Chemists distort the chances of preventing cancer

The path to cancer prevention is long and difficult for legions of researchers, but new work from Rice University scientists shows there may be short paths.

Rice chemist Anatoly Kolomeisky, lead author and Dr. Hamid Teymouri and researcher Cade Spaulding develop theoretical foundations to explain how cancers caused by more than one genetic mutation can be more easily identified and possibly stopped.

In essence, it does this by detecting and ignoring transitional pathways that do not promote the fixation of mutations in the cell that further creates the tumor.

Research in Biophysical Journal describes their analysis of efficient energy landscapes of cellular transformation pathways relevant to different cancers. The ability to limit the number of pathways to the few that are most likely to cause cancer can help find ways to stop the process before it actually begins.

“In a sense, cancer is a failure,” said Kolomeisky, a professor of chemistry and chemical and biomolecular engineering. “We believe we can reduce the likelihood of this failure by looking for collections of low-probability mutations that usually lead to cancer. Depending on the type of cancer, it can range from two to 10 mutations.”

Calculating the effective energy that determines the interactions in biomolecular systems can predict how they behave. The theory is commonly used to predict how a protein will coagulate, based on the sequence of its atoms and how they interact.

Rice’s team applies the same principle to cancer initiation pathways that act in cells but sometimes carry mutations missed by the body’s defenses. When two or more of these mutations are fixed in a cell, they are transferred as cells divide and tumors grow.

According to their estimates, the chances contribute to the most dominant pathway, those that carry mutations forward, while consuming the least amount of energy, said Kolomeisky.

“Instead of looking at all the possible chemical reactions, we identify the few that we may need to look at,” he explained. “It seems to us that most of the tissues involved in cancer initiation strive to be as homogeneous as possible. The rule is that the path that reduces heterogeneity will always be the fastest on the path to tumor formation. ”

The sheer number of possible paths seems to make narrowing them an unsolvable problem. “But it turned out that the use of our chemical intuition and the creation of an efficient landscape of free energy helped us to calculate where in the process the mutation can be fixed in the cell,” said Kolomeisky.

The team simplified the calculations by initially focusing on pathways that involve only two mutations that, when fixed, initiate tumor. Kolomeisky said mechanisms involving more mutations would complicate the calculations, but the procedure remains the same.

Much credit goes to Spaulding, who under the leadership of Teymour has created algorithms that greatly simplify calculations. The invited researcher was 12 years old when he first met with Kolomeisky to ask for guidance. After graduating from high school in Houston two years earlier, he joined Rice’s lab last year at age 16 and will be studying at Trinity University in San Antonio this fall.

“Cade has excellent skills in computer programming and the implementation of complex algorithms, despite their very young age,” said Kolomeisky. “He came up with the most effective Monte Carlo simulation to test our theory, where the size of a system could include up to a billion cells.”

Spaulding said the project combined chemistry, physics and biology in a way that matched his interests as well as his programming skills. “It was a good way to bring together all branches of science as well as programming, which I find most interesting,” he said.

The study follows Fr. Paper 2019 in which Rice’s lab simulated stochastic (random) processes to find out why some of them cancer cells overcome the body’s defenses and provoke the spread of the disease.

But understanding how these cells primarily become cancerous could help repel them on the pass, Kolomeisky said. “It matters to personalized medicine,” he said. “If tissue analysis can find mutations, our database can tell you whether there is a chance of developing a tumor and whether you need to be examined more often. I think this powerful structure can be a tool of prevention. ”