Machine learning predicts cancer subtypes and progression from blood immune signatures

David A. Simon Davis; Sahngeun Mun; Julianne M. Smith; Dillon Hammill; Jessica Garrett; Katharine Gosling; Jason Price; Hany Elsaleh; Farhan M. Syed; Ines I. Atmosukarto; Benjamin J. C. Quah

doi:10.1371/journal.pone.0264631

Peer Review History

Original SubmissionNovember 4, 2021
5 Jan 2022 Decision Letter - Afsheen Raza, Editor PONE-D-21-35248Machine learning predicts cancer outcomes from blood immune signaturesPLOS ONE Dear Dr. Quah, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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.The reviewers have asked for adding the limitations of the study as well as some clarifications. Kindly address the reviewer comments and resubmit the manuscript by Feb 19 2022 11:59PM. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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: Yes ****** 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 ****** 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: This study is well designed and described. The major limitations include using (1) cell lines, (2) using only two cell lines, and (3) using mouse xenograft models. The authors allude to (1) in the Discussion (last paragraph), but all of these limitations should be further discussed. Additionally, the potential translation of this approach to clinical practice should be discussed in greater detail. What are the steps to get there and how would this workflow be applied to actual patients? Reviewer #2: This is an excellent study that establish a blood immune signature predicting cancer outcomes by machine learning. But I still have some advice about this study: 1.It’s better to valid the application of this signature in patients. 2.You can add some experiments to certify the result of this study in vivo and in vitro. ****** 6. 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: 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.] 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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0264631.r001
Revision 1
23 Jan 2022 Author Response Dear Editor, Please find below responses to the specific queries by the reviewers. We have modified the manuscript’s discussion to address all of these points with track changes. Reviewer #1: This study is well designed and described. The major limitations include using (1) cell lines, (2) using only two cell lines, and (3) using mouse xenograft models. The authors allude to (1) in the Discussion (last paragraph), but all of these limitations should be further discussed. Response: We have now extensively broadened the discussion to include a section (from pg 30) on the use of cell lines and murine cancer models, highlighting their limitations and benefits in cancer research. We have also highlighted the benefits of using more cell line models in this context. It should also be noted that xenografts were not used in this study as suggested by the reviewer, only syngeneic models, and so only syngeneic models were addressed in the new discussion section. Additionally, the potential translation of this approach to clinical practice should be discussed in greater detail. What are the steps to get there and how would this workflow be applied to actual patients? Response: We have included details of our clinical approach in the above-mentioned new section of the discussion (from pg 30). Reviewer #2: This is an excellent study that establish a blood immune signature predicting cancer outcomes by machine learning. But I still have some advice about this study: 1.It’s better to valid the application of this signature in patients. Response: We agree very much with this statement and it is indeed what we are currently working towards. The current report was to provide evidence in order to help gain funding and resources to pursue such a human study. Therefore, we feel that to include a human study in this particular report is beyond the scope of the current work. However, we have now included an extensive additional discussion section (as mentioned above from pg 30) highlighting the limitation of our current study and the need for validation in the clinic, and suggesting steps to pursue this. 2.You can add some experiments to certify the result of this study in vivo and in vitro. Response: While such a general statement is difficult to address specifically, we have modified our discussion section (on pg 26), in which we previously addressed the need for further experimentation to support for our findings, to include a brief detail of the experiments that could be done to support the numerous features we identified that may be involved in cancer development. We note that these experiments would be vast given the number of features involved and are out of the scope of the current study. Instead support our conclusions based on reference to independent peer-reviewed studies that support our findings and which we have summarised in the discussion. Response: Please note, as requested, we would like to notify the journal that five new references are included in the revised manuscript to help support the changes to the discussion. Finally, we have taken this opportunity to make some minor amendments to the title, abstract, introduction and discussion, as well as making some minor grammatical changes, which we believe improve the readability of the manuscript without changing the essence of these sections. If this goes beyond what is appropriate, we are happy to exclude these changes and can send this to you instead, please let us know if this is preferred. Thank you for considering our work for publication in PLOS ONE. Yours sincerely Dr Ben Quah Principal Investigator, I-Cube lab ACRF Department of Cancer Biology & Therapeutics The John Curtin School of Medical Research The Australian National University Attachments Attachment Submitted filename: Response to Reviewers_edits.docx https://doi.org/10.1371/journal.pone.0264631.r002
15 Feb 2022 Decision Letter - Afsheen Raza, Editor Machine learning predicts cancer subtypes and progression from blood immune signatures PONE-D-21-35248R1 Dear Dr. Quah, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Afsheen Raza, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0264631.r003
Formally Accepted
17 Feb 2022 Acceptance Letter - Afsheen Raza, Editor PONE-D-21-35248R1 Machine learning predicts cancer subtypes and progression from blood immune signatures Dear Dr. Quah: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Afsheen Raza Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0264631.r004

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