Algorithm Could Help Identify Dominating Mutations for Tumor Formation

Researchers in Texas have described a theoretical method to increase the potential of identifying cancer-causing mutations before tumors take hold.

As described in Biophysical Journal, the theoretical framework essentially identifies and ignores transition pathways that don’t contribute much to the fixation of mutations in a cell that goes on to establish a tumor. “The dynamics of tumor formation is a very complex phenomenon due to multiple involved biochemical and biophysical processes,” according to the paper’s abstract. “It leads to a very large number of possible pathways on the road to final fixation of all mutations that marks the beginning of the cancer, complicating the understanding of microscopic mechanisms of tumor formation.”

The paper notes that although there are many possible pathways for the fixation of all mutations in the system, there are only a few dominating pathways on the road to tumor formation. By analyzing the effective energy landscapes of cellular transformation pathways implicated in a variety of cancers, the tool would essentially limit the number of pathways to the few most likely to lead to cancer. 

“In some sense, cancer is a bad-luck story,” says Anatoly Kolomeisky, professor of chemistry and of chemical and biomolecular engineering at Rice University. “We think we can decrease the probability of this bad luck by looking for low-probability collections of mutations that typically lead to cancer. Depending on the type of cancer, this can range between two mutations and 10.”

Calculating the effective energies that dictate interactions in biomolecular systems can predict how they behave. The theory is commonly used to predict how a protein will fold, based on the sequence of its constituent atoms and how they interact. The Rice team is applying the same principle to cancer initiation pathways that operate in cells but sometimes carry mutations missed by the body’s safeguards. When two or more of these mutations are fixed in a cell, they are carried forward as the cells divide and tumors grow.

By their calculations, the odds favor the most dominant pathways, those that carry mutations forward while expending the least amount of energy, Kolomeisky said. “Instead of looking at all possible chemical reactions, we identify the few that we might need to look at,” he explained. “It seems to us that most tissues involved in the initiation of cancer are trying to be as homogenous as possible. The rule is a pathway that decreases heterogeneity is always going to be the fastest on the road to tumor formation.”

The huge number of possible pathways seems to make narrowing them down an intractable problem. “But it turned out that using our chemical intuition and building an effective free-energy landscape helped by allowing us to calculate where in the process a mutation is likely to become fixated in a cell,” Kolomeisky said.

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

Much of the credit goes to research assistant Cade Spaulding, who under the direction of lead author and postdoctoral researcher Hamid Teimouri created the algorithms that greatly simplify the calculations. Having graduated from a Houston high school two years early, Spaulding joined the Rice lab last year at 16 and will attend Trinity University in San Antonio this fall.

“Cade has outstanding ability in computer programming and in implementing sophisticated algorithms despite his very young age,” Kolomeisky said. “He came up with the most efficient Monte Carlo simulations to test our theory, where the size of the system can involve up to a billion cells.”

The recent publication follows a 2019 paper in which the Rice lab modeled stochastic (random) processes to learn why some cancerous cells overcome the body’s defenses and trigger the spread of the disease.

But understanding how those cells become cancerous in the first place could help head them off at the pass, Kolomeisky said. “This has implications for personalized medicine,” he said. “If a tissue test can find mutations, our framework might tell you if you are likely to develop a tumor and whether you need to have more frequent checkups. I think this powerful framework can be a tool for prevention.”