Jean Hausser Lab

Quantitative tumor immune control

Fluorescence microscopy of tumor spheroid

Research field

Immunotherapy has transformative potential in cancer, as illustrated by the durable remission of ~30% of metastatic patients with immune checkpoint blockade therapy in several cancer types. Yet, most patients do not respond to immune checkpoint blockade. Engineering effective immunotherapy is challenging due to the complexity of tumor immunology as a biosystem, with dozens of cell types whose activity is potentially controlled by hundreds of molecular signals and thousands of genes.

Historically, controlling and engineering complex systems – from electronic circuits to space vessels – has benefited from articulating these systems into quantitative theories. A quantitative theory of tumor immunology is presently missing. Formulating such a theory can be catalyzed by the growing tumor single-cell and spatial omics data which can quantify thousands of signals and genes in the local contexts of human tumors. We are still missing, however, a quantitative framework to turn this data into predictive models of how signals and genes determine tumor fate.

Approach and vision

To address this, we are researching a quantitative framework of tumor immune control designed to maximize the utility of human tumor spatial and single-cell transcriptomics data for engineering personalized immunotherapy. We take inspiration from physics-style mathematical modeling which we implement into new data science and machine-learning methods. We calibrate and validate these through experiments in vitro and in vivo.

Our vision is for our quantitative framework to accelerate immunotherapeutic innovation, by identifying targets from patient material within weeks compared to years with traditional clinical trials.

Selected publications

Full list of our publications on Google Scholar.

Niche-phenotype mapping (NIPMAP)

The spatial architecture of tumors has high relevance for diagnostic and therapy and can be surveyed by multiplex histology techniques such as imaging mass cytometry and multiplex immunofluorescence. This produces spatial maps of dozens to hundreds of cellular and phenotypic markers. But surveying these spatial maps exhaustively requires browsing through 10’000+ images per sample. To address this, NIPMAP uses unsupervised machine-learning to (1) concisely and accurately summarize the architecture of tissues, and (2) automatically identify phenotypes with salient spatial architecture spheroids.

Nature Communications 2023, accepted

Multi-task tumor evolution

We previously researched a quantitative framework to explain the diversity of gene expression, mutations and drug sensitivities of tumors, grounded in evolutionary. This framework suggests approaches to overcome cancer diversity, which represents a major challenge to therapy.

Universal cancer tasks, evolutionary trade-offs, and the function of driver mutations. Jean Hausser, Pablo Szekely, Noam Bar, Hila Sheftel, Carlos Caldas, Uri Alon. Nature Communications 2019.

Tumor heterogeneity and the evolutionary trade-offs of cancer. Jean Hausser, Uri Alon. Nature Reviews Cancer 2020.

We demonstrated that this framework can guide the design of drug combinations effective against heterogeneous populations of cancer cells using 3D tumor spheroids.

Microscopy-based phenotypic monitoring of MDA-MB-231 spheroids allows the evaluation of phenotype-directed therapy. Loay Mahmoud, Antony Cougnoux, Christina Bekiari, Paloma Araceli Ruiz de Castroviejo Teba, Anissa El Marrahi, Guilhem Panneau, Louise Gsell, Jean Hausser. Experimental Cell Research 2023.


Jean Hausser
principal investigator (CV)

Antony Cougnoux
staff scientist (immunology, cancer)

Jakob Rosenbauer
EMBO post-doctoral fellow (physics)

Alper Eroglu
PhD student (bioinformatics)

Louise Gsell
lab engineer (computational biology)

Anissa El Marrahi
lab engineer (computational biology), consultant


Mathilda Stigenberg (2023)
master student (bioinformatics)

Felix Waern (2023)
master student (bioinformatics)

Sofie Blahova (2023)
master student (bioengineering)

Raziyeh Mohseni (2023)
bachelor student (bioinformatics)

Petter Säterskog (2021-2023)
post-doctoral fellow (theoretical physics)

Loay Mahmoud (2021-2023)
research assistant (experimental biomedicine)
position after the lab: PhD student, NTNU Trondheim

Ziqi Kang (2022-2023)
master thesis student, research assistant (bioinformatics)
position after the lab: PhD student, Helsinki University

Iva Sutevski (2022-2023)
research assistant (biology)
position after the lab: PhD student, KTH Stockholm

Benjamin Maier (2021-2022)
visiting student (molecular life science)
position after the lab: PhD student, EMBL Heidelberg

Javier Escudero Morlanes (2021-2022)
master student (molecular life science)
position after the lab: research engineer, KTH/SciLifeLab Stockholm

Christina Bekiari (2022)
master student (biomedicine)
position after the lab: research assistant, Stockholm University/SciLifeLab

Guilhem Panneau (2021-2022)
research assistant (computational biology)
position after the lab: PhD student, University of Lausanne

Paloma Araceli Ruiz de Castroviejo Teba (2021)
visiting student (biomedicine)
position after the lab: product development scientist, Basic Genomics Stockholm

Fabio Lipreri (2020-2021)
research assistant (computer science)
position after the lab: data scientist, FrescoFrigo Milano

Letizia Orsini (2020-2021)
research assistant (biostatistics)
position after the lab: biostatistician, Karolinska Institutet

Axel de Tonnac (2020-2021)
consultant (bioengineering / bioinformatics)
position after the lab: sales executive, Sophia Genetics Lausanne

Tagore Sanketh Bandaru (2020)
master thesis student (molecular life science)
position after the lab: PhD student, University Hospital Basel

Maximilian Reck (2020)
master thesis student (molecular life science)
position after the lab: PhD student, University of Edinburgh

Fredrik Carlsson (2020)
summer student and developper
position after the lab: medical student, Karolinska Institutet

David Alber (2020)
research student (computational physics)
position after the lab: data scientist, IBM Vienna

Panos Kalogeropoulos (2020)
summer student (molecular life science)
position after the lab: PhD student, Stockholm University

Joining us

We are currently looking for:

  • A postdoctoral fellow Email Jean about 1. which of your research project was most exciting to you so far and why, 2. why you'd like to join us (1000 words max) and 3. attach a concise CV.
  • Master students enrolled in biology / biomedical programs or computational programs (bioinformatics, physics, applied mathematics, computer science, engineering, ...).
    Email Jean about 1. what course or projects you most enjoyed in your studies so far, 2. why you'd like to join us (1000 words maximum), and 3. attach a concise CV.

How to find us

View of the Stockholm old town

The lab is located at Karolinska Institutet and SciLifeLab in Stockholm, Sweden. Within Karolinska Institutet, we are affiliated to the department of Cell and Molecular Biology.

Visiting address

Gamma 6, Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden

Mailing address

Science for Life Laboratory, Box 1031, 17121 Solna, Sweden