Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis

WV is a SULSA Systems Biology Prize PhD Student; VAS is supported by the BBSRC Research Council [grant number BB/F001398/1] and Medical Research Scotland [grant number FRG353]. DJH is supported by CASyM Concerted Action [grant number EU HEALTH-F4-2012-305033] and the Chief Scientist Office of Scotland.Current clinical practice in cancer stratifies patients based on tumour histology to determine prognosis. Molecular profiling has been hailed as the path towards personalised care, but molecular data are still typically analysed independently of known clinical information. Conventional clinical and histopathological data, if used, are added only to improve a molecular prediction, placing a high burden upon molecular data to be informative in isolation. Here, we develop a novel Monte Carlo analysis to evaluate the usefulness of data assemblages. We applied our analysis to varying assemblages of clinical data and molecular data in an ovarian cancer dataset, evaluating their ability to discriminate one-year progression-free survival (PFS) and three-year overall survival (OS). We found that Cox proportional hazard regression models based on both data types together provided greater discriminative ability than either alone. In particular, we show that proteomics data assemblages that alone were uninformative (p = 0.245 for PFS, p = 0.526 for OS) became informative when combined with clinical information (p = 0.022 for PFS, p = 0.048 for OS). Thus, concurrent analysis of clinical and molecular data enables exploitation of prognosis-relevant information that may not be accessible from independent analysis of these data types.Publisher PDFPeer reviewe

A dual role for Caspase8 and NF-κB interactions in regulating apoptosis and necroptosis of ovarian cancer, with correlation to patient survival

Ovarian cancer is a deadly disease characterized by primary and acquired resistance to chemotherapy. We previously associated NF-κB signaling with poor survival in ovarian cancer, and functionally demonstrated this pathway as mediating proliferation, invasion and metastasis. We aimed to identify cooperating pathways in NF-κB-dependent ovarian cancer cells, using genome-wide RNA interference as a loss-of-function screen for key regulators of cell survival with IKKβ inhibition. Functional genomic screen for interactions with NF-κB in ovarian cancer showed that cells depleted of Caspase8 died better with IKKβ inhibition. Overall, low Caspase8 was associated with shorter overall survival in three independent gene expression data sets of ovarian cancers. Conversely, Caspase8 expression was markedly highest in ovarian cancer subtypes characterized by strong T-cell infiltration and better overall prognosis, suggesting that Caspase8 expression increased chemotherapy-induced cell death. We investigated the effects of Caspase8 depletion on apoptosis and necroptosis of TNFα-stimulated ovarian cancer cell lines. Inhibition of NF-κB in ovarian cancer cells switched the effects of TNFα signaling from proliferation to death. Although Caspase8-high cancer cells died by apoptosis, Caspase8 depletion downregulated NF-κB signaling, stabilized RIPK1 and promoted necroptotic cell death. Blockage of NF-κB signaling and depletion of cIAP with SMAC-mimetic further rendered these cells susceptible to killing by necroptosis. These findings have implications for anticancer strategies to improve outcome for women with low Caspase8-expressing ovarian cancer

The O-Linked Glycome and Blood Group Antigens ABO on Mucin-Type Glycoproteins in Mucinous and Serous Epithelial Ovarian Tumors

Mucins are heavily O-glycosylated proteins where the glycosylation has been shown to play an important role in cancer. Normal epithelial ovarian cells do not express secreted mucins, but their abnormal expression has previously been described in epithelial ovarian cancer and may relate to tumor formation and progression. The cyst fluids were shown to be a rich source for acidic glycoproteins. The study of these proteins can potentially lead to the identification of more effective biomarkers for ovarian cancer.In this study, we analyzed the expression of the MUC5AC and the O-glycosylation of acidic glycoproteins secreted into ovarian cyst fluids. The samples were obtained from patients with serous and mucinous ovarian tumors of different stages (benign, borderline, malignant) and grades. The O-linked oligosaccharides were released and analyzed by negative-ion graphitized carbon Liquid Chromatography (LC) coupled to Electrospray Ionization tandem Mass Spectrometry (ESI-MSn). The LC-ESI-MSn of the oligosaccharides from ovarian cyst fluids displayed differences in expression of fucose containing structures such as blood group ABO antigens and Lewis-type epitopes.The obtained data showed that serous and mucinous benign adenomas, mucinous low malignant potential carcinomas (LMPs, borderline) and mucinous low-grade carcinomas have a high level of blood groups and Lewis type epitopes. In contrast, this type of fucosylated structures were low abundant in the high-grade mucinous carcinomas or in serous carcinomas. In addition, the ovarian tumors that showed a high level of expression of blood group antigens also revealed a strong reactivity towards the MUC5AC antibody. To visualize the differences between serous and mucinous ovarian tumors based on the O-glycosylation, a hierarchical cluster analysis was performed using mass spectrometry average compositions (MSAC).Mucinous benign and LMPs along with mucinous low-grade carcinomas appear to be different from serous and high-grade mucinous carcinomas based on their O-glycan profiles