A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
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Curated by eLife
Evaluation Summary:
This paper will be of interest to scientists across translational medicine and cancer treatment. It describes a standardized method to identify drugs that could be potentially repositioned for tumor treatment. In addition, using this new method and experimental manipulations the authors identify homoharringtonine as a new potential therapy for liver cancer and the underlying liver disease. However, while bioinformatic analysis was really comprehensive, the results and conclusions obtained are based on public datasets and therefore limited by the data available. In addition, the experimental approach to test the potential new treatments are currently based on in vitro assays and would be strengthened by in vivo validations.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL 4 )-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.
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Evaluation Summary:
This paper will be of interest to scientists across translational medicine and cancer treatment. It describes a standardized method to identify drugs that could be potentially repositioned for tumor treatment. In addition, using this new method and experimental manipulations the authors identify homoharringtonine as a new potential therapy for liver cancer and the underlying liver disease. However, while bioinformatic analysis was really comprehensive, the results and conclusions obtained are based on public datasets and therefore limited by the data available. In addition, the experimental approach to test the potential new treatments are currently based on in vitro assays and would be strengthened by in vivo validations.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers …
Evaluation Summary:
This paper will be of interest to scientists across translational medicine and cancer treatment. It describes a standardized method to identify drugs that could be potentially repositioned for tumor treatment. In addition, using this new method and experimental manipulations the authors identify homoharringtonine as a new potential therapy for liver cancer and the underlying liver disease. However, while bioinformatic analysis was really comprehensive, the results and conclusions obtained are based on public datasets and therefore limited by the data available. In addition, the experimental approach to test the potential new treatments are currently based on in vitro assays and would be strengthened by in vivo validations.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
In the present work the authors have defined several objectives. First, to define an appropriate benchmarking standard for drug repositioning based on signature reversion in silico studies Second, apply the method previously defined to determine the best practice approach for LINCS data-based computational drug repositioning. Last, use the methods defined in the second objective to identify novel drugs that can be used for liver cancer treatment. As results of this approaches the authors defined two independent benchmarking standards (AUC-based and KS statistic-based) that shown a good agreement of the evaluation results. Then, since the use of reference signatures showed a high degree of cell-type specific effect the authors demonstrated that XSum is the optimal method for matching compound and disease …
Reviewer #1 (Public Review):
In the present work the authors have defined several objectives. First, to define an appropriate benchmarking standard for drug repositioning based on signature reversion in silico studies Second, apply the method previously defined to determine the best practice approach for LINCS data-based computational drug repositioning. Last, use the methods defined in the second objective to identify novel drugs that can be used for liver cancer treatment. As results of this approaches the authors defined two independent benchmarking standards (AUC-based and KS statistic-based) that shown a good agreement of the evaluation results. Then, since the use of reference signatures showed a high degree of cell-type specific effect the authors demonstrated that XSum is the optimal method for matching compound and disease signatures. Additionally, the authors also investigated the optimal clinical features that should recapitulate the query signature and demonstrate that a good signature should possess the ability to comprehensively recapitulate the clinical features of corresponding disease, rather than only reflect the disease characteristic from single perspective such as normal vs diseased states or prognosis. Finally, using the methods developed the above findings together with in vitro assays the authors determined that homoharringtonine could be not only a promising anti liver cancer agent but also a treatment for the underlying liver chronic disease. All in all, the data provided a good pipeline for the drug repositioning using a in silico approach, and the authors have discussed most of the strengths and weaknesses of their work. Therefore, the conclusions of this paper are mostly well supported, however some aspects would need to be clarified and validated.
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Reviewer #2 (Public Review):
In this paper, the authors aimed at developing an optimal strategy for LINCS data-based therapeutic discovery. They thoroughly analyzed available drug pertubation expression data and also suggested a new therapeutic for HCC. This paper is of potential interest to a broad audience in cancer biology, cancer drug development and associated research.The conclusions of this paper are well supported by the data presented. The paper presents new benchmarking standards regarding methodology and parameters for quantitatively estimating drug retrieval performance. Furthermore, new therapeutics for liver cancer were suggested.
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