New DDLS Fellow: Fredrik Edfors
The recruitment of Fellows to the SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) continues. Our newest DDLS Fellow, Fredrik Edfors (Royal Institute of Technology, KTH), who will join the Precision medicine and diagnostics DDLS research area, is featured in our latest Q&A-style article.
Fredrik holds a Master of Science and Engineering and a Ph.D. from the Royal Institute of Technology (KTH). After his dissertation, he joined Prof. Michael Snyder’s lab at Stanford University School of Medicine. At Stanford, he worked on novel at-home sampling strategies combined with longitudinal personal omics profiling to better understand metabolic diseases and obesity. Upon his return to Sweden in 2020, he joined the national pandemic center at KI during the SARS-COV-2 pandemic, where he established a rapid at-home-sampling diagnostic pipeline for Covid-19 diagnostics, which has processed over 1.5 million diagnostic tests.
The Edfors lab focuses on blood-based protein profiling and data-driven integration of comprehensive data resources created internally and externally, including different analytical platforms and national registries. Fredrik is the current President of the Swedish Proteomics Society, a part of the Swedish Pharmaceutical Organization.
How do you think your expertise can contribute to the program?
Throughout my academic career, I have worked on different proteomics analyses of human plasma to better understand how it can be used to study human health and disease. Precise and accurate measurements of proteins in a multiplex can expedite our understanding of human health and disease. Within the DDLS program, I will focus on the dynamic behavior of proteins in our circulatory system to explore how it can improve the diagnosis of different diseases. My future contribution to the DDLS program will therefore be driven by the use of proteomic strategies to change how we diagnose patients and shift away from the one-size-fits-all model of stratified diagnoses and therapies and toward applicable customized proteomics techniques. We can already see that the area of precision medicine is going toward different proteomics integrations, which I am confident will be an excellent supplement to more traditional genetic precision medicine approaches.
Shortly describe your research in an easy to understand way.
Because proteins reflect intermediate phenotypes, proteomics is the next likely candidate to be added to the fast-growing precision medicine portfolio. Proteins, in particular, are gene expression products that mediate the biochemical activities of all cells and tissues. Historically, the primary focus of Precision Medicine activities in the last decade has been to utilize genomics, driven by next-generation sequencing, to investigate the genetic make-up of individual tumors and the implications of these variations and mutations for patient survival and therapy.
Other disorders, such as liver and cardiovascular disease, should not be monitored with a single genetic test in a precision medicine context because it only reflects a risk factor over time. This has led to the conclusion in the field of Precision Medicine that a multi-omics study is required. I have dedicated my career to researching and analyzing data from proteomics technologies capable of characterizing the deep plasma proteome. The combination of data generated by data-independent mass spectrometry and affinity-based technologies enables us to annotate molecular signals and detect changes from a patient’s baseline.
This can be done by integrating molecular and clinical data for translational research and diagnostic development, simplifying tailored patient treatment. The focus lies on liquid biopsies and how we may use this information to map the molecular landscape of health and disease. We can improve patient stratification by combining data from mass spectrometry and affinity-based approaches, which are often driven by longitudinal monitoring of an individual’s baseline. My research has focused on the dynamic proteome of individuals and how we might use blood indicators to develop improved prediction models. This data integration strategy and analysis can be utilized for patient stratification, diagnostic biomarker identification, and longitudinal monitoring and follow-up.
How do you think the program and interactions with the other DDLS-Fellows will benefit you?
I look forward to working and interacting with the DDLS-fellows, who are all experts in their respective disciplines. With all potential linkages, this type of network is critical for being active in the diverse area of precision medicine and diagnostics. I am excited to be creating new diagnostic approaches driven by ML and AI. I am particularly intrigued about developing novel strategies for blood plasma monitoring, which will be helpful for next-generation precision medicine diagnostics.
Name one thing that people generally do not know about you.
I am a huge fan of longitudinal sampling and how much we can learn from simple biological data sources, as well as how we can use them in n-of-1 studies. For example, I track myself as much as possible and collect any biometrical data I can get my hands on, to better understand how the body responds to perturbations such as intense training or different diets.
Where do you see yourself in five years regarding the DDLS aspect?
I am sure that this program will accelerate research and add proteomics to the arsenal of precision medicine. I anticipate seeing multiplex proteomics diagnostic testing for blood used in hospitals for some diseases, likely liver injury or cardiovascular disease. These diagnostic tests will thus be enabled by technological leaps in recent years, but will be driven by improved data analysis strategies and modeling.
In one word, describe how you feel about becoming a DDLS-Fellow.
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