{"id":49636,"date":"2019-10-29T00:00:00","date_gmt":"2019-10-28T23:00:00","guid":{"rendered":"http:\/\/www.pcb.ub.edu\/scientists-build-machine-learning-based-algorithm-to-predict-which-cancer-patients-benefit-from-immunotherapy\/"},"modified":"2020-11-18T14:56:06","modified_gmt":"2020-11-18T13:56:06","slug":"scientists-build-machine-learning-based-algorithm-to-predict-which-cancer-patients-benefit-from-immunotherapy","status":"publish","type":"post","link":"https:\/\/www.pcb.ub.edu\/en\/scientists-build-machine-learning-based-algorithm-to-predict-which-cancer-patients-benefit-from-immunotherapy\/","title":{"rendered":"Scientists build machine learning-based algorithm to predict which cancer patients benefit from immunotherapy"},"content":{"rendered":"

Mutations in our DNA can disrupt protein synthesis, sometimes causing truncated proteins which don\u2019t 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\u00a0nonsense-mediated mRNA decay\u00a0(NMD).<\/p>\n

To better understand the effect of NMD on human disease, researchers at the Institute for Research in Biomedicine (IRB Barcelona), in collaboration with the Centre for Genomic Regulation (CRG) and Radboud University,\u00a0built\u00a0NMDetective, a tool describing every possible nonsense mutation that can occur in the human genome.\u00a0\u00a0Developed by large-scale statistical analyses based on machine learning, the algorithm identifies which mutations in the genome are susceptible to NMD.\u00a0<\/p>\n

As described today in\u00a0Nature Genetics, scientists used\u00a0NMDetective\u00a0to analyse thousands of genetic variants that give rise to\u00a0hereditary diseases\u00a0in humans. \u201cWe were surprised to observe that, in many cases, NMD activity was predicted to lead to a greater severity of the disease,\u201d says\u00a0Fran Supek, ICREA researcher,\u00a0head of the\u00a0Genome Data Science <\/a>laboratory\u00a0at IRB Barcelona and leader of the team that built the tool.<\/p>\n

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\u00a0precision medicine\u00a0approach\u00a0to determine the mutation responsible for the disease and the effect of NMD on this mutation, and this is precisely where\u00a0NMDetective\u00a0comes into play.<\/p>\n

Researchers also studied the role of NMD in\u00a0cancer\u00a0and the interaction between the tumour and the immune system. \u201cWe discovered that NMD activity is important for the prediction of successful outcome of\u00a0immunotherapy\u00a0in cancer,\u201d explains\u00a0Supek. Researchers found NMD hides mutations that would otherwise trigger the immune system. Therefore,\u00a0NMDetective\u00a0can be used to analyse the mutations present in the tumour, in order to better distinguish between cancer patients that respond to immunotherapy from those who do not respond to immunotherapy.\u00a0<\/p>\n

\u201cTumours are riddled with genetic mutations that\u00a0should\u00a0make all sorts of weird proteins. The immune system should pick these up, identifying the cells gone wrong and destroying them,\u201d says\u00a0Ben Lehner, a researcher at the Centre for Genomic Regulation who also took party in the study. \u201cBut many of\u00a0these weird proteins never actually get made because of NMD.\u201d\u00a0<\/p>\n

\u201cThis algorithm can distinguish which mutations will and won\u2019t trigger this error-checking system. What\u2019s 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,\u201d explains\u00a0Lehner.<\/p>\n

\u201cHere we see an example where publicly available cancer genomics data\u00a0can be \u2018reused\u2019 through machine learning approaches to better understand\u00a0 biological processes such as NMD,\u201d says lead author\u00a0Rik\u00a0Lindeboom, a researcher at Radboud University in the Netherlands. \u201cWhat makes this study especially exciting, is that we could\u00a0directly translate this fundamental research into insights that are\u00a0relevant for clinicians and patients.\u201d<\/p>\n

\u25ba Reference article: <\/strong>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<\/a>“. Nature Genetics (2019) DOI:10.1038\/s41588-019-0517-5<\/p>\n

\u25ba For further information: IRB Barcelona website\u00a0[+]<\/a><\/strong><\/p>\n

\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

Scientists at the Institute for Research in Biomedicine (IRB Barcelona<\/a>) in the Barcelona Science Park, in collaboration with the Centre for Genomic Regulation (CRG) and Radboud University, have built a tool that detects genetic mutations that trigger the immune system, helping identify which cancer patients are more likely to benefit from immunotherapy. The algorithm also reveals which people living with hereditary diseases may benefit from drugs that already exist. The new technology\u2019s potential is described today in Nature Genetics.<\/p>\n

\u00a0<\/p>\n","protected":false},"author":1,"featured_media":53663,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-49636","post","type-post","status-publish","format-standard","has-post-thumbnail","category-uncategorized"],"acf":[],"yoast_head":"\nScientists build machine learning-based algorithm to predict which cancer patients benefit from immunotherapy - Parc Cient\u00edfic de Barcelona<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pcb.ub.edu\/en\/scientists-build-machine-learning-based-algorithm-to-predict-which-cancer-patients-benefit-from-immunotherapy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Scientists build machine learning-based algorithm to predict which cancer patients benefit from immunotherapy - Parc Cient\u00edfic de Barcelona\" \/>\n<meta property=\"og:description\" content=\"Scientists at the Institute for Research in Biomedicine (IRB Barcelona) in the Barcelona Science Park, in collaboration with the Centre for Genomic Regulation (CRG) and Radboud University, have built a tool that detects genetic mutations that trigger the immune system, helping identify which cancer patients are more likely to benefit from immunotherapy. 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