An mri-based radiomic prognostic index predicts poor outcome and specific genetic alterations 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 < 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 < 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.publishedVersio

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.publishedVersio

Patient-derived organoids reflect the genetic profile of endometrial tumors and predict patient prognosis

Background: A major hurdle in translational endometrial cancer (EC) research is the lack of robust preclinical models that capture both inter- and intra-tumor heterogeneity. This has hampered the development of new treatment strategies for people with EC. Methods: EC organoids were derived from resected patient tumor tissue and expanded in a chemically defined medium. Established EC organoids were orthotopically implanted into female NSG mice. Patient tissue and corresponding models were characterized by mor- phological evaluation, biomarker and gene expression and by whole exome sequencing. A gene signature was defined and its prognostic value was assessed in multiple EC cohorts using Mantel-Cox (log-rank) test. Response to carboplatin and/or paclitaxel was measured in vitro and evaluated in vivo. Statistical difference between groups was calculated using paired t-test. Results: We report EC organoids established from EC patient tissue, and orthotopic organoid-based patient-derived xenograft models (O-PDXs). The EC organoids and O-PDX models mimic the tissue architecture, protein biomarker expression and genetic profile of the original tissue. Organoids show heterogenous sensitivity to conventional chemotherapy, and drug response is reproduced in vivo. The relevance of these models is further supported by the identification of an organoid-derived prognostic gene signature. This signature is vali- dated as prognostic both in our local patient cohorts and in the TCGA endometrial cancer cohort. Conclusions: We establish robust model systems that capture both the diversity of endo- metrial tumors and intra-tumor heterogeneity. These models are highly relevant preclinical tools for the elucidation of the molecular pathogenesis of EC and identification of potential treatment strategies.publishedVersio

An mri-based radiomic prognostic index predicts poor outcome and specific genetic alterations 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 < 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 < 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

Novel PCDH10-Wnt-MALAT1 regulatory axis in endometrioid endometrial adenocarcinoma

Key Messages: 1. Protocadherin 10 (PCDH10) is silenced in endometrioid endometrial cancer through promoter hypermethylation. 2. Ectopic expression of PCDH10 inhibits tumour growth and induces cell apoptosis. 3. Transcriptomic analysis revealed that PCDH10 expression down-regulates metastasis-associated lung adenocarcinoma transcript 1 (MALAT1). Further mechanistic studies uncovered that MALAT1 expression is transcriptionally induced by Wnt/β-catenin signalling. 4. We uncovered a novel PCDH10-Wnt/β-cateninMALAT1 regulatory axis that contributes to development and progression of endometrioid endometrial cancer

Novel PCDH10-Wnt-MALAT1 regulatory axis in endometrioid endometrial adenocarcinoma

Key Messages: 1. Protocadherin 10 (PCDH10) is silenced in endometrioid endometrial cancer through promoter hypermethylation. 2. Ectopic expression of PCDH10 inhibits tumour growth and induces cell apoptosis. 3. Transcriptomic analysis revealed that PCDH10 expression down-regulates metastasis-associated lung adenocarcinoma transcript 1 (MALAT1). Further mechanistic studies uncovered that MALAT1 expression is transcriptionally induced by Wnt/β-catenin signalling. 4. We uncovered a novel PCDH10-Wnt/β-cateninMALAT1 regulatory axis that contributes to development and progression of endometrioid endometrial cancer

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

High-Throughput Mutation Profiling of Primary and Metastatic Endometrial Cancers Identifies KRAS, FGFR2 and PIK3CA to Be Frequently Mutated

Background: Despite being the most common pelvic gynecologic malignancy in industrialized countries, no targeted therapies are available for patients with metastatic endometrial carcinoma. In order to improve treatment, underlying molecular characteristics of primary and metastatic disease must be explored. Methodology/Principal Findings: We utilized the mass spectrometric-based mutation detection technology OncoMap to define the types and frequency of point somatic mutations in endometrial cancer. 67 primary tumors, 15 metastases corresponding to 7 of the included primary tumors and 11 endometrial cancer cell lines were screened for point mutations in 28 known oncogenes. We found that 27 (40.3%) of 67 primary tumors harbored one or more mutations with no increase in metastatic lesions. FGFR2, KRAS and PIK3CA were consistently the most frequently mutated genes in primary tumors, metastatic lesions and cell lines. Conclusions/Significance: Our results emphasize the potential for targeting FGFR2, KRAS and PIK3CA mutations in endometrial cancer for development of novel therapeutic strategies

ATAD2 overexpression links to enrichment of B-MYBtranslational signatures and development of aggressive endometrial carcinoma

We have explored the potential for clinical implementation of ATAD2 as a biomarker for aggressive endometrial cancer by investigating to what extent immunohistochemical (IHC) staining for ATAD2 is feasible, reflects clinical phenotype and molecular subgroups of endometrial carcinomas. Increased expression of the ATAD2 gene has been implicated in cancer development and progression in a number of tissues, but few studies have investigated ATAD2 expression using IHC. Here we show that high ATAD2 protein expression is significantly associated with established clinical-pathological variables for aggressive endometrial cancer, also in the subset of estrogen receptor α (ERα) positive tumors. Protein and mRNA expression of ATAD2 were highly correlated (P < 0.001), suggesting that IHC staining may represent a more clinically applicable measure of ATAD2 level in routinely collected formalin fixed paraffin embedded specimens. Gene expression alterations in samples with high ATAD2 expression revealed upregulation of several cancer-related genes (B-MYB, CDCs, E2Fs) and gene sets that previously have been linked to aggressive disease and potential for new targeting therapies. Our results support that IHC staining for ATAD2 may be a clinically applicable biomarker reflecting clinical phenotype and targetable alterations in endometrial carcinomas to be further explored in controlled clinical trials

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC