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Models and Technologies Cancer In Silico Drug Discovery : A Systems Biology Tool for Identifying Candidate Drugs to Target Speci fi c Molecular Tumor Subtypes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lucas, F. Anthony San Fowler, Jerry Chang, Kyle Kopetz, Scott Vilar, Eduardo Scheet, Paul |
| Copyright Year | 2014 |
| Abstract | Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on awide range of clinical andmolecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silicoDrugDiscovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determiningwhether specific clinical phenotypes ormolecular characteristics are associatedwith unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidatedrugs anda list of cell lines for in vitro experimentation.WeappliedCiDD to identify candidatedrugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposingfive cell lines for in vitro testing.CiDD facilitates phenotype-driven, systematic drugdiscovery based on clinical and molecular data from TCGA. Mol Cancer Ther; 13(12); 3230–40. 2014 AACR. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://mct.aacrjournals.org/content/molcanther/13/12/3230.full.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
