PONE-D-19-32944

Predicting cancer origins with a DNA methylation-based deep neural network model

PLOS ONE

Dear Dr Xu,

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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

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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

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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: Authors should make a case study which can use the method. For example, CTC methylation information could be used for showing the usefulness of the application. One of the example, CTC was used (PMID: 28389380).

Reviewer #2: General:

The authors proposed a deep neural network-based model to predict origin of cancers using DNA methylation data available in TCGA repository. They divided the dataset into train. development and test sets according to 60:20:20 splits. Other than using 10-fold cross-validation, they also used an independent set of data available in GEO database.

Strength:

• Author clearly mentioned that how cancer site classification study is useful

• Idea of model development and independent validation is good

• Prediction of metastatic samples provides another level of validation

Weakness:

• Major drawback of this model is that it cannot tell which features are responsible for classification or related to the origin of cancers.

• Though the DNN model configuration is mentioned, the author did not specify which version of DNN is used, for example MLP, Autoencoder, etc.

• Design of DNN architecture was not explained clearly. How the number of hidden layers and number of nodes in each hidden layer are decided?

• Feature selection is the key component of this study, which was not explained clearly. Given the information provided, it would be difficult to reproduce the results

• There is no section with “Conclusion.”

Minor Comments:

• Line - 61: “All these molecular platforms have shown better performance in identifying tissue of origin as compared to pathology-based methods.” � This sentence does not make sense with the context of material presented in this section. Needs rephrasing.

• Line-62: “However, gene expression- or microRNA-bases classifiers need to handle RNA that is unstable and less convenient in clinic settings.” �This statement requires some references to support. There is also a spelling mistake, should be … microRNA-based …..

• Line – 114: “Then we used the Tukey honest test to remove….” � Little explanation of “Tukey honest test” and how it works would improve the quality of the paper.

• Line-262: “High performance of our DNA methylation based DNN model may provide an opportunity in this scenario when pathology-based approach fails.” � But it does not provide which DNA methylation sites are related to producing high performance.

Major Comments:

• Need to add a section called “Conclusion”

• Feature Selection: Line-110: “To make the model with good compatibility and also reduce the dimensionality, we firstly reduced CpG sites to 27K for 450K derived samples.” � Concern-1: The authors did not mention what technique was used to reduce the dimension from 450K to 27K. Concern-2: After reducing features 450K to 27K, there will be two sets of 27K features. It is not clear – What do they do with these two sets? Do they combine? Step-by-step procedure to get to the final set of features, 10,360 CpG sites, needs to be stated/explained clearly so that the results can be reproduced.

• Line-127: “In addition, three hyperparameters (learning rate, number of hidden layer and hidden layer unit) were optimized to obtain best performance according to development set performance.” � More details are needed on how each of these hyperparameters are obtained so that the results can be reproduced.

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Reviewer #1: Yes: Edwin Wang

Reviewer #2: No

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Predicting cancer origins with a DNA methylation-based deep neural network model

PONE-D-19-32944R1

Dear Dr. Xu,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Serdar Bozdag, 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 #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Yes

Reviewer #3: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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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 #2: (No Response)

Reviewer #3: (No Response)

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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 #2: (No Response)

Reviewer #3: (No Response)

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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 #2: (No Response)

Reviewer #3: (No Response)

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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 #2: No

Reviewer #3: No