HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
Sam F Greenbury, Mauricio Barahona, Iain G Johnston
Cell Systems (2019)
Many diseases that take a substantial human toll can be viewed as “progressive”. That is, a patient starts out healthy, then disease-related problems and/or symptoms develop over time. For example, a given case of cancer may begin with a patient acquiring a particular mutation, then other mutations building up in their genome over time.
How the same disease progresses in different patients often varies widely. Understanding this variability is important for precision medicine, where detailed knowledge of individual patients is used to design the best targeted treatments. However, learning the varied pathways of diseases and using them to predict future outcomes is challenging. Human researchers usually cannot hope to remember or analyse enough examples of patient data to provide the most reliable picture.
We previously developed an algorithm called HyperTraPS (hypercubic transition path sampling) to explore how biological systems evolve over time. We reasoned that HyperTraPS could also be used to learn the pathways of disease progression. In a new study in Cell Systems (free preprint available here) we used HyperTraPS to analyse biomedical data from many patients – hundreds, or thousands of individuals – to build a ‘road map’ of the different pathways that a disease takes over time.
Picture a river that branches out into a wide delta. Patients start out healthy – upstream in the river – and different patients go down different branches as the disease progresses and they acquire more symptoms. HyperTraPS learns the structure of the river delta from data, and predicts which river branches are more or less likely – and, importantly, where you'll end up if you're currently at a particular point.
By learning these branching patterns of disease progression, HyperTraPS has helped provide a refined risk assessment for malaria, based on data from thousands of Gambian children – as we’ve written about before. The approach also revealed diverse pathways of ovarian cancer progression, where the first mutation to occur appears to play a large role in determining subsequent mutations.
HyperTraPS is very generalisable and can be used to learn pathways by which mutations, symptoms, or other features develop over time from an initial state. We further used this generalisability to understand a biomedically important example of evolution – specifically, how tuberculosis evolves to become resistant to antibiotics.
Tuberculosis acquires resistance through mutations, and HyperTraPS has revealed the patterns of these mutations in TB bacteria reported from a group of 1000 Russian patients. These patterns help predict which mutation a bacterium will acquire next, and hence which drugs may be more effective for a given case. We’re following up with other applications of HyperTraPS, to learn about other progressive diseases, ageing, and evolution, and even to analyse how students complete tasks in online courses.
Sam F Greenbury, Mauricio Barahona, Iain G Johnston
Cell Systems (2019)
Many diseases that take a substantial human toll can be viewed as “progressive”. That is, a patient starts out healthy, then disease-related problems and/or symptoms develop over time. For example, a given case of cancer may begin with a patient acquiring a particular mutation, then other mutations building up in their genome over time.
How the same disease progresses in different patients often varies widely. Understanding this variability is important for precision medicine, where detailed knowledge of individual patients is used to design the best targeted treatments. However, learning the varied pathways of diseases and using them to predict future outcomes is challenging. Human researchers usually cannot hope to remember or analyse enough examples of patient data to provide the most reliable picture.
We previously developed an algorithm called HyperTraPS (hypercubic transition path sampling) to explore how biological systems evolve over time. We reasoned that HyperTraPS could also be used to learn the pathways of disease progression. In a new study in Cell Systems (free preprint available here) we used HyperTraPS to analyse biomedical data from many patients – hundreds, or thousands of individuals – to build a ‘road map’ of the different pathways that a disease takes over time.
Picture a river that branches out into a wide delta. Patients start out healthy – upstream in the river – and different patients go down different branches as the disease progresses and they acquire more symptoms. HyperTraPS learns the structure of the river delta from data, and predicts which river branches are more or less likely – and, importantly, where you'll end up if you're currently at a particular point.
By learning these branching patterns of disease progression, HyperTraPS has helped provide a refined risk assessment for malaria, based on data from thousands of Gambian children – as we’ve written about before. The approach also revealed diverse pathways of ovarian cancer progression, where the first mutation to occur appears to play a large role in determining subsequent mutations.
The "waterfall" in the foreground shows paths from one stage of a disease to the next, learnt by HyperTraPS using data from a high number of patients. Each dot of the illustration represents different stages of disease, for example a specific set of symptoms or a given set of mutations. The thickness of the lines indicate the probability of moving from one specific stage of disease to the next.
HyperTraPS is very generalisable and can be used to learn pathways by which mutations, symptoms, or other features develop over time from an initial state. We further used this generalisability to understand a biomedically important example of evolution – specifically, how tuberculosis evolves to become resistant to antibiotics.
Tuberculosis acquires resistance through mutations, and HyperTraPS has revealed the patterns of these mutations in TB bacteria reported from a group of 1000 Russian patients. These patterns help predict which mutation a bacterium will acquire next, and hence which drugs may be more effective for a given case. We’re following up with other applications of HyperTraPS, to learn about other progressive diseases, ageing, and evolution, and even to analyse how students complete tasks in online courses.
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