New Algorithm Matches Tumor Cells to Best Anticancer Treatments

Cancer patients don’t have time to waste. Many go through several different treatments, however, to find one that is more effective against their particular type of tumor.

Thus, an algorithm that could help rapidly sort molecular information about a patient’s particular tumor and could help match this information to the right drug treatment would be a breakthrough of enormous value.

Dan Theodorescu, M.D., Ph.D., a University of Virginia oncologist and cancer biologist, and Jae Lee, Ph.D., a computational biologist and bioinformatics statistician, have pioneered just such a system. This work involved collaboration with colleagues at the National Cancer Institute, GeneLogic Inc. and the University of Virginia Computer Sciences Department. They published their results the week of July 23 in the Early Edition of the Proceeedings of the National Academy of Sciences, found online.

Using a panel of 60 diverse, human cancer cell lines from the National Cancer Institute (NCI-60), the researchers devised and tested an algorithm designed to match the best potential treatment(s) for a particular tumor in a particular patient.
Previously, the NCI-60 cell lines were used to screen more than 100,000 chemical compounds for their anticancer activity. These drug responses, however, were not definitely linked to clinical effectiveness in patients. Another issue is that the 60 cell lines did not include all important cancer types (for example, certain bladder cancers, lymphomas, and small cell lung cancers were not among the 60 lines studied).

The researchers investigated whether the drug sensitivity data of the 60 cancer cell lines could be extrapolated into useful information on other tumors or cancer cell lines. In fact, they found that their “coexpression extrapolation (COXEN) system” could be used to accurately predict drug sensitivity for bladder cancer cell lines to two common chemotherapies, cisplatin and paclitaxel.

“Even though this NCI cell set wasn’t 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.