Abstract
BackgroundGenomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature.ResultsWe have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3.ConclusionsOur results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0097-1) contains supplementary material, which is available to authorized users.
Highlights
Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments
We demonstrate the utility of GOPredict by stratifying Cancer Genome Atlas (TCGA) breast and ovarian cancer samples and prioritizing drugs in these two cohorts [17,18,19]
The knowledgebase is gene-centric because this allows taking into account published results that are GOPredict uses signaling pathway information defined with Gene Ontology (GO) biological processes [22]
Summary
Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Genomic alterations affecting the production of drug target proteins occur in several tumor histological types as exemplified by druggable HER2 mutations in both breast and metastatic gastric cancer [3]. These drug target proteins, which are genomically altered in multiple cancers, are prime candidates for precision medicine drug repositioning [7, 8]. Utilization of signaling networks offers possibilities for improving cancer drug treatments [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
