Researchers silence leading cancer-causing gene

Using bioluminescence, researchers showed that the novel molecule “KRAS silencing RNA” or "KRAS siRNA” (right) reduced the size of a tumor in mice. Researchers used a “non-KRAS silencing” molecule as the control (left) as a comparison. Credit: Image courtesy of University of North Carolina Health Care

Researchers from the UNC School of Medicine and colleagues at The University of Texas MD Anderson Cancer Center have developed a new approach to block the KRAS oncogene, one of the most frequently mutated genes in human cancer. The approach, led by Chad Pecot, MD, an assistant professor of medicine at UNC, offers another route to attack KRAS, which has proven to be an elusive and frustrating target for drug developers.

The new method relies upon a specifically sequenced type of small interfering RNA -- or siRNA. The findings, published in the journal Molecular Cancer Therapeutics, show that using a form of siRNA to halt KRAS not only dramatically stunted the growth of lung and colon cancers in cultured cells and mice but also stopped metastasis -- the main cause of cancer deaths.

"KRAS has been widely regarded as an undruggable protein, but we show that that's simply not the case," said Pecot, the study lead author and member of the UNC Lineberger Comprehensive Cancer Center.

KRAS is a signaling molecule -- a protein switch that triggers a cascade of molecular events that tell cells to grow and survive. Mutations in the KRAS gene create a switch that is perpetually "on," causing cells to divide uncontrollably. KRAS mutations are present in roughly 30 percent of human cancers, particularly lung, colon, pancreatic, and thyroid.

"It is the elephant in the room," Pecot said. "KRAS was one of the first cancer-causing genes ever discovered, and it was the obvious target to go after. People have been trying for decades to hit it, but they haven't had much luck."

Inhibiting KRAS signaling has been tricky because it lacks good pockets or crevices for small molecules and drugs to bind to. Some researchers have tried instead to target the proteins downstream in the KRAS signaling cascade, but those attempts have also had limited success.

Rather than try another conventional approach, Pecot decided to use a new genetic tool known as RNA interference -- or RNAi -- to destroy the KRAS protein before it fully forms. RNAi uses bits of synthetically engineered RNA -- the single-stranded molecule transcribed from DNA -- to silence specific genes. These bits of RNA bind to specific genetic messages called mRNA in the cell and direct enzymes to recognize the messages as enemies. In this context, the enzymes destroyed the genetic messages of KRAS mRNA so that KRAS can't be made. As a result, the cells don't grow, replicate, or move nearly as well.

RNAi has shown great promise in the treatment of liver diseases, viral infections, and cancers. To see if this approach could thwart the KRAS oncogene, Pecot and his colleagues first had to test different sequences of RNA to determine which one most effectively tagged KRAS for destruction. Of five RNA sequences, the researchers identified two candidates worthy to take into cancer models.

When they delivered these sequences into tissue culture cells, they found that the siRNAs destroyed more than 90 percent of the KRAS gene messages, significantly impairing the growth of cancer cell lines. The technique also led to marked reduction of two signaling molecules called pERK and pMEK, which lie downstream of KRAS and have been implicated in cancer cell proliferation and tumor growth.

Next, Pecot and his colleagues tested the siRNAs in mouse models of lung and colon cancer. They wrapped the sequences in protective lipid nanoparticles and delivered the siRNA solution into the mice. The researchers found that this treatment significantly slowed the growth of primary tumors. For example, tumors from colon cancer models that had been treated with the KRAS siRNAs were 69 percent smaller than tumors treated with control RNA sequences.

In addition, the researchers discovered that silencing KRAS stemmed the spread of cancer cells to other organs. The siRNA treatment reduced the number of these secondary malignant growths by about 80 percent in mice with lung cancer and to a similar degree in colon cancer models.

Pecot's findings come on the heels of two other papers using siRNAs to target KRAS, one from Frank McCormick's laboratory at the University of California at San Francisco and the other from Tyler Jacks' laboratory at the Massachusetts Institute of Technology. What sets the UNC study apart is that it demonstrates that this approach can be used to control the development of metastatic disease.

"Having all three papers come out at about the same time is encouraging because it means that KRAS is druggable if you use outside-the-box methods," Pecot said. "Now, we essentially have three platforms for targeting KRAS with siRNAs that may get to the clinic."

Pecot said the results, while promising, are just a first step in combating this cancer-causing gene. Ultimately, the siRNA sequences will have to be designed to specifically target the mutant form of KRAS without disrupting the normal form of the gene, which is necessary for maintaining normal growth in healthy cells.

Source: University of North Carolina Health Care

CT scans could bolster forensic database to ID unidentified remains

Cranium image reconstructed from CT scans. Credit: North Carolina State University

A study from North Carolina State University finds that data from CT scans can be incorporated into a growing forensic database to help determine the ancestry and sex of unidentified remains. The finding may also have clinical applications for craniofacial surgeons.

"As forensic anthropologists, we can map specific coordinates on a skull and use software that we developed -- called 3D-ID -- to compare those three-dimensional coordinates with a database of biological characteristics," says Dr. Ann Ross, a professor of anthropology at NC State and senior author of a paper describing the work. "That comparison can tell us the ancestry and sex of unidentified remains using only the skull -- which is particularly valuable when dealing with incomplete skeletal remains."

However, the size of the 3D-ID database has been limited by the researchers' access to contemporary skulls that have clearly recorded demographic histories.

To develop a more robust database, Ross and her team launched a study to determine whether it was possible to get good skull coordinate data from living people by examining CT scans.

The University of Pennsylvania Museum's Morton Collection provided the NC State researchers with CT scans of 48 skulls. Researchers mapped the coordinates of the actual skulls manually using a digitizer, or electronic stylus. Then they compared the data from the CT scans with the data from the manual mapping of the skulls.

The researchers found that eight bilateral coordinates on the skull -- those found on either side of the head -- were consistent for both the CT scans and manual mapping.

"This will allow us to significantly expand the 3D-ID database," Ross says. "And these bilateral coordinates give important clues to ancestry, because they include cheekbones and other facial characteristics."

However, the five midline coordinates the researchers tested showed inconsistencies between the CT scans and manual mapping. Midline coordinates are those found along the center of the skull, such as the bridge of the nose.

"More research is needed to determine what causes these inconsistencies, and whether we'll be able to retrieve accurate midline data from CT scans," says Amanda Hale, a former master's student at NC State and lead author of the paper.

This research may also help craniofacial surgeons. "An improved understanding of the flaws in how CT scans map skull features could help surgeons more accurately map landmarks for reconstructive surgery," Hale says.

Source: North Carolina State University

Mysteries of 'molecular machines' revealed: Phenix software uses X-ray diffraction spots to produce 3-D image

This is a membrane protein called cysZ, imaged in 3 dimensions with Phenix software using data that could not previously be analyzed. Credit: Los Alamos National Laboratory

Scientists are making it easier for pharmaceutical companies and researchers to see the detailed inner workings of molecular machines.

'Inside each cell in our bodies and inside every bacterium and virus are tiny but complex protein molecules that synthesize chemicals, replicate genetic material, turn each other on and off, and transport chemicals across cell membranes,' said Tom Terwilliger, a Los Alamos National Laboratory scientist.

'Understanding how all these machines work is the key to developing new therapeutics, for treating genetic disorders, and for developing new ways to make useful materials.'

To understand how a machine works you have to be able to see how it is put together and how all its parts fit together. This is where the Los Alamos scientists come in. These molecular machines are very small: a million of them placed side by side would take up less than an inch of space. Researchers can see them however, using x-rays, crystals and computers. Researchers produce billions of copies of a protein machine, dissolve them in water, and grow crystals of the protein, like growing sugar crystals except that the machines are larger than a sugar molecule.

Then they shine a beam of X-rays at a crystal and measure the brightness of each of the thousands of diffracted X-ray spots that are produced. Then researchers use the powerful Phenix software, developed by scientists at Los Alamos, Lawrence Berkeley National Laboratory, Duke and Cambridge universities, to analyze the diffraction spots and produce a three-dimensional picture of a single protein machine. This picture tells the researchers exactly how the protein machine is put together.

The 3-D Advance

Recently Los Alamos scientists worked with their colleagues at LBNL and Cambridge University to make it even easier to visualize a molecular machine. In a report in the journal Nature Methods this month, Los Alamos scientists and their team show that they can obtain three-dimensional pictures of molecular machines using X-ray diffraction spots that could not previously be analyzed.

Some molecular machines contain a few metal atoms or other atoms that diffract X-rays differently than the carbon, oxygen, nitrogen, and hydrogen atoms that make up most of the atoms in a protein. The Phenix software finds those metal atoms first, and then uses their locations to find all the other atoms. For most molecular machines, however, metal atoms have to be incorporated into the machine artificially to make this all work.

The major new development to which Los Alamos scientists have contributed was showing that powerful statistical methods could be applied to find metal atoms even if they do not scatter X-rays very differently than all the other atoms. Even metal atoms such as sulfur that are naturally part of almost all proteins can be found and used to generate a three-dimensional picture of a protein. Now that it will often be possible to see a three-dimensional picture of a protein without artificially incorporating metal atoms into them, many more molecular machines can be studied.

Cracking the Cascade

Molecular machines that have recently been seen in three-dimensional detail include a 'huge' molecular machine called Cascade that was reported in the journal Science this summer. The Cascade machine is present in bacteria and can recognize DNA that comes from viruses that infect the bacteria. The Cascade machine is made up of 11 proteins and an RNA molecule and looks like a seahorse, with the RNA molecule winding through the whole 'body' of the seahorse. If a foreign piece of DNA in the bacterial cell is complementary to part of the RNA molecule then another specialized machine can come by and chop up the foreign DNA, saving the bacterium from infection.

Los Alamos and Cambridge University scientists who were developing the Phenix software were part of the team that visualized this protein machine for the first time. The Phenix software has been used to determine the three-dimensional shapes of over 15,000 different protein machines and has been cited by over 5000 scientific publications.

Source: DOE/Los Alamos National Laboratory