5 Cardio-oncology: Network-Based Prediction …
91
bias of the human interactome warrants inspection in the future. Second, our current
network-based in silico models cannot separate therapeutic effects from side effects
owing to the lack of detailed functional effects of drug targets and disease proteins.
Drug targets representing nodes within cellular networks are often intrinsically coupled in both therapeutic and adverse profiles. For example, drugs can inhibit or activate protein functions (including antagonists vs. agonists), while disease alleles from
genetic or genomic studies contain loss-of-function or gain-of-function. An inhibitor
that targets loss-of-function disease proteins often causes adverse effects. In addition, dose-dependent cardiotoxicity cannot be evaluated by the current network-based
systems pharmacology framework. Finally, translating network-based prediction to
regulatory science during drug discovery and development remains challenging.
There are several new directions to improve network proximity approach further.
Adding genome/proteome-wide drug-induced transcriptome or proteome data such
as the Connectivity Map may help overcome data incompleteness of known targets on
approved drugs (Table 5.1). In addition, integration of functional genomic assays or
large-scale disease gene expression profiles (upregulation or downregulation), along
with patient data validation and in vitro or in vivo mechanistic studies will improve
network proximity approaches further. Utilizing network proximity approaches to
investigate the metabolic intervention and dietary regulation may offer novel chemical intervention strategies for cancer treatment-related cardiotoxicities. In addition,
implementing dynamics data (e.g., time series drug-protein binding affinity [k on and
k off ]) via network control approaches [96, 97] and pharmacokinetics-based mathematical modeling into the network-based systems pharmacology framework can
be used to assess dose-dependent cardiotoxicities. Finally, assembling multi-omics
data, including genomics, transcriptomics, and proteomics from individual patients,
under a network proximity framework, may offer novel actionable biomarkers for
characterization of heterogeneities of cancer treatment-induced cardiotoxicities, in
the personalized cardio-oncology era.
Acknowledgements This work was supported by the National Heart, Lung, and Blood Institute
of the National Institutes of Health under Award Number K99HL138272 and R00HL138272.
Competing Interests The author has declared that no conflict of interest exists.
References
1. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66(1):7–30
2. Brown SA, Sandhu N, Herrmann J (2015) Systems biology approaches to adverse drug effects,
the example of cardio-oncology. Nat Rev Clin Oncol 12(12):718–731
3. Cheng F, Loscalzo J (2018) Pulmonary comorbidity in lung cancer. Trends Mol Med
24(3):239–241
4. Cheng F, Nussinov R (2018) KRAS activating signaling triggers arteriovenous malformations.
Trends Biochem Sci 43(7):481–483
5. Pullamsetti SS, Kojonazarov B, Storn S, Gall H, Salazar Y, Wolf J et al (2017) Lung cancerassociated pulmonary hypertension, Role of microenvironmental inflammation based on
tumor cell-immune cell cross-talk. Sci Transl Med 9(416):eaai9048
91
bias of the human interactome warrants inspection in the future. Second, our current
network-based in silico models cannot separate therapeutic effects from side effects
owing to the lack of detailed functional effects of drug targets and disease proteins.
Drug targets representing nodes within cellular networks are often intrinsically coupled in both therapeutic and adverse profiles. For example, drugs can inhibit or activate protein functions (including antagonists vs. agonists), while disease alleles from
genetic or genomic studies contain loss-of-function or gain-of-function. An inhibitor
that targets loss-of-function disease proteins often causes adverse effects. In addition, dose-dependent cardiotoxicity cannot be evaluated by the current network-based
systems pharmacology framework. Finally, translating network-based prediction to
regulatory science during drug discovery and development remains challenging.
There are several new directions to improve network proximity approach further.
Adding genome/proteome-wide drug-induced transcriptome or proteome data such
as the Connectivity Map may help overcome data incompleteness of known targets on
approved drugs (Table 5.1). In addition, integration of functional genomic assays or
large-scale disease gene expression profiles (upregulation or downregulation), along
with patient data validation and in vitro or in vivo mechanistic studies will improve
network proximity approaches further. Utilizing network proximity approaches to
investigate the metabolic intervention and dietary regulation may offer novel chemical intervention strategies for cancer treatment-related cardiotoxicities. In addition,
implementing dynamics data (e.g., time series drug-protein binding affinity [k on and
k off ]) via network control approaches [96, 97] and pharmacokinetics-based mathematical modeling into the network-based systems pharmacology framework can
be used to assess dose-dependent cardiotoxicities. Finally, assembling multi-omics
data, including genomics, transcriptomics, and proteomics from individual patients,
under a network proximity framework, may offer novel actionable biomarkers for
characterization of heterogeneities of cancer treatment-induced cardiotoxicities, in
the personalized cardio-oncology era.
Acknowledgements This work was supported by the National Heart, Lung, and Blood Institute
of the National Institutes of Health under Award Number K99HL138272 and R00HL138272.
Competing Interests The author has declared that no conflict of interest exists.
References
1. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66(1):7–30
2. Brown SA, Sandhu N, Herrmann J (2015) Systems biology approaches to adverse drug effects,
the example of cardio-oncology. Nat Rev Clin Oncol 12(12):718–731
3. Cheng F, Loscalzo J (2018) Pulmonary comorbidity in lung cancer. Trends Mol Med
24(3):239–241
4. Cheng F, Nussinov R (2018) KRAS activating signaling triggers arteriovenous malformations.
Trends Biochem Sci 43(7):481–483
5. Pullamsetti SS, Kojonazarov B, Storn S, Gall H, Salazar Y, Wolf J et al (2017) Lung cancerassociated pulmonary hypertension, Role of microenvironmental inflammation based on
tumor cell-immune cell cross-talk. Sci Transl Med 9(416):eaai9048