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A computational model developed by researchers at the Institute for Biomedical Research (IRB Barcelona) and the Center for Genome Regulation (CRG) can predict which drugs are most effective in treating diseases caused by mutations that cause protein synthesis to stop, leading to disease. unfinished protein.
The research results published today in Nature Genetics mark an important step in helping personalize treatment by matching patients with specific mutations with the most promising drug candidates. The predictive model is a public resource called RTDetective that accelerates the design, development and effectiveness of clinical trials for many different types of genetic diseases and cancer.
Truncated proteins are the result of a sudden stop in protein synthesis. In our bodies, this is caused by the emergence of “meaningless mutations” that act like stop signs or roadblocks, causing cellular machines to suddenly brake. In many cases, these unfinished proteins stop working and lead to disease.
The presence of these stop signals is the basis of up to one-fifth of single-gene diseases, including certain types of cystic fibrosis and Duchenne Muscular Dystrophy. They also often appear in tumor suppressor genes and often help control cell growth. Stop signs deactivate these genes and are a major cause of cancer.
Diseases caused by truncated proteins can be targeted through meaningless inhibitory therapies, which help cells ignore or “interpret” stop signals that appear during protein production. Cells with higher read-through rates will produce more full-length or near-full-length proteins.
The study suggests that clinical trials of nonsense inhibitory therapies to date may have used ineffective patient drug combinations. This is because the effectiveness of a drug in promoting read-through depends not only on the nonsense mutation, but also on the genetic code immediately surrounding it.
The researchers made the discovery after studying 5,800 premature cessation symptoms that lead to the disease and testing the efficacy of eight different drugs on each symptom. The data comes from patient reports submitted to freely accessible public archives such as ClinVar, as well as research projects such as the Cancer Genome Atlas (TCGA), which collects and analyzes genetic information, including premature termination codons, from thousands of patients with cancer and genetic diseases.
They found that a drug that works on one premature stop signal may not work on another drug, even within the same gene, because of the local sequence background surrounding the premature stop signal.
“Think of the DNA sequence as a road, and one of the stops mutations appears as a roadblock. We showed that crossing this obstacle depends largely on the surrounding environment. Some mutations are surrounded by obvious detours, while others are full of potholes or deaths. Student at the IRB and Genome Regulation Center in Barcelona.
The researchers generated a large amount of data by testing multiple different drug combinations to bypass stop signs, obtaining a total of more than 140,000 individual measurements. The data was big enough to train accurate predictive models, which they used to create RTDetective.
The researchers used the algorithm to predict the effectiveness of different drugs on each of the 32.7 million possible stop signals that may be generated in the human genomic RNA transcripts. It is expected that at least one of the six drugs tested will be able to achieve a read-through rate of more than 1% in 87.3% of all possible stop signs and a read-through rate of 2% in nearly 40% of cases.
The results are promising because higher read-through rates are often associated with better treatment outcomes. For example, Hurler syndrome is a serious genetic disease caused by nonsense mutations in the IDUA gene. Previous research has shown that with just 0.5% reading, individuals can partially reduce the severity of the disease by producing very small amounts of functional protein. RTDetective predicts that at least one of the drugs can achieve read-through above this threshold.
“Imagine a patient being diagnosed with a genetic disease. Genetic testing determines the exact mutation, and then computer models suggest which drug is best to use. This wise decision is what we hope to achieve in this area the promise of personalized medicine.
The study also suggests ways to quickly deliver new drugs to the right patients. “When a new read-through drug is discovered, we can use this approach to quickly model it and identify all patients who are most likely to benefit,” Professor Reiner added.
Researchers next plan to confirm the function of the protein produced by reading through the drug, a key step in verifying its clinical applicability. The team also plans to explore other strategies that can be used in conjunction with nonsense suppression therapy to further improve treatment outcomes, especially in cancer.
Fran, a research professor at ICREA, concluded: “Our research not only opens up new avenues for treating inherited genetic diseases, but it is also important for treating tumors because most cancers have mutations that cause the protein to terminate prematurely.” Supek of the Barcelona IRB is one of the study’s lead authors.
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Original text:https://medicalxpress.com/news/2024-08-algorithm-drugs-genetic-disorders-cancer.html
More information: Ignasi Toledano et al., Genome-size quantification and prediction of small-molecule pathogenic stop codon read-through, Nature Genetics (2024). DOI：10.1038/s41588-024-01878-5
Journal information: Nature Genetics
Journal information: Nature Genetics
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