Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE.
Translational models directly relating drug response specific processes that can be observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, current studies often focus on the optimization of isolated model cha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007803 |
| _version_ | 1818579079712997376 |
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| author | Lisa-Katrin Schätzle Ali Hadizadeh Esfahani Andreas Schuppert |
| author_facet | Lisa-Katrin Schätzle Ali Hadizadeh Esfahani Andreas Schuppert |
| author_sort | Lisa-Katrin Schätzle |
| collection | DOAJ |
| description | Translational models directly relating drug response specific processes that can be observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, current studies often focus on the optimization of isolated model characteristics instead of examining the overall modeling workflow and the interplay of the individual model components. Moreover, they are often limited to specific data sets only. Therefore, they are often confined by the irreproducibility of the results and the non-transferability of the approaches into other contexts. In this study, we present a thorough investigation of translational models and their ability to predict the drug responses of cancer patients originating from diverse data sets using the R-package FORESEE. By systematically scanning the modeling space for optimal combinations of different model settings, we can determine models of extremely high predictivity and work out a few modeling guidelines that promote simplicity. Yet, we identify noise within the data, sample size effects, and drug unspecificity as factors that deteriorate the models' robustness. Moreover, we show that cell line models of high accuracy do not necessarily excel in predicting drug response processes in patients. We therefore hope to motivate future research to consider in vivo aspects more carefully to ultimately generate deeper insights into applicable precision medicine. |
| first_indexed | 2024-12-16T06:56:00Z |
| format | Article |
| id | doaj.art-5125a6b8b8fa4e7e8915ecc6b391ea10 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:56:00Z |
| publishDate | 2020-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5125a6b8b8fa4e7e8915ecc6b391ea102022-12-21T22:40:17ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-04-01164e100780310.1371/journal.pcbi.1007803Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE.Lisa-Katrin SchätzleAli Hadizadeh EsfahaniAndreas SchuppertTranslational models directly relating drug response specific processes that can be observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, current studies often focus on the optimization of isolated model characteristics instead of examining the overall modeling workflow and the interplay of the individual model components. Moreover, they are often limited to specific data sets only. Therefore, they are often confined by the irreproducibility of the results and the non-transferability of the approaches into other contexts. In this study, we present a thorough investigation of translational models and their ability to predict the drug responses of cancer patients originating from diverse data sets using the R-package FORESEE. By systematically scanning the modeling space for optimal combinations of different model settings, we can determine models of extremely high predictivity and work out a few modeling guidelines that promote simplicity. Yet, we identify noise within the data, sample size effects, and drug unspecificity as factors that deteriorate the models' robustness. Moreover, we show that cell line models of high accuracy do not necessarily excel in predicting drug response processes in patients. We therefore hope to motivate future research to consider in vivo aspects more carefully to ultimately generate deeper insights into applicable precision medicine.https://doi.org/10.1371/journal.pcbi.1007803 |
| spellingShingle | Lisa-Katrin Schätzle Ali Hadizadeh Esfahani Andreas Schuppert Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. PLoS Computational Biology |
| title | Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. |
| title_full | Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. |
| title_fullStr | Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. |
| title_full_unstemmed | Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. |
| title_short | Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE. |
| title_sort | methodological challenges in translational drug response modeling in cancer a systematic analysis with foresee |
| url | https://doi.org/10.1371/journal.pcbi.1007803 |
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