Abstract
Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ‘cancer hallmarks’. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody–related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
Highlights
The Cancer Genome Atlas (TCGA) Research Network has recently profiled and analyzed large numbers of human tumors both within and across tumor lineages to elucidate the landscape of cancer associated alterations at the DNA, RNA, protein, and epigenetic levels [1]
Pan-cancer proteomic datasets from The Cancer Genome Atlas provide a unique opportunity to study the functions of proteins in human cancers
Such datasets, where proteins are measured in different conditions and where correlations are informative, can enable the discovery of potentially causal protein-protein interactions, which may in turn shed light on the function of proteins
Summary
The Cancer Genome Atlas (TCGA) Research Network has recently profiled and analyzed large numbers of human tumors both within and across tumor lineages to elucidate the landscape of cancer associated alterations at the DNA, RNA, protein, and epigenetic levels [1]. The RPPA platform used in the Akbani et al analysis was subsequently expanded to include 187 high-quality antibodies, 51 of which are phosphospecific and 136 of which are non-phosphospecific These antibodies were selected with a focus on cancer-related pathway and signaling events and analyzed with the intent to discover new therapeutic opportunities. This dataset is available for download from The Cancer Proteome Atlas [5], and referred to as PANCAN11 from here on
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