Donnelly Investigator Hannes Röst Named Canada Research Chair to Advance Molecular Tracking for Personalized Medicine
Instead of occasional blood tests measuring only a handful of parameters, more frequent molecular tracking could hold the key to long-lasting health.
This is how one U of T researcher envisions a path to personalized medicine in which disease diagnosis and treatment will be tailored to an individual’s unique physiology. But that requires collecting a lot of data first.
“To make medicine truly personalized, we need to profile people at the molecular level as much as possible to understand molecular changes that occur over lifetime,” says Hannes Röst, Assistant Professor at the Donnelly Centre for Cellular and Biomolecular Research, who is developing tools to make this happen.
As a newly appointed Canada Research Chair in Mass Spectrometry-based Personalized Medicine, Röst seeks to improve mass spectrometry methods for molecular profiling by increasing both their accuracy and throughput.
Röst is the thirteenth investigator in the Donnelly Centre to be named CRC, with more than a third of its faculty now holding the prestigious appointment. Created two decades ago, the CRC program seeks to attract and retain best researchers from diverse disciplines to make Canada a leader in research and innovation.
"To make medicine truly personalized, we need to profile people at the molecular level as much as possible to understand molecular changes that occur over lifetime" - Assistant Professor Hannes Röst
Mass spectrometry is used to identify diverse molecules present in, say, blood or other complex clinical samples, based on their size and electric charge. Although widely used in research labs, its slow pace and reproducibility issues have marred its uptake in the clinic.
Solving these problems will enable measuring countless proteins and metabolites, products of enzyme reactions, which are made by the body and hold clues to health and disease. Scientists now think that each person has a unique ‘molecular barcode’, a baseline that could help define what normal health looks like at the molecular level in a way that is more informative for detecting disease onset than comparing individuals to the population-level data.
“If you know you were healthy 10 years ago and if you start to change in some way, then maybe this type of change relative to yourself is much more informative in terms of disease development than comparing you to the rest of the population,” says Röst.
Such individual baselines are still a distant prospect and require collecting vast amounts of personal multi-omics data including gene, protein and metabolite levels, over long periods of time from healthy and patient populations. A technology that is fast and accurate is key to achieving this goal.
Fortunately, Röst is well-equipped to tackle the remaining challenges having trained with leading mass-spectrometry researchers, first as a PhD student with proteomics pioneer Ruedi Aebersold, at the Swiss Federal Institute of Technology (ETH) in Zurich, and then as a postdoctoral fellow working with Michael Snyder, Director of the Center for Genomics and Personalized Medicine at Stanford University.
At Stanford, Röst helped track sweeping molecular changes in 30 women over the course of their pregnancies. The largest of its kind, the study measured several thousand hormones and other molecules from about 1000 blood samples which had been collected from every woman for every week of pregnancy starting from the first trimester until the delivery date. The data revealed what that while pregnancy progressed through the same molecular states in all women, the timing and duration of hormone surges was different in each individual, indicating that each pregnancy is unique.
"We still don’t know what 'healthy' or 'normal' looks like at the molecular level" - Assistant Professor Hannes Röst
After joining the Donnelly Centre as faculty in 2017, Röst has been working to expand this type of personal omics research while at the same time improving the technology for it.
Earlier this year, the first research paper published by his lab described a computational method which can correct sampling errors introduced by mass spectrometry instruments for a more accurate reading of the levels of thousands of different proteins in one sample.
He is also developing machine-learning tools to improve the quality of the analysis in metabolomics datasets, in collaboration with Amy Caudy and Quaid Morris, both professors in the Donnelly Centre and Philip Awadalla, of the Ontario Institute for Cancer Research.
Röst was recently also named an inaugural recipient of the $250,000 research grant from the New Frontiers in Research Fund, established last year by the federal government in support of early career researchers pursuing high-risk, high-reward research. The funding will support a project seeking to transform cancer diagnostics by analyzing blood samples donated by a large cohort of patients several months before they were diagnosed with one of many types of cancer. If successful, the project will reveal a diagnostic method that could detect cancer before any currently available test.
In another project, Röst and collaborators from Spain, Italy, Germany and Romania are looking to identify biomarkers associated with the recurrence of glioblastoma, an aggressive form of brain cancer, to better understand the molecular changes leading to relapse and target them for therapy.
It’s early days but the data generated by Röst and researchers like him is making personalized medicine a more likely prospect.
“We still don’t know what “healthy” or “normal” looks like at the molecular level,” says Röst. “But we’re working on it.”
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