Genetic variants can be classified into two different groups according to whether they affect the parts of the genome that tell cells how to make proteins or not. The former are called coding mutations, and they can have large effects in an individual. The latter are called non-coding mutations and their effects are usually more modest. So far, we have identified hundreds of non-coding variants associated to higher or lower cancer risk, but we have had limited success in finding coding variants.
We believe that our limited success with coding variants is due to the way we analyze them. So far, we either look at the effect of each coding variant on its own, or we group them according to the protein they affect. However, proteins have different parts with different functions, and the effect of each coding variant likely depends on which precise part of the protein it affects. We have recently developed a new computational tool that uses the information of the different parts of the protein to group the genetic coding variants. We will use this new tool to identify new protein regions that are associated with higher or lower cancer risk.
Finally, we will develop an artificial intelligence model that integrates the effects of both, the coding and non-coding variants of each individual, to predict his or her risk of suffering cancer in the future. If successful, this model could help physicians decide whether a person has a high or low risk of cancer. This is extremely important, as people with high risk should be screened more often, whereas people with low risk might be able to avoid some screens and the discomfort that they might have associated.