Research Papers:

CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling

Limeng Pu, Manali Singha, Jagannathan Ramanujam and Michal Brylinski _

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Oncotarget. 2022; 13:695-706. https://doi.org/10.18632/oncotarget.28234

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Limeng Pu1,*, Manali Singha2,*, Jagannathan Ramanujam1,3 and Michal Brylinski1,2

1 Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA

2 Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

3 Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA

* These authors contributed equally to this work

Correspondence to:

Michal Brylinski, email: michal@brylinski.org

Keywords: cancer growth rate; kinase inhibitors; differential gene expression; gene-disease association; cancer-specific networks

Received: March 22, 2022     Accepted: May 03, 2022     Published: May 19, 2022

Copyright: © 2022 Pu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet.

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