Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

No abstract

245 Background: Next generation sequencing has revolutionised genomic studies of cancer, having facilitated the development of precision oncology treatments based on a tumour’s molecular profile. We aimed to develop a targeted sequencing panel for application to treatment-refractory solid tumor types with particular focus on tumours of the stomach cancer, plus test for utility in FFPE, fluid sample and cancer cell lines. Methods: “CancerMaster” is custom RNA probes for target enrichment sequencing. It consists of all unions (7,811 regions, 2.5Mb) of reported exons of 524 tumor and immune related genes. The panel contains special RNA probes, which enables detection of microsatellite instability, Epstein-Barr virus and Human papillomavirus. Also, the CancerMaster panel analyzes 524 genes as well as genomic signatures including microsatellite instability (MSI), tumor mutational burden (TMB) and HLA allele. FFPE samples and 49 gastric cancer (GC) cell lines were collected for performance and application testing. Panel sensitivity and precision were measured using well-characterised DNA controls (n = 117), and specificity by Sanger sequencing, pyrosequencing, FoundationOne CDx. Results: We achieved a mean coverage of 1,032x, with sensitivity and specificity of >99% and precision of >97%. In this study, we evaluated 26 Yonsei Cancer Center (YCC) GC cell lines and 23 GC cell lines from other sources (ATCC, KCLB, and JCRB). Application to 49 gastric cancers cell lines resulted in detection of copy number variant (CNV), single number variant (SNV) and small InDel aligned to whole exome sequencing data. In addition to previous novel EBV infected cell line (YCCEL1/YCC-10, J Gen Virol. 2013), we observed amplification and overexpression of receptor tyrosine kinase (RTK) including HER2 (YCC-19, 32, 33, 38), EGFR (YCC-11, 21), Met (YCC-31, 34), and FGFR2 (YCC-28, 30) in YCC GC cell lines; confirming that protein was overexpression by Western blot or Flow cytometry in 19/49 (38.8%). Interestingly, amplification of RTKs was detected mutually exclusive pattern with other RTK amplification. Conclusions: We have developed and validated an analytically-validated panel for application to cancers of treatment-refractory types.

No abstract

No abstract