News from CRG
Scientists at the Institute for Research in Biomedicine (IRB Barcelona), in collaboration with the Centre for Genomic Regulation (CRG) and Radboud University, have developed an algorithm that can predict which cancer patients are more likely to benefit from immunotherapy.
Mutations in our DNA can disrupt protein synthesis, sometimes causing truncated proteins which don’t work as intended. Known as nonsense mutations, these types of alterations can give rise to hereditary diseases and different types of cancer. To keep the number of truncated proteins to a minimum, human cells recognise and remove RNAs with nonsense mutations through a quality control process known as nonsense-mediated mRNA decay (NMD).
To better understand the effect of NMD on human disease, researchers built NMDetective, a tool describing every possible nonsense mutation that can occur in the human genome. Developed by large-scale statistical analyses based on machine learning, the algorithm identifies which mutations in the genome are susceptible to NMD.
As described today in Nature Genetics, scientists used NMDetective to analyse thousands of genetic variants that give rise to hereditary diseases in humans. “We were surprised to observe that, in many cases, NMD activity was predicted to lead to a greater severity of the disease,” says Fran Supek, ICREA researcher, head of the Genome Data Science laboratory at IRB Barcelona and leader of the team that built the tool.
The results of the study suggests that pharmacological NMD inhibition could slow the progression of many different genetic diseases. To distinguish which patients would benefit from this therapy, it is necessary to apply a precision medicine approach to determine the mutation responsible for the disease and the effect of NMD on this mutation, and this is precisely where NMDetective comes into play.
Rik G.H. Lindeboom, Michiel Vermeulen, Ben Lehner & Fran Supek. "The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer immunotherapy." Nature Genetics (2019) DOI: 10.1038/s41588-019-0517-5