To investigate whether specific obesity/metabolism‐related gene expression patterns affect the survival of patients with ovarian cancer. Clinical and genomic data of 590 samples from the high‐grade ovarian serous carcinoma (HGOSC) study of The Cancer Genome Atlas (TCGA) and 91 samples from the Australian Ovarian Cancer Study were downloaded from the International Cancer Genome Consortium (ICGC) portal. Clustering of mRNA microarray and reverse‐phase protein array (RPPA) data was performed with 83 consensus driver genes and 144 obesity and lipid metabolism‐related genes. Association between different clusters and survival was analyzed with the Kaplan–Meier method and a Cox regression. Mutually exclusive, co‐occurrence and network analyses were also carried out. Using RNA and RPPA data, it was possible to identify two subsets of HGOSCs with similar clinical characteristics and cancer driver mutation profiles (e.g. TP53), but with different outcome. These differences depend more on up‐regulation of specific obesity and lipid metabolism‐related genes than on the number of gene mutations or copy number alterations. It was also found that CD36 and TGF‐ß are highly up‐regulated at the protein levels in the cluster with the poorer outcome. In contrast, BSCL2 is highly up‐regulated in the cluster with better progression‐free and overall survival. Different obesity/metabolism‐related gene expression patterns constitute a risk factor for prognosis independent of the therapy results in the Cox regression. Prognoses were conditioned by the differential expression of obesity and lipid metabolism‐related genes in HGOSCs with similar cancer driver mutation profiles, independent of the initial therapeutic response.
still treated according to a "one size fits all" approach. While tumor staging offers some stratification, the development of personalized treatment concepts remain elusive. Our group has recently validated the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), to distinguish clinically relevant prognostic groups. ENOC shares risk factors, genomics, and histology with it's endometrial counterpart. The aim of our study was to apply and investigate ProMisE in ovarian endometrioid carcinoma. Methods ProMisE was applied to n=509 ENOC after biomarker-assisted review of endometrioid histotype. Cases were aligned into four groups: low risk POLE mutant (POLE); moderate risk mismatch repair deficient (MMRd); high-risk p53 abnormal (p53abn); and a final moderate risk category lacking these biomarkers (p53wt). Kaplan-Meier and multivariable survival analyses were performed. Results 4% of cases were POLE, 16% MMRd, 10% p53abn and 71% p53wt. Groups showed distinct progression-free and overall survival (p<0.001), near-identical to profiles of endometrial carcinoma. 5-year PFS was 54% in p53abn, 81% in MMRd, 84% in p53wt, and 100% in POLE cases. ProMisE classes of ENOC were independent of stage and residual disease in multivariable analysis. Conclusions ProMisE risk classification provides additional prognostic information in a large cohort of ENOC. Our findings support the introduction of ProMisE-stratified treatment algorithms to ultimately improve endometrioid ovarian carcinoma patient care. Further, ENOC may benefit from parallel efforts under investigation in endometrial carcinoma.
In obesity, the increase in the number and / or size of adipocytes leads to chronic low-grade systemic inflammation that conditions the development and evolution of a series of pathologies such as coronary heart disease or cancer. The change from hyperplasia to hypertrophy has been characterized by the expression of early and late adipogenic biomarkers, but little is known about the communications between cells. Exosomes are small extracellular vesicles (30-100 nm in diameter) released by exocytosis in almost all cell types. Due to its wide distribution and absorption by different cell types, exosomes have been considered an attractive tool for diagnosis, therapy and response evaluation. Objective: To characterize the exosomes released by human fat cells during differentiation and the effect of hyperplasia on the function of these. Material and methods: Human cells (SW-872) were cultured for 24h in DMEM-F12- with FBS serum (free of steroids and exosomes), differentiated with adipogenic cocktail for 7 days, and hypertrophy for further 24h G1 agonist treatment. Exosomes were isolated from conditioned medium of SW872 cells by ultracentrifugation and characterized by immunoblotting against exosomal markers, (Nanoparticle Tracking Analysis (NTA) and transmission electron microscopy (TEM). Cancer initiating cells (CICs) were isolated from HeyA8 ovarian cancer cells using culture selecting conditions. CICs were 24h treated with exosomes (1x1011 particles/mL) and then seeded over matrigel to carry out 3Dmigration assays. Results: SW872 cells showed the morphological characteristics described for this cell line and MR expression was observed. Successful isolation of SW872-derived exosomes was confirmed by assessing the particle size distribution by NTA, the morphology by TEM and the presence of exosome markers (Alix, HSP70, TSG101, and CD36) by immunoblotting. Preadipocyte differentiation showed a significant decrease in the exosome concentration (pre,1.3x1011 particles/ml vs adipo, 1.5x1010 particles/ml p <0.0001) and in the size of these nanovesicles (102.2 ±3.1nm vs 69.8 ±20.1nm p = 0.05). When the differentiated cells become hydrophobic the concentration of exosomes released showed a significant increase in the concentration (1.5x1010 part/ml vs 5.5x1010 part/ml p <0.0001) and no changes in size were observed (139.5±15.2 nm vs 119.9±14.3). A functional 3D analysis shows that exosomes from hypertrophic adipocytes induce an increase on the migration of HEYA8 o-derived CICs.Conclusions: These preliminary results show that during the differentiation of the adipocyte the position of exosomes probably change as a reflection of cell specialization. Hypertrophic cells-derived exosome can modulate the migratory capacity of CICs a reflex of change of exosome content during differentiation. Further analysis will analyze the exosome cargo (i.e. miRNA) during this process.
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