Dear Dr. Luo,

Thank you very much for submitting your manuscript "PESSA: A Shiny App for Pathway Enrichment Score-Based Survival Analysis in Cancer" 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.

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[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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Sincerely,

Elena Papaleo, PhD

Academic Editor

PLOS Computational Biology

Jason Haugh

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: Yang et al. presented PESSA, a shiny app for correlating pathway enrichment scores with survival outcomes. The team has calculated ssGSEA scores for the entire TCGA and 214 datasets from GEO, which covers 21 cancer types based on gene sets from MsigDB. The Shiny app provides an excellent interface for users to search pathways that might be associated with survival. The tool will be an excellent resource to perform high-level pan-cancer analysis. However, the current analysis neglects the heterogeneity of the patient cohort in each study, specifically related to histology, clinical grade, and treatment context, which can affect the interpretation of the results. There should be more effort to assess these covariates as additive effects and interactions within the systematic analysis via the CoxH model. Overall, the tool is of interest to the scientific community, but I would recommend the author to significantly expand its functionalities.

Major Comments.

1. The heterogeneity of the patient cohort needs to be included in the current resource. The patients need to be subsetted according to histology, clinical grade, and treatment context. I would also recommend incorporating these features as additive and interacting covariates in the CoxH model.

2. If Cox analysis is used, the author may include additional test statistics, such as the concordance index and chi-square test, to evaluate the proportion hazard assumption.

3. As the author states, the pathway tools might also be helpful in prioritizing gene features. It might be useful to perform a systematic screen for individual genes.

4. The provided MsigDB gene set is limited, and many of the immune-associated gene sets (also available to MsigDB) were not included. Given the significant interest in immunotherapy, the authors should also include results associated with these gene sets.

6. Since the current resource is designed for pan-cancer analysis, there should be an additional summary of pathways relevant in each histology, such as a) pathways implicated across squamous malignancies, b) pathways implicated in Herpes virus, c) pathways implicated in samples derived from metastatic events, d) pathways implicated in samples from pre or post immunotherapy treatment. There should be existing literature on the four studies, and it would be nice to provide examples in which PESSA was able to confirm or reject existing studies.

Minor comments:

1. The download feature is limited to the high-level summary with only 20-100 items that are viewable/downloadable at a time. I would recommend the author add the ability for the users to download the complete table.

2. Endpoints to the survival analysis should be defined, such as OS, PFS, DMFS, DFS, MFS, and BCR.

Reviewer #2: This manuscript describes a simple but useful tool to aid in analyzing gene set activation as a biomarker in cancer for prognosis. The tool is built as an R Shiny app that comes preloaded with many gene sets and expression data from a range of cancers. The app is easy to use and can provide statistical results and figures. Overall, I think the approach does have the potential to be of wide interest to cancer researchers. However, I do have some concerns.

Minor:

1. On line 63, there is a typo "820,2023"

2. On line 146, it is unclear what normalization was applied to arrays (which needs a citation) vs. sequencing data.

3. On line 174, it is not stated how the optimal cut-off is obtained.

4. There is no ability to control for confounding in models.

5. Gene sets are only given by name and there is no description, making them hard to use.

6. There is no ability to upload user data, which limites the utility of the tool.

Major:

7. The manuscript assumes that the overall method of using ssGSEA is useful compared to existing methods, of which there are many. However, it is not really established in either the background or the results.

8. Results from Cox proportional hazards models are given without the ability to check the assumptions. Making statistics easy does not make them correct.

9. In the app, for many gene sets, there are unreasonable HRs. For example, for C2CP - Biocarta Bad Pathway, in AML, with the IlluminaHiSeq platform, the HR is 8.16e+04 on the continuous Cox model. This is despite the fact that it is only 1.43 with the median cut-off. I suspect this is due to an issue with pre-processing the data, but there are other possibilities.

Reviewer #3: Benefits of Using PESSA

Gene Set Activation Levels

The authors argue that PESSA is a tool that distinguishes itself by focusing on gene set activation levels, offering a holistic view of pathway activities rather than individual gene expression levels. This approach can potentially provide more comprehensive insights into the functional status of biological pathways in the context of cancer.

The authors also state that PESSA shows robustness in pan-cancer analysis suggesting its effectiveness in handling diverse cancer types. The main feature is to analyze and compare survival outcomes across different cancer types, potentially revealing common or distinct pathway signatures.

Overall Considerations

PESSA may be particularly valuable for researchers interested in pathway-centric survival analysis across multiple cancer types, but KM Plotter, GEPIA, Prognoscan, and UCSC Xena offer a more extensive range of analyses, making them suitable for diverse research questions. The authors should be able to prove the need for a tool like PESSA and the benefit their tool provided against the specific functionalities and performances offered by each of the abovementioned tools KM Plotter, GEPIA, Prognoscan, and UCSC Xena.

In conclusion, the choice of genomic analysis tools depends on the specific goals and nature of the analyses researchers intend to conduct. PESSA's strengths in gene set activation and pancancer analysis are noteworthy, but authors must carefully consider the trade-offs in comparison to the broader functionalities offered by KM Plotter, GEPIA, Prognoscan, and UCSC Xena.

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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: No: Would recommend the author in providing the source code used to generate the result.

Reviewer #2: No: I don't see the code.

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure Files:

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To enhance the reproducibility of your results, we recommend that you deposit your 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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Luo,

Thank you very much for submitting your manuscript "PESSA: A Web Tool for Pathway Enrichment Score-Based Survival Analysis in Cancer" for consideration at PLOS Computational Biology. Your manuscript was reviewed by members of the editorial board and by two of the three previous reviewers. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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,

Jason M. Haugh

Section Editor

PLOS Computational Biology

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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:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors has addressed all my questions.

Reviewer #3: Based on Supplementary Table 4, it is apparent that UCSC Xena provides more advanced analytical features compared to PESSA. While both platforms are user-friendly, UCSC Xena is distinguished by its extensive functionality and depth of information.

Summary endpoints from Supp.Table 4.

UCSC Xena: It aggregates data from over 1500 datasets covering 50+ cancer types, with nearly 85,000 samples. This extensive dataset coverage encompasses various genomic and clinical data types, providing researchers with a wealth of information for survival analysis.

UCSC Xena:

Supports Cox analysis, enabling researchers to assess the relationship between variables and survival outcomes.

Provides subgroup analysis functionality, allowing for the exploration of survival differences based on factors such as tumor grading, treatment measures, and tumor histology.

PESSA: has 233 datasets covering 51 cancer types, with over 44,000 samples. While still significant, it falls short in terms of dataset size compared to UCSC Xena.

Does not provide subgroup analysis based on tumor grading, treatment measures, and tumor histology, which may hinder the exploration of heterogeneity in survival outcomes.

The authors should highlight more on how their software offers a more thorough analysis of the different available online datasets, highlighting its advantages over UCSC Xena.

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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: No: The team should consider making the Shiny app source code available on zenodo prior to publication. This will ensure continuity of their online service when the shiny app is no longer available.

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 #3: No

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, we recommend that you deposit your 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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

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.

Dear Dr. Luo,

We are pleased to inform you that your manuscript 'PESSA: A Web Tool for Pathway Enrichment Score-Based Survival Analysis in Cancer' 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.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Jason M. Haugh

Section Editor

PLOS Computational Biology

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