2. Academic Validation
  3. Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

  • BMC Cancer. 2022 Nov 24;22(1):1211. doi: 10.1186/s12885-022-10293-0.
Manali Singha # 1,  Limeng Pu # 2,  Brent A Stanfield 3,  Ifeanyi K Uche 3 4 5,  Paul J F Rider 3 4,  Konstantin G Kousoulas 3 4,  J Ramanujam 2 6,  Michal Brylinski 7 8
Affiliations

Affiliations

  • 1 Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • 2 Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • 3 Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • 4 Division of Biotechnology and Molecular Medicine, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • 5 School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA.
  • 6 Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • 7 Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA. michal@brylinski.org.
  • 8 Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA. michal@brylinski.org.
  • # Contributed equally.
PMID: 36434556 DOI: 10.1186/s12885-022-10293-0
Abstract

Background: Vast amounts of rapidly accumulating biological data related to Cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform Other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

Methods: CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

Results: Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate Cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src Inhibitor PP1 being the most potent against the pancreatic Cancer cell line Panc 04.03.

Conclusions: CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Keywords

Artificial intelligence; Cancer growth rate; Cancer-specific networks; Differential gene expression; Gene signature; Gene-disease association; Graph neural network; Kinase inhibitors; Live-cell time course assay; Network biology; Precision oncology.

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