PONE-D-24-40566Optimal Adaptive Cancer Therapy Based on Evolutionary Game TheoryPLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Title: Optimal Adaptive Cancer Therapy Based on Evolutionary Game Theory

Summary:

Authors incorporate pharmacokinetics into a cancer evolutionary game theory model of adaptive cancer therapy. The model framework builds upon a previous game theoretic model of adaptive therapy but goes beyond previous work by 1) incorporating drug decay dynamics and 2) solving an optimal control problem. The work is thus novel and a very useful and welcome addition to the literature in this field.

Major comments

1. Firstly, can J in equation 10 be weighted between the two terms? I’m concerned that the first term is by definition between 0 and 1, while the second term can be infinite as time tF goes to infinity – does this essentially negate the effect of the first term in the control problem?

2. The most interesting part of the manuscript to me is found in lines 308-310 where the optimal treatment schedule maintains the same value for m1 m2 m3 and m4. This is remarkably reminiscent to the “dose-skipping” adaptive strategy proposed in the original series of papers. In many follow ups, the goal has been to compare dose-skipping versus dose modulation adaptive strategies. I think this should be noted in the discussion, with a comment on how the optimal strategy is a dose-skipping one. For example see this paper: https://www.cell.com/cell-systems/abstract/S2405-4712(24)00118-2 (there are many other examples as well).

Minor comments:

1. Missing reference in line 246

2. I am curious to know what “environmental factors” referenced in line 247 means. It’s not important for the paper as long as the payoff matrix criteria in lines 80 - 82 are satisfied, so maybe this comment can be removed.

Reviewer #2: Review report on Optimal Adaptive Cancer Therapy Based on Evolutionary Game Theory by Zhiging Li, Xuewen Tan and Yangtao Yu

In this paper, authors adopted an evolutionary game theory approach combined with optimal control theory to model cancer growth and chemotherapeutic treatment mechanism. Also incorporated pharmacokinetics of the drug into the modeling frame work. They proposed an optimal adaptive therapy strategy. Existence of the solution of optimal control problem is derived and characterization of it using Pontryagin’s minimum principle is established. They conducted a numerical simulation to substantiate the theoretical proposal and compared with other standard approaches.

The theoretical derivation seems to be correct and from numerical simulation we can see some advantage of the proposed model compared to other models but not completely eradicate the tumor population. The concern of the reviewer is the following: Will game theory model can actually deal with practical cases as we need to deal with human beings? Game theory has applied to other applicable areas but we need to be little bit cautious while applying in human beings. Even other types of mathematical models have limitations while applying to real-time situations as mathematical models basically works based on assumptions. Since, cancer is a killer disease, no harm in studying a new approach for solving the problem and proposing treatment strategies. Any new approach in studying the problem should always welcome. In that perspective, although it has certain shortfalls, this reviewer would like to recommend the paper for publication.

Correction: The authors should mention the missing reference paper on page number 10, line 246 regarding the parameters used for simulation.

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Reviewer #1: No

Reviewer #2: No

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Optimal Adaptive Cancer Therapy Based on Evolutionary Game Theory

PONE-D-24-40566R1

Dear Dr. Li,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Nikos Kavallaris, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

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Reviewer #1: No

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