The best way to increase the odds of identifying oncogenic mutations before tumor formation

For many researchers, the road to cancer prevention has been long and arduous, but new work by Rice University scientists shows that there may be shortcu.

Rice chemist Anatoly Kolomeisky, lead author and postdoctoral researcher Hamid Teimouri, and research associate Cade Spaulding are developing a theoretical framework to explain how cancers caused by mutations in more than one gene are more easily identified and prevent.

Essentially, it does this by identifying and ignoring those transformational pathways that don't do much for the fixation of mutations in cells that go on to form tumo.

A study in the Journal of Biophysics describes their analysis of the efficient energy landscape of cellular transformation pathways involved in multiple cance.

"In a sense, cancer is a bad luck story," said Kolomeiski, a professor of chemistry and chemical and biomolecular engineeri.

Calculating the effective energies of indicated interactions in biomolecular systems can predict their behavi.

Rice's group is applying the same principle to cancer-initiating pathways within cells that sometimes carry mutations that are missed by the body's defens.

According to their calculations, the odds skewed toward the most dominant pathways, those carrying the mutation that expended the least energy, Kolomeiski sa.

"Instead of looking at all possible chemistries, we're going to identify a few that might need to be studied," he explain.

The sheer number of possible pathways seems to make narrowing them down a tricky probl.

The team initially focused on paths involving only two mutations, simplifying the calculatio.

This is thanks in large part to Spaulding, who, under the direction of Teimouri, invented algorithms that greatly simplified calculatio.

"Cade has an outstanding ability to program computers and implement complex algorithms, despite his young age," Kolomeiski sa.

Spaulding said the project combines chemistry, physics and biology in a way that suits his interests, as well as his computer programming skil.

The research follows a 2019 paper from Rice's lab in which the lab simulated stochastic (random) processes to understand why some cancer cells overcome the body's defenses and trigger the spread of disea.

But understanding how these cells became cancerous in the first place could help stop their spread, Kolomeisky sa.

"This has implications for personalized medicine," he sa.

"If a tissue test can find a mutation, our framework might tell you if you're likely to develop a tumor and if you need more frequent testi.

I think this powerful Frameworks can be tools for preventi.

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article title

Optimal pathways control fixation of multiple mutations during cancer initiation