PGENETICS-D-25-01035

Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer

PLOS Genetics

Dear Dr. Li,

Thank you for submitting your manuscript to PLOS Genetics. After careful consideration, we feel that it has merit but does not fully meet PLOS Genetics'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.

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We look forward to receiving your revised manuscript.

Kind regards,

Xiang Zhou, Ph.D.

Academic Editor

PLOS Genetics

Xiaofeng Zhu

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

Additional Editor Comments:

Please note that Reviewer #1's detailed comments are included in the attached file.

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At this stage, the following Authors/Authors require contributions: Qing Li, Deshan Perera, Zhishan Chen, Wanqing Wen, Zilong Zhang, Dinghao Wang, Jun Yan, Xiao-Ou Shu, Wei Zheng, Xingyi Guo, and Quan Long. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: Please see the attachment word file.

Reviewer #2: Li, et al. adapt the Enformer model for variant effect prediction using transfer learning in order to focus on transcription factor tracks from breast and prostate cancer cell lines. They then evaluate the predicted variant effects from this new model compared to the original Enformer model.

Major comments

1) Clarify what training data was used.

The authors state:

“For breast cancer, we obtained tracks for 18 TFs … yielding a total of 275 tracks”

“For prostate cancer, we selected 17 TFs … totaling 357 tracks”

The Supplementary Tables indicate multiple cell lines were used for each TF, but it looks like many of the tracks a biological or technical replicates. How many biologically distinct tracks were used and how were replicates handled?

In Figure 2, the performance often varies widely across many tracks for the same TF. Is this biological or technical?

2) Demonstrate contribution of transfer learning

The authors seek to demonstrate the value of adapting a pretrained Enformer model using transfer learning. While they demonstrate that training on their specific cancer tracks is useful, they don’t directly demonstrate the benefit of transfer learning compared to training a new model without the pretraining step. Does using transfer learning instead of a new set of parameters just reduce training time or meaningfully increase predictive performance?

Reviewer #3: This paper adapted and fine-tuned existing deep learning tool Enformer to breast and prostate cancer using 275 and 357 tissue-specific transcription factor (TF) ChIP–seq tracks. Further application of the fine-tuned Enformer include tissue-specific prioritization of variants using these variants for improving TWAS. It is claimed the proposed approach enhances regulatory variant prioritization and improve susceptibility gene discovery in a tissue-specific manner. I have some comments as follows,

1. As so many TFs are involved in calculating the tCRAs for variant prioritization. However, the formula on line 400 only indicate one variant and one TF. Please clarify how the variant score is calculated considering so many TFs are collected and for each TF, there are multiple biological replicates (tracks). Please formularize these in a more rigorous way.

2. More formula for loss function for TF-breast and TF-prostate are needed. Only mention “The model was trained to predict ChIP-seq signals using a Poisson negative log-likelihood loss function is not enough.” A follow-up question is, as processed ChIP-seq signals are not count data, why use Poisson distribution?

3. One key concern is the prediction performance is very low (Fig2) but the downstream analysis of variant effect is based on the difference of accurate prediction between ref and alt, which make the variant effect analysis less reliable.

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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 Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes: Yichao Zhou

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: Reviewer_comments.docx

PGENETICS-D-25-01035R1

Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer

PLOS Genetics

Dear Dr. Li,

Thank you for submitting your manuscript to PLOS Genetics. After careful consideration, we feel that it has merit but does not fully meet PLOS Genetics'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.

Please submit your revised manuscript by Apr 17 2026 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 plosgenetics@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgenetics/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to any formatting updates and technical items listed in the 'Journal Requirements' section below.

* 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, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Xiang Zhou, Ph.D.

Academic Editor

PLOS Genetics

Xiaofeng Zhu

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Qing Li, Dinghao Wang, Zilong Zhang, Deshan Perera, Zhishan Chen, Wanqing Wen, M. Ethan MacDonald, Weijia Cai, Jun Yan, Xiao-Ou Shu, Wei Zheng, Xingyi Guo, and Quan Long. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

The list of CRediT author contributions may be found here: https://journals.plos.org/plosgenetics/s/authorship#loc-author-contributions

2) We notice that your supplementary Figures are included in the manuscript file. Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list.

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3) If any authors received a salary from any of your funders, please state which authors and which funders..

If you did not receive any funding for this study, please simply state: u201cThe authors received no specific funding for this work.u201d

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have addressed all my comments and suggestions. I thank them for their valuable contribution to the statistical genetics and broader computational biology communities.

Reviewer #2: I am satisfied with the revised version

Reviewer #3: For comment3, the response from the reviewers "as average correlations around ~0.4

are well within the expected range for TF ChIP-seq prediction and align with previously

published deep-learning models." However, why using TF ChIP-seq for prediction is the first question of concern. If prediction only ~0.4, how to expect predicted TF under ref and alt alleles are confident, and how the difference is confident to evaluate variant effect? As ChIP-seq is usually measured by binding sites (binary), not sure if what is predicted ChIP-seq track in a continuous manner here? Moreover, other deep learning models report on binary peak prediction for evaluating variant effect. More explanation and investigation towards this direction would be helpful.

**********

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 Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: None

Reviewer #3: None

**********

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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: 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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While revising your submission, we strongly recommend that you use PLOS’s NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.

After uploading your figures to PLOS’s NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete. If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above. When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies 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. 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 Li,

We are pleased to inform you that your manuscript entitled "Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Yours sincerely,

Xiang Zhou, Ph.D.

Academic Editor

PLOS Genetics

Xiaofeng Zhu

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

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----------------------------------------------------

Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #3: None

**********

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 Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #3: None

**********

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.

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Reviewer #3: No

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