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Mutations in the TP53 tumor suppressor gene are common in ovarian carcinoma (OC) but their impact on outcomes is controversial. We sought to define the relationship of TP53 mutations to cancer outcomes and their interactions with co-occurrent BRCA1 or BRCA2 (BRCA) mutations, comparing three different TP53 mutation classification schemes.
Women with fallopian tube carcinoma (FTC) are reported to have a higher frequency of inherited BRCA mutations than those with ovarian carcinoma (OC) or primary peritoneal carcinoma (PPC). We hypothesized that routine serial sectioning of fallopian tubes would increase the proportion of cases designated as FTC and change the frequency of inherited mutations between carcinoma types.
The ever-increasing size and scale of biological information have popularized network-based approaches as a means to interpret these data. We develop a network propagation method that integrates kinase-inhibitor-focused functional screens with known protein-protein interactions (PPIs). This method, dubbed KiRNet, uses an a priori edge-weighting strategy based on node degree to establish a pipeline from a kinase inhibitor screen to the generation of a predictive PPI subnetwork. We apply KiRNet to uncover molecular regulators of mesenchymal cancer cells driven by overexpression of Frizzled 2 (FZD2). KiRNet produces a network model consisting of 166 high-value proteins. These proteins exhibit FZD2-dependent differential phosphorylation, and genetic knockdown studies validate their role in maintaining a mesenchymal cell state. Finally, analysis of clinical data shows that mesenchymal tumors exhibit significantly higher average expression of the 166 corresponding genes than epithelial tumors for nine different cancer types.
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