This resource, dubbed the Cancer Cell Line Encyclopedia(CCLE), provides a powerful tool for the design of cancer drug trials and willhelp researchers identify patients who could benefit most from specific drugsin development, according to the two organizations, which published the resultsof their collaboration March 28 in the journal Nature. The CCLE project was specifically a collaboration betweenthe Broad Institute, the Novartis Institutes for Biomedical Research (NIBR) andthe Genomics Institute of the Novartis Research Foundation to conduct adetailed genetic and pharmacologic characterization of a large panel of humancancer models, develop integrated computational analyses that link distinctpharmacologic vulnerabilities to genomic patterns and translate cell lineintegrative genomics into cancer patient stratification.
"The systematic translation of cancer genomic data intoknowledge of tumor biology and therapeutic possibilities remains challenging,"the CCLE's creators wrote in their article, "The Cancer Cell Line Encyclopediaenables predictive modeling of anticancer drug sensitivity."
"Such efforts," the researchers added, "should be greatlyaided by robust preclinical model systems that reflect the genomic diversity ofhuman cancers and for which detailed genetic and pharmacological annotation isavailable. Here, we describe the Cancer Cell Line Encyclopedia (CCLE): acompilation of gene expression, chromosomal copy number and massively parallelsequencing data from 947 human cancer cell lines. When coupled withpharmacological profiles for 24 anticancer drugs across 479 of the cell lines,this collection allowed identification of genetic, lineage andgene-expression-based predictors of drug sensitivity."
On the website, http://www.broadinstitute.org/ccle—whichanyone can access—researchers can enter a keyword to search for genes, newsitems and publications; search results for a gene, including links toannotations and analyses; and browse, analyze and download studies and datasets.
"The goals of the CCLE project are twofold: one is toassemble a resource of genomic characteristics of 1,000 cancer cell lines, andthe second is to develop tools to predict the sensitivity of those cell linesto cancer drugs based on genomic alterations," explains Dr. Nicolas Stransky, acomputational biologist in the Cancer Program at the Broad and a co-firstauthor of the paper. "Certainly, people have been doing these kinds of thingsin the past, but on a much smaller scale. There are many applications here. Oneis to better inform the clinical trials that are taking place in thedevelopment of drugs. The use of the CCLE here would be to better select whichpatients are more likely to respond when they are given a specific drug,because you can tell which cancer cell lines are being killed by a drug."
The team purchased cell lines and their associatedinformation directly from several commercial vendors in the United States,Europe, Japan and Korea, says William Sellers, global head of oncology at theNIBR. Cell lines represent a diverse picture of cancer as a disease, as theyinclude many subtypes of both common and rare forms of cancer.
"If someone buys a vial of cells that we characterized, itshould be very close with as minimal drift as possible to what we used in ourgenetic studies. It was expensive to do this, but it was done to make this apublicly valuable resource," he adds.
Each cell line was genetically characterized through aseries of high-throughput analyses at the Broad Institute, including global RNAexpression patterns, changes in DNA copy number, as well as DNA sequencevariations in about 1,600 genes associated with cancer, and pharmacologicprofiling for several drugs in about half of the cell lines. Algorithms weredeveloped to predict drug responses based on the genetic and molecular makeupof cancer cells.
The collaboration was "exciting" for Novartis "because weare in the drug discovery arena," says Sellers. "We are good at team-orientedand project-oriented science. It turned out to be a lot of fun because bothteams worked together as a single project team."
In fact, a number of Novartis' clinical trials have alreadybeen influenced by the data generated during the collaboration, he adds. Forexample, Novartis used the data in the development of BYL719, a novel, oral,targeted anticancer agent that selectively inhibits thephosphatidylinositol-3-kinase (PI3K) pathway. The compound, which is beinginvestigated in advanced solid tumor patients, has shown significant cellgrowth inhibition and induction of apoptosis in a variety of tumor cell linesas well as in animal models. In addition, in preclinical models, it has beenshown to possess antiangiogenic properties.
"While this result wasn't unexpected, the power of theencyclopedia results motivated a specific trial design," Sellers says. "Thestrength of association was so compelling, suggesting that not only was themolecule a good molecule, but also that the best thing to do with respect toits clinical development was to focus the trial on patients who had a certainmutation, so we had the best chance of seeing early efficacy in patients."
Sellers acknowledges that "there is a lot of debate aboutwhether cell lines are OK to use in cancer research." While human cancer celllines represent a mainstay of tumor biology and drug discovery through facileexperimental manipulation, global and detailed mechanistic studies and varioushigh-throughput applications, many previous efforts have been limited in their depth of geneticcharacterization and pharmacological interrogation, he notes.
"It is important to remember that while cell lines are notalways the best tools, they are the most widely used by cancer researchers," hestresses. "People know about their limitations, but it is important to rememberthat they are tremendously useful tools for studying cancer therapies and howdrugs work. In certain cases, cell lines don't fully represent the genomicheterogeneity of cancers. Again, in certain cases, looking at cell lines won'tgive you a complete picture, but what we show in our paper is that in manycases, they are reasonable models."
"Our biggest hope for this project is that the data will beused by the community, but also that the biggest discoveries in the data areyet to come. This is likely to generate a lot of enthusiasm and be widelyused," says Sellers.
Stransky notes that the CCLE is still an ongoing project,and its repository of data is neither final nor complete. In the second phaseof the project, "our goal is to perform a much deeper genomic characterizationusing several sequencing techniques including whole-genome, transcriptome,exome sequencing, etc. We're also looking at other data types such asepigenetic alterations, phosphoproteomics and metabolomics," he says.
Pairing this information with ways to rapidly genotypepatient tumor samples represents the next step in the effort to enable thepersonalization of cancer treatment, according to the researchers. Some majorresearch hospitals already genetically profile cancer patients' tumorsroutinely, and many more are likely to follow, says the CCLE team.
"What we're trying to do here," Stransky concludes, "is lay down thebasis of what personalized medicine would be in the future, which means we'retrying to have the best match between a particular drug and which tumors arelikely to respond. This is going to be tremendously useful for drug developmentin the future."