Creating a way to obtain data for generalized cases rather than individuals would increase the possibility of using this method for a variety of pathogens.
“We ran the model and it matched clinical data to a degree much better than I ever expected,” said Pritchard. “We did this from first principles (basic assumptions).”
As cancer cells divide, errors that are made in the copying of DNA result in mutations. One letter of DNA might be mistakenly replaced with another, but these mistakes are not completely random. Some letters are more easily substituted for others, and so these mutations happen more often. This creates a mutation bias — some substitutions are more likely. Thus the likeliness of a mistake, and not the reduction in sensitivity to drugs, can predict the resistance mutations that real patients develop.
“We shouldn't always focus on the strongest resistance mutation because there are other evolutionary forces that dictate what happens in the real world,” said Pritchard. “Sometimes drug resistance relies on biased random events.”
The researchers found that biased random mutations played a big part in the evolution of resistance in leukemia. They found similar results with breast, prostate and stomach cancers, although the effect size was not as large.
“The data are not quite as strong in the prostate and breast cancer setting,” said Pritchard. “In non-small cell lung cancer we didn't see this effect at all.”
According to the researchers, there are lots of places where evolutionary bias creates an abundance of mutations that are not the most resistant strains, but it is a spectrum with leukemias on one end; breast, prostate and stomach cancers in the middle; and non-small cell lung cancer on the other end.
“Our analysis establishes a principle for rational drug design: When evolution favors the most probable mutant, so should drug design,” the researchers said.
Source: Penn State University