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. 2024 Jan 26;19(1):e0295629.
doi: 10.1371/journal.pone.0295629. eCollection 2024.

Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma

Bin Baek  1 , Eunmi Jang  2 , Sejin Park  1 , Sung-Hye Park  3   4 , Darren Reece Williams  2 , Da-Woon Jung  2 , Hyunju Lee  1   5
Affiliations

Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma

Bin Baek et al. PLoS One. .

Abstract

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the analysis process.
Gene Exp: gene expression; CNV: copy number variation; AE-NN: a predictive model consisting of an autoencoder and a fully connected neural network. Flowchart of drug response prediction process and drug screening. Genomics of Drug Sensitivity in Cancer (GDSC) multi-omics data were used to train the computational predictive models. The upper left corner illustrates multi-omics data of two generated human embryonal RMS (RD) and human alveolar RMS (SJCRH30) cell lines. The responses of the RD and SJCRH30 cell lines to all possible drugs were predicted using both trained models. The upper right shows that the predicted probability values are used to classify binary drug responses (’resistant’ and ’sensitive’). Finally, in vitro testing was conducted to validate RD and SJCRH30 responses to these potential candidate drugs (bottom of figure). Cell viability and IC50 were assessed via MTT assay. Drug resistance and sensitivity were verified. For testing the sensitivity eliciting drugs with high anti-cancer activity, we evaluated selectivity using the human colon fibroblast cell line (CCD-18Co) as a normal control, thereby validating the drug classifications.
Fig 2
Fig 2. Comparison of RMS cell lines with that from the Genomics of Drug Sensitivity in Cancer (GDSC) database.
corr: correlation. AE-NN: A predictive model comprising an autoencoder and a neural network classifier. (A) Spearman correlation of GDSC-RD and RD gene expression. (B) Spearman correlation of GDSC-RD and RD copy number. (C) Spearman correlation of GDSC-SJCRH30 and SJCRH30 gene expression. (D) Spearman correlation of GDSC- SJCRH30 and SJCRH30 copy number.
Fig 3
Fig 3. FDA-approved candidate drug selection.
AUC; area under the curve of AE-NN. (A) Candidate drugs for the RD cell line. (B) Candidate drugs for the SJCRH30 cell line.
Fig 4
Fig 4. RD proliferation assay: Cell viability tests for the candidate drugs.
(A) Human embryonal RMS cells were treated with RD-R drugs. (B) Human embryonal RMS cells were treated with RD-S drugs. (C) CCD-18Co normal human colon fibroblasts, as controls. (D) Comparison with the clinical drugs vincristine and cyclophosphamide. Data represent the mean ± SD. *P<0.05, **P<0.01, ***P<0.001 (t-test).
Fig 5
Fig 5. SJCRH30 proliferation assay: Cell viability tests for the candidate drugs.
(A) Human alveolar RMS cells were treated with SJCRH30-R drugs. (B) Human alveolar RMS cells were treated with SJCRH30-S drugs. (C) CCD-18Co normal human colon fibroblasts, as controls. (D) Comparison with the clinical drugs vincristine and cyclophosphamide. Data represent the mean ± SD. *P<0.05, **P<0.01, ***P<0.001 (t-test).
Fig 6
Fig 6. Caspase-3/7 activity assay for the validation of NSC207895, vorinostat and belinostat.
(A) Representative images of caspase-3/7 activity after NSC207895 treatment in RD cells. (B) Quantification of the percentage of apoptotic cells per field of view. (C) Representative images of caspase-3/7 activity after vorinostat treatment in SJCRH30 cells. (D) Quantification of the percentage of apoptotic cells per field of view. (E) Representative images of caspase-3/7 activity after belinostat treatment in SJCRH30 cells. (F) Quantification of the percentage of apoptotic cells per field of view. Scale bar = 100 μm. Data represent the mean ± SD. *P<0.05, **P<0.01, ***P<0.001 (t-test).
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Grants and funding

This work was funded by the Korea government (MSIP) through the Institute for Information and communications Technology Promotion (IITP) grant (No. 2019-0-00567, Development of Intelligent SW systems for uncovering genetic variation and developing personalized medicine for cancer patients with unknown molecular genetic mechanisms). This work was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) [grant no NRF-2022R1A2C1008322], the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) [grant no. NRF-2020M3A9G3080282], and a ‘GIST Research Institute (GRI) IIBR’ grant funded by the GIST in 2023.