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Emory Team Builds Interaction Network of Key Lung Cancer Proteins


NEW YORK (GenomeWeb) – Emory University researchers are developing a targeted protein-protein interaction network focused on the behavior of known oncogenes across a variety of cancers.

In a study published last week in Nature Communications, they presented an initial version of the resource, called OncoPPi. Containing data on 397 protein-protein interactions in lung cancer, including more than 260 novel interactions, the network could be useful for a variety of research purposes including identifying new disease biomarkers as well as potential drug targets, said Haian Fu, professor of pharmacology, hematology, and medical oncology at Emory and senior author on the paper.

While protein-protein interaction work is most commonly done using methods like immunoprecipitation mass spec or yeast two-hybrid screening, the Emory team used time-resolved Förster resonance energy transfer (TR-FRET), a method that Fu noted is commonly used for drug screening applications, though less frequently used for protein interaction work.

In TR-FRET, molecules of interest are tagged with paired fluorophores. When these fluorophores come into close contact, one will excite the other causing an emission of energy that can be detected. In the case of protein interaction work, the notion is that this emission indicates interaction between the two proteins under investigation.

Often protein interaction studies take a broad view, collecting information on the interaction partners of thousands of molecules. However, Fu said, while this provides a wealth of data, validating this data can be challenging.

He and his colleagues opted instead for a more limited approach, looking specifically at the products of 83 genes either known to be involved in lung cancer signaling or that had high frequencies of alterations in lung cancer. Using the TR-FRET-based platform, they expressed tagged version of their proteins of interest in H1299 lung cancer cells, looking at them in pairwise fashion for a total of 3,486 interactions examined.

"Unlike established proteomics approaches which are unbiased — you have the bait and you don't know what will come with it — in our case it is a targeted discovery with cancer-focused genes," Fu said.

The experiment's limited scope allowed the researchers to test each PPI pair with two fusion tags for each gene in triplicate and across three different rounds of screening. This, Fu noted, "allowed us to really increase the statistical power and the quality of the data that came out of our screening."

That, he suggested, was what allowed the team to identify the more than 260 interactions not seen in previous research.

Of the 3,486 interactions the researchers investigated, 798 were statistically significant. Of these, 128 had been identified through previous efforts as direct interactors in lung cancer cells, while 670 were novel. Using more stringent criteria, they narrowed the set down to a high-confidence collection consisting of 397 lung cancer protein-protein interactions, 269 of which had not been previously observed.

Fu cited the targeted approach as key to uncovering these novel interactions, and added that he and his colleagues as well as outside researchers have a number of hypotheses to follow up on.

"We validated a number of interactions while at the same time revealing new binding partners," he said. "So this gives the scientific community potential new regulators of these well-studied oncogenes and also new pathways to investigate."

He suggested that one particularly interesting angle for follow up is investigating new interactions that could allow drug makers to go after previously undruggable targets. For instance, Fu said, "one of the major challenges in cancer therapeutic discovery is how to target tumor suppressors, which are usually lost in tumors."

By placing such suppressors in their OncoPPI network, they can be linked to actionable drug targets that could offer alternative ways of regulating these molecules' pathways.

The interaction data also suggests new biomarkers that could be useful for, predicting or monitoring drug response in clinical trials, for instance. Fu gave the example of the connection he and his colleagues observed between the proteins STK11 and CDK4 that suggested that STK11-silenced lung cancer cells could be sensitive to CDK4 inhibition.

To further explore this connection, they treated lung cancer cells with and without STK11 expression with the CDK4 inhibitor palbociclib (marketed by Pfizer as Ibrance and approved by the US Food and Drug Administration for treatment of ER-positive and HER2-negative breast cancer). They found that STK11 silenced cells were more sensitive to the drug compared to wild type cells while overexpression of STK11 reduced palbociclib sensitivity.

Typically, HER2 is measured to assess a patient's likelihood of responding to CDK4 inhibition, Fu noted, but, he said, given the link the study established between STK11 and CDK4, it might be useful to look at the expression and activation of STK11 and its substrates, as well.

The Emory researchers generated the initial version of the OncoPPi resource in lung cancer, but Fu said they now plan to expand it to cover a variety of tumor types. Next they hope to generate interaction data for cancer-associated proteins known to be implicated in a variety of tumor types and those known to be particularly important to specific tumor types, he said.

The OncoPPi research is co-led by Fadlo Khuri, a medical oncologist and president of the American University of Beirut. Fu and his colleagues are part of the National Cancer Institute's Cancer Targeted Discovery and Development (CTD2) Network, and they also plan to integrate their OncoPPI data with data from other researchers in that network and other NCI initiatives. Data from the project has been deposited at the CTD2 Network data portal to facilitate sharing with the larger community.

The researchers will also place the OncoPPi data in a web portal that Fu said links their data with other existing databases and to clinical and genomic data for each of the oncogenes they investigated.