A systems biology approach to discovering pathway signaling dysregulation in metastasis

Robert Clarke, Pavel Kraikivski, Brandon C. Jones, Catherine M. Sevigny, Surojeet Sengupta, Yue Wang

Research output: Contribution to journalReview articlepeer-review

10 Scopus citations


Total metastatic burden is the primary cause of death for many cancer patients. While the process of metastasis has been studied widely, much remains to be understood. Moreover, few agents have been developed that specifically target the major steps of the metastatic cascade. Many individual genes and pathways have been implicated in metastasis but a holistic view of how these interact and cooperate to regulate and execute the process remains somewhat rudimentary. It is unclear whether all of the signaling features that regulate and execute metastasis are yet fully understood. Novel features of a complex system such as metastasis can often be discovered by taking a systems-based approach. We introduce the concepts of systems modeling and define some of the central challenges facing the application of a multidisciplinary systems-based approach to understanding metastasis and finding actionable targets therein. These challenges include appreciating the unique properties of the high-dimensional omics data often used for modeling, limitations in knowledge of the system (metastasis), tumor heterogeneity and sampling bias, and some of the issues key to understanding critical features of molecular signaling in the context of metastasis. We also provide a brief introduction to integrative modeling that focuses on both the nodes and edges of molecular signaling networks. Finally, we offer some observations on future directions as they relate to developing a systems-based model of the metastatic cascade.

Original languageEnglish (US)
Pages (from-to)903-918
Number of pages16
JournalCancer and Metastasis Reviews
Issue number3
StatePublished - Sep 1 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.


  • Bioinformatics
  • Breast cancer
  • Computational modeling
  • Mathematical modeling
  • Metastasis
  • Multiscale modeling
  • Pathway analysis
  • Signaling
  • Systems biology


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