PONE-D-20-30580

An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

PLOS ONE

Dear Dr. Yankeelov,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In particular, the proposed experimental-computational approach is very interesting but the modelling assumptions need to be justified and detailed more. We would also like to encourage you to include more details on data availability.  

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We look forward to receiving your revised manuscript.

Kind regards,

Aurélie Carlier

Academic Editor

PLOS 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: Partly

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: N/A

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3. 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

Reviewer #2: No

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4. 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

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: This is an interesting paper focusing on fitting different models to the experimental data on the temporal change of the number of live and dead tumor cells under different initial glucose concentrations and seeding densities. However, I have number of concerns regarding the modelling, which I believe have to be addressed.

Major concerns:

1. Unreadable mathematical expressions: Practically all equations ([1]-[6]) in the manuscript are unreadable , as many squares and rectangles appear instead of signs +,-, and =, and possibly also brackets. As a consequence, the rationale behind those equations cannot be checked, which makes it impossible for me to give my full judgement of the models used. This needs to be corrected.

2. Unmotivated manipulation of death rates as opposed to growth rates: I do not understand why when you experimentally manipulate the glucose concentration which should contribute to faster proliferation, your models do not differ in their growth rate, but instead in their death rate. Please explain or switch to more standard interpretation where glucose contributes to faster proliferation of cells.

3. Unmotivated simplification of the model by Mendoza-Juez et al: It is unclear why the authors do not use 2 phenotypes as Mendoza-Juez et al. but instead assume that all cells use anaerobic glycolysis.

Is that inherent to the cell lines used? Is it 100% sure that no cells use oxidative phosphorylation?

If not, why isn't it better to estimate the subpopulations pf Mendoze-Juez as opposed to removing one type? (Here I note that my problem with this may be a consequence of inability to read the equations, as mentioned in 1., thus correction of that problem as asked in 1. may help me to understand the simplification better)

4. Strong dependence on the results on the percentage of training and validation data: The authors report that with the selected model, the achieved accuracy (defined as the fraction of

37 measured data that fell within the 95% confidence intervals of the predicted growth curves) is 77.2

38 ± 6.3% and 87.2 ± 5.1% for live BT-474 and MDA-MB-231 cells, respectively. This accuracy will surely depend on the percentage of data used for training/validation. Assuming that there are reasons for having the model form the authors use (see concerns I express in my previous points), could the authors explain why exactly 75% of data are used for training (and not more or less)? And if not, could they perform sensitivity analysis with respect to this percentage? Also, I do not perceive 77.2 % as particularly high. Could the authors comment why they think their model outperforms other, perhaps more standard models when fitting the data?

Minor concerns:

5. I would be more modest in statements like this: "To the best of our knowledge, this represents the first time these important cellular parameters have been quantified within a rigorous modeling framework." I know of many studies where cellular parameters are quantified with a modeling framework (eg Kaznatcheev et al, Nature Eco Evo 3(3), 2019; or works by Brady and Enderling....), and I am sure the authors know many, too.

6. It is interesting to see a paper on Warburg effect which does not mention a large body of literature of game theoretical models that attempt to explain/quantify it, for example Archetti at al, Epstein et al. I would recommend adding these references.

7. "The major source of energy for most cancer cells comes from a high rate of glycolysis

followed by lactate fermentation in the cytosol, even in the presence of sufficient oxygen—a

phenomenon known as the Warburg effect [1,2]." - Are you sure of the word "most" here?

Reviewer #2: It is not clear to me that the authors have made their data available

I would encourage them to include details explaining how people can access the data

and use it to perform their own analysis of the data.

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Attachments

Attachment

Submitted filename: Yankeelov_PONE_Nov2020.pdf

PONE-D-20-30580R1

An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

PLOS ONE

Dear Dr. Yankeelov,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that the reviewer comments were addressed and that the manuscript can be accepted pending the small suggested revisions below.

Please submit your revised manuscript by Aug 02 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Aurélie Carlier

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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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 #2: All comments have been addressed

Reviewer #3: (No Response)

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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 #2: Yes

Reviewer #3: Yes

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

Reviewer #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: Yes

**********

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 #2: The authors have done a good job of revising their manuscript in line with my comments and those of the other reviewer. As a result, I am happy to recommend it for publication.

I have only one minor request for further clarification: on page 15, the authors talk about 'reliance of the model parameters on the initial conditions' - what does this mean? that the model parameters are correlated with the initial conditions?

Reviewer #3: Overview:

The authors present a combined experimental and modeling approach to determine rates of proliferation, death, and glucose consumption for tumor cells, in varying glucose conditions. The paper achieves this goal, and the results are clearly presented. Though I did not review the first version, I have read the reviewers' comments and the authors' responses. I believe the authors have addressed the previous concerns.

Specific comments:

Minor points:

(1) the equations (still?) do not appear correctly, with operation signs (addition, multiplication) missing. I assume this will be corrected in the proofing stage.

(2) in the text, p values should be shown as numerical values (10^-4) rather than "1e-4".

Larger points:

(1) My primary major concern is in the presentation of the utility of the work. I would like to see some explanation about the impact and utility of the modeling work. In its current state, the paper does a great job in presenting the model and results. However, how others could take advantage of the work and use it to gain biological insight is missing. I feel this should be addressed in order to increase the impact of the work. How would the model do with a different cell line, say MCF7?

(2) The abstract, intro, and methods emphasize the family of models and three approaches for calibration, but all of that (interesting stuff!) is relegated to a single table in the supplemental info (S1 Table). I would prefer to see a bit more description of those steps in the main text, as the work raises some interesting questions. For example, why do the model that neglects the bystander effect or the model that neglects death due to low glucose NOT fit the data well? These mechanistic questions are of interest, especially when considering the previous point - the utility of the work. If this is not the focus, but rather the goal is to show results from a single model, the intro and methods should be revised.

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Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

PONE-D-20-30580R2

Dear Dr. Yankeelov,

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,

Aurélie Carlier

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: