Dear PhD Lu,

Thank you very much for submitting your manuscript "Construct Prognostic Models of Multiple Myeloma with Pathway Information Incorporated" 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 pathway-based approach was found interesting by all the reviewers, while pointing out shortcomings that, if addressed, may significantly strengthen the paper. Please address these suggestions as best as you can.

Reviewer 2 noted that a discussion related to the computational complexity of the presented model. and how it compares to existing models, considering its accuracy, is missing. This is an important point to address, as it has a direct impact on clinical translation, as noted also by Reviewer 3.

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).

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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,

Gianfilippo Coppola

Guest Editor

PLOS Computational Biology

Mark Alber

Section 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 manuscript provides insights into the use of pathway information for developing prognostic models in Multiple Myeloma. However, I have three minor points for revision:

1) Clarity on Batch Correction Application: Please specify where batch correction was applied in your analyses. Your manuscript mentions adjusting batch effects for the training data of competitor models, when applicable (Page 15, Line 15). Clarifying exactly where batch correction was applied (and where it was not) would greatly aid in understanding the methodological rigor and ensuring the robustness of your comparative analysis.

2) Pathway Scoring Justification: Could you provide a more detailed motivation for your choice of pathway scoring methods? While the comparison with gene-based models is clear, understanding the specific rationale behind selecting these pathway scoring methods would enhance the context and depth of your study.

3) The sentence in Page 5, Line 9 seems to have grammatical issues

Best regards

Reviewer #2: In this paper, the authors explore novel prognostic modeling approaches for Multiple Myeloma (MM), focusing on the utilization of pathway information rather than conventional gene-level data. Two primary strategies are assessed: the pathway score method and group lasso with pathway information. The study tests various methods for calculating pathway scores and integrates this information into predictive models, using microarray data (GSE136324) for MM. The best-performing model, based on group lasso incorporating Vax pathway information (Vax(grp)), demonstrates superior predictive accuracy compared to gene-based models and previously published approaches, both in internal and external validations. Despite the manuscript presenting high levels of accuracy, a good level of English and providing a valuable contribution to current research, it needs some improvements:

- Improve the introductory section of the manuscript by highlighting and listing both the “contributions'” points of this research and the work's limitations;

- While the study presents promising results, the generalizability of the findings to other types of hematological diseases remains to be explored. Please discuss them in the possible limitations of the work or as possible future tasks;

- Considerating the complexity of Pathway Analysis, please discuss how the complexity of these models may affect their translation from research to clinical practice, considering the computational load that may be placed on clinical systems by offering potential solutions or areas for future development that may help reduce the computational burden, such as algorithmic improvements or the use of more efficient computing frameworks. So, explain better the complexity of the proposed framework, which might make it difficult to implement in real-world scenarios;

- Please to include specific statistical tests to quantify the effectiveness of the proposed method and simulation;

- This study could benefit from comparisons with the latest deep learning or machine learning techniques in prognostic modeling. Please to discuss them.

- Given the complexity of pathway-based models, the study could delve deeper into the interpretability of these models, which is crucial for clinical acceptance and application. How do the authors overcome and discuss that difficulty?

- The transition from a research setting to clinical implementation requires further validation, especially in terms of how these models might influence therapeutic decision-making in real-world scenarios. Please discuss that;

- The "Conclusions" section should be expanded to offer a more comprehensive view and analytical perspective on future prospects by considering and adding a comparative analysis.

Reviewer #3: The paper from Wang et al is of interest and describe a new method to infer and interpret the pathway analysis and how they can explain cancer development and biology.

The paper is well written and despite the high complexity of the applied model it is easy to understand.

I have only a concern based on the idea that this model could be applied for decision making and also could outperformed compared with the previous models.

This could be right from a technical point f view, which is the scope of the work, but for really say that outperformed it would be nice if the authors could provide some survival predictions based on they new model.

The knowledge of how a cancer work (deregulated pathways) is absolutely more important rather than the knowledge of a some gene deregulation. Nevertheless, the application of other scores, even if gene-based, has clinical and prognostic impact. Is also the case for the author's model?

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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 #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Giovanni Cicceri

Reviewer #3: No

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Dear PhD Lu,

We are pleased to inform you that your manuscript 'Construct Prognostic Models of Multiple Myeloma with Pathway Information Incorporated' 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,

Gianfilippo Coppola

Guest Editor

PLOS Computational Biology

Marc Birtwistle

Section 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: Dear Authors,

Thank you for your thorough responses and the revisions made to the manuscript. After reviewing the updated version, I am pleased to see that my previous comments and concerns have been addressed satisfactorily.

I have no further comments at this time and believe the manuscript has been improved.

Best regards

Reviewer #2: The manuscript has been revised to enhance its clarity and organization of findings. All suggested sections have been refined to elevate the paper's quality. Additionally, the discussion has been updated and the conclusions strengthened.

Reviewer #3: I thank the authors to have substantially accomplished to my previous comments.

Nevertheless a multivariate analysis to demonstrate a clinical significance and a real ability to overcoming other risk stratification methods would still be useful.

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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 #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.

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 #2: Yes: Giovanni Cicceri

Reviewer #3: Yes: Matteo Claudio Da Viá