PCOMPBIOL-D-24-02209

A Boolean network model of hypoxia, mechanosensing and TGF-β signaling captures the role of phenotypic plasticity and mutations in tumor metastasis

PLOS Computational Biology

Dear Dr. Ravasz Regan,

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Sylvain Soliman

Academic Editor

PLOS Computational Biology

Stacey Finley

Section Editor

PLOS Computational Biology

Additional Editor Comments :

The reviewers point out several strengths of this paper and are supportive of acceptance. There are a few minor issues (Reviewer 3), which the authors are invited to address in a revised submission.

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Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: This manuscript proposes Boolean model that integrates hypoxic signaling with a mechanosensitive model of

EMT. The model is impressively comprehensive, it which includes the EMT-promoting crosstalk of mitogens and biomechanical signals, cell cycle control, and apoptosis. The model is validated by showing that it reproduces multiple phenotypes and processes, for example hypoxic protection from anoikis.

The model has high explanatory power, as well as predictive power. For example, the model yields experimentally testable predictions about the effect of loss of the Von Hippel Landau protein (VHL) on cancer hallmarks, with or without secondary oncogene activation.

This model is a key advance, integrating multiple cancer hallmarks. It can be further extended to cover DNA damage response and senescence, which would then cover all single-cell cancer hallmarks.

The methods and results are well-documented and the writing is clear.

In addition to its clear biological/medical implications, this manuscript is a very good example of successful modeling.

Reviewer #2: In this work, the authors built a Boolean network model that extends their previously published model to integrate a hypoxic response module and study the dynamics of signaling pathways associated with the epithelial-mesenchymal transition.

Their model successfully reproduces opposite outputs that depend on multiple signals and integrates the influence of biomechanical features of the environment, such as the stiffness of the extracellular matrix. The links and nodes in the Boolean network can be traced to experimental evidence in the literature, and the authors validated the results of their simulations by matching them to experimental data.

The article is well-structured and well-written. The introduction is thorough and provides the reader with enough context to understand the results, particularly the paradoxical hypoxia responses. Every claim in the results can be easily traced to a figure, and the computational methods and tools that were used are well-described and referenced. Additionally, the supplementary files include all the data needed to run the model and ensure reproducible results. The Discussion section, although somewhat lengthy, recapitulates the results, comments on their relationship with the hallmarks of cancer, and discusses the limitations of the model. Overall, I must admit that I really enjoyed reading and reviewing this article. It is a great example of the relevance of Boolean networks and their application in cancer systems biology, and I hope the authors continue expanding the use of this model.

Reviewer #3: The manuscript presents a single-cell Boolean network model that incorporates hypoxia signaling in a previously published mechanosensitive epithelial to mesenchymal transition (EMT) model. The authors aim to model the signaling crosstalk between environmental factors, such as extracellular matrix stiffness, cell density, hypoxia, and growth signals, that are central for tumor metastasis, and they also aim to model the effect of these factors on the cell cycle, cell death, and EMT.

The integration of these processes in a single model is a significant advancement; in the literature, these processes are mostly studied in isolation. The authors used their in-house simulation software dynmod and provided sufficient detail for the reproducibility of their work. The model underwent extensive validation and could reproduce key findings from the literature. The authors provide predictions for interactions, between environmental factors and mutations, that affect cancer hallmarks. Original experimental validations for the model and predictions were not included.

Overall, this manuscript is written well for the most part and its results provide significant insight. However, it has a few issues, which the authors should address so that it can be recommended for publication.

Major issues:

1. The authors include a detailed discussion about the limitations of the model; however, an extra discussion should be added about the applicability of the predictions of a general model to a specific type of cancer cell, which can have numerous cell-type specific interactions that are not captured by the general model.

2. In line 511, “apoptosis of a neighbor” is mentioned as an example of how an epithelial cell can gain the room necessary for EMT. However, on Figure 6, no apoptosis can be observed in the corresponding pulse, Pulse 3. This may lead to confusion, and the authors should clarify how cell density can decrease at this pulse.

Minor issues:

1. On page 8, the sentence: “Under moderate hypoxia, localization of Hif-1⍺ to the mitochondria prevents oxidative-stress induced apoptosis through…” seems to be unfinished.

2. Figures 2, 3, 4, 6: y axis is labelled “% time in phenotype”, but values are 0–1 and not in percentage. Figure 4B: values along the y axis are partially obstructed. On SM Fig. 5B-C the x axis label is missing.

3. In line 300, authors should introduce the abbreviation SAC for those unfamiliar.

4. On page 28, the line “We predict that the loss of TGF-β signaling can… slow down hypoxia-induced EMT in cells with weak mitogenic signaling (SM Fig. 3).” seems to incorrectly refer to SM Fig. 3 based on the line on page 15: “…TGF-β receptor I knockdown does not block hypoxia-induced EMT – though it slows it in mild hypoxia (Fig. 3B-C).”

5. Authors should proofread the manuscript for typos and omissions.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes:  Diana Garcia-Cortes

Reviewer #3: No

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Dear Whitmore-Williams Assistant Professor Ravasz Regan,

We are pleased to inform you that your manuscript 'A Boolean network model of hypoxia, mechanosensing and TGF-β signaling captures the role of phenotypic plasticity and mutations in tumor metastasis' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Sylvain Soliman

Academic Editor

PLOS Computational Biology

Stacey Finley

Section Editor

PLOS Computational Biology

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