Epithelial ovarian cancer (EOC) is still considered the most lethal gynecological malignancy and improved early detection of ovarian cancer is crucial to improving patient prognoses. To address this need, we tested whether candidate EOC biomarkers can be identified using three-dimensional (3D) in vitro models. We quantified changes in the abundance of secreted proteins in a 3D genetic model of early-stage EOC, generated by expressing CMYC and KRAS(G) (12) (V) in TERT-immortalized normal ovarian epithelial cells. Cellular proteins were labeled in live cells using stable isotopic amino acid analogues, and secreted proteins identified and quantified using liquid chromatography-tandem mass spectrometry. Thirty-seven and 55 proteins were differentially expressed by CMYC and CMYC+KRAS(G) (12) (V) expressing cells respectively (p < 0.05; >2-fold). We evaluated expression of the top candidate biomarkers in ∼210 primary EOCs: CHI3L1 and FKBP4 are both expressed by >96% of primary EOCs, and FASN and API5 are expressed by 86 and 75% of cases. High expression of CHI3L1 and FKBP4 was associated with worse patient survival (p = 0.042 and p = 0.002, respectively). Expression of LGALS3BP was positively associated with recurrence (p = 0.0001) and suboptimal debulking (p = 0.018) suggesting that these proteins may be novel prognostic biomarkers. Furthermore, within early stage tumours (I/II), high expression of API5, CHI3L1 and FASN was associated with high tumour grade (p = 3 × 10(-4) , p = 0.016, p = 0.010, respectively). We show in vitro cell biology models of early-stage cancer development can be used to identify novel candidate biomarkers for disease, and report the identification of proteins that represent novel potential candidate diagnostic and prognostic biomarkers for this highly lethal disease.
Keywords: 3D models; MAPK; SILAC; biomarkers; early detection; ovarian cancer.
© 2014 UICC.