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A multi-method approach for proteomic network inference in 11 human cancers

View ORCID ProfileYasin Şenbabaoğlu, View ORCID ProfileSelçuk Onur Sümer, View ORCID ProfileGiovanni Ciriello, View ORCID ProfileNikolaus Schultz, View ORCID ProfileChris Sander
doi: https://doi.org/10.1101/015214
Yasin Şenbabaoğlu
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Selçuk Onur Sümer
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Giovanni Ciriello
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Nikolaus Schultz
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Chris Sander
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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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 literature-curated pathway 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. A consensus network from this high-performing group reveals that signal transduction events involving receptor tyrosine kinases (RTKs), the RAS/MAPK pathway, and the PI3K/AKT/mTOR pathway, as well as innate and adaptive immunity signaling, are the most significant PPIs shared across all tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

Availability PPI networks from the TCGA or user-provided data can be visualized with the ProtNet web application at http://www.sanderlab.org/protnet/.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 15, 2015.
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A multi-method approach for proteomic network inference in 11 human cancers
Yasin Şenbabaoğlu, Selçuk Onur Sümer, Giovanni Ciriello, Nikolaus Schultz, Chris Sander
bioRxiv 015214; doi: https://doi.org/10.1101/015214
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A multi-method approach for proteomic network inference in 11 human cancers
Yasin Şenbabaoğlu, Selçuk Onur Sümer, Giovanni Ciriello, Nikolaus Schultz, Chris Sander
bioRxiv 015214; doi: https://doi.org/10.1101/015214

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