Largest Data Set of Cancer-Related Genetic Variations Generated for Researchers

The investigators, from the US National Cancer Institute (NCI; Bethesda, MD, USA), published their study’s findings July 15, 2013, online in Cancer Research, a journal of the American Association for Cancer Research.

This new technology will help cancer researchers better illuminate drug response and resistance to cancer treatments. “To date, this is the largest database worldwide, containing six billion data points that connect drugs with genomic variants for the whole human genome across cell lines from nine tissues of origin, including breast, ovary, prostate, colon, lung, kidney, brain, blood, and skin,” said Yves Pommier, MD, PhD, chief of the laboratory of molecular pharmacology at the NCI in an interview. “We are making this data set public for the greater community to use and analyze. Opening this extensive data set to researchers will expand our knowledge and understanding of tumorigenesis, as more and more cancer-related gene aberrations are discovered. This comes at a great time, because genomic medicine is becoming a reality, and I am very hopeful this valuable information will change the way we use drugs for precision medicine.”

Dr. Pommier and colleagues conducted whole-exome sequencing of the NCI-60 human cancer cell-line panel, which is an assortment of 60 human cancer cell lines, and generated a comprehensive list of cancer-specific genetic variations. Early research conducted by the researchers show that the extensive data set has the potential to greatly enhance understanding of the links between specific cancer-related genetic variations and drug response, which will hasten the drug development process.

The NCI-60 human cancer cell-line panel is used extensively by cancer researchers to discover novel anticancer drugs. To conduct whole-exome sequencing, Dr. Pommier and his NCI team extracted DNA from the 60 different cell lines from tumors of the lung, colon, brain, ovary, prostate, breast, and kidney, as well as melanoma and leukemia, and cataloged the genetic coding variants for the complete human genome. The genetic variations identified were of two types: type I variants corresponding to variants found in the normal population, and type II variants, which are cancer-specific.

The scientists then employed the Super Learner algorithm to predict the sensitivity of cells harboring type II variants to 103 anticancer drugs approved by the US Food and Drug Administration (FDA) and an additional 207 investigational new pharmaceutical agents. They were able to assess the correlations between key cancer-related genes and clinically pertinent anticancer drugs, and predict the outcome.

The data generated in this project provide a way to identify new determinants of response and processes of drug resistance, and offer opportunities to target genomic defects and overcome acquired resistance, according to Dr. Pommier. To accomplish this, the researchers are making these data available to all researchers by way of two database portals, called the CellMiner database and the Ingenuity systems database.

Related Links:
US National Cancer Institute