Data integration to prioritize drugs using genomics and curated data

Background: Genomic 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. Results: We 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. Conclusions: Our 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/.Peer reviewe

Data integration to prioritize drugs using genomics and curated data

Background: Genomic 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. Results: We 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. Conclusions: Our 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/.Peer reviewe

Hepsin regulates TGF beta signaling via fibronectin proteolysis

Transforming growth factor-beta (TGF beta) is a multifunctional cytokine with a well-established role in mammary gland development and both oncogenic and tumor-suppressive functions. The extracellular matrix (ECM) indirectly regulates TGF beta activity by acting as a storage compartment of latent-TGF beta, but how TGF beta is released from the ECM via proteolytic mechanisms remains largely unknown. In this study, we demonstrate that hepsin, a type II transmembrane protease overexpressed in 70% of breast tumors, promotes canonical TGF beta signaling through the release of latent-TGF beta from the ECM storage compartment. Mammary glands in hepsin CRISPR knockout mice showed reduced TGF beta signaling and increased epithelial branching, accompanied by increased levels of fibronectin and latent-TGF beta 1, while overexpression of hepsin in mammary tumors increased TGF beta signaling. Cell-free and cell-based experiments showed that hepsin is capable of direct proteolytic cleavage of fibronectin but not latent-TGF beta and, importantly, that the ability of hepsin to activate TGF beta signaling is dependent on fibronectin. Altogether, this study demonstrates a role for hepsin as a regulator of the TGF beta pathway in the mammary gland via a novel mechanism involving proteolytic downmodulation of fibronectin.Peer reviewe

Additional file 3 of Data integration to prioritize drugs using genomics and curated data

A spreadsheet file containing the cancer-essentiality results for genes and processes. (XLS 10752 kb

Additional file 1 of Data integration to prioritize drugs using genomics and curated data

A PDF-document containing the Supplementary Figures, Results and Methods. (PDF 273 kb

Additional file 4 of Data integration to prioritize drugs using genomics and curated data

A spreadsheet file containing the drug prioritization results. (XLS 275 kb

Additional file 2 of Data integration to prioritize drugs using genomics and curated data

A spreadsheet file containing the list of studies. (XLS 15 kb