In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome
In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete pa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/11/9/1361 |
| _version_ | 1797766408137867264 |
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| author | Elena Piñeiro-Yáñez María José Jiménez-Santos Gonzalo Gómez-López Fátima Al-Shahrour |
| author_facet | Elena Piñeiro-Yáñez María José Jiménez-Santos Gonzalo Gómez-López Fátima Al-Shahrour |
| author_sort | Elena Piñeiro-Yáñez |
| collection | DOAJ |
| description | In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility. |
| first_indexed | 2024-03-12T20:24:53Z |
| format | Article |
| id | doaj.art-e469ec8e9ee94ce4a7ca078ce6b13b0a |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-12T20:24:53Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-e469ec8e9ee94ce4a7ca078ce6b13b0a2023-08-02T00:38:25ZengMDPI AGCancers2072-66942019-09-01119136110.3390/cancers11091361cancers11091361In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical OutcomeElena Piñeiro-Yáñez0María José Jiménez-Santos1Gonzalo Gómez-López2Fátima Al-Shahrour3Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, SpainBioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, SpainBioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, SpainBioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, SpainIn silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.https://www.mdpi.com/2072-6694/11/9/1361precision medicinecancer genomicsintra-tumour heterogeneityin silico prescriptionbioinformaticspharmacogenomicsdruggable genome |
| spellingShingle | Elena Piñeiro-Yáñez María José Jiménez-Santos Gonzalo Gómez-López Fátima Al-Shahrour In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome Cancers precision medicine cancer genomics intra-tumour heterogeneity in silico prescription bioinformatics pharmacogenomics druggable genome |
| title | In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome |
| title_full | In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome |
| title_fullStr | In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome |
| title_full_unstemmed | In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome |
| title_short | In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome |
| title_sort | in silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome |
| topic | precision medicine cancer genomics intra-tumour heterogeneity in silico prescription bioinformatics pharmacogenomics druggable genome |
| url | https://www.mdpi.com/2072-6694/11/9/1361 |
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