GAN-Enhanced Machine Learning and Metabolic Modeling Identify Reprogramming in Pancreatic Cancer

PLOS Computational Biology

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Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewer #1:

Reviewer #2:

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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: This paper described novel GAN-Enhanced ML and metabolic modeling to study pancreatic cancer. The ML classifier achieved high accuracy in distinguishing between cell states and revealed key dysregulated pathways. The simulation provided mechanistic explanation for pancreatic cancer. The paper is well written and I have a few minor questions.

1. Biomarkers usually refer to unique metabolites or proteins. For cancer cells, the disease is due to genetic mutations. Therefore, the use of FBA to identify biomarkers is confusing to me. The flux value is determined by known multiple enzyme reactions as well as reaction thermodynamics. FBA may not have good resolution to reveal biomarker.

2. FBA flux is highly related to nutrient conditions. In cancel cell, the substrates can be complex and I am not sure if the FBA can simulate the realistic cellular status. What is the objective function for cancer cell metabolism.

3. The model discovered the disease is associated with fatty acid transporters and acyl-CoA synthetases that drive metabolic reprogramming. Some key metabolic reactions are controlled by cell organelles (such as mitochondria and peroxisome, etc.). It is unclear to me how FBA can sensitively predict cellular responses associated with compartmentalization.

4. Augmentation of realistic synthetic healthy samples (n=4) can be a bit risky. Augmentation techniques may lead models to learn patterns that do not generalize well to actual data.

5. lactate production in the cytosolic glycolysis pathway has been known as cancer- metabolism for many years. Can the model identify some new pathways that can be validated by recent experiments?

Reviewer #2: Summary:

This manuscript offers new computational insights into cellular metabolism with potential relevance to pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). The authors analyze gene expression profiles from PDAC and healthy tissue samples, applying a computational pipeline that constructs tissue-specific metabolic models tailored to each sample. A notable innovation in this study is the application of a machine learning method—generative adversarial networks (GANs)—to address dataset imbalance. This enables the development of a random forest classifier that effectively distinguishes between cancerous and healthy states. The study further explores the metabolic reactions, pathways, and genes identified as key features by the classifier. Drawing on existing literature, the authors propose mechanistic hypotheses that differentiate PDAC metabolism from normal metabolism, highlighting roles for heparan sulfate, O-glycan biosynthesis, heme metabolism, and nervonic acid transport. These hypotheses point toward promising avenues for future mechanistic research.

Comments:

• In line 63, the manuscript states that late diagnosis and delayed treatment are major contributors to poor outcomes. Could the authors provide supporting evidence for this claim? Is it established that these factors are among the leading causes of mortality in PDAC? Clarifying this would reinforce the rationale for the study.

• The manuscript mentions a lack of gene expression data from healthy samples. Is this limitation specific to the dataset used in this study? Could publicly available data from healthy individuals in other studies be incorporated? What challenges would arise from doing so?

• Regarding the filtering of synthetic data, why were ATP, NADH, and NADPH production levels chosen as criteria? What makes these particular metabolites critical, and why were other currency metabolites not considered?

• More detail is needed on how flux balance analysis was conducted following the construction of tissue-specific metabolic models using iMAT. What objective function was employed? What constraints were applied to uptake and production rates? Given the discussion of hypoxia in PDAC, was oxygen uptake treated differently across sample groups?

• Could the authors elaborate on how inherent coupling between reaction fluxes—due to the structure of the metabolic network—might influence the random forest model? For instance, reactions that are tightly coupled across all samples may exhibit similar feature importance scores. How should this affect the interpretation of the model’s outputs?

• For Figure 4A, would a box plot or a probability density plot for each flux provide a clearer and more intuitive visualization of the results?

• Do the observed trends in reaction fluxes and pathway differences hold when analyzing only the real (non-synthetic) samples? While statistical significance may be limited due to the small number of healthy samples, does the real data support the same patterns?

• The study presents a method for analyzing gene expression data across conditions with unequal sample sizes by incorporating validated synthetic data generated via GANs. It would be helpful for the authors to discuss the risks, limitations, and necessary precautions when interpreting results derived from synthetic data.

• Line 152: The phrase should be revised to “deal with the class imbalance issue.”

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Saha,

We are pleased to inform you that your manuscript 'GAN-Enhanced Machine Learning and Metabolic Modeling Identify Reprogramming in Pancreatic Cancer' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Sunil Laxman, PhD

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: My comments have been addressed.

Reviewer #2: The authors have satisfactorily addressed the issues raised by the reviewer

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code 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 and code 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 or code —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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Reviewer #1: No

Reviewer #2: No