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Chemoresistance remains the major barrier to effective ovarian cancer treatment. The molecular features and associated biological functions of this phenotype remain poorly understood. We developed carboplatin-resistant cell line models using OVCAR5 and CaOV3 cell lines with the aim of identifying chemoresistance-specific molecular features. Chemotaxis and CAM invasion assays revealed enhanced migratory and invasive potential in OVCAR5-resistant, compared to parental cell lines. Mass spectrometry analysis was used to analyse the metabolome and proteome of these cell lines, and was able to separate these populations based on their molecular features. It revealed signalling and metabolic perturbations in the chemoresistant cell lines. A comparison with the proteome of patient-derived primary ovarian cancer cells grown in culture showed a shared dysregulation of cytokine and type 1 interferon signalling, potentially revealing a common molecular feature of chemoresistance. A comprehensive analysis of a larger patient cohort, including advanced in vitro and in vivo models, promises to assist with better understanding the molecular mechanisms of chemoresistance and the associated enhancement of migration and invasion.

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

Ovarian cancer is the most lethal gynecologic malignancy. Early detection would improve survival, but an effective diagnostic test does not exist. Novel biomarkers for early ovarian cancer diagnosis are therefore warranted. We performed intraoperative blood sampling from ovarian veins of stage I epithelial ovarian carcinomas and analyzed the serum proteome. Junction plakoglobin (JUP) was found to be elevated in venous blood from ovaries with malignancies when compared to those with benign disease. Peripheral plasma JUP levels were validated by ELISA in a multicenter international patient cohort. JUP was significantly increased in FIGO serous stage IA+B (1.97-fold increase; p < 0.001; n = 20), serous stage I (2.09-fold increase; p < 0.0001; n = 40), serous stage II (1.81-fold increase, p < 0.001, n = 23) and serous stage III ovarian carcinomas (1.98-fold increase; p < 0.0001; n = 34) vs. normal controls (n = 109). JUP plasma levels were not increased in early stage breast cancer (p = 0.122; n = 12). In serous ovarian cancer patients, JUP had a sensitivity of 85% in stage IA+B and 60% in stage IA-C, with specificities of 76 and 94%, respectively. A logistic regression model of JUP and Cancer Antigen 125 (CA125) revealed a sensitivity of 70% for stage IA+B and 75% for stage IA-C serous carcinomas at 100% specificity. Our novel ovarian blood sampling - proteomics approach identified JUP as a promising new biomarker for epithelial ovarian cancer, which in combination with CA125 might fulfill the test criteria for ovarian cancer screening.© Copyright © 2020 Weiland, Lokman, Klingler-Hoffmann, Jobling, Stephens, Sundfeldt, Hoffmann and Oehler.

Ovarian cancer is the most lethal gynecologic malignancy. Early detection would improve survival, but an effective diagnostic test does not exist. Novel biomarkers for early ovarian cancer diagnosis are therefore warranted. We performed intraoperative blood sampling from ovarian veins of stage I epithelial ovarian carcinomas and analyzed the serum proteome. Junction plakoglobin (JUP) was found to be elevated in venous blood from ovaries with malignancies when compared to those with benign disease. Peripheral plasma JUP levels were validated by ELISA in a multicenter international patient cohort. JUP was significantly increased in FIGO serous stage IA+B (1.97-fold increase; p < 0.001; n = 20), serous stage I (2.09-fold increase; p < 0.0001; n = 40), serous stage II (1.81-fold increase, p < 0.001, n = 23) and serous stage III ovarian carcinomas (1.98-fold increase; p < 0.0001; n = 34) vs. normal controls (n = 109). JUP plasma levels were not increased in early stage breast cancer (p = 0.122; n = 12). In serous ovarian cancer patients, JUP had a sensitivity of 85% in stage IA+B and 60% in stage IA-C, with specificities of 76 and 94%, respectively. A logistic regression model of JUP and Cancer Antigen 125 (CA125) revealed a sensitivity of 70% for stage IA+B and 75% for stage IA-C serous carcinomas at 100% specificity. Our novel ovarian blood sampling - proteomics approach identified JUP as a promising new biomarker for epithelial ovarian cancer, which in combination with CA125 might fulfill the test criteria for ovarian cancer screening.© Copyright © 2020 Weiland, Lokman, Klingler-Hoffmann, Jobling, Stephens, Sundfeldt, Hoffmann and Oehler.

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.

The gp130(F/F) genetically engineered mouse (GEM) model reproducibly and predictably develops a gastric adenoma phenotype resembling the primary lesions of human intestinal-type gastric cancer (GC). Accordingly, changes to the serum proteome of gp130(F/F) mice may uncover early-stage GC biomarkers. Here, we have employed several double and compound mutant GEM strains that display distinct phenotypes with respect to gastric tumour load and inflammatory response, thereby mimicking different states of inflammation-associated early-stage GC in humans. This allowed us to distinguish between proteomic changes associated with tumourigenesis rather than confounding systemic inflammation. The comparative proteomic workflow involved depletion of high abundance proteins, 2D-DIGE analysis and protein identification by LC-MS/MS. The differential expression of 112 2D-DIGE spots specifically correlated with the tumour-bearing phenotype, corresponding to 31 murine proteins and their 28 human orthologues. Eight proteins were selected for validation in GC patient sera versus healthy controls. Significant increases in serum apolipoprotein E and haptoglobin, and decreases in afamin and clusterin, were confirmed by ELISA. Receiver operating characteristic analysis revealed that these proteins may be more sensitive and specific discriminators of GC than the existing clinical marker CA72-4.