Prediction of PIK3CA mutations from cancer gene expression data

Jun Kang; Ahwon Lee; Youn Soo Lee

doi:10.1371/journal.pone.0241514

Peer Review History

Original SubmissionJuly 21, 2020
26 Aug 2020 Decision Letter - Nandini Dey, Editor PONE-D-20-22669 Prediction of PIK3CA mutations from cancer gene expression data PLOS ONE Dear Dr. Lee, 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. Please respond to the comments item-by-item to satisfy the reviewers' concerns. Please carefully edit the MS for English corrections and typos. Please submit your revised manuscript by Oct 10 2020 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 plosone@plos.org. 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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: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know 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 is a regulated regression analysis to find out the most significant variable(s) and that is kept in the final model. This is a very concise article and lack of detail methodology. It has been known for quite some times that PIK3CA GOF mutation is very common (in fact next to TP53) in solid tumors. It is also known that PIK3CA is very much related to PTEN and IGF1R signaling. The finding is not new and the rationale for this article is not very clear. More importantly, this type of article is not suitable for PLOS ONE audience. Authors may consider to some bio-informatics or bio-statistics journal. Reviewer #2: The authors present a succinct study on the prediction of PIK3CA mutations from gene expression data. This study applies an elastic net penalized logistic regression classifier to the cancer genome atlas (TCGA) pan-cancer gene expression dataset, a method that was previously established for detecting RAS pathway activation. The methods used and the results presented in the figures appear to be appropriate for the work performed. Both the AUROC and AUPRC demonstrate predictive performance well above baseline. Limitations of the approach used were also appropriately discussed. It may be questionable why PIK3CA mutation prediction from mRNA expression is useful when targeted sequencing panels can assay these mutations directly, but it has been proposed elsewhere that clinical transcriptomics may add important functional or phenotypic information. Overall this work demonstrates that machine learning approaches can predict PIK3CA mutation status from gene expression data with a reasonably good level of performance. ****** 6. 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Revision 1
9 Sep 2020 Author Response Reviewer #1: Comment 1 This is a regulated regression analysis to find out the most significant variable(s) and that is kept in the final model. This is a very concise article and lack of detail methodology. Response Thank you for your constructive feedback considering the lack of detailed methodology. TCGA is a widely used public data of cancer genomics. The detail of the TCGA pan-cancer data is described in the reference. For the method of prediction modeling, we tried to follow guidelines for developing and reporting machine learning predictive models in biomedical research. We add a supplementary figure to help understand hyperparameter tuning. Luo, Wei, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, et al. “Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.” Journal of Medical Internet Research 18, no. 12 (2016): e323. https://doi.org/10.2196/jmir.5870. Comment 2 It has been known for quite some times that PIK3CA GOF mutation is very common (in fact next to TP53) in solid tumors. It is also known that PIK3CA is very much related to PTEN and IGF1R signaling. The finding is not new and the rationale for this article is not very clear. Response Thank you for your opinion. Our study aims to build the PIK3CA mutation prediction model not to search for important variables. Because the penalized logistic regression model is highly interpretable, we were able to find significant variables like IGF1R and PTEN. But this findings of significant variables is not the primary purpose of this study. The purpose of this study is to investigate the PIK3CA mutation prediction performance of machine learning models. The purpose of the study is further described in the manuscript. Comment 3 More importantly, this type of article is not suitable for PLOS ONE audience. Authors may consider to some bio-informatics or bio-statistics journal. Response Thank you for your suggestion. Since machine learning modeling is complex and has begun to be widely used relatively recently, the audience may lack an understanding of detailed methods. However, our study used a widely used data set (TCGA) and modeling framework (R tidymodels package). We believe our research will benefit audiences interested in applying machine learning to patient care. We also believe that publishers targeting a broad audience are publishing predictive model studies using machine learning. Reviewer #2: Comment 1 The authors present a succinct study on the prediction of PIK3CA mutations from gene expression data. This study applies an elastic net penalized logistic regression classifier to the cancer genome atlas (TCGA) pan-cancer gene expression dataset, a method that was previously established for detecting RAS pathway activation. The methods used and the results presented in the figures appear to be appropriate for the work performed. Both the AUROC and AUPRC demonstrate predictive performance well above baseline. Limitations of the approach used were also appropriately discussed. Response Thank you for your opinion. Comment 2 It may be questionable why PIK3CA mutation prediction from mRNA expression is useful when targeted sequencing panels can assay these mutations directly, but it has been proposed elsewhere that clinical transcriptomics may add important functional or phenotypic information. Response As you pointed out, the clinical utility of PIK3CA mutation prediction from mRNA expression is unclear because most direct genomic tests are more specific and sensitive than predictive models. Our prediction model is not an application that is immediately applicable to a cancer patient for the detection of PIK3CA mutation. It is not known how it will be used, but finding out the mutation prediction performance using gene expression data could play a role in advancing machine learning to be helpful in patient treatment. We discussed further the limitations of this study in the manuscript. Comment 3 Overall this work demonstrates that machine learning approaches can predict PIK3CA mutation status from gene expression data with a reasonably good level of performance. Response Thank you for your opinion. Attachments Attachment Submitted filename: Response-to-Reviewers.pdf https://doi.org/10.1371/journal.pone.0241514.r002
16 Oct 2020 Decision Letter - Nandini Dey, Editor Prediction of PIK3CA mutations from cancer gene expression data PONE-D-20-22669R1 Dear Dr. Lee, 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, Nandini Dey, MS., Ph.D Academic Editor PLOS ONE Additional Editor Comments (optional): 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 #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 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 #1: No Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: Yes ****** 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 #1: Yes Reviewer #2: Yes ****** 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 #1: Yes Reviewer #2: Yes ****** 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 #1: As a reviewer I am not satisfied the overall approach of the MS and I am also not satisfied after the revision too. Reviewer #2: In my original review I questioned the utility of PIK3CA mutation prediction from mRNA data. The authors have added text to the discussion section that has addressed this comment appropriately. ****** 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 #1: No Reviewer #2: No https://doi.org/10.1371/journal.pone.0241514.r003
Formally Accepted
23 Oct 2020 Acceptance Letter - Nandini Dey, Editor PONE-D-20-22669R1 Prediction of PIK3CA mutations from cancer gene expression data Dear Dr. Lee: 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. Nandini Dey Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0241514.r004

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