Altered thymic niche synergistically drives the massive proliferation of malignant thymocytes
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eLife Assessment
This valuable study combines agent-based modelling and in vivo experiments in medaka embryos to provide new insights into the role of the thymic niche in T cell development. The modelling yields some interesting findings regarding the importance of thymic epithelial cells, for some of which the evidence is incomplete. This study would be of interest to oncologists, immunologists, and mathematical modelers.
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Abstract
The discovery of genetic alterations in patient samples over the last 20 years has promoted a cell-autonomous view of proliferative expansion during T-cell acute lymphoblastic leukemia (T-ALL) development in the thymus. However, the potential contribution of non-cell-autonomous factors, particularly the impact of thymic epithelial cells (TECs) within the thymic niche during the initiation phase, remains unexplored. In this study, we employ a unique combination of a cell-based computational model of the thymus and in vivo experiments. We systematically analyze the impact of 12 cell-autonomous and non-autonomous factors, either alone or in combinations, on the proliferation of normal and malignant thymocytes with interleukin-7 receptor (IL7R) gain-of-function mutations or elevated IL7R levels, as observed in T-ALL patients. By simulating over 1500 scenarios, we show that while a dense TEC network favored the proliferation of normal thymocytes, it inhibited the proliferation of malignant lineages, which achieved their maximal proliferative capacity when TECs were sparsely distributed. Our in silico model predicts that certain mutations could accelerate proliferative expansion within a few days. This was experimentally validated, revealing rapid onset of thymus lymphoma and infiltration of malignant T-cells into other organs within 8 days of medaka ( Oryzias latipes ) embryonic development, thus revealing that modifications in the thymic niche and oncogenes in thymocytes together accelerate the disease development. Our results also suggest that negative feedback from the proliferative state inhibits differentiation of thymocytes, thereby prolonging the proliferative state and further fueling malignant expansion. Overall, this work reveals the critical impact of TEC-thymocyte interactions in both the initiation and progression of disease.
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Author Response:
We thank the reviewers for their thoughtful comments on our manuscript. In this provisional response, we aim to address the major concerns raised and outline a plan for a revised version of the manuscript. A more detailed point-by-point response will follow with the revision.
The reviewers appreciated our efforts to combine computational modelling with experimental work. However, they also expressed the need for more clarity in explaining how the model was set up, what was simulated, and what the insights and limitations are. In the revision, we plan to improve the discussion section to clarify all of these points.
The reviewers also highlighted the need for more transparency regarding the code and the mathematical formulas used in this study. We agree that this is an important issue. While we have already made the …
Author Response:
We thank the reviewers for their thoughtful comments on our manuscript. In this provisional response, we aim to address the major concerns raised and outline a plan for a revised version of the manuscript. A more detailed point-by-point response will follow with the revision.
The reviewers appreciated our efforts to combine computational modelling with experimental work. However, they also expressed the need for more clarity in explaining how the model was set up, what was simulated, and what the insights and limitations are. In the revision, we plan to improve the discussion section to clarify all of these points.
The reviewers also highlighted the need for more transparency regarding the code and the mathematical formulas used in this study. We agree that this is an important issue. While we have already made the software and code for our computational model, along with instructions on how to run it, available in Zenodo (see Ref. 1), and have extensively described the original computational model and formulas in a 13-page supplementary file in our previous study (see Ref. 2), we recognize from the reviewers’ comments that additional transparency is needed. To address this, we will provide an appendix in the revision that includes a full model description, covering the incorporation of cell differentiation and death, a list of parameters, and details on how parameter values were chosen.
Additionally, in the revised manuscript, we will add a paragraph to more thoroughly discuss the limitations of our approach, as well as avenues for future studies. We hope this will clarify both capabilities and limitations of our model in a way that is more accessible to readers of eLife.
References:
1. Virtual Thymus Model (version 2.0). Published: Jun 14, 2024. doi:10.5281/zenodo.11656320
2. Aghaallaei, Narges, et al. "αβ/γδ T cell lineage outcome is regulated by intrathymic cell localization and environmental signals." Science Advances 7.29 (2021): eabg3613.
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eLife Assessment
This valuable study combines agent-based modelling and in vivo experiments in medaka embryos to provide new insights into the role of the thymic niche in T cell development. The modelling yields some interesting findings regarding the importance of thymic epithelial cells, for some of which the evidence is incomplete. This study would be of interest to oncologists, immunologists, and mathematical modelers.
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Reviewer #1 (Public review):
Summary:
This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.
Strengths:
The strength of the paper is the use of computational modelling to …
Reviewer #1 (Public review):
Summary:
This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.
Strengths:
The strength of the paper is the use of computational modelling to obtain unique insights into the niche parameters that control T cell development, such as the role of TEC architecture, while anchoring those findings with in vivo experiments. I can't comment on the model itself, as I am not an expert in modelling, however, the conclusions of the paper seem to be well-supported by the model.
Weaknesses:
One potential issue is that many of the conclusions are drawn from the number of thymocytes, or related parameters such as the thymic size or proliferation of the thymocytes. The study only touches briefly on the influence of the thymic niche on other aspects of thymocyte behaviour, such as their differentiation and death.
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Reviewer #2 (Public review):
Summary:
The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneiety. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.
In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and …
Reviewer #2 (Public review):
Summary:
The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneiety. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.
In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and monopolise the IL-7 source. The effect will be more noticeable if sources are rare, so is seen when the TEC network is sparse.
Strengths:
Thymic disfunctions are of interest, not least because of T-ALL. New cells are added, one at a time, to simulate the conveyor belt of thymocytes on a background of stationary cells. They are thus able to follow cell lineages, which is interesting because one progenitor can give rise to many progeny.
There are some experimental results in Figures 4,5 and 6. For example, il7 crispant embryos have fewer thymocytes and smaller thymii; but increasing IL-7 availability produces large thymii.
Weaknesses:
On the negative side, like most agent-based models, there are dozens of parameters and assumptions whose values and validity are hard to ascertain.
The stated aim is to mimic a 2.5-to-11 day-old medaka thymus, but the constructed model is a geometrical subset that holds about 100 cells at a time in a steady state. The manuscript contains very many figures and lengthy descriptions of simulations run with different parameters values and assumptions. The abstract and conclusion did not help me understand what exactly has been done and learned. No attempt to synthesise observations in any mathematical formula is made.
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Reviewer #3 (Public review):
Summary:
Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.
Strengths:
A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the …
Reviewer #3 (Public review):
Summary:
Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.
Strengths:
A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the agent-based model, the authors also tested the effects of varying a few key parameters of cell proliferation.
Weaknesses:
A minor weakness is that the authors did not conduct a global sensitivity analysis of all parameters in their agent-based model to show that the model is robust to variation, which would demonstrate that their results would still hold under a reasonable level of variation in the model and model parameters. This is a minor point, and such a supporting study would end in an appendix or supplement.
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