Dear Dr. Gallo,

Thank you very much for submitting your manuscript "Epigenetic instability may alter cell state transitions and anticancer drug resistance" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

Douglas Lauffenburger

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have developed stochastic models for cancer cell population that capture transition among various states that are capable of switching among multiple cell states, and in the presence of drug, can undergo an irreversible transition to a drug-resistant state. They investigate the impact of introducing epigenetic instability in their model and quantify the effect on population state distribution under many conditions and suggest mechanisms to potentially reduce the dominance of drug-resistant population. The study endeavours to ask important questions for therapeutic management of cancer; however, it seems to falls short on both fronts in its current form – a) comparing their model to experimental data that may be available for combinatorial therapies (not necessarily in gliomas though) and b) generating specific predictions that can be experimentally tested. Thus, the authors need to conduct additional analysis before the manuscript can be considered for publication:

1. The authors should discuss more of the relevant literature in terms of modeling the effects of epigenetic on cell-fate decisions (Miyamoto et al. PLoS Comp Biol 2015; Jia et al. Phys Biol 2019) and its effect in designing epigenetic-targeting drugs (Gunnarsson et al. J Theor Biol 2020). They should discuss conceptual and technical similarities and differences in their work vis-à-vis previous literature.

2. Cell-state transitions are also possible during cell division (Tripathi et al. PLoS Comp Biol 2020). How does incorporating such effects alter the behavior of the models shown here?

3. Have the authors investigated the impact of having intermittent therapy (i.e. drug holidays) which has been included as a part of various clinical trials now? (Michor & Beal, Cell 2015)

4. What is so specific about this model in terms of gliomas, as the authors claim?

5. Is the only major difference between the first model and the multi-step model that there is one more intermediate step that needs to be completed before the lock-in? If true, how do the results of the model change when more than one such intermediaries are included? Also, how do the effects of bidirectional transitions aggregate in terms of such n-state multi-state models? Can bidirectional transitions and multi-state modes sufficient to largely delay tumor resurgence?

Reviewer #2: Modeling without experimental validation is just modeling.

Reviewer #3: The paper needs to be re-written for certain sections as it is not very clear to the reader at the first instance. Also the proposed model needs to be reconsidered as to how much does this statistical model help in understanding drug resistance with respect to other aspects apart from epigenetic instability. Moreover, citing other published articles on the same lines in the manuscript will be appreciated. The idea is good but the authors need to answer certain questions: 1) Is the concept novel or are their other papers that have been published on the same lines been published earlier? 2) How will epigenetic instability in terms of dysregulation alter the drug resistance mechanism? 3) Have the author thought of doing certain experiments to justify their results in terms of any in vitro drug resistance model and epigenetic instability.

Reviewer #4: Authors focus on the most important problem that affects anticancer therapy which is resistance. In this manuscript, the authors offer a new approach focused on causing epigenetic instability to cancer cells. Authors developed a mathematical model of glioblastoma cells that can transition from one state to another based on cell stresses, such as drug therapy and epigenetic plasticity. According to this model, when epigenetically instability was allowed, the number of resistant cells decreased.

Introduction should be improved:

It is important to give information about the epigenetic modifications that authors will introduce in their model, arguing why they decided to introduce histone post-translational modifications instead of other types of epigenetic modifications (DNA methylation, DNA acetylation, etc.).

Authors should also give information about the stochastic cell state models considering permissive or restrictive states.

Discussion should be improved:

It is also important to discuss up to what point the introduction of histone post-translational modifications will not affect health tissue, or which could be the effect of increasing epigenetic instability in health tissue. It would be interesting that authors present a model where epigenetic instability is introduced and increased in normal cells and to report what happens in these cells.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #4: No: Authors did not introduce in the manuscript the statement of data availability.

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Dear Dr. Gallo,

We are pleased to inform you that your manuscript 'Epigenetic instability may alter cell state transitions and anticancer drug resistance' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

Douglas Lauffenburger

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed my comments.

Reviewer #3: The revisions have been properly addressed and the necessary changes have been made accordingly. Moreover, one suggestion that I would like to give the authors is to back up their model with relevant experimental work that can be published later.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No