Fem SUND-forskere henter 42 millioner kroner til spændende projekter

Ascending Investigator

Associate Professor Erin Gabriel, IFSV - DKK 7,618,938
The project aims to develop statistical methods to improve personalized treatment decision-making while considering patient burden and accounting for the shortcomings of the data being used.

As medical data and treatment options grow, a vital question becomes how to use the information available to make the best treatment decision. Methods exist to help select the best treatment for each patient based on patient and disease characteristics. However, these methods often do not consider the patient’s burden for collecting those characteristics, nor do they account for the potential shortcomings of the data used in the selection. Both issues can lead to the selection of sub-optimal treatments and potential harm to patients. To avoid this, selected decisions should be tested in clinical trials, and the patient burden should always be considered. Randomized clinical trials can be costly, untimely, or simply impossible. The use of validated surrogate endpoints can make randomized clinical trials feasible, but in the setting of personalized treatment, improved statistical evaluation methods are needed. Regardless of the data collection type, there is also a need for statistical tools that account for patient burden in treatment selection. Finally, when observational data must be used, improved methods are needed for treatment selection that account for biases that may occur due to the lack of randomization.

Associate Professor Fernando Racimo, Globe Institute - DKK 8,276,123
The genomes of organisms contain information about their past history: migrations, displacements and expansions of populations can be discerned from the footprints they left in genetic sequences – including our own genomes. Space is thus a crucial dimension of evolution: organisms interact, mate and compete with organisms that are closest to them in their landscape. Yet, tools for analyzing genomes in space are scarce or highly limited in scope.

Which types of genetic patterns are most informative of spatial aspects of the history of a species? And how can we best harness them to better understand the movement and past distribution of those species? To answer these questions, our research program will generate an array of computational tools for simulating, analyzing and modelling genomes on real geographic landscapes. These tools will be applicable to genetic data from both present-day living organisms and from extinct populations, allowing us to better understand population processes with unprecedented detail.

We will then apply our newly developed methods to a specific case-study: ancient epidemics in recent human prehistory. We will infer the spatial distribution and expansion of ancient pathogens and their hosts, using a combination of present-day and ancient genomic data. We will seek to understand how past epidemics have affected human populations over the last 50,000 years, how humans – in turn – have responded to these epidemics, and how future epidemics might unfold over time, as a consequence of climate change and ecological breakdown.

Associate Professor Hiren Joshi, ICMM - DKK 10,000,00
The “third language of life” after genes and proteins is that of complex sugars. This language (the glycocode) describes myriad ways that organisms have fine-tuned proteins and cellular functions to allow complex life to thrive. We know how 100s of enzymes generate sugars in cells, but we do not know how the individual cell regulates its enzymes and glycosylation network to make specific sugars required in health and diseases. The goal of this project is to learn how the glycocode is regulated, and in doing so reveal its functions. Using data science, the project team will build a foundational machine learning model for in silico glycoscience: GolgiNet. Capturing the regulatory patterns of cellular glycosylation, GolgiNet will be used to predict biological functions, reveal the sugars of a single cell, and predict the sugar-coated proteins a cell is programmed to make. GolgiNet will transform our ability to understand the third language of life, providing a Rosetta stone to decipher how sugars can mediate biological interactions.

Emerging Investigator

Associate Professor Tibor V. Varga, IFSV - DKK 10,000,000
In the EU, health inequalities account for 20% of total healthcare costs and related welfare losses amount to nearly 1 trillion EUR per year. The European Commission considers health inequalities to be one of the greatest challenges facing European healthcare systems. Navigating this challenge requires improved data and smarter methods and tools for evaluating inequalities in health as well as practical ways for narrowing healthcare gaps. The vision of the Algorithmic Fairness in Diabetes Prediction (ALFADIAB) research program is a society where access to healthcare and quality of care do not depend on ethnicity, race, sex, or wealth. Even in Denmark, with its universal healthcare system, this is not yet a reality, and minorities with diabetes, and those who are the poorest, are affected more than others. As an example, immigrants, their descendants, and those who are the poorest have higher rates of developing type 2 diabetes, experience more severe complications (diseases of the heart, eye, and kidney), and benefit less from the Danish healthcare system. In this research program, I will investigate whether established risk prediction models, that are used to forecast which individuals are at high risk of diabetes, are underperforming for minorities and those with lower socioeconomic status. By utilizing Danish registry-based data on millions of people I will assess inequalities in diabetes management and care, and deploy artificial intelligence techniques to develop improved predictive models that are equitable and perform equally well between subgroups.

Distinguished Innovator

Professor Ali Salanti, ISIM - DKK 6,000,000
The grant is t for investigating whether a newly discovered carbohydrate on the surface of cancer cells could become a target for therapies to combat many types of cancer. Ali Salanti’s research has shown that cancer cells typically express the carbohydrate because it helps them to penetrate the surrounding tissue and therefore plays an essential role in metastasising cancer. Ali Salanti’s research group has already developed an antibody that binds to this specific carbohydrate, and the aim of the future research is to determine how this antibody can be developed to kill the cancer cells.