New CU study illuminates how cancer-killing gene may actually work
Scientists armed with a supercomputer and a vast trove of newly collected data on the body’s most potent “tumor suppressor” gene have created the best map yet of how the gene works, an accomplishment that could lead to new techniques for fighting cancers, which are adept at disabling the gene in order to thrive.
Scientists from the University of Colorado Cancer Center and the University of Colorado Boulder used a new technology to tease out how the p53 gene—which is responsible for recognizing damaged DNA in cells and then marking them for death—is actually able to suppress tumors by determining what other genes p53 regulates. The study, published in the journal eLife, describes dozens of new genes directly regulated by p53.
The study authors say further research can explore which of these genes are necessary for p53’s cancer-killing effect, how cancer cells evade these p53-activated genes, and how doctors may be able to moderate cancer cells’ ability to stay safe from these genetic attempts at suppression.
The exhaustively studied p53 gene—which has been the subject of 50,000 papers over more than 30 years of research—is the most commonly inactivated gene in cancers. When p53 acts, cells are stopped or killed before they can survive, grow, replicate and cause cancer.
As such, all cancers must deal with p53’s anti-tumor effects. Generally, there are two ways that cancer cells do this: by mutating p53 directly or by making a protein called MDM2, which stops p53 from functioning.
The current study explores cancer cells’ second strategy of blocking p53 function by producing the protein MDM2. Researchers have reasoned that treating a patient with an MDM2 inhibitor should allow p53 to restart its anti-cancer activities.
“MDM2 inhibitors, which are through phase I human trials, effectively activate p53 but manage to kill only about one-in-20 tumors,” said Joaquín Espinosa, an investigator at the CU Cancer Center, an associate professor of molecular, cellular and developmental biology at CU-Boulder, and the paper’s co-senior author. “The question is why. What else is happening in these cancer cells that allow them to evade p53?”
The answer is in what are called “downstream” effects of this gene, Espinosa said. The gene p53 doesn’t act against cancer alone. Instead, it is the master switch that sets in motion a cascade of genetic events that lead to the destruction of cancer cells. And until now, it was unclear exactly which other genes were directly activated by p53.
The imperfect knowledge of p53’s effects isn’t for lack of research interest. Researchers have written thousands of papers exploring p53’s targets and, in fact, many genetic targets are previously known. Most of these studies determine genetic targets by measuring levels of RNA.
When a gene is activated, it creates a protein. But between the gene and its protein product is the measurable step of RNA—the more gene-specific RNA, the more often a gene’s informational blueprint is carried to the cell’s manufacturing centers, and the more protein is eventually made. Researchers measure RNA to see which genes are being turned up or down by any other gene.
“But the problem is, measuring overall RNA levels is like looking in a huge bucket full of water—you see the water but you don’t really know where it came from. And imagine you are dripping water into this bucket—it takes a long time for those drips to create a measurable change in the overall water level,” Espinosa said.
Also, it’s very difficult with traditional methods to tell whether increased RNA is a direct effect of a gene or whether more RNA in the bucket is a product of two- or three-steps removed signaling. The p53 gene may activate another gene, which activates another, and down the line until the far downstream result is increased RNA.
“Instead, to measure the direct genetic targets of p53, we measured not the water in the bucket, but the faucet dripping into it,” Espinosa said.
The technique is called GRO-Seq, or Global Run-On Sequencing, and it measures new RNA being created, not overall RNA levels.
“Many teams around the world have been getting cancer cells, treating them with MDM2 inhibitors and waiting hours and hours to see what genes turn on and then only imprecisely. GRO-Seq lets us do it in minutes and the discoveries are massive,” Espinosa said.
The technique also generates an astounding quantity of data. That’s because it requires counting tens of thousands of RNA molecules before and after p53 activation.
So the experiment required designing algorithms—sets of instructions for solving problems—to sort through the data, and a computational biologist driving a supercomputer.
Espinosa partnered with computational biologist Robin Dowell at CU-Boulder’s BioFrontiers Institute. Together they co-mentored a postdoctoral fellow, Mary Allen, who was capable of doing both the molecular biological and computational aspects of the work.
“The data collection took a year and the computational analysis took a year and a half,” Dowell said.
The results helped the scientists pinpoint dozens of new genes directly regulated by p53, which may lead to future cancer-fighting strategies.
The technique of GRO-Seq also may have additional, far-reaching applications. For example, the Dowell lab plans to find RNAs whose synthesis is changed by a third copy of chromosome 21 in Down Syndrome individuals.
The study was supported by the Howard Hughes Medical Institute, the National Institutes of Health, the National Science Foundation, a Boettcher Foundation Seed Grant and the Boettcher Foundation’s Webb-Waring Biomedical Research program.