NEW YORK (GenomeWeb News) – RNA interference-based screens are an effective way to delve into the functional underpinning of cancer cells, according to new research published by members of the RNAi Consortium.
The researchers, led by scientists at the Broad Institute, used RNAi screening with short hairpin RNAs to come up with a list of essential genes in a dozen cancer cell lines. They also used the screen to dig up a handful of genes involved in chronic myelogenous leukemia cell response to Novartis’ Gleevec (imatinib). The work is scheduled to appear online this week in the Proceedings of the National Academy of Sciences.
“We anticipate that systematic efforts to apply these approaches to study other cancer phenotypes will eventually lead to a more complete view of the Achilles’ heels of different types of cancers,” the authors wrote.
While projects such as The Cancer Genome Atlas and International Cancer Genome Consortium work towards characterizing the structural genetic changes behind various cancer types, the authors argued that there is also a need for corresponding studies into the associated functional changes in these cells.
In an effort to get at this functional information, the researchers applied a pooled screening approach developed by the RNAi Consortium, tapping into the consortium’s shRNA libraries. Overall, the libraries house roughly 170,000 shRNAs targeting 17,200 human genes and thousands more targeting 16,000 mouse genes.
But the team took advantage of a smaller sub-library that held 45,000 shRNAs targeting about 9,500 human genes, infecting various cell lines with the shRNA library and determining which shRNAs were over- and under-represented in surviving cells after a given amount of time. By assessing the genes targeted by these hairpin RNAs, the team was able to pick out genes whose expression influences survival under different conditions in different cell lines.
The team first tested the approach by screening Jurkat cells, a T lymphocyte cell line, to unearth genes involved in T cell resistance to apoptosis. Then, they screened a small cell lung cancer line to find genes conferring resistance to a compound called etoposide, which alters topoisomerase IIA activity. Because both screens pulled out plausible genes, the researchers were confident that their approach was feasible.
Next, the team turned their attention to 12 different cancer cells lines — representing everything from lung cancers to leukemias — to look for genes that were essential for survival in each. They screened the lines using ten or more rounds of infection with the shRNA library.
By amplifying shRNA sequences from surviving cells after roughly a month, digesting the hairpins with restriction enzymes, and using high-density, custom Affymetrix microarrays to measure the amount of these so-called half-hairpin barcodes, the researchers determined which shRNAs were more or less abundant in surviving cells.
The team then used unsupervised clustering and consensus clustering to group the various cell lines based on their shRNA abundance. They also came up with a new statistical score called the RNAi gene enrichment ranking to define essential genes based on the shRNA profiles detected in the cells.
Using this approach, the researchers found 268 “commonly essential genes” involved in pathways such as those for ribosomal proteins, mRNA processing, translation, and proteasome degradation. Known or suspected oncogenes, including KRAS, MYC, and MYB, were also among the top one percent of essential genes in one or more of the cell lines.
In addition, the team pinpointed genes that were specifically required in different types of cancer cells. For instance, they found 63 genes that were essential in four different non-small cell cancer cell lines and 32 genes that were essential in four different leukemia lines. The researchers also found instances of cell line-specific gene requirements in which certain genes were essential in just one of the 12 cancer cell lines.
By tweaking the approach slightly, the researchers were able to use the screen to find genes involved in imatinib response in a CML cell line. Imatinib, which is marketed as Gleevec by Novartis, inhibits a fusion protein that would otherwise keep a tyrosine kinase active in the cells, promoting cancer growth.
When they picked out and screened CML cells that were resistant to imatinib treatment, the researchers uncovered eight genes involved in imatinib response, providing new information about the pathways affected by the drug. And, they noted, RNAi screening may be useful for finding genes and pathways that interact with one another in various cell types and for fleshing out data from structural studies of cancer.
“When combined with the increasingly complete structural analyses of cancer genomes by the Cancer Genome Atlas and other such efforts, the experimental and analytical strategies for pooled shRNA screens described herein provide a feasible strategy to systematically identify the key genes involved in cancer initiation, maintenance, and progression and likely targets for therapeutic intervention,” the team concluded.