Researchers develop a tool to predict which tumour patients will benefit from immunotherapy

Researchers develop a tool to predict which tumour patients will benefit from immunotherapy

Scientists have built an algorithm that detects genetic mutations that activate the immune system, facilitating the identification of cancer patients more prone to benefit from immunotherapy

Predictive decision trees optimized to decide if a cancer mutation will trigger the immune system. Rik G.H. Lindeboom, Radboud University

European researchers have developed an algorithm that identifies genetic alterations that trigger the immune system, helping distinguish which cancer patients are more likely to benefit from immunotherapy.
This algorithm also predicts which people affected by hereditary diseases may profit from treatments with commercially available medications.

Genetic alterations can perturb protein synthesis, sometimes leading to the formation of truncated proteins which do not operate as expected. Known as nonsense mutations, these types of modifications can give rise to different typologies of tumour and hereditary diseases. In order to maintain the percentage of truncated proteins to a minimum level, human cells recognise and remove RNAs with nonsense mutations via a ‘quality check’ process known as nonsense-mediated mRNA decay (NMD).

To study the effect of this surveillance mechanism on different human diseases, researchers at Institute for Research in Biomedicine (IRB, Barcelona), in collaboration with the Centre for Genomic Regulation (CRG, Barcelona) and Radboud University (Nijmegen), have developed NMDetective, an algorithm describing every possible nonsense mutation that can occur in our genome.  The purpose of NMDetective is to identify all the genetic alterations that are susceptible to NMD process. 

As described in the study published in Nature Genetics, researchers employed this tool to analyse thousands of genetic mutations causing 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, head of the Genome Data Science laboratory at IRB and leader of the team that developed the algorithm. This study shows that pharmacological inhibition of NMD could slow the progression of various genetic diseases. Employing NMDetective, researchers could define the mutation responsible for a specific disease and the effect of NMD on this alteration, eventually distinguishing which patients would benefit from the inhibition of this error-checking system.  

»What makes this study especially exciting, is that we could directly translate this fundamental research into insights that are relevant for clinicians and patients»

Scientists also investigated the role of NMD in cancer and the interaction between the malignant lesion and the immune system. “We discovered that NMD activity is important for the prediction of successful outcome of immunotherapy in cancer”, explains Supek. Researchers discovered that NMD hides genetic alterations that would otherwise activate the immune system. Therefore, NMDetective can be used to analyse the mutations present in the tumour, in order to better predict which cancer patients could positively respond to immunotherapy. 

This algorithm can distinguish which mutations will and won’t trigger this error-checking system. What’s exciting is that drugs that block NMD already exist, which could be used in conjunction with other treatments to help the immune system better recognise tumour cells”, explains Ben Lehner, a researcher at the CRG who also participated in the study. As stated by lead author and researcher at Radboud University Rik Lindeboom: “What makes this study especially exciting, is that we could directly translate this fundamental research into insights that are relevant for clinicians and patients”.

Reference: 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).

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