A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

Macfarlane, Fiona; Chaplain, M. A. J.; Lorenzi, Tommaso

doi:10.1016/j.jtbi.2019.07.019

Cited by 16 publications

(19 citation statements)

References 106 publications

(136 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, neoantigen-related subclonal death takes into account antigen load and frequency across the tumour while keeping the number of model parameters small. Our framework contemplates computational methods and data in both epitope immunogenicity [17] and immune search efficiency [18,29,30] to translate previous evidence into a mathematical model able to produce insight into the underlying subclonal dynamics. Analytical results of the new model indicate that a heterogeneity threshold separates cancer growth from immune control, so that highly heterogeneous neoantigen landscapes might impair immune efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…We postulate that a higher antigen load increases the probability of presenting more immunogenic epitopes, thus increasing subclonal dominance D. Additionally, it has been shown that even highly recognizable antigens fail at inducing a T cell response if they are not present in a sufficient fraction of the tumour [14,15]. Following research on immune search mechanisms [18,29,30], we hypothesize that increased epitope heterogeneity leading to more private antigens will result in a loss of T cell efficiency E.…”

Section: Mathematical Framework 21 Neoantigen Heterogeneity and The Cancer-immune Ecologymentioning

confidence: 98%

“…In order to make these relations explicit, we study a spatial simulation framework of a tumour surface built upon previous research [30] that minimizes the estimated parameters at play (see electronic supplementary material). In it, dendritic and T cell agents search for cancer antigens royalsocietypublishing.org/journal/rsif J. R. Soc.…”

Section: Immune Search Efficiency Ementioning

confidence: 99%

See 2 more Smart Citations

Tumour neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Aguadé-Gorgorió

Solé

2020

J. R. Soc. Interface.

View full text Add to dashboard Cite

Following the advent of cancer immunotherapy, increasing insight has been gained on the role of mutational load and neoantigens as key ingredients in T cell recognition of malignancies. However, not all highly mutational tumours react to immune therapies, and initial success is often followed by eventual relapse. Heterogeneity in the neoantigen landscape of a tumour might be key in the failure of immune surveillance. In this work, we present a mathematical framework to describe how neoantigen distributions shape the immune response. The model predicts the existence of an antigen diversity threshold level beyond which T cells fail at controlling heterogeneous tumours. Incorporating this diversity marker adds predictive value to antigen load for two cohorts of anti-CTLA-4 treated melanoma patients. Furthermore, our analytical approach indicates rapid increases in epitope heterogeneity in early malignancy growth following immune escape. We propose a combination therapy scheme that takes advantage of preexisting resistance to a targeted agent. The model indicates that the selective sweep for a resistant subclone reduces neoantigen heterogeneity, and we postulate the existence of a time window before tumour relapse where checkpoint blockade immunotherapy can become more effective.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Mathematical Framework 21 Neoantigen Heterogeneity and The Cancer-immune Ecologymentioning

confidence: 98%

Section: Immune Search Efficiency Ementioning

confidence: 99%

See 1 more Smart Citation

Tumour neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Aguadé-Gorgorió

Solé

2020

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…A spatial model for the cancer-immune interaction has been recently proposed in [28], where T cells walk on a twodimensional grid in search for antigens. Here, we present a similar model able to explicitly compute the dependency of T cell search efficiency on antigen clonality.…”

Section: Immune Search Efficiency Ementioning

confidence: 99%

Tumor neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Aguadé-Gorgorió

Solé

2019

Preprint

View full text Add to dashboard Cite

Following the advent of immunotherapy as a possible cure for cancer, remarkable insight has been gained on the role of mutational load and neoantigens as key ingredients in T cell recognition of malignancies. However, not all highly mutational tumors react to immune therapies, and even initial success is often followed by eventual relapse. Recent research points out that high heterogeneity in the neoantigen landscape of a tumor might be key in understanding the failure of immune surveillance. In this work we present a mathematical framework able to describe how neoantigen distributions shape the immune response. Modeling T cell reactivity as a function of antigen dominancy and distribution across a tumor indicates the existence of a diversity threshold beyond which T cells fail at controling heterogeneous cancer growth. Furthemore, an analytical estimate for the evolution of average antigen clonality indicates rapid increases in epitope heterogeneity in early malignancy growth. In this scenario, we propose that therapies targeting the tumor prior to immunotherapy can reduce neoantigen heterogeneity, and postulate the existence of a time window, before tumor relapse due to de novo resistance, rendering immunotherapy more effective. Major FindingsGenetic heterogeneity affects the immune response to an evolving tumor by shaping the neoantigen landscape of the cancer cells, and highly heterogeneous tumors seem to escape T cell recognition. Mathematical modeling predicts the existence of a well defined neoantigen diversity threshold, beyond which lymphocites are not able to counteract the growth of a population of highly heterogeneous subclones. Furthermore, evolutionary dynamics predict a fast decay of neoantigen clonality, rendering advanced tumors hard to attack at the time of immunotherapy. Within this mathematical framework we propose that targeted therapy forcing a selective pressure for resistance might as well increase neoantigen homogeneity, providing a novel possibility for combination therapy.The authors declare no potential conflicts of interest.

show abstract

“…Spatial structure in biological populations includes both clustering and segregation [32][33][34][35][36][37]. Stochastic individual-based models (IBM) offer a straightforward means of exploring population dynamics without invoking a meanfield approximation [38,39]. However, IBM approaches are computationally prohibitive for large populations and provide limited mathematical insight into the population dynamics, for example how particular biological mechanisms affect the carrying capacity or the Allee threshold [40].…”

Section: Introductionmentioning

confidence: 99%

Population dynamics with spatial structure and an Allee effect

Plank

Surendran

Simpson

2020

Preprint

View full text Add to dashboard Cite

Allee effects describe populations in which long-term survival is only possible if the population density is above some threshold level. A simple mathematical model of an Allee effect is one where initial densities below the threshold lead to population extinction, whereas initial densities above the threshold eventually asymptote to some positive carrying capacity density. Mean field models of population dynamics neglect spatial structure that can arise through short-range interactions, such as short-range competition and dispersal. The influence of such non mean-field effects has not been studied in the presence of an Allee effect. To address this we develop an individual-based model (IBM) that incorporates both short-range interactions and an Allee effect. To explore the role of spatial structure we derive a mathematically tractable continuum approximation of the IBM in terms of the dynamics of spatial moments. In the limit of long-range interactions where the mean-field approximation holds, our modelling framework accurately recovers the mean-field Allee threshold. We show that the Allee threshold is sensitive to spatial structure that mean-field models neglect. For example, we show that there are cases where the mean-field model predicts extinction but the population actually survives and vice versa. Through simulations we show that our new spatial moment dynamics model accurately captures the modified Allee threshold in the presence of spatial structure.

show abstract

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

Cited by 16 publications

References 106 publications

Tumour neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Tumour neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Tumor neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Population dynamics with spatial structure and an Allee effect

Contact Info

Product

Resources

About