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Full publication list:
LABORATORY OF
CELL STRESS & IMMUNITY
Translating systems immunology & artificial intelligence-driven biomarkers into precision cancer patient management & therapeutic care
At the CSI Lab, we believe that transformative immuno-oncology research must ultimately be tested in patients. Our patient-centred, reverse-translational philosophy bridges fundamental immunology, systems medicine, AI-driven biomarker discovery, and clinical investigation to understand immunotherapy resistance and translate biological insights into therapeutic opportunities. Working closely with clinical collaborators at UZ Leuven (Belgium), Heinrich Heine University Hospital (Germany), MAASTRO (Netherlands), and other international centres, we pursue a continuum of clinical translation spanning retrospective and prospective observational cohorts, investigator-initiated exploratory and early-phase interventional studies, and biomarker-integrated early-phase clinical trials.
A cornerstone of this strategy is the development and clinical evaluation of high-precision immune biomarkers. These include our blood-based serum Functional Immunodynamics Status (sFIS) assay (see Case #1 below for more details), investigated across several malignancies, and AI and/or multi-omics/spatial omics driven clinical resources/biomakers (see Case #2 below for more details) designed to identify clinically relevant tumour–immune ecosystems and predict therapeutic responsiveness. Our translational activities encompass three complementary directions:
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Clinical biomarker validation: Retrospective and prospective evaluation of blood-based, tissue-derived, and AI-driven biomarkers for patient stratification, prognostic assessment, and longitudinal monitoring of immunotherapy-associated immune responses.
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Investigator-initiated clinical exploration: Integration of mechanistic biomarkers into observational studies and exploratory interventional trials, including the EU-supported GLIOMATCH glioblastoma immunotherapy programme, to investigate treatment-induced immune changes, resistance mechanisms, and biological proof of concept.
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Biomarker-enabled therapeutic development: Advancing precision immunotherapy strategies, including dendritic cell (DC) vaccination, rational combinations, and immunotherapy approaches for kidney cancer, with the ambition of incorporating validated biomarkers into larger prospective clinical trials.
Across these programmes, our biomarkers are positioned as exploratory pharmacodynamic readouts, longitudinal monitoring modalities, and candidate predictors of treatment benefit. Their relationship with established clinical outcomes (including objective treatment response, progression-free survival, and overall survival) can be systematically assessed to determine their clinical utility and potential development as surrogate efficacy endpoints. Our ultimate ambition is to establish a continuous clinical–experimental feedback loop in which patient observations guide mechanistic discovery, and mechanistic discoveries guide better patient stratification, clinical trial design, and next-generation immunotherapies.
Case #1: The first-in-class sFIS biomarker assay for patient stratification & management
Click on above image to go to a YouTube video introducing our sFIS biomarker assay!
The sFIS assay is a novel blood-based immuno-oncology biomarker platform developed to enable patient-centred immune monitoring in cancer. By capturing clinically relevant systemic immune signals, sFIS has shown promise for improving patient stratification, treatment follow-up and biomarker-guided therapeutic assessment. Clinical evidence already supports its application in ovarian (JITC 2021), lung (Cancers 2021), breast (Genes & Immunity 2023) and head & neck (Commun Med 2026) cancers, highlighting its broader pan-cancer potential. As validation expands, sFIS may become an important tool for precision patient management, and prospective clinical studies.
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For patent information, click here!
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For simplified technology offering statement, click here!
Case #2: Clinically-relevant multi-omics, spatial omics & AI/ML resources
At the CSI Lab, we have developed large-scale, clinically anchored single-cell and spatial immunology atlases, integrative multi-omics resources, and AI/ML frameworks to decode human tumour ecosystems and uncover mechanisms of immunotherapy resistance (links to resources are available below).
Our pan-cancer research established a multi-omics & spatial atlas distinguishing diverse CD8+T-cell functional and dysfunctional states across immune "supportive vs. hostile" malignancies (Science Translational Medicine 2023). This atlas and associated resources are currently being expanded (e.g., in terms of omics dimensions and AI-based automatization), optimized for clinical scability (e.g., through digital pathology, pathomics or radiomics) as well as rigorously validated (e.g., via functional assays involving human and mouse models) for the benefit of glioblastoma patients through participation in the Leuven Cancer Institute's Brain Tumor Research Hub (click here for the website).
Extending these discoveries to colorectal cancer, we integrated single-cell transcriptomics, T-cell receptor repertoires, and proteomic analyses to distinguish tumour-reactive versus bystander T-cell immunity, generating an AI-enabled immune subtype classification with prognostic and immunotherapy-response relevance (Cell Discovery 2023). In kidney cancer, our integration of large-scale clinical transcriptomics, single-cell and spatial omics, HLA immunogenetics, and machine learning uncovered clinically relevant macrophage <-> T-cell interactions and an HLA-associated biomarker signature predictive of immunotherapy outcomes across independent patient cohorts (Nature Medicine 2024).
Together, these patient-linked atlases and computational frameworks form a foundation for our reverse-translational research, connecting complex human immunology with mechanistic discovery, actionable immune niches, precision biomarkers, and next-generation immunotherapy design.
This is one of the first and most comprehensive multi-omics and spatial biomarker workflow for mapping clinically-relevant CD8+T cell states in cancer patient samples. Click on above image to go to the Synapse Database page of this project.
This is the first AI-based immuno-stratification tool for colorectal cancer (CRC) patients, based on single-cell CD8+T cell states in tumour samples. Click on above image to go to the Synapse Database page of this project.
This is the first multi-omics driven AI-based immuno-stratification tool for renal cell carcinoma (RCC) patients, based on bulk omics, single-cell and spatial mapping of tumour samples as well as HLA-genotyping of blood samples. Click on above image to go to the Synapse Database page of this project.
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