Program can also be used for screening new compounds.

Researchers at the University of Virginia created an algorithm that can rapidly sort molecular information about a patient’s tumor and match this data to the right drug treatment.

Using a panel of 60 diverse human cancer cell lines from the NCI, called NCI-60, the investigators 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 nor did they include all important cancer types. For example, certain bladder cancers, lymphomas, and small-cell lung cancers were not among the these lines.

The scientists 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. 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. The team was also able to predict the clinical responses of breast cancer patients treated with commonly used chemotherapies, docetaxel and tamoxifen.

The researchers used the COXEN to screen 45,545 compounds. They identified several new compounds that have activity against human bladder cancer. The team is thus planning clinical trials for these bladder cancer candidates. They are also expecting to initiate a trial that would examine patients with a variety of cancers receiving COXEN personalized, second-line drug combinations to beat their cancers.

The scientists also are developing a web-based COXEN system where investigators with genomic profiling data from cancer cells or patient tumors can obtain chemosensitivity prediction results on FDA-approved chemotherapeutic compounds.

The study involved collaboration with colleagues at the NCI and GeneLogic and is published in the early online edition of the Proceedings of the National Academy of Sciences.

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