Chapter 5
Cardio-oncology: Network-Based
Prediction of Cancer Therapy-Induced
Cardiotoxicity
Feixiong Cheng
Abstract The growing awareness of cardiotoxicities associated with cancer treatment has led to the emerging field of cardio-oncology (also known onco-cardiology),
which centers on screening, monitoring, and treating cancer patients with cardiac
dysfunction before, during, or after cancer treatment. The classical approach centered on the hypothesis of ‘one gene, one drug, one disease’ in the traditional drug
discovery paradigm may have contributed to unanticipated off-target cardiotoxicity.
However, there are no guidelines in terms of how to prevent and efficiently treat
new cardiotoxicities in drug discovery and development. Novel approaches, such
as network-based drug-disease proximity, shed light on the relationship between
drugs and diseases, offering novel tools for risk assessment of drug-induced cardiotoxicity. In this chapter, we will introduce an integrated, network-based, systems
pharmacology approach that incorporates disease-associated proteins/genes, drugtarget networks, and the human protein-protein interactome, for risk assessment of
drug-induced cardiotoxicity. Specifically, we will introduce available bioinformatics
resources and quantitative network analysis tools. In addition, we will showcase how
to use network proximity for risk assessment of drug-induced cardiotoxicity and
for understanding of their underlying cardiotoxicity-related mechanism-of-action
(e.g., multi-targeted kinase inhibitors). Finally, we will discuss existing challenges
and highlight future directions of network proximity approaches for comprehensive
assessment of oncological drug-induced cardiotoxicity in the early stage of drug
discovery, clinical trials, and post-marketing surveillance.
F. Cheng (B)
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue
Cleveland, Cleveland, OH 44195, USA
e-mail: chengf@ccf.org
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106,
USA
Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western
Reserve University, Cleveland, OH 44195, USA
Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine,
Cleveland, OH 44106, USA
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_5
75
Cardio-oncology: Network-Based
Prediction of Cancer Therapy-Induced
Cardiotoxicity
Feixiong Cheng
Abstract The growing awareness of cardiotoxicities associated with cancer treatment has led to the emerging field of cardio-oncology (also known onco-cardiology),
which centers on screening, monitoring, and treating cancer patients with cardiac
dysfunction before, during, or after cancer treatment. The classical approach centered on the hypothesis of ‘one gene, one drug, one disease’ in the traditional drug
discovery paradigm may have contributed to unanticipated off-target cardiotoxicity.
However, there are no guidelines in terms of how to prevent and efficiently treat
new cardiotoxicities in drug discovery and development. Novel approaches, such
as network-based drug-disease proximity, shed light on the relationship between
drugs and diseases, offering novel tools for risk assessment of drug-induced cardiotoxicity. In this chapter, we will introduce an integrated, network-based, systems
pharmacology approach that incorporates disease-associated proteins/genes, drugtarget networks, and the human protein-protein interactome, for risk assessment of
drug-induced cardiotoxicity. Specifically, we will introduce available bioinformatics
resources and quantitative network analysis tools. In addition, we will showcase how
to use network proximity for risk assessment of drug-induced cardiotoxicity and
for understanding of their underlying cardiotoxicity-related mechanism-of-action
(e.g., multi-targeted kinase inhibitors). Finally, we will discuss existing challenges
and highlight future directions of network proximity approaches for comprehensive
assessment of oncological drug-induced cardiotoxicity in the early stage of drug
discovery, clinical trials, and post-marketing surveillance.
F. Cheng (B)
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue
Cleveland, Cleveland, OH 44195, USA
e-mail: chengf@ccf.org
Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106,
USA
Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western
Reserve University, Cleveland, OH 44195, USA
Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine,
Cleveland, OH 44106, USA
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_5
75