Genetic diseases that result from truncated proteins can be targeted by so-called nonsense suppression therapies—drugs that prevent protein translation from terminating prematurely. A new computational model built by scientists at the Institute of Research in Biomedicine (IRB Barcelona) and the Centre for Genomic Regulation (CRG) may be able to use this information to predict which therapies will likely work best for some hereditary disorders as well as cancer.
Details of the model, which is called RTDetective, are provided in a new paper published in Nature Genetics titled, “Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules.” Its developers believe that the tool could be helpful in the design, development, and efficacy of clinical trials of drugs referred to as nonsense suppression therapies.
Understanding these drugs requires some background on truncated protein translation due to premature termination codons. This phenomenon has been linked to approximately 10–20% of inherited diseases including some types of cystic fibrosis and Duchenne muscular dystrophy. It is also a major mechanism by which tumor suppressor genes are inactivated in cancer.
Nonsense suppression therapies effectively target the problem by helping cells ignore or “read through” the instructions to stop that appear during protein production. Previous studies show that cells with higher readthrough rates go on to make more full-length or near full-length proteins. But many clinical trials of nonsense suppression therapies likely use ineffective patient-drug combinations. This is because a drug’s effectiveness in promoting readthrough depends not just on the nonsense mutation but also its environment.
According to the authors, this was one of the key findings from “quantifying the readthrough of roughly 5,800 human pathogenic stop codons by eight drugs.” The data for this study came from patient reports submitted to large public databases like ClinVar and the Cancer Genome Atlas. Recognizing the impact of the local sequence context enabled them to develop “models that predict readthrough efficacy by the best-performing drugs with very good performance genome-wide.”
Sequence context proved to be important for another reason. According to other results reported in the paper, while a drug might work well for one premature stop codon, it may not be effective for another within the same gene because of the local sequence. “We show that navigating through this obstacle depends heavily on the immediate surroundings,” said Ignasi Toledano, first author of the study and joint PhD student at IRB Barcelona and the Centre for Genomic Regulation. Using roads as an analogy, he explained that “some mutations are surrounded by well-marked detour routes while others are full of potholes or dead ends. This is what marks a drug’s ability to bypass obstacles and work effectively.”
Training computational models requires lots of data. To train RTDective, the scientists tested thousands of combinations of drugs and stop codons resulting in over 140,000 individual measurements. They then used the algorithm to predict how different drugs were likely to perform against each of the 32.7 million possible stop codons that can be generated in human RNA transcripts. Among other findings, RTDetective predicted that at least one drug could achieve more than 1% readthrough in just over 87% of all possible stop codons, and 2% readthrough for nearly 40% of cases.
Those are promising numbers, according to the research team. They could mean potential relief for patients with conditions like Hurler syndrome, a severe genetic disorder caused by a nonsense mutation in the IDUA gene. Forms of the disorder, which was formerly known as gargoylism, can be characterized by features such as developmental delays, cognitive decline, joint stiffness, and shorter life expectancy. Studies show that just 0.5% readthrough is enough to create a functional protein that mitigates the severity of the condition. In the study, RTDetective predicted that at least one of the drugs tested could achieve a readthrough higher than 0.5%.
“Imagine a patient is diagnosed with a genetic disorder. The exact mutation is identified through genetic testing and then a computer model suggests which drug is the best to use. This informed decision-making is the promise of personalized medicine we hope to unlock in the future,” said Ben Lehner, PhD, group leader at the CRG and the Wellcome Sanger Institute and one of the study’s main authors. Furthermore, “when a new readthrough drug is discovered, we can use this approach to rapidly build a model for it and to identify all the patients that are most likely to benefit,” he added.
For their next steps, the researchers plan to confirm that the proteins produced after administering nonsense suppressor therapies are functional. This is important for establishing the clinical applicability of RTDetective’s predictions. They’ll also explore other strategies that can be used in combination with the therapies to boost their effectiveness, particularly in cancer.
