PONE-D-20-32564

A Computational Method for Drug Sensitivity Prediction of Cancer Cell Lines Based on Various Molecular Information

PLOS ONE

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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: Yes

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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: No

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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: The authors introduced the CDSML method to predict drug sensitivity of cancer cell lines. They compared it with two types of methods – classification and regression methods on the GDSC dataset. It shows the CDSML performs better than other methods when using the six metrics. This manuscript is well organized and easy to follow. It provides us an effective computational method for drug sensitivity prediction on cancer cell lines. I have the following comments.

Major:

1. In the objective function, it seems that you could solve this problem without dealing with missing values. Because in the first term in Eq.(8), one could ignore the drug-cell line pair for which B(i,j) value is missing.

2. Following the above question, could the author compare the predicted values for missing pairs and the imputing values by Eq.(1) to show the reasonability of the imputing method in the paper.

3. The idea of the method in this paper ”Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model” is very similar to the imputing way (Eq.(1)) for missing values, could you compare CDSML with it?

4. Regarding to the objective function, how to ensure the entries in the estimated matrix B range from 0 to 1? There is no constraint about the scale and sign for values of X(i) and Y(j).

5. In Table 2, CDSML performs similarly when using gene expression, mutation and CNV to calculate cell line similarity. How about estimating B(i,j) without the matrix SC in the objective function? It will test the power of cell line similarity matrix SC and drug similarity matrix SD when prediction.

6. When performing the 5-fold CV procedure, did you make sure that the percentage of positive and negative cases for training dataset is the same with the testing dataset? I think this is important for classification problem. Under your threshold C_{max}, please provide the percentage of sensitive drug-cell line pairs.

7. CCLE is another commonly used dataset for drug sensitivity prediction study. It is better to apply the methods on this dataset to show its efficiency.

Minor:

1. In the Abstract, “achieving AUC of 0.92%, xxxxx 0.94%, xxxx”. The percent sign % should be removed.

2. Please check the compared method names, and make sure they are the same in the manuscript, for example, SRMF and SRMf.

3. Could you provide the values for the parameters \\alpha, \\beta and the latent number k in Eq.(8) in the Table 4? And in Table 4, which metric do you use to select the best hyper-parameters for these methods since there are 6 metrics in Table 5?

4. The figures in this paper have very low resolution, please improve that.

5. The section number is strange, 0.1, 0.2 ….

6. I downloaded the data from the github website, all the entries in the table “SC_CopyNumberVariation.csv” are zero. Did you upload a wrong table?

Reviewer #2: 1. Avoid using abbreviation in abstract

2. Performance gain in the new method (CDSML) compared to the old one (ADRML) should be made clear

3. Author should clarify what is the novelty in this work compared to the previous approach (i.e. ADRML).

4. Writing should be improved in several places, e.g. several short sentences should be paraphrased in a better way.

5. Data preprocessing steps should be clearly mentioned in the method section

6. Including two types of cell-line similarity profiles, authors have used only one type of drug similarity profiles based on chemical substructure. I would suggest to include other types of multi-modal drug similarities, e.g. target protein sequence similarity, target pathway similarity and functional similarity would affect the overall perfomance. See Azad et. al, 2020 [Brief. in Bioinf.] for reference.

7. Author need to merge 0.5.1.c and 0.5.2

8. Author should provide experimental justification for the threshold of 0.6 at line 177.

9. In the case studies, literature evidence should be sought for the predicted drug-cell line associations, not just drug-tissue association in general.

10. More detail discussion should be made about the methodologies, results found and various assumptions.

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Reviewer #1: No

Reviewer #2: No

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A Computational Method for Drug Sensitivity Prediction of Cancer Cell Lines Based on Various Molecular Information

PONE-D-20-32564R1

Dear Dr. Eslahchi,

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.

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Kind regards,

Fatemeh Vafaee, 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

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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 #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

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: (No Response)

Reviewer #2: All comments are rigorously addressed.

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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 #1: No

Reviewer #2: Yes: A K M Azad