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Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults and one of the most aggressive cancers in man. Despite technological advances in surgical management, combined regimens of radiotherapy with new generation chemotherapy, the median survival for these patients is 14.6 months. This is largely due to a highly deregulated tumour genome with opportunistic deletion of tumour suppressor genes, amplification and/or mutational hyper-activation of receptor tyrosine kinase receptors. The net result of these genetic changes is augmented survival pathways and systematic defects in the apoptosis signalling machinery. The only randomised, controlled phase II trial conducted targeting the epidermal growth factor receptor (EGFR) signalling with the small molecule inhibitor, erlotinib, has showed no therapeutic benefit. Survival signalling and apoptosis resistance in GBMs can be viewed as two sides of the same coin. Targeting increased survival is unlikely to be efficacious without at the same time targeting apoptosis resistance. We have critically reviewed the literature regarding survival and apoptosis signalling in GBM, and highlighted experimental, preclinical and recent clinical trials attempting to target these pathways. Combined therapies simultaneously targeting apoptosis and survival signalling defects might shift the balance from tumour growth stasis to cytotoxic therapeutic responses that might be associated with greater therapeutic benefits.

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). 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=0.60, and 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.

Several studies have highlighted the frequent alterations of the PI3K pathway in endometrial cancer leading to increased signaling activation with potential for targeted treatment. The objective of this meta-study was to evaluate how PIK3CA exon 9/20 mutations affect survival in endometrial cancer patients, based on available literature. Topic-based search strategies were applied to databases including CENTRAL, MEDLINE, Embase, Web of Science and COSMIC. All studies assessing the impact of mutations in exon 9 and exon 20 of PIK3CA on survival rates of endometrial cancer patients were selected for inclusion. Statistical meta-analysis was performed with the ‘ meta’ package in RStudio. Overall, 7 of 612 screened articles were included in the present study, comprising 1098 women with endometrial cancer. Meta-analysis revealed a tendency of impaired survival for patients with PIK3CA exon 9 and/or exon 20 mutations (RR 1.28; 95% CI 0.84, 1.94; p = 0.25). This tendency was consistent in subgroup analyses stratified by histologic type or -grade, with the most prominent effect in low-grade endometrial cancers (RR 2.04; 95% CI 0.90, 4.62; p = 0.09). In summary, these results suggest that PIK3CA mutations negatively influence survival outcomes of patients with endometrial cancer, including those with low-grade tumors.

Helga Salvesen, Rameen Beroukhim, Scott Carter and colleagues study the evolutionary landscape of endometrial cancer by performing whole-exome sequencing of complex atypical hyperplasias, primary tumors and metastases. They identify recurrent alterations in primary tumors and suggest that driver events are generally shared by primary and metastatic tumors. Recent studies have detailed the genomic landscape of primary endometrial cancers, but the evolution of these cancers into metastases has not been characterized. We performed whole-exome sequencing of 98 tumor biopsies including complex atypical hyperplasias, primary tumors and paired abdominopelvic metastases to survey the evolutionary landscape of endometrial cancer. We expanded and reanalyzed The Cancer Genome Atlas (TCGA) data, identifying new recurrent alterations in primary tumors, including mutations in the estrogen receptor cofactor gene NRIP1 in 12% of patients. We found that likely driver events were present in both primary and metastatic tissue samples, with notable exceptions such as ARID1A mutations. Phylogenetic analyses indicated that the sampled metastases typically arose from a common ancestral subclone that was not detected in the primary tumor biopsy. These data demonstrate extensive genetic heterogeneity in endometrial cancers and relative homogeneity across metastatic sites.

Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal–Wallis tests, Kaplan–Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.

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-DQB1 high 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.

Human papillomavirus (HPV)-associated cervical cancer is a leading cause of cancer deaths in women. Here we present an integrated multi-omic analysis of 643 cervical squamous cell carcinomas (CSCC, the most common histological variant of cervical cancer), representing patient populations from the USA, Europe and Sub-Saharan Africa and identify two CSCC subtypes (C1 and C2) with differing prognosis. C1 and C2 tumours can be driven by either of the two most common HPV types in cervical cancer (16 and 18) and while HPV16 and HPV18 are overrepresented among C1 and C2 tumours respectively, the prognostic difference between groups is not due to HPV type. C2 tumours, which comprise approximately 20% of CSCCs across these cohorts, display distinct genomic alterations, including loss or mutation of the STK11 tumour suppressor gene, increased expression of several immune checkpoint genes and differences in the tumour immune microenvironment that may explain the shorter survival associated with this group. In conclusion, we identify two therapy-relevant CSCC subtypes that share the same defining characteristics across three geographically diverse cohorts. Human papillomavirus (HPV) is a known cause of cervical cancer. Here, the authors perform a multi-omic analysis using published cervical squamous cell carcinoma cohorts from the USA, Europe, and SubSaharan Africa and identify two cervical squamous cell carcinoma subtypes that display prognostic differences.

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.Hoivik, Hodneland and colleagues employ convoluted neural networks alongside radiogenomic biomarkers to profile endometrial tumors. They identify an 11-gene high-risk signature validated with retrospective patient data.

The presence of cancer stem cells is linked to aggressive disease and higher risk of recurrence, and multiple markers have been proposed to detect cancer stem cells. However, a detailed evaluation of the expression patterns and the prognostic value of markers relevant for endometrial cancer is lacking. As organoid models are suggested to be enriched in cancer stem cells, such models may prove valuable to define tissue‐specific cancer stem cells. To address this, imaging mass cytometry and multiplex single‐cell analyses were performed on an endometrial cancer patient series including both tumor biopsies and corresponding patient‐derived organoids. An antibody panel focused on cancer stem cell markers was used to identify cancer stem cell phenotypes. Over 70% of epithelial cells in the tumor biopsies expressed at least one putative cancer stem cell marker. We identified distinct cancer cell phenotypes with heterogeneous expression within individual patients and between patient samples. Few differences in the distribution of cancer cell phenotypes were observed between tumor biopsies and corresponding organoids. Cells expressing aldehyde dehydrogenase 1 (ALDH1) were more prevalent in high‐grade tumors, while expression of CD44 was more prevalent in grade 1 tumors. Spatial analysis revealed significantly less interaction between ALDH1‐ and CD44‐expressing cells. Gene expression data was used to further investigate selected markers. CD44 gene expression was associated with a favorable prognosis and was further validated using immunohistochemistry. High expression of CD44 was significantly associated with better survival. The general high expression of proposed stem cell markers may indicate alternative roles for these in endometrial cancer. Cancer stem cells are associated with aggressive disease, but a deep characterization of such markers is lacking in endometrial cancer. This study uses imaging mass cytometry to explore putative cancer stem cell markers in endometrial tumors and corresponding organoid models. Analyses showed a high number of cancer cells expressing stemness markers. CD44 expression associated with patient prognosis.

Background:Despite successful implementation of drugs targeting the human epidermal growth factor receptor 2 (HER2) receptor in breast and gastric cancers, the potential of HER2 as a therapeutic target in other cancers has been less studied, including endometrial cancer. We investigated expression levels of HER2 (ERBB2) in a large cohort of endometrial cancer lesions, also including complex atypical hyperplasia and metastatic lesions.Methods:67 precursor lesions, 790 primary endometrial cancers and 383 metastatic lesions were investigated for HER2 expression in relation to clinicopathologic features and outcome. Protein levels were assessed by immunohistochemistry (using the HercepTest and staining index (SI) criteria), mRNA levels by microarrays and amplification status by chromogenic in situ hybridisation.Results:High HER2 protein levels were significantly associated with features of aggressive disease and increased mRNA ERBB2 levels. HER2 expression defined by the SI proved to be a better predictor of survival compared with the HercepTest. A discordant HER2 expression pattern between paired primary and metastatic lesions was detected, revealing substantial reduction in HER2 expression from primary to metastatic disease.Conclusions:Loss of HER2 expression is common in metastatic endometrial cancer lesions and assessment of HER2 levels in the metastatic lesions may be important to define the potential benefit of anti-HER2 treatments in endometrial cancer patients.