Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods
The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that resu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/3/1074 |
| _version_ | 1797487669733752832 |
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| author | Maryam Pouryahya Jung Hun Oh James C. Mathews Zehor Belkhatir Caroline Moosmüller Joseph O. Deasy Allen R. Tannenbaum |
| author_facet | Maryam Pouryahya Jung Hun Oh James C. Mathews Zehor Belkhatir Caroline Moosmüller Joseph O. Deasy Allen R. Tannenbaum |
| author_sort | Maryam Pouryahya |
| collection | DOAJ |
| description | The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses. |
| first_indexed | 2024-03-09T23:51:02Z |
| format | Article |
| id | doaj.art-c8364f29c0304c458ee9961ee4b8562f |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T23:51:02Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-c8364f29c0304c458ee9961ee4b8562f2023-11-23T16:34:39ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-01-01233107410.3390/ijms23031074Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based MethodsMaryam Pouryahya0Jung Hun Oh1James C. Mathews2Zehor Belkhatir3Caroline Moosmüller4Joseph O. Deasy5Allen R. Tannenbaum6Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USASchool of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UKDepartment of Mathematics, University of California at San Diego, La Jolla, CA 92093, USADepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY 11794, USAThe development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.https://www.mdpi.com/1422-0067/23/3/1074drug sensitivityoptimal mass transportnetwork-based clusteringcell lines |
| spellingShingle | Maryam Pouryahya Jung Hun Oh James C. Mathews Zehor Belkhatir Caroline Moosmüller Joseph O. Deasy Allen R. Tannenbaum Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods International Journal of Molecular Sciences drug sensitivity optimal mass transport network-based clustering cell lines |
| title | Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods |
| title_full | Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods |
| title_fullStr | Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods |
| title_full_unstemmed | Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods |
| title_short | Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods |
| title_sort | pan cancer prediction of cell line drug sensitivity using network based methods |
| topic | drug sensitivity optimal mass transport network-based clustering cell lines |
| url | https://www.mdpi.com/1422-0067/23/3/1074 |
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