System-level approaches for biomarker discovery in complex and malignant diseases

Early diagnosis and effective treatment of complex and malignant diseases are critical challenges in modern medicine. These diseases exhibit significant heterogeneity, manifesting differently across patients and involving diverse genes and pathways. This variability complicates the identification of universal biomarkers, necessitating innovative, system-level approaches to uncover robust and predictive biomarkers across various biological contexts.

This dissertation addresses these challenges by leveraging both knowledge- based and data-driven methods to analyze multi-omics data, including spatial and single-cell transcriptomics, as well as bulk data. The goal is to prioritize potential biomarkers that can provide system-level insights into disease mechanisms, ultimately facilitating personalized medicine. The findings of this thesis promise to transform biomarker discovery, leading to earlier diagnoses, improved treatment outcomes, and enhanced patient care.

The thesis encompasses four comprehensive studies:

1. Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases: We analyzed scRNA-seq data from mice and humans with immune-mediated inflammatory diseases (IMIDs). We identified upstream regulators acting as on/off switches, which collectively serve as candidate biomarkers and potential drug targets. These findings were validated in a study involving sera from nearly 300 systemic lupus erythematosus patients.

2. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases: Using multiomics data from UK Biobank (UKBB), we identified reliable biomarkers for nine complex diseases and constructed an interactive atlas to explore their performance. Proteomics outperformed other omics types in most of the diseases.

3. Multiomics biomarkers were not superior to clinical variables for pan- cancer screening: We analyzed multiomics data to find early diagnostic biomarkers for cancers. While proteomics data from peripheral blood did not outperform routine clinical variables for most cancers, promising biomarkers were identified for cancers in highly vascularized organs.

4. Combining spatial transcriptomics and pseudotime to find urine biomarkers for prostate cancer: We analyzed spatial transcriptomics data of prostate cancer to identify transcripts correlated with malignant transformation. Using pseudotime and machine learning, we validated the diagnostic accuracy of these transcripts in various samples from over 2,000 patients and controls.

In conclusion, this thesis demonstrates the potential of systems-level approaches in biomarker discovery, providing insights into disease mechanisms and paving the way for personalized medicine. The studies highlight the importance of considering disease heterogeneity and the need for tailored biomarker strategies, ultimately contributing to earlier diagnoses and improved patient outcomes.

List of scientific papers

I. Sandra Lilja*, Xinxiu Li*, Martin Smelik*, Eun Jung Lee, Joseph Loscalzo, Pratheek Bellur Marthanda, Lang Hu, Mattias Magnusson, Oleg Sysoev, Huan Zhang, Yelin Zhao, Christopher Sjöwall, Danuta Gawel, Hui Wang, Mikael Benson. Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases. Cell Rep Med. 2023 Mar 21;4(3):100956. https://doi.org/10.1016/j.xcrm.2023.100956

II. Martin Smelik*, Yelin Zhao*, Xinxiu Li, Joseph Loscalzo, Oleg Sysoev, Firoj Mahmud, Dina Mansour Aly, Mikael Benson. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Sci Rep. 14, 12710 (2024). https://doi.org/10.1038/s41598-024-63399-9

III. Martin Smelik, Yelin Zhao, Dina Mansour Aly, AKM Firoj Mahmud, Oleg Sysoev, Xinxiu Li, Mikael Benson. Multiomics biomarkers were not superior to clinical variables for pan-cancer screening. Communications Medicine. 4, 234 (2024). https://doi.org/10.1038/s43856-024-00671-z

IV. Martin Smelik*, Daniel Diaz-Roncero Gonzalez*, Xiaojing An, Rakesh Heer, Lars Henningsohn, Xinxiu Li, Hui Wang, Yelin Zhao, Mikael Benson. Combining spatial transcriptomics, pseudotime and machine learning to find biomarkers for prostate cancer. [Manuscript]

* Shared first-author