellular and LCHF groups, respectively. There were no significant between-group differences in VAT volume after 6 months (cellular vs. acellular [95% CI]: −55 cm³ [−545, 436]; LCHF vs. acellular [95% CI]: −225 cm³ [−703, 253]) or after 12 months (cellular vs. acellular [95% CI]: −122 cm³ [−757, 514]; LCHF vs. acellular [95% CI]: −317 cm³ [−943, 309]). VAT volume decreased significantly within all groups by 14–18% and 12–17% after 6 and 12 months, respectively. Waist circumference was reduced to a significantly greater degree in the LCHF vs. acellular group at 6 months (LCHF vs. acellular [95% CI]: −2.78 cm [−5.54, −0.017]). Conclusions: Despite modest energy restriction, the three isocaloric eating patterns, differing in carbohydrate cellularity and amount, decreased visceral fat volume significantly and to a similar clinically relevant degree. publishedVersion

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p &lt, 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p &lt, 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, $$p = 0.06$$ p = 0.06 ). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, $$p=0.08$$ p = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ p = 0.05 ). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcriptional alterations associated with the RPI were investigated in the study cohort. Potential prognostic markers were further explored for validation in an mRNA validation cohort (n = 161). The RPI included four tumor texture features, and a high RPI was significantly associated with poor disease-specific survival in both the study cohort (p &lt, 0.001) and the MRI validation cohort (p = 0.030). The association between RPI and gene expression profiles revealed 46 significantly differentially expressed genes in patients with a high RPI versus a low RPI (p &lt, 0.001). The most differentially expressed genes, COMP and DMBT1, were significantly associated with disease-specific survival in both the study cohort and the mRNA validation cohort. In conclusion, a high RPI score predicts poor outcome and is associated with specific gene expression profiles in endometrial cancer patients. The promising link between radiomic tumor profiles and molecular alterations may aid in developing refined prognostication and targeted treatment strategies in endometrial cancer.

Background Obesity is an important risk factor for the development of endometrial cancer and is associated with poor outcomes. However, the impact of body fat distribution on survival and surgical outcomes in endometrial cancer patients is unclear. Methods This is a retrospective study in women diagnosed with primary endometrial cancer between February 2006 and August 2017 at the Royal Cornwall Hospital who had abdominal CT-scan as part of routine diagnostic work-up. Subcutaneous abdominal fat volumes and visceral abdominal fat volumes were quantified, and visceral fat percentage calculated. Results A total of 302 patients with high grade endometrial cancer were included. The median age was 70 years and median Body Mass Index (BMI) was 29.7 kg/m2. The majority of patients (60%) had endometrioid type histology. High visceral fat percentage was associated with poor overall- and disease-free survival (p Conclusion Obesity with high visceral fat percentage is an independent negative prognostic factor in endometrial cancer and high visceral fat volumes are associated with increased postoperative complication rates. The additional association of high visceral fat with multiple comorbidities might be reflecting an unhealthy macroenvironment.

Imaging of clinically relevant preclinical animal models is critical to the development of personalized therapeutic strategies for endometrial carcinoma. Although orthotopic patient-derived xenografts (PDXs) reflecting heterogeneous molecular subtypes are considered the most relevant preclinical models, their use in therapeutic development is limited by the lack of appropriate imaging modalities. Here, we describe molecular imaging of a near-infrared fluorescently labeled monoclonal antibody targeting epithelial cell adhesion molecule (EpCAM) as an in vivo imaging modality for visualization of orthotopic endometrial carcinoma PDX. Application of this near-infrared probe (EpCAM-AF680) enabled both spatio-temporal visualization of development and longitudinal therapy monitoring of orthotopic PDX. Notably, EpCAM-AF680 facilitated imaging of multiple PDX models representing different subtypes of the disease. Thus, the combined implementation of EpCAM-AF680 and orthotopic PDX models creates a state-of-the-art preclinical platform for identification and validation of new targeted therapies and corresponding response predicting markers for endometrial carcinoma. publishedVersion

Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival. Results For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p p MRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival. Conclusions Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC. Key Points • Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than VMRI> 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management. publishedVersion

Objective: Correct preoperative identification of high-risk patients is important to optimise surgical treatment and improve survival. We wanted to explore if asparaginase-like protein 1 (ASRGL1) expression in curettage could predict lymph node metastases and poor outcome, potentially improving preoperative risk stratification. Design: Multicentre study. Setting: Ten hospitals in Norway, Sweden and Belgium. Population: Women diagnosed with endometrial carcinoma. Methods: ASRGL1 expression in curettage specimens from 1144 women was determined by immunohistochemistry. Main outcome measures: ASRGL1 status related to disease-specific survival, lymph node status, preoperative imaging parameters and clinicopathological data. Results: ASRGL1 expression had independent prognostic value in multivariate survival analyses, both in the whole patient population (hazard ratio (HR) 1.63, 95% CI 1.11–2.37, P = 0.012) and in the low-risk curettage histology subgroup (HR 2.54, 95% CI 1.44–4.47, P = 0.001). Lymph node metastases were more frequent in women with low expression of ASRGL1 compared with women with high ASRGL1 levels (23% versus 10%, P

Background Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer. Purpose To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients. Study type Prospective cohort study. Population/subjects In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017. Field strength/sequences Preoperative pelvic MRI including contrast-enhanced T1 -weighted (T1 c), T2 -weighted, and diffusion-weighted imaging at 1.5T. Assessment Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated. Statistical tests Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis. Results High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T1 c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1 c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy. Data conclusion MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer. Level of evidence 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Background Ewing sarcoma (ES) is currently staged using Radiography, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) and skeletal scintigraphy (bone scan). 18F- fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET-CT) is increasingly used for staging and follow-up, but its role is still under evaluation. Objective To evaluate the added information from 18F-FDG PET-CT studies compared to conventional imaging and to estimate radiation doses received from radiological and nuclear medicine imaging during staging of Ewing sarcoma. Material and methods Sixty-one patients under the age of 30 years (mean 16, range 5–26) were diagnosed with Ewing sarcoma in Norway during the period 2005–2012. Nineteen patients met the inclusion criteria for this population-based study: pre therapeutic 18F-FDG PET-CT and a minimum follow-up of 12 months. Imaging reports, medical records and pathology reports were collected and compared for all patients. Biopsy histology, supplementary imaging and long-term follow-up (median 27 months) were taken as composite gold standard. Results 18F-FDG PET-CT detected more lesions than conventional imaging in four patients (21%) but this did not change planned treatment as they all had extensive metastatic disease. The 18F-FDG PET-CT study was false negative in one patient and showed false positive lesions in three patients (16%). The estimated mean (range) effective total radiation dose was from CT: 7 mSv (2–16), skeletal scintigraphy: 3 mSv (0–5) and 18F-FDG PET-CT: 5 mSv (4–6). Conclusion 18F-FDG PET-CT is useful for staging of Ewing sarcoma and increase detection of metastases. False positive lesions are quite common, emphasizing the need for supplementary imaging or biopsy of suspected FDG positive metastases.

Purpose: We aimed to study the potential influence of tumour blood flow –obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)- in the metabolomic profiles of endometrial tumours. Methods: Liquid chromatography coupled to mass spectrometry established the metabolomic profile of endometrial cancer lesions exhibiting high (n=12) or low (n=14) tumour blood flow at DCE-MRI. Univariate and multivariate statistics (ortho-PLS-DA, a random forest (RF) classifier and hierarchical clustering) and receiver operating characteristic (ROC) curves were used to establish a panel for potentially discriminating tumours with high versus low blood flow. Results: Tumour blood flow is associated with specific metabolomic signatures. Ortho-PLS-DA and RF classifier resulted in well-defined clusters with an out-of-bag error lower than 8%. We found 28 statistically significant molecules (False Discovery Rate corrected p

// Anna Berg 1, 2 , Ankush Gulati 3 , Sigmund Ytre-Hauge 3, 4 , Kristine E. Fasmer 3 , Karen K. Mauland 1, 2 , Erling A. Hoivik 1, 2 , Jenny A. Husby 3, 4 , Ingvild L. Tangen 1, 2 , Jone Trovik 1, 2 , Mari K. Halle 1, 2 , Ingunn Stefansson 5, 6 , Lars A. Akslen 5, 6 , Kathrine Woie 2 , Line Bjorge 1, 2 , Helga B. Salvesen 1, 2 , Oyvind O. Salvesen 7 , Henrica M.J. Werner 1, 2 , Ingfrid S. Haldorsen 3, 4, * and Camilla Krakstad 1, 2, * 1 Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway 2 Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway 3 Department of Radiology, Haukeland University Hospital, Bergen, Norway 4 Section of Radiology, Department of Clinical Medicine, University of Bergen, Norway 5 Department of Pathology, Haukeland University Hospital, Bergen, Norway 6 Department of Clinical Medicine, Centre for Cancer Biomarkers, Bergen, Norway 7 Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway * These authors have contributed equally to this work Correspondence to: Camilla Krakstad, email: camilla.krakstad@uib.no Keywords: endometrial carcinoma, endometrial hyperplasia, PBK, MRI, FDG-PET/CT Received: April 27, 2017 Accepted: June 19, 2017 Published: July 31, 2017 ABSTRACT Purpose: Distinguishing complex atypical hyperplasia (CAH) from grade 1 endometrioid endometrial cancer (EECG1) preoperatively may be valuable in order to prevent surgical overtreatment, particularly in patients wishing preserved fertility or in patients carrying increased risk of perioperative complications. Material and methods: Preoperative histological diagnosis and radiological findings were compared to final histological diagnosis in patients diagnosed with CAH and EECG1. Imaging characteristics at preoperative magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography/computer tomography (FDG-PET/CT) were compared with tumor DNA oligonucleotide microarray data, immunohistochemistry findings and clinicopathological annotations. Results: MRI assessed tumor volume was higher in EECG1 than in CAH (p=0.004) whereas tumor apparent diffusion coefficient value was lower in EECG1 (p=0.005). EECG1 exhibited increased metabolism with higher maximum and mean standard uptake values (SUV) than CAH (p≤0.002). Unsupervised clustering of EECG1 and CAH revealed differentially expressed genes within the clusters, and identified PDZ-binding kinase (PBK) as a potential marker for selecting endometrial lesions with less aggressive biological behavior. Conclusion: Both PBK expression and preoperative imaging yield promising biomarkers that may aid in the differentiation between EECG1 and CAH preoperatively, and these markers should be further explored in larger patient series.

Background Obesity is an important cause of multiple cancer types, amongst which endometrial cancer (EC). The relation between obesity and cancer is complicated and involves alterations in insulin metabolism, response to inflammation and alterations in estradiol metabolism. Visceral obesity is assumed to play the most important role in the first two mechanisms, but its role in estradiol metabolism is unclear. Therefore, this retrospective study explores the relationship of body mass index (BMI), visceral fat volume (VAV) and subcutaneous fat volume (SAV) and serum levels of sex steroids and lipids in patients with endometrial cancer. Methods Thirty-nine postmenopausal EC patients with available BMI, blood serum and Computed Tomography (CT) scans were included. Serum was analyzed for estradiol, dehydroepiandrosterone sulfate (DHEAS), androstenedione, testosterone, cholesterol, triglycerides and high (HDL), low (LDL) and non-high density (NHDL) lipoprotein. VAV and SAV were quantified on abdominal CT scan images. Findings were interpreted using pearson correlation coefficient and linear regression with commonality analysis. Results Serum estradiol is moderately correlated with BMI (r = 0.62) and VAV (r = 0.58) and strongly correlated with SAV (r = 0.74) (p

ObjectiveBetter 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.MethodsEDTA-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.ResultsPreoperative 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) (pConclusionElevated 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.

Background The key metabolic enzyme lactate dehydrogenase A (LDHA) is overexpressed in many cancers, and several preclinical studies have shown encouraging results of targeted inhibition. However, the mechanistic importance of LDHA in melanoma is largely unknown and hitherto unexplored in brain metastasis. Methods We investigated the spatial, temporal, and functional features of LDHA expression in melanoma brain metastasis across multiple in vitro assays, in a robust and predictive animal model employing MRI and PET imaging, and in a unique cohort of 80 operated patients. We further assessed the genomic and proteomic landscapes of LDHA in different cancers, particularly melanomas. Results LDHA expression was especially strong in early and small brain metastases in vivo and related to intratumoral hypoxia in late and large brain metastases in vivo and in patients. However, LDHA expression in human brain metastases was not associated with the number of tumors, BRAF(V600E) status, or survival. Moreover, LDHA depletion by small hairpin RNA interference did not affect cell proliferation or 3D tumorsphere growth in vitro or brain metastasis formation or survival in vivo. Integrated analyses of the genomic and proteomic landscapes of LDHA indicated that LDHA is present but not imperative for tumor progression within the CNS, or predictive of survival in melanoma patients. Conclusions In a large patient cohort and in a robust animal model, we show that although LDHA expression varies biphasically during melanoma brain metastasis formation, tumor progression and survival seem to be functionally independent of LDHA.

Endometrial cancer is the most common gynecologic malignancy in industrialized countries. Most patients are cured by surgery; however, about 15% of the patients develop recurrence with limited treatment options. Patient-derived tumor xenograft (PDX) mouse models represent useful tools for preclinical evaluation of new therapies and biomarker identification. Preclinical imaging by magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), single-photon emission computed tomography (SPECT) and optical imaging during disease progression enables visualization and quantification of functional tumor characteristics, which may serve as imaging biomarkers guiding targeted therapies. A critical question, however, is whether the in vivo model systems mimic the disease setting in patients to such an extent that the imaging biomarkers may be translatable to the clinic. The primary objective of this review is to give an overview of current and novel preclinical imaging methods relevant for endometrial cancer animal models. Furthermore, we highlight how these advanced imaging methods depict pathogenic mechanisms important for tumor progression that represent potential targets for treatment in endometrial cancer. publishedVersion

Hypoxia is frequent in solid tumors and linked to aggressive phenotypes and therapy resistance. We explored expression patterns of the proposed hypoxia marker HIF-1α in endometrial cancer (EC) and investigate whether preoperative functional imaging parameters are associated with tumor hypoxia. Expression of HIF-1α was explored both in the epithelial and the stromal tumor component. We found that low epithelial HIF-1α and high stromal HIF-1α expression were significantly associated with reduced disease specific survival in EC. Only stromal HIF-1α had independent prognostic value in Cox regression analysis. High stromal HIF-1α protein expression was rare in the premalignant lesions of complex atypical hyperplasia but increased significantly to invasive cancer. High stromal HIF-1α expression was correlated with overexpression of important genes downstream from HIF-1α, i.e. VEGFA and SLC2A1 (GLUT1). Detecting hypoxic tumors with preoperative functional imaging might have therapeutic benefits. We found that high stromal HIF-1α expression associated with high total lesion glycolysis (TLG) at PET/CT. High expression of a gene signature linked to hypoxia also correlated with low tumor blood flow at DCE-MRI and increased metabolism measured by FDG-PET. PI3K pathway inhibitors were identified as potential therapeutic compounds in patients with lesions overexpressing this gene signature. In conclusion, we show that high stromal HIF-1α expression predicts reduced survival in EC and is associated with increased tumor metabolism at FDG-PET/CT. Importantly; we demonstrate a correlation between tissue and imaging biomarkers reflecting hypoxia, and also possible treatment targets for selected patients. publishedVersion

Endometrial cancer (EC) is the most common gynaecological malignancy in Western society and the majority of cases are estrogen dependent. While endocrine drugs proved to be of insufficient therapeutic value in the past, recent clinical research shows promising results by using combinational regimens and pre-clinical studies and identified potential novel endocrine targets. Relevant pre-clinical models can accelerate research in this area. In the present study we describe an orthotopic and estrogen dependent xenograft mouse model of EC. Tumours were induced in one uterine horn of female athymic nude mice using the well-differentiated human endometrial adenocarcinoma Ishikawa cell line—modified to express the luciferase gene for bioluminescence imaging (BLI). BLI and contrast-enhanced computed-tomograph (CE-CT) were used to measure non-invasive tumour growth. Controlled estrogen exposure was achieved by the use of MedRod implants releasing 1.5 μg/d of 17β-estradiol (E2) in ovariectomized mice. Stable E2 serum concentration was demonstrated by LC-MS/MS. Induced tumours were E2 responsive as increased tumour growth was observed in the presence of E2 but not placebo, assessed by BLI, CE-CT, and tumour weight at sacrifice. Metastatic spread was assessed macroscopically by BLI and histology and was seen in the peritoneal cavity, in the lymphovascular space, and in the thoracic cavity. In conclusion, we developed an orthotopic xenograft mouse model of EC that exhibits the most relevant features of human disease, regarding metastatic spread and estrogen dependency. This model offers an easy to manipulate estrogen dosage (by simply adjusting the MedRod implant length), image-guided monitoring of tumour growth, and objectively measurable endpoints (including tumour weight). This is an excellent in vivo tool to further explore endocrine drug regimens and novel endocrine drug targets for EC.

Objective Blood-based biomarkers are attractive due to ease of sampling and standardized measurement technology, reducing obstacles to clinical implementation. The objective of this study was to evaluate a clinically available method of steroid hormone measurement for its prognostic potential in endometrial cancer. Methods We quantified seven steroid hormones by liquid chromatography-tandem mass spectrometry in 100 endometrial cancer patients from a prospective cohort. Abdominal fat distribution was assessed from abdominal computed tomography (CT) scans. Steroid hormone levels were compared to clinical characteristics, fat distribution and gene expression in primary tumor samples. Results Low levels of 17OH-progesterone, 11-deoxycortisol and androstenedione were associated with aggressive tumor characteristics and poor disease specific survival (p = .003, p = .001 and p = .02 respectively). Adjusting for preoperative risk based on histological type and grade, low 17OH-progesterone and 11-deoxycortisol independently predicted poor outcome with hazard ratios of 2.69 (p = .033, 95%CI: 1.09–6.68) and 3.40 (p = .020, 1.21–9.51), respectively. Tumors from patients with low steroid level displayed increased expression of genes related to mitosis and cell cycle progression, whereas high steroid level was associated with upregulated estrogen signaling and genes associated with inflammation. Estrone and estradiol correlated to abdominal fat volume in all compartments (total, visceral, subcutaneous, p