2024-03-28T10:12:49Zhttp://repisalud.isciii.es/oai/requestoai:repisalud.isciii.es:20.500.12105/93362022-11-07T11:02:11Zcom_20.500.12105_2174com_20.500.12105_2051com_20.500.12105_2173col_20.500.12105_2175
Repisalud
author
Piñeiro-Yáñez, Elena
author
Jiménez-Santos, María José
author
Gómez-López, Gonzalo
author
Al-Shahrour, Fatima
funder
Instituto de Salud Carlos III
funder
Unión Europea
2020-03-25T15:44:43Z
2020-03-25T15:44:43Z
2019-09-13
Cancers (Basel). 2019;11(9).
2072-6694
http://hdl.handle.net/20.500.12105/9336
31540260
10.3390/cancers11091361
Cancers
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.
eng
Bioinformatics
Cancer genomics
Druggable genome
In silico prescription
Intra-tumour heterogeneity
Pharmacogenomics
Precision medicine
In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome
journal article
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URL
https://repisalud.isciii.es/bitstream/20.500.12105/9336/1/insilicodrug_2019.pdf
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