Dear Dr. Pulkkinen,

Thank you very much for submitting your manuscript "Multiobjective Optimization Identifies Cancer-Selective Combination Therapies" 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.

The reviewers appreciate the importance of optimizing drug combinations and providing greater quantitative insight in this area. However, they raise major concerns regarding the core equations used to establish the drugs' therapeutic and nonselective effects and what the regression model is. Additionally, both reviewers have questions about validating the model predictions.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[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).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

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,

Stacey Finley, Ph.D.

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

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: Drug combination is critical for overcoming drug resistance, especially in cancer treatment. In this study, the authors proposed to predict drug combination for specific cancer cells using the multi-objective optimization method. It is a good exploratory design. Some major comments are:

Majors:

Equation (3) have no biological meaning, and would be wrong for most drug combinations. Also it is not a regression model (as there is no parameter, and just simply summarization). Also for the question (4).

Whereas the whole model is built based on this objective function (or minor changes), thus the design of the model might not be reasonable.

Another limitation/concern is the 'pure' data driven, which means a lot of drug combinations should be tested first before the prediction. No new knowledge can be mined from this model (considering the biased/ too simple objective function). Though some predictions are validated, this model is still kind of limited considering the definition of the objective functions (which is unknown for many synergistic or non-effective drug combinations).

Minors:

Drug i at concentration ci is not correct (for each drug there are multiple doses). The same notation error for Ei(ci,cj, l).

Reviewer #2: Reproducibility Report has been uploaded as an attachment.

Reviewer #3: This manuscript explores how non-selective synergistic drug combinations can be identified for a given cancer cell line using NCI-Almanac data. This is posed as a two-objective optimisation problem aiming at identifying combinations that maximise both therapeutic effect and selectivity for the intended cancer cell line. The study focuses on a BRAFV600E-mutant melanoma cell line (MALME-3M), for which they find several optimal solutions (vemurafenib monotherapy and some of its combinations). Two three-drug combinations (triplets) were tested in vitro and the compromises between their non-selectivity and therapeutic effect discussed.

This is definitely an interesting study. I feel however that it has to be polished before publication.

This study employs non-selectivity (essentially which proportion of NCI60 cell lines is inhibited by the drug or combination) as a surrogate of drug safety (toxicity). This assumption is appealing because enables the analysis without requiring further data (e.g. activity of the drug/s on a non-tumoural cell line). Ideally, this plausible hypothesis should be supported by some form of validation. Looking at adverse effects arising from clinical practice (drugs.com) and Figure 1C, NCI60-selective vemurafenib has a higher number of important adverse effects than NCI60-promiscuous mythramycin, and thus the assumption does not seem to hold in this case. The other aspect to discuss is related to intra-tumour heterogeneity, which is minimal in cancer cell lines (one clone at low passages) but typically high in primary tumours (clones with different drug sensitivies). If we see each melanoma cell line as a tumour clone, it is not unreasonable to think that in vitro non-selective drugs might be able to delay the emergence of acquired resistance more than in vitro selected drugs due to the former neutralising a larger proportion of the tumour clones. In that case, non-selectivity would be positive rather than negative. I think that the study would benefit from discussing the limitations of this assumption.

In Figure 1, all 104 NCI-Almanac are being used, which come from three screening centres (mostly FG and FF). For a given drug-cell line pair, the single-drug activities from FF exhibit much higher variability than those from FG (https://doi.org/10.3389/fchem.2019.00509). Were all single-drug activities for a given cell line considered in calculating Qmin? If not, how were extreme values discarded? The authors should discuss how Figure 1C changes depending on the level of variability in single-drug activities.

On the other hand, pages 3 to 6 (up to ‘Application to melanoma’) present a sound methodological development, but should be made clearer and better organised. Adding subsections such as Monotherapy, Two-drug Combinations and Higher-order Combinations would be helpful, each with their specific therapeutic effect and non-selectivity equations.

The two-objective optimization problem is solved for monotherapies and two-drug combinations by inspecting NCI-Almanac data (a reference to the epsilon-constrained method is required). It is not clear either how this is done for the three-drug combinations, as such data is not available at NCI-Almanac. In page 3, a regression model is mentioned, but which quantity is predicted and how? (algorithm, data, features). Also, I am missing the definition of M in equation 6. M=2 seem to cover monotherapies and two-drug combinations, that is N=1 and N=2 in equation 3, but why?

Page 7 (last paragraph) – very interesting result to discuss: vemurafenib + gefitinib’s synergy depends on their concentration to the extent of being antagonistic for some concentration pairs. Could the authors discuss which of these in vitro concentrations would be relevant in vivo? That is, any way to anticipate whether synergy or antagonism in vivo given these in vitro results?

The conclusions are summarised in the last paragraph of the discussion. I am missing a summary of which two-objective-optimal monotherapies, two-drug combinations and higher-order combinations have been identified in NCI-Almanac and, in the latter case, confirmed in vitro by the authors.

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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: None

Reviewer #2: None

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.

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Reviewer #1: No

Reviewer #2: Yes: Anand K Rampadarath

Reviewer #3: Yes: Pedro J. Ballester

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methods

Attachments

Attachment

Submitted filename: Reproducible_report_PCOMPBIOL_D_20_00963.pdf

Dear Dr. Pulkkinen,

Thank you very much for submitting your manuscript "Multiobjective Optimization Identifies Cancer-Selective Combination Therapies" 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. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Overall, the reviewers agree that the updated manuscript is much improved. One issue remains, related to the limitations of the work, including the model's ability to predict new combination treatments. This can be addressed in a revised manuscript.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. 

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[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).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. 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,

Stacey Finley, Ph.D.

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The revision adressed the comments, and is acceptable with minor revisions.

Minor comments:

1) it is better to discuss the potential limitations mentioned in the review.

2) It is better to discuss whether the model can predict new combinations (without combination experimental data), or select the 'best' combinations based on the screening data of drug combinations.

Reviewer #3: The authors have done an excellent job at addressing all my comments and I agree that the paper has improved very much as a result. I do not have any further comment.

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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: None

Reviewer #3: Yes

**********

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: Yes: Pedro J. Ballester

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Dear Dr. Pulkkinen,

We are pleased to inform you that your manuscript 'Multiobjective Optimization Identifies Cancer-Selective Combination Therapies' 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,

Stacey Finley, Ph.D.

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

PLOS Computational Biology

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