An updated set of web-based analysis tools rapidly finds drugs with the potential to combat certain cancers, matching the drugs with tumors based on vast amounts of genetic information about the cancers and data on thousands of tested compounds. The NIH’s National Cancer Institute developed the software called CellMiner for use with its extensive collection of cancer cell samples, but researchers anywhere can use the tools for free as they drill into existing and new datasets to identify anti-cancer drugs.
If the nirvana of personalized medicine is to match patients with the right drugs based on the specific genetic traits of their disease, the NCI has advanced some technology in CellMiner to help realize that ideal in oncology.
The software takes advantage of the way new drugs are developed to target specific genes rather than entire organs where cancers crop up, enabling researchers to find uses of drugs in whichever tumor type the misfit genes appear. For instance, researchers used CellMiner to uncover that an experimental drug called selumetinib, which has been trialed in patients with colon cancer, might also work as an attack against deadly skin cancer or melanoma. The findings were published in the journal Cancer Research.
The NCI’s tools have been developed with information overload in mind. Drug researchers have access to many massive datasets on cancer genomes and other molecular information to aid their hunt for new treatments, yet some of the existing analysis software might require scads of bioinformatics specialists to correlate target gene data with information on compounds. And the NCI says that CellMiner is easier to use than those previous tools. The ease of use alone could speed up data-driven discovery of potential cancer drugs.
“Previously you would have to hire a bioinformatics team to sort through all of the data, but these tools put the entire database at the fingertips of any researcher,” the NCI’s Dr. Yves Pommier said in a statement. “These tools allow researchers to analyze drug responses as well as make comparisons from drug to drug and gene to gene.”
– here’s the release
– see the abstract
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