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Genome Med. 2018 May 31;10(1):41. doi: 10.1186/s13073-018-0546-1.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.

Author information

1
Spanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain.
2
Computer Science Department - University of Vigo, Vigo, Spain.
3
Biomedical Research Centre (CINBIO), Vigo, Spain.
4
Spanish National Bioinformatics Institute (INB), Madrid, Spain.
5
Loyola University Chicago Stritch School of Medicine, Maywood, IL, USA.
6
Department of Physiology - University of Valencia, Valencia, Spain.
7
Beth Israel Deaconess Medical Center, Boston, USA.
8
Spanish National Cancer Research Centre (CNIO), 3rd Melchor Fernandez Almagro st., E-28029, Madrid, Spain. falshahrour@cnio.es.

Abstract

BACKGROUND:

Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.

RESULTS:

We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.

CONCLUSIONS:

PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

KEYWORDS:

Cancer genomics; Druggable genome; In silico prescription; Personalized medicine; Precision oncology; Targeted therapy; Translational bioinformatics

PMID:
29848362
PMCID:
PMC5977747
DOI:
10.1186/s13073-018-0546-1
[Indexed for MEDLINE]
Free PMC Article

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