New algorithm matches any tumor cells to best possible anti-cancer treatments

platin and paclitaxel.

Even though this NCI cell set wasnt an exhaustive encyclopedia of cancer cells, we found we could use the available data to draw conclusions about other cell types we were exploring. The algorithm is a Rosetta stone for translating from the NCI-studied drugs to any other cell line or human tumor, says Dr. Theodorescu, director of the UVa Paul Mellon Prostate Cancer Institute and senior author of the study. We believe we have found an effective way to personalize cancer therapy. The UVa research team was able to predict the clinical responses of breast cancer patients with treated with commonly used chemotherapies, docetaxel and tamoxifen.

The most exciting aspect of this research is that in addition to predicting patient responses to therapy, the COXEN algorithm can be used to discover effective compounds in any form of cancer. By the nature of the algorithm, which examines both cancer cells and drug activity at the molecular level, these newly discovered drugs should be effective in patients. This pre-screening for effectiveness using COXEN should greatly lower the failure rate of clinical trials testing new compounds. Likewise, as the drug discovery times are decreased in research laboratories, the cost of drugs also will come down. Basically it brings the chemists making the drugs much closer to the clinic, saving time.

Because the NCI-60 set of cells has been used to screen thousands of chemically defined compounds and natural extracts for anticancer activity, we were able to make significant predictions about what compounds might work on real patients who might have other types of cancer, Theodorescu said. The researchers used the COXEN to screen 45,545 compounds, and they identified a several new compounds that have activity against human bladder cancer. To share this exciting capability with the scientific community, Dr. Lee is leading the development of a web-based COXEN system (

Contact: Mary Jane Gore
University of Virginia Health System

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