Lectio Magistralis: Disease trajectories – the future of humans as model organisms | Prof. Søren Brunak | University of Copenhagen

Mercoledì 2 aprile 2025 | 17.00 | aula Magna Torre Biologica ‘F. Latteri’ | via S. Sofia 89 - Catania

Bio: Søren Brunak, Ph.D., is a professor of Disease Systems Biology and Research Director in the Novo Nordisk Foundation Center for Protein Research at University of Copenhagen. His program combines molecular level systems biology data with analysis of healthcare sector phenotypic data (electronic patient records, registry information and biobank questionnaires) to understand multimorbidities and discriminate between treatment related disease correlations. This stratifies patients not only from their genotype, but also based on the clinical descriptions in their medical records and is particularly relevant in the context of the precision medicine agenda. He has been a Member of the Royal Swedish Academy of Sciences since 2016, a Member of the European Molecular Biology Organization since 2009, and a Member of the Royal Danish Academy of Sciences and Letters since 2004.

Abstract: As populations are aging disease patterns are becoming increasingly complex. Patients often suffer from many illnesses simultaneously over the life course. Health data on heterogeneous phenotypes are at the same time accumulating by way of electronic patient records in the healthcare sector, in biobanks and in numerous multi-omics cohort studies. This data richness has given rise to the idea of looking at humans as model organisms, where results can be of direct relevance to healthcare without the need to transfer results from the animal setting. The talk will discuss how to analyze multimorbidity data at scale from millions of patients, and present machine learning approaches of relevance for the understanding of complex disease etiologies and the precision medicine agenda.

Reading:
Network biology concepts in complex disease comorbidities
Hu JX, Thomas CE, Brunak S. Nat Rev Genet. 2016 Oct;17(10):615-29. doi: 10.1038/nrg.2016.87.

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients.
Siggaard T, Reguant R, Jørgensen IF, Haue AD, Lademann M, Aguayo-Orozco A, Hjaltelin JX, Jensen AB, Banasik K, Brunak S. Nat Commun. 2020 Oct 2;11(1):4952. doi: 10.1038/s41467-020-18682-4.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.
Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Leal Rodríguez C, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S; IMI DIRECT Consortium. Nat Biotechnol. 2023 Mar;41(3):399-408. doi: 10.1038/s41587-022-01520-x.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.
Placido D (*), Yuan B (*), Hjaltelin JX (*), Zheng C (*), Haue AD, Chmura PJ, Yuan C, Kim J, Umeton R, Antell G, Chowdhury A, Franz A, Brais L, Andrews E, Marks DS, Regev A, Ayandeh S, Brophy MT, Do NV, Kraft P, Wolpin BM, Rosenthal MH, Fillmore NR, Brunak S (*), Sander C (*). Nat Med. 2023 May;29(5):1113-11