New combination therapy developed for multiple myeloma

This is Steven Grant, M.D., Shirley Carter Olsson and Sture Gordon Olsson Chair in Cancer Research, associate director for translational research and program co-leader of Developmental Therapeutics at VCU Massey Cancer Center. Credit: VCU Massey Cancer Center

Each year, more than 25,000 Americans are diagnosed with multiple myeloma, a form of blood cancer that often develops resistance to therapies. However, researchers at Virginia Commonwealth University Massey Cancer Center are reporting promising results from laboratory experiments testing a new combination therapy that could potentially overcome the resistance hurdle.

While several drugs are effective against multiple myeloma, including the proteasome inhibitor bortezomib, multiple myeloma cells are often able to survive by increasing the production of a protein known as Mcl-1. Mcl-1 regulates a number of processes that promote cell survival and has been implicated in resistance to anti-myeloma drugs that were initially effective. However, a team of researchers led by Xin-Yan Pei, M.D., Ph.D., and Steven Grant, M.D., recently published the findings of a study in the journal PLoS ONE demonstrating that a novel drug combination both reduces Mcl-1 expression and disrupts its interactions with other proteins to effectively kill multiple myeloma cells. The therapy combines a type of drug known as a Chk1 inhibitor with another called a MEK inhibitor. Chk1 inhibitors prevent cells from arresting in stages of the cell cycle that facilitate the repair of DNA damage, while MEK inhibitors prevent cells from activating a variety of proteins that regulate DNA repair processes while promoting the accumulation of pro-death proteins.

"This research builds on our previous studies that showed exposing multiple myeloma and leukemia cells to Chk1 inhibitors activated a protective response through the Ras/MEK/ERK signaling pathway," says Pei, instructor in the Department of Internal Medicine at the VCU School of Medicine. "By combining a Chk1 inhibitor with a MEK inhibitor, we have developed one of only a limited number of strategies shown to circumvent therapeutic resistance caused by high expressions of Mcl-1."

In laboratory experiments, the scientists enforced overexpression of Mcl-1 in human multiple myeloma cells. They found that this caused the cells to become highly resistant to bortezomib, but it failed to protect them from the Chk1/MEK inhibitor regimen. 

Additionally, the combination therapy was able to completely overcome resistance due to microenvironmental factors associated with increased expression of Mcl-1. A cell's microenvironment consists of surrounding cells and the fluids in which they reside, and the communication between cancer cells and their surrounding cells can significantly impact resistance. Mcl-1 plays a key role in this communication by facilitating events that promote cancer cell survival.

"Not only was the combination therapy effective against multiple myeloma cells, it notably did not harm normal bone marrow cells, raising the possibility of therapeutic selectivity," says Grant, the study's lead investigator and Shirley Carter Olsson and Sture Gordon Olsson Chair in Cancer Research, associate director for translational research and program co-leader of Developmental Therapeutics at VCU Massey Cancer Center. "We are hopeful that this research will lead to better therapies for multiple myeloma, and help make current therapies more effective by overcoming resistance caused by Mcl-1."

The researchers have started initial discussions with clinical investigators and drug manufacturers with hopes of developing a clinical trial testing a combination of Chk1 and MEK inhibitors in patients with refractory multiple myeloma. It is too early to estimate when the trial will open.

Source: Virginia Commonwealth University

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Weigh-in once a week or you'll gain weight

The researchers found that weight loss was related to how often individuals weighed themselves. Credit: Image courtesy of Cornell Food & Brand Lab

Stepping on the scale is common among dieters but how does the frequency of weigh-ins impact weight? A new study in PLOS ONE showed that the more frequently dieters weighed themselves the more weight they lost, and if participants went more than a week without weighing themselves, they gained weight.

The researchers analyzed 2,838 weight measurements (up to a years' worth of weigh-ins) from 40 overweight individuals (with a body mass index of 25 and over) who indicated that weight loss was a personal goal or concern. The researchers found that weight loss was related to how often individuals weighed themselves. "The more often you weigh yourself the more weight you lose," says to lead author Elina Helander from Tempere Univeristy of Technology in Finland. This observational study cannot prove causation -- it may be that less serious dieters weight themselves less or that dieters who stop losing weight stop weighting themselves. The average time that participants could go between weighting without gaining weight was 5.8 days or about a weekly weigh-in.

Previous Findings by the Research Team

Weigh yourself at least once a week if you wish to lose weight, and weighing yourself everyday may help you stay on track. A previous study by the same research team found that your weight naturally fluctuates throughout the week and that most people weigh the least on Wednesday. To summarize both studies Brian Wansink, PhD, Director of the Cornell Food and Brand Lab and author of Slim by Design: Mind Eating Solutions for Everyday Life advises, "The bottom line is: If you want to lose weight, it's best to weigh yourself every day. But if you weigh yourself only once a week, do it on Wednesday because that will give you the most accurate reading."

Source: Cornell Food & Brand Lab

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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

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