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Teams Develop Resources Linking Cancer Cell Line Features to Treatment Response

NEW YORK (GenomeWeb News) – In Nature online today, studies from two independent research groups introduce new genomics-based resources designed to more accurately match treatments to cancer cell characteristics.

For the first of these, an international group led by investigators from the Wellcome Trust Sanger Institute and the Massachusetts General Hospital Cancer Center characterized gene mutation, expression, and copy number changes in more than 600 cancer cell lines. They then used these and other features in the cell lines to look for patterns that marked treatment sensitivity or resistance when cancer lines were exposed to 130 clinical or pre-clinical drugs.

"There is a compelling need to identify, in a systematic fashion, whether observed mutations affect the likelihood of a patient's response to a given drug treatment," Wellcome Trust Sanger Institute researcher Ultan McDermott, co-corresponding author on the study, said in a statement.

"We have therefore developed a unique online open-access resource for the research and medical community that can be used to optimize the clinical application of cancer drugs as well as the design of clinical trials of investigational compounds being developed as treatments," McDermott said.

The research was done as part of a collaboration between Sanger Institute researchers involved with The Cancer Genome Project and researchers at Massachusetts General Hospital Cancer Center's Center for Molecular Therapeutics.

"Our key focus is to find how to use cancer therapeutics in the most effective way by correctly targeting patients that are most likely to respond to a specific therapy," Wellcome Trust Sanger Institute researcher Mathew Garnett, co-first author on the study, said in a statement.

Using 639 human cancer cell lines from a wide range of common and rare cancers, researchers did capillary sequencing of coding exons from 64 genes that are often altered in cancer. They also looked for rearrangements in several other genes, assessed microsatellite stability patterns, and used Affymetrix microarrays to generate copy number and expression data in the cell lines.

The team then screened the genetically characterized cell lines for drug sensitivity or resistance, generating data on between 275 and 507 cell lines each for 130 compounds with known or suspected activity against cancer.

From there, they were able to see how various cancer types responded to the drugs in general and to track down more specific information on the genetic and molecular profiles corresponding to treatment outcomes for the drugs tested.

In addition to explaining some known treatment response patterns, the study pointed to new applications for existing cancer treatments, researchers reported.

For instance, they found that a fusion between the EWS and FLI1 that typically turns up in Ewing's sarcoma also signals sensitivity to PARP inhibitors, drugs that are currently used to treat some forms of breast and ovarian cancer. That, in turn, suggests the drugs might also prove effective against pediatric bone cancer.

Additional research and clinical trials will be needed to verify such predictions and to better understand if and how a tumor's environment might affect the patterns detected in isolated cell lines, researchers explained. In the meantime, they are continuing to screen cancer lines using other compounds, including drug combinations.

Data from the study is being made available through the Genomics of Drug Sensitivity in Cancer website.

Meanwhile, another team headed by researchers at the Broad Institute, Dana-Farber Cancer Institute, and Novartis has established a resource that relies on mutation, expression, copy number, and lineage-related characteristics to help predict sensitivity or resistance to cancer-targeting drugs.

That group's Cancer Cell Line Encyclopedia brings together data on almost 950 human cancer cell lines, along with treatment response information for just over half of the lines, which were screened using two-dozen anti-cancer drugs.

"We hope that the Cancer Cell Line Encyclopedia will be a preclinical resource that could guide clinical trials," co-corresponding author Levi Garraway of the Broad Institute and the Dana-Farber Cancer Institute said in a statement.

"We can ask questions not only about emerging targeted therapies, but also about standard chemotherapy drugs," he added. "There may be ways to identify patients who are more likely to respond to conventional chemotherapy versus those who might not."

Garraway and his team focused on 947 human cancer cell lines representing three-dozen tumor types, using massively parallel sequencing to tackle more than 1,600 genes believed to contribute to cancer.

Along with this targeted gene sequencing, the researchers did mass spectrometry-based genotyping to look for mutations in 33 oncogenes or tumor suppressor genes as well as additional genotyping and copy number analyses on the lines using Affymetrix microarrays.

To ensure that the cell lines being tested were appropriate stand-ins for tumor samples, they also compared some of their genomic findings with those reported for primary tumors of the same cancer type in the past.

After getting dose-response profiles for 479 cell lines treated with 24 anti-cancer drugs, the team integrated data on tens of thousands of cancer cell features to see which ones offered predictive information about drug response.

For example, their analyses hint that elevated expression of the aryl hydrocarbon receptor gene AHR serves as a marker for MEK inhibitor sensitivity in cancers containing NRAS gene mutations. On the other hand, higher-than-usual levels of a gene called SLFN11 appeared to coincide with sensitivity to topoisomerase inhibitor drugs in a range of cancers, including Ewing's sarcoma.

"With this initial effort, we have taken some critical first steps," co-author Todd Golub, director of the cancer program at the Broad Institute, said in a statement. "The challenge now is to greatly expand the number of compounds tested across the panel of cell lines."

That team also plans more in-depth analyses on the lines, including deep sequencing, epigenetic, and metabolic profiling studies. The Cancer Cell Line Encyclopedia is available online through a Broad Institute website.

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