Immunotherapy using antibodies that block immune checkpoints is an emerging success story for some patients with cancer; however, the majority of patients gain no benefit while they can experience considerable toxicity.
Biomarkers to predict whether a patient will respond or not would therefore be extremely helpful. Current biomarkers are assessed from tumour tissue or peripheral blood, usually taken before treatment, and many studies have used archival samples.
The antitumour immune response after checkpoint blockade displays features of a critical state transition, similar to other complex systems.
Complex systems are highly sensitive to the initial conditions, and critical state transitions are notoriously difficult to predict far in advance.
Recent advances in mathematics and network biology are making it possible to identify dynamic states moving towards critical transitions.
We propose that mapping dynamic biomarkers will prove to be useful for differentiating responding from non-responding patients, and will facilitate the identification of new therapeutic targets to improve the efficacy of current treatments.
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.
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W.J.L is supported by a John Stocker Fellowship from the Australian Science and Industry Endowment Fund and by grants from Cure Cancer/Cancer Australia. R.A.L. is supported by the Insurance Commission of Western Australia. W.J.L., A.K.N. and R.A.L. are supported by project grants and Centre for Research Excellence funding from Australia's National Health and Medical Research Council.
W.J.L., A.B. and R.A.L. hold a patent on a method for the identification of immunotherapy and drug combinations using a network approach. the other authors declare no competing interests.
- Immune checkpoint
A stimulatory or inhibitory pathway that regulates the scale of adaptive immune responses.
- Cytotoxic T lymphocyte-associated antigen 4
(CTLA4). An inhibitory immune checkpoint molecule that is upregulated on effector T cells after activation. CTLA4 competes for ligands with the stimulatory receptor CD28.
- Programmed cell death protein 1
(PD1). An inhibitory immune checkpoint receptor expressed on activated lymphocytes and highly expressed on exhausted T lymphocytes. Ligation results in an impairment of proliferation, cytokine production, cytolytic function and survival of T cells.
A measurable indicator of normal biological processes, pathogenic processes or biological responses to a therapeutic intervention. In oncology, biomarkers are often used as predictors of disease outcome, such as prognosis or response to treatment.
This describes a phenomenon in which the tumour volume increases due to infiltrating immune cells and oedema, before a subsequent response occurs.
- Cytotoxic T cells
A subset of T cells expressing CD8 that has the capacity to directly kill target cells, such as cancer cells or virus-infected cells, after recognizing antigenic peptides on that cell.
- Regulatory T cells
A subset of CD4+ T cells that inhibit immune responses; they can down modulate the induction of a response and can limit the proliferation of effector T cells.
- T cell receptor repertoires
T cell receptors are formed by the random recombination of a set of germline-encoded elements with mechanisms to enhance junctional diversity. Thus, genetically identical individuals can create unique T cell receptor repertoires.
- Complex system
A system characterized by emergent behaviour. The system itself may consist of a large number of very simple parts interacting in simple ways, but with a large number of such interactions. The observed behaviour of the entire system is not what one would immediately expect from the individual components.
- Chaotic system
A system that exhibits irregular and apparently random variation but is in fact completely described by a small number of deterministic equations. Chaotic systems are characterized by sensitive dependence on initial conditions (a small change in the initial state leads to a large change in outcome), extreme mixing (different initial conditions will eventually become arbitrarily close, for a short time), and, exhibiting bounded, deterministic, but aperiodic behaviour.
- Critical state transition
The behaviour of complex systems typically changes smoothly (gradually) with changes in the system parameters. However, at a critical state transition a small perturbation to the system parameters will result in a sudden and dramatic change in the systems behaviour.
- Tipping point
A tipping point is the (biological) state at which a critical state transition occurs.
- Graph theory
The mathematical study of graphs, abstract structures used to model pairwise relations between members of a set. A graph in this context is made up of nodes that are connected by edges. Mathematical graph theory tends to focus on analysis of symmetry and structure, whereas in physics the same objects are called networks and the emphasis is on structures with a very large number of nodes and the description of the statistical interaction between them.
Many complex systems are characterized by complex networks. A complex network is represented by a series of nodes connected by edges. One can visualize these nodes and edges as points (nodes) connected by line segments (edges). The edges determine which nodes are connected to which other nodes. Nodes that have a relatively large number of edges are called hubs.
- Bottleneck nodes
A bottleneck node is a node with a central role in connecting other parts of a network. A large number of paths between random pairs of nodes will pass through a bottleneck node.
Any subsets of the nodes in a network and the edges connecting nodes within that subset. They form a part a network.
- Transient hubs
Hubs that rise to prominence only for a short time. In a time varying network, a transient hub will have a large number of edges only for some of the time.
- Dynamic network biomarkers
Predictive biomarkers (that is, associated with response to treatment) that are obtained after treatment has been initiated.
- Static biomarkers
Predictive biomarkers obtained at a single time point.
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Lesterhuis, W., Bosco, A., Millward, M. et al. Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat Rev Drug Discov 16, 264–272 (2017). https://doi.org/10.1038/nrd.2016.233
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