Jean Hausser Lab

Quantitative principles in tumor biology

Magnified view of a tumor

We are currently recruiting at all levels: master students, PhD students, postdocs, staff scientists.

Research interests

The field of cancer has blossomed during the last decade, thanks to advances in genome technology and to successful application of fundamental insights from immunology to the clinic. Cancer genomics studies have revealed how tumors differ in their molecular make-up. These differences can explain why different tumors respond to different drugs, and suggest how to personalize therapy. Decades of immunology research have revealed how the immune system protects the organism against cancer and explored the molecular signals that coordinate immune response. These fundamental insights are now starting to bear fruit in the clinic in the form of immunotherapy.

At the same time, these advances have produced new challenges. One challenge is that cancer cells can escape anti-cancer therapy, especially in advanced cancer patients. Another challenge is that only ~20% of patients benefit from immunotherapy, in a limited number of cancer types.

Our research has the potential to impact both challenges.

Evolutionary constraints on cancer cells

Within a tumor, single cancer cells express different genes. These differences can provide resistance against anti-cancer therapy because a given therapy may fail to eliminate the diversity of cancer cells. We are interested in mapping the evolutionary limits of this diversity, and in exploring how these limits can be exploited to treat heterogeneous tumors.

We previously researched the evolutionary constraints of solid tumors. We found that tumors from different cancer types face universal trade-offs and that these trade-offs provide a theoretical framework to integrate gene expression, drug sensitivities and genetic alterations. We are now interested in mapping the evolutionary trade-offs faced by single cancer cells.

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.

Quantitative rules in tumor biology

We research quantitative rules in tumor biology. We aim to formulate these rules in the form of equations that state how key tumor properties relate to each other: cellular composition of the tumor, functional heterogeneity among cells found in the tumor, spatial organization, tumor growth rate, tumor size, shedding rate of metastatic cells, and so on. Finding mathematical order in the complexity of the tumor micro-environment could support diagnostics, prognosis and inform the design of immunotherapy in the future, much like the laws of mechanics and aerodynamics presently support the design of cars and airplanes. It could also enhance ongoing efforts to apply artificial intelligence and machine-learning techniques to cancer, for example by providing constraints to artificial intelligence and identify new features for machine learning.

In the past, we researched quantitative rules in gene regulation. We now apply this research approach to tumor biology. By doing so, we hope to explain why certain immunotherapies fail on certain tumors and suggest what therapies may be effective.

Central dogma rates and the trade-off between precision and economy. Jean Hausser, Avi Mayo, Leeat Keren, Uri Alon. Nature Communications 2019, 10(68).

Time-scales and bottlenecks in microRNA mediated regulation of gene expression. Jean Hausser, Afzal Pasha Syed, Nathalie Selevsek, Erik van Nimwegen, Lukasz Jaskiewicz, Ruedi Aebersolf and Mihaela Zavolan. Molecular Systems Biology 2013, 9:711.

Research tools

We employ a systems biology approach that combines computation and experiments:

  • developing new data analysis approaches
  • performing mouse experiments to follow tumors in time and characterize their response to therapeutic or genetic perturbation
  • physics-inspired mathematical modeling


A full list of our publications is available on Google Scholar.


Jean Hausser
principal investigator

Antony Cougnoux
senior lab manager / staff scientist

Tagore Sanketh Bandaru
master thesis student (molecular life science)

Maximilian Reck
master thesis student (molecular life science)

Fredrik Carlsson
summer student (medicine) and developper

Axel de Tonnac
consultant (bioengineering / bioinformatics)


David Alber (2020)
research student (computational physics)

Panos Kalogeropoulos (2020)
summer student (molecular life science)

Joining us

We are currently looking for:

  • A computational biologist / bioinformatician More information.
  • Two PhD students interested in combining experiments with data analysis and theory More information.
  • Postdocs with computational and/or experimental background.
  • 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