Neutrophil Granulocytes in Ovarian Cancer - Induction of Epithelial-To- Mesenchymal-Transition and Tumor Cell Migration

Background: Ovarian cancer (OvCa) is a highly aggressive malignoma with a tumor-promoting microenvironment. Infiltration of polymorphonuclear neutrophils (PMN) is frequently seen, raising the question of their impact on tumor development. In that context, effects of PMN on human ovarian cancer cells were assessed. Methods: Human epithelial ovarian cancer cells were incubated with human PMN, lysate of PMN, or neutrophil elastase. Morphological alterations were observed by time-lapse video-microscopy, and the underlying molecular mechanism was analyzed by flow cytometry and Western blotting. Functional alternations were assessed by an in vitro wound healing assay. In parallel, a large cohort of n=334 primary OvCa tissue samples of various histological subtypes was histologically evaluated. Results: Co-cultivation of cancer cells with either PMN or PMN lysate causes a change of the polygonal epithelial phenotype of the cells towards a spindle shaped morphology, causing a cribriform cell growth. The PMN-induced alteration could be attributed to elastase, a major protease of PMN. Elastase-induced shape change was most likely due to the degradation of membranous E-cadherin, which results in loss of cell contacts and polarity. Moreover, in response to elastase, epithelial cytokeratins were downmodulated, in parallel with a nuclear translocation of β-catenin. These PMN-elastase induced alterations of cells are compatible with an epithelial-to-mesenchymal transition (EMT) of the cancer cells. Following EMT, the cells displayed a more migratory phenotype. In human biopsies, neutrophil infiltration was seen in 72% of the cases. PMN infiltrates were detected preferentially in areas with low E-cadherin expression. Conclusion: PMN in the microenvironment of OvCa can alter tumor cells towards a mesenchymal and migratory phenotype

Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery

Neutrophil gelatinase-associated lipocalin (NGAL) predicts response to neoadjuvant chemotherapy and clinical outcome in primary human breast cancer

In our previous work we showed that NGAL, a protein involved in the regulation of proliferation and differentiation, is overexpressed in human breast cancer (BC) and predicts poor prognosis. In neoadjuvant chemotherapy (NACT) pathological complete response (pCR) is a predictor for outcome. The aim of this study was to evaluate NGAL as a predictor of response to NACT and to validate NGAL as a prognostic factor for clinical outcome in patients with primary BC. Immunohistochemistry was performed on tissue microarrays from 652 core biopsies from BC patients, who underwent NACT in the GeparTrio trial. NGAL expression and intensity was evaluated separately. NGAL was detected in 42.2% of the breast carcinomas in the cytoplasm. NGAL expression correlated with negative hormone receptor (HR) status, but not with other baseline parameters. NGAL expression did not correlate with pCR in the full population, however, NGAL expression and staining intensity were significantly associated with higher pCR rates in patients with positive HR status. In addition, strong NGAL expression correlated with higher pCR rates in node negative patients, patients with histological grade 1 or 2 tumors and a tumor size <40 mm. In univariate survival analysis, positive NGAL expression and strong staining intensity correlated with decreased disease-free survival (DFS) in the entire cohort and different subgroups, including HR positive patients. Similar correlations were found for intense staining and decreased overall survival (OS). In multivariate analysis, NGAL expression remained an independent prognostic factor for DFS. The results show that in low-risk subgroups, NGAL was found to be a predictive marker for pCR after NACT. Furthermore, NGAL could be validated as an independent prognostic factor for decreased DFS in primary human BC

High-grade ovarian serous carcinoma patients exhibit profound alterations in lipid metabolism

Ovarian cancer is a very severe type of disease with poor prognosis. Treatment of ovarian cancer is challenging because of the lack of tests for early detection and effective therapeutic targets. Thus, new biomarkers are needed for both diagnostics and better understanding of the cellular processes of the disease. Small molecules, consisting of metabolites or lipids, have shown emerging potential for ovarian cancer diagnostics. Here we performed comprehensive lipidomic profiling of serum and tumor tissue samples from high-grade serous ovarian cancer patients to find lipids that were altered due to cancer and also associated with progression of the disease. Ovarian cancer patients exhibited an overall reduction of most lipid classes in their serum as compared to a control group. Despite the overall reduction, there were also specific lipids showing elevation, and especially alterations in ceramide and triacylglycerol lipid species were dependent on their fatty acyl side chain composition. Several lipids showed progressive alterations in patients with more advanced disease and poorer overall survival, and outperformed CA-125 as prognostic markers. The abundance of many serum lipids correlated with their abundance in tumor tissue samples. Furthermore, we found a negative correlation of serum lipids with 3-hydroxybutyric acid, suggesting an association between decreased lipid levels and fatty acid oxidation. In conclusion, here we present a comprehensive analysis of lipid metabolism alterations in ovarian cancer patients, with clinical implications.Peer reviewe

Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging

With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC > 0.6 or 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC

Identification of biology-based breast cancer types with distinct predictive and prognostic features: role of steroid hormone and HER2 receptor expression in patients treated with neoadjuvant anthracycline/taxane-based chemotherapy

Introduction: Reliable predictive and prognostic markers for routine diagnostic purposes are needed for breast cancer patients treated with neoadjuvant chemotherapy. We evaluated protein biomarkers in a cohort of 116 participants of the GeparDuo study on anthracycline/taxane-based neoadjuvant chemotherapy for operable breast cancer to test for associations with pathological complete response (pCR) and disease-free survival (DFS). Particularly, we evaluated if interactions between hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression might lead to a different clinical behavior of HR+/HER2+ coexpressing and HR+/HER2- tumors and whether subgroups of triple negative tumors might be identified by the help of Ki67 labeling index, cytokeratin 5/6 (CK5/6), as well as cyclooxygenase-2 (COX-2), and Y-box binding protein 1 (YB-1) expression. Methods: Expression analysis was performed using immunohistochemistry and silver-enhanced in situ hybridization on tissue microarrays (TMAs) of pretherapeutic core biopsies. Results: pCR rates were significantly different between the biology-based tumor types (P = 0.044) with HR+/HER2+ and HR-/HER2- tumors having higher pCR rates than HR+/HER2-tumors. Ki67 labeling index, confirmed as significant predictor of pCR in the whole cohort (P = 0.001), identified HR-/HER- (triple negative) carcinomas with a higher chance for a pCR (P = 0.006). Biology-based tumor type (P = 0.046 for HR+/HER2+vs. HR+/HER2-), Ki67 labeling index (P = 0.028), and treatment arm (P = 0.036) were independent predictors of pCR in a multivariate model. DFS was different in the biology-based tumor types (P < 0.0001) with HR+/HER2- and HR+/HER2+ tumors having the best prognosis and HR-/HER2+ tumors showing the worst outcome. Biology-based tumor type was an independent prognostic factor for DFS in multivariate analysis (P < 0.001). Conclusions: Our data demonstrate that a biology-based breast cancer classification using estrogen receptor (ER), progesterone receptor (PgR), and HER2 bears independent predictive and prognostic potential. The HR+/HER2+ coexpressing carcinomas emerged as a group of tumors with a good response rate to neoadjuvant chemotherapy and a favorable prognosis. HR+/HER2- tumors had a good prognosis irrespective of a pCR, whereas patients with HR-/HER- and HR-/HER+ tumors, especially if they had not achieved a pCR, had an unfavorable prognosis and are in need of additional treatment options. Trial registration ClinicalTrials.gov identifier: NCT0079337

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging.

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment

Exploring the clonal evolution of CD133/aldehyde-dehydrogenase-1 (ALDH1)-positive cancer stem-like cells from primary to recurrent high-grade serous ovarian cancer (HGSOC). A study of the Ovarian Cancer Therapy–Innovative Models Prolong Survival (OCTIPS) Consortium

Background High-grade serous ovarian cancer (HGSOC) causes 80% of all ovarian cancer (OC) deaths. In this setting, the role of cancer stem-like cells (CSCs) is still unclear. In particular, the evolution of CSC biomarkers from primary (pOC) to recurrent (rOC) HGSOCs is unknown. Aim of this study was to investigate changes in CD133 and aldehyde dehydrogenase-1 (ALDH1) CSC biomarker expression in pOC and rOC HGSOCs. Methods Two-hundred and twenty-four pOC and rOC intrapatient paired tissue samples derived from 112 HGSOC patients were evaluated for CD133 and ALDH1 expression using immunohistochemistry (IHC); pOCs and rOCs were compared for CD133 and/or ALDH1 levels. Expression profiles were also correlated with patients' clinicopathological and survival data. Results Some 49.1% of the patient population (55/112) and 37.5% (42/112) pOCs were CD133+ and ALDH1+ respectively. CD133+ and ALDH1+ samples were detected in 33.9% (38/112) and 36.6% (41/112) rOCs. CD133/ALDH1 coexpression was observed in 23.2% (26/112) and 15.2% (17/112) of pOCs and rOCs respectively. Pairwise analysis showed a significant shift of CD133 staining from higher (pOCs) to lower expression levels (rOCs) (p < 0.0001). Furthermore, all CD133 + pOC patients were International Federation of Gynaecology and Obstetrics (FIGO)-stage III/IV (p < 0.0001) and had significantly worse progression-free interval (PFI) (p = 0.04) and overall survival (OS) (p = 0.02). On multivariate analysis, CD133/ALDH1 coexpression in pOCs was identified as independent prognostic factor for PFI (HR: 1.64; 95% CI: 1.03–2.60; p = 0.036) and OS (HR: 1.71; 95% CI: 1.01–2.88; p = 0.045). Analysis on 52 pts patients with known somatic BRCA status revealed that BRCA mutations did not influence CSC biomarker expression. Conclusions The study showed that CD133/ALDH1 expression impacts HGSOC patients' survival and first suggests that CSCs might undergo phenotypic change during the disease course similarly to non stem-like cancer cells, providing also a first evidence that there is no correlation between CSCs and BRCA status