In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
Entity
UAM. Departamento de BioquímicaPublisher
MDPI, Basel, SwitzerlandDate
2019-09-13Citation
10.3390/cancers11091361
Cancers 11.9 (2019): 1361
ISSN
2072-6694DOI
10.3390/cancers11091361Funded by
This work was supported by the Instituto de Salud Carlos III (ISCIII); Marie-Curie Career Integration Grant (CIG334361); and Paradifference FoundationEditor's Version
https://doi.org/ 10.3390/cancers11091361Subjects
Precision medicine; Cancer genomics; Intra-tumour heterogeneity; Iin silico prescription; Bioinformatics; Pharmacogenomics; Druggable genome; MedicinaRights
© 2019 The AuthorsAbstract
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
Files in this item
Google Scholar:Piñeiro-Yáñez, Elena
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Jiménez-Santos, María José
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Gómez López, Gonzalo
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Al-Shahrour, Fátima
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