PONE-D-21-14217Selecting predictive biomarkers from genomic dataPLOS ONE

Dear Dr. Frommlet,

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“PS and FK were supported by the European Union’s 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552, co-financed by the Polish Ministry of Science and Higher Education under Grant Agreement 2932/7.PR/2013/2. MB gratefully gratefully acknowledges the support by the grant Nr 2016/23/B/ST1/00454 of the Polish National Center of Science”

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Reviewers' comments:

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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

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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

Reviewer #3: Yes

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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

Reviewer #3: 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: The paper is beyond my competence in mathematical statistics, but it seems to be of very good quality. It is well written and the methods are advance. To the best of my knowledge the manuscript is satisfactory, but a specialist in mathematical statistics should do a final assessment.

Reviewer #2: Summary:

This paper focuses on the selection of prognostic (main effect) and predictive (interaction with treatment) biomarkers from a large number of candidate SNPs. SLOBE and mBIC2 are compared with adaptive LASSO, and have good performance in the simulation in terms of biomarker selection and subgroup identification. The paper is nicely written with a wide range of simulation scenarios.

Major comments:

1. It is assumed all the SNPs are independent, while in reality the multicollinearity of neighboring SNPs makes them highly correlated. The Bonferroni correction is overly conservative in association studies in which the tests are correlated. Adaptive LASSO and SLOBE have advantages with correlated covariates. I am wondering what’s the performance of the proposed methods with correlated SNPs.

2. In the simulation, half of the causal variants were prognostic and the other half predictive. So there is no overlap in the biomarkers for main effect and interaction with dose. In reality a biomarker can be both prognostic and predictive. I am wondering if the proposed methods can identify both effect and what’s the performance.

3. For biomarker identification, Fig 1 doesn’t distinguish between prognostic and predictive biomarkers. However, predictive biomarkers play a role in the personalized treatment selection and are of interest in clinical settings. It would be good to know the selection of predictive biomarkers.

4. From part 1 results, mBIC2 has best performance under different scenarios and is adopted in part 2. Method 1, 2, 2a tested treatment effect in whole population, method 3 & 4 tested treatment effect in predicted responders R(X)>0, and method 5 & 6 tested treatment effect overall or in subgroup. They are different hypotheses and methods within each hypothesis are comparable. It is confusing to compare method 2 vs method 4 and put all methods in one figure.

5. When test treatment effect overall or in subgroup, each test is performed at α=0.025 with Bonferroni correction. Those two tests are correlated and Bonferroni correction might be too conservative. For the adaptive clinical trial as in the discussion, different α-spending functions can be used and should be discussed.

6. SLOBE and mBIC2 are compared with adaptive LASSO, it would be helpful to provide the equation of adaptive LASSO too and compare with equation (3) SLOPE.

Minor comments:

The resolution of the figures needs to be improved.

Reviewer #3: This paper needs to be rewritten as many terms are not introduced at all. For example, I am not sure what are ‘prognostic index’, ‘Treatment Efficacy’, etc. In addition, no real-world genomic data were used in this paper but only some simulated SNPs. This paper will be greatly improved if the authors can apply the machine learning models directly to human genomics data. There are many published human genomic datasets in the GEO database, the authors can take advantage of them.

The content of this paper seems to overlap with the authors’ other paper (Frommlet and Bogdan, 2020) titled ‘Identifying important predictors in large data bases - multiple testing and model selection’.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Selecting predictive biomarkers from genomic data

PONE-D-21-14217R1

Dear Dr. Frommlet,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Fabio Rapallo, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Please check the notation in Equation 4 (see the reviewer comment) and do a final check on the English language before sending your files to the editorial process.

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: N/A

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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: No

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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 #2: The additional simulation of real SNP data is very informative and the definition of true positive detections in the highly correlated markers is important. The relaxed definition of TP is more consistent than strict definition with correlated SNPs. There is no much difference whether the SNP is prognostic, predictive, or both. mBIC2 has slightly better performance than SLOBE with faster computation time. Thank you for fully corresponding to my questions.

Equation 4, should the beta_j be b_j comparing to equation 3?

Reviewer #3: I would like to thank the authors for addressing my comments. Still, no real-world genomic data were used in this paper but only some simulated SNPs.

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7. 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 #2: No

Reviewer #3: No