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Updated: Mar 9, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity
W Joost Lesterhuis1, Anthony Bosco2, Michael J Millward1,3
1School of Medicine and Pharmacology and National Centre for Asbestos Related Diseases, University of Western Australia, 5th Floor QQ Block, 6 Verdun Street, Nedlands, Perth, Western Australia 6009, Australia.
Abstract:
Recently, there has been a coordinated effort from academic institutions and the pharmaceutical industry to identify biomarkers that can predict responses to immune checkpoint blockade in cancer. Several biomarkers have been identified; however, none has reliably predicted response in a sufficiently rigorous manner for routine use. Here, we argue that the therapeutic response to immune checkpoint blockade is a critical state transition of a complex system. Such systems are highly sensitive to initial conditions, and critical transitions are notoriously difficult to predict far in advance. Nevertheless, warning signals can be detected closer to the tipping point. Advances in mathematics and network biology are starting to make it possible to identify such warning signals. We propose that these dynamic biomarkers could prove to be useful in distinguishing responding from non-responding patients, as well as facilitate the identification of new therapeutic targets for combination therapy.
Insights
Predicting patient response to immune checkpoint blockade (ICB) is challenging. This study proposes dynamic biomarkers, identified using network biology, to signal critical transitions and improve ICB treatment prediction.
Area of Science:
- Oncology
- Systems Biology
- Computational Biology
Background:
- Coordinated efforts seek biomarkers for immune checkpoint blockade (ICB) response prediction in cancer.
- Current biomarkers lack the reliability for routine clinical use.
Purpose of the Study:
- To propose a novel framework for predicting ICB response by viewing it as a critical state transition.
- To introduce dynamic biomarkers for improved patient stratification and identification of new therapeutic targets.
Main Methods:
- Applying principles of complex systems theory to understand therapeutic response.
- Leveraging advances in mathematics and network biology to identify predictive warning signals.
- Developing dynamic biomarkers to detect critical transitions in patient response.
Main Results:
- Therapeutic response to ICB is characterized as a critical state transition in complex biological systems.
- Critical transitions are sensitive to initial conditions, making early prediction difficult.
- Warning signals preceding critical transitions can be detected closer to the tipping point.
Conclusions:
- Dynamic biomarkers offer a promising approach to distinguish responders from non-responders to ICB.
- This approach may facilitate the discovery of novel therapeutic targets for combination therapies.
- Integrating systems biology and network analysis can enhance cancer immunotherapy prediction.
