and released in advance online today¹, may allow scientists around the world to use this information to improve cancer clinical trial design and further cancer research.
"Cancer is a genetic disease. Cell lines reflect the genetic disturbances that drive cancers. Probing cell lines with medicines targeted at specific pathways, as done for the Cancer Cell Line Encyclopedia, provides a powerful tool for design of cancer treatment," said Mark Fishman, President of the Novartis Institutes for BioMedical Research (NIBR). "We are placing this information in the public domain. We hope that many in industry and academia will use these data to discover new drug targets, to evaluate current therapies, and to facilitate treatment for their patients with cancer."
The genetic and molecular profiling data from the cell lines is freely available to the scientific community here on the Broad Institute's website².
Investigators use cell lines to shed light on how new or existing cancer drugs might best be used in patients. "Without access to a systematically collected set of molecular data, researchers can't match experiments from cell lines with patient tumors when new medicines become available," said William Sellers, Global Head of Oncology, NIBR. "The Cancer Cell Line Encyclopedia will provide scientists with the ability to build predictive models of what types of patients will respond to a particular class of drugs."
The cell lines were acquired from commercial vendors in the U.S., Europe, Japan and Korea and represent a diverse picture of cancer as a disease as they include many subtypes of both common and rare forms of cancer. According to lead authors and NIBR researchers Jordi Barretina and Giordano Caponigro, each cell line was genetically characterized through a series of high-throughput analyses at the Broad Institute, including global RNA expression patterns, changes in DNA copy number, as well as DNA sequence variations in about 1,600 genes associated with cancer, and pharmacologic profiling for several drugs in about half of the cell lines. Algorithms were developed to predict drug responses based on the genetic and molecular makeup of cancer cells.
Pairing this information with ways to rapidly genotype patient tumor samples represents the next step in the effort to enable the personalization of cancer treatment. Some major research hospitals already routinely genetically profile cancer patients' tumors, and many more are likely to follow, according to the researchers.