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. 2021 Aug 31:12:668221.
doi: 10.3389/fimmu.2021.668221. eCollection 2021.

Predator-Prey in Tumor-Immune Interactions: A Wrong Model or Just an Incomplete One?

Irina Kareva  1 , Kimberly A Luddy  2   3 , Cliona O'Farrelly  3 , Robert A Gatenby  4 , Joel S Brown  4
Affiliations

Predator-Prey in Tumor-Immune Interactions: A Wrong Model or Just an Incomplete One?

Irina Kareva et al. Front Immunol. .

Abstract

Tumor-immune interactions are often framed as predator-prey. This imperfect analogy describes how immune cells (the predators) hunt and kill immunogenic tumor cells (the prey). It allows for evaluation of tumor cell populations that change over time during immunoediting and it also considers how the immune system changes in response to these alterations. However, two aspects of predator-prey type models are not typically observed in immuno-oncology. The first concerns the conversion of prey killed into predator biomass. In standard predator-prey models, the predator relies on the prey for nutrients, while in the tumor microenvironment the predator and prey compete for resources (e.g. glucose). The second concerns oscillatory dynamics. Standard predator-prey models can show a perpetual cycling in both prey and predator population sizes, while in oncology we see increases in tumor volume and decreases in infiltrating immune cell populations. Here we discuss the applicability of predator-prey models in the context of cancer immunology and evaluate possible causes for discrepancies. Key processes include "safety in numbers", resource availability, time delays, interference competition, and immunoediting. Finally, we propose a way forward to reconcile differences between model predictions and empirical observations. The immune system is not just predator-prey. Like natural food webs, the immune-tumor community of cell types forms an immune-web of different and identifiable interactions.

Keywords: cancer ecology; first principles; immune-web; immunoediting; predator-prey dynamics; tumor-immune interactions.

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Conflict of interest statement

IK is an employee of EMD Serono, US subsidiary of Merck KGaA. Views presented in this manuscript do not necessarily reflect the views of EMD Serono. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Numerical vs functional response. (A) Numerical response describes conversion of prey density into predator density. (B) Functional response captures relationship between rate of consumption and food density. Type 1 response implies that rate of consumption of predator is proportional to prey density. Type II response implies that the number of prey consumed increases rapidly with increased prey population density but plateaus at a carrying capacity. Type III response is similar to Type II but assumes that at low prey density rate of prey consumption is slower than in Type II.
Figure 2
Figure 2
Complete phase-parameter portrait of Lotka-Volterra model with Allee effect that captures the possible dynamical regimes possible in the model subject to variation of predator inefficiency (c/db) and the ratio of the extinction threshold to carrying capacity (L/K). In this figure the predators have a type I functional response and so there is no handling time (h=0). Adapted from original study by (23), reprinted in (24), Section 3.5.5. The diagram highlights that there is a predictable sequence of regimes between predator efficiency and inefficiency.
Figure 3
Figure 3
The dynamical regimes of cancer immunoediting paralleled by the sequence of regimes predicted by predator-prey models in response to decreasing predator efficiency.
Figure 4
Figure 4
Summary of the tumor-immune cycle adapted from Mellman and Chen (56), delineating key actors and mechanisms that defined the tumor-immunity cycle within and outside of the tumor microenvironment.
Figure 5
Figure 5
(A) Proposed set of minimally sufficient variables and mechanisms to be included in future models of tumor-immune interactions. (B) Ecological abstraction of the full model, where it is assumed that interactions with APCs and subsequent T cell activation reach a quasi-steady state before impacting interactions between CTLs and cancer cells in the tumor microenvironment. The resulting model has features of a classical predator-prey type model with a caveat that CTLs are indirectly stimulated by cancer cells.
See this image and copyright information in PMC

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