PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05-31

AUTHORS

Elena Piñeiro-Yáñez, Miguel Reboiro-Jato, Gonzalo Gómez-López, Javier Perales-Patón, Kevin Troulé, José Manuel Rodríguez, Héctor Tejero, Takeshi Shimamura, Pedro Pablo López-Casas, Julián Carretero, Alfonso Valencia, Manuel Hidalgo, Daniel Glez-Peña, Fátima Al-Shahrour

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 . More... »

PAGES

41

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13073-018-0546-1

    DOI

    http://dx.doi.org/10.1186/s13073-018-0546-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1104296688

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29848362


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