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Cancer Dependency Maps Developed by Two Research Teams

NEW YORK (GenomeWeb) – Two research teams have mapped out genes that cancer cells depend on for growth and survival.

Using genome-wide RNA interference screens, research teams from both the Broad Institute and Novartis knocked down thousands of genes in hundreds of cancer cell lines. By then examining which cells survived, the researchers could gauge whether cancer cells were dependent upon the silenced genes. The teams reported their results today in separate Cell papers.

Both groups said that these dependencies could help identify drug targets.

"We wanted to identify vulnerabilities — the genes and proteins that cancer cells care most about," Rob McDonald, senior researcher in oncology at the Novartis Institutes for BioMedical Research and a co-corresponding author on one of the Cell papers, said in a statement.

In their Cell paper, the Broad team silenced more than 17,000 genes in 501 cell lines that represented more than 20 cancer types. The cells were passaged for 40 days before being sequenced to assess which short hairpin RNAs were depleted from the cell population.

"The simplest thing one can do with perturbed cells is allow them to keep growing over time and see which ones thrive," David Root, a Broad researcher and study co‐senior author, said in a statement. "If cells with a certain gene silenced disappear, for example, it means that gene is essential for proliferation."

He and his colleagues also developed and used a computational approach called DEMETER to tease out on- and off-target RNAi effects to eliminate false-positive results.

The Broad researchers noted that a number of the dependencies they uncovered — 769 genes were differentially required in the cell lines — were cancer specific. At the same time, though, they reported that most of the cell lines were also dependent upon a small group of shared genes. This suggested to them that a small number of therapeutics might work across a large swath of tumors. 

The researchers also used a set of molecular features to develop biomarker-based predictive models for a portion of these dependencies. These biomarkers fell into four categories — gene mutations, gene copy number loss or reduced gene expression, increased expression, or a functional or structural reliance on a lost paralog.

Additionally, 20 percent of the dependencies they uncovered were associated with genes that have been previously tagged as possible drug targets.

The Novartis research team similarly knocked down 7,837 genes in 398 cancer cell lines for their effort, called Project DRIVE. In all, they used an average 20 different RNAi reagents per gene across the cell lines to boost their confidence that they could accurately identify the effects of silencing those genes.

They passaged the cells for 14 days before assessing gene-level activity using both ATARiS and RSA to account for false positives, and identified genes that cancer cell lines depend upon. Mutated oncogenes such as NRAS, BRAF, and KRAS were among the most robust dependencies they identified.

The Novartis team developed a bioinformatics pipeline to identify features that made the cell lines sensitive to the loss of certain genes. "[W]e might ask 'What do each of those cell lines have in common?" McDonald said. "If they share a feature that the other cells don't, then you can form a hypothesis — specifically that the feature predicts sensitivity to gene silencing that might have therapeutic implications."

Through this, they identified four classes that cancer dependency genes fall into: genetic dependence, expression-based dependence, metabolic genes and enzymes, and synthetic lethals.

The researchers also constructed a global network of essential genes that began to tease out how they were related to one another. For instance, their network recapitulated much of the p53 pathway.

Novartis' Jeff Engleman added that Project DRIVE has "informed our thinking about the targets we want to go after to cripple cancer cells." He and his colleagues noted that they've developed a web portal where others can examine their data.

In their paper, the Broad researchers call for an international effort to create a definitive cancer dependency map.

"Much of what has been and continues to be done to characterize cancer has been based on genetics and sequencing. That's given us the parts list," Hahn said. "Mapping dependencies ascribes function to the parts and shows you how to reverse engineer the processes that underlie cancer."