Abstract
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.
Highlights
Large-scale cancer genome projects such as The Cancer Genome Atlas (TCGA) and TheInternational Cancer Genome Consortium (ICGC) have revealed that cancers are characterized by a high multidimensional genomic heterogeneity among different tumours and within the same patient [1]
Besides PanDrugs classifies druggable genes as (a) direct targets, genes that can be directly targeted by a drug, (b) biomarkers, genes which genetic status is associated with a drug response and (c) pathway member, a targetable gene located downstream to the altered one
Anticancer in silico prescription methods represent a promising group of evidence-guided of tools to such methods is currently hampered the lack of public with complete clinical data propose treatments based on tumour by genomic profiles, butdatabases the systematic performance evaluation of records
Summary
Large-scale cancer genome projects such as The Cancer Genome Atlas (TCGA) and TheInternational Cancer Genome Consortium (ICGC) have revealed that cancers are characterized by a high multidimensional genomic heterogeneity among different tumours and within the same patient [1]. It is widely admitted that the massive analysis and integration of patients’ genomics profiles and clinical data will promote cancer precision medicine approaches [3,4] by guiding the development of prognostic, diagnostic and therapeutic strategies in cancer. Significant progress towards this goal has been made by exploiting the data collection and analysis efforts of large cancer genomics consortia such as TCGA and ICGC. These discoveries are limited by the lack of Cancers 2019, 11, 1361; doi:10.3390/cancers11091361 www.mdpi.com/journal/cancers. In silico drug prescription tools have recently emerged to prioritize patients’ specific genomic alterations with matched therapies and candidate drugs [5,6,7,8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
