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 identified how the immune system protects the organism againts cancer and 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.
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 heterogenous 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, in print.
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 immunotherapy in the future, much like the laws of mechanics and aerodynamics presently support the design of cars and airplanes.
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.
We employ a systems biology approach that combines computation and experiments:
A full list of our publications is available on Google Scholar.
We are currently recruiting:
Gamma 6, Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden
Science for Life Laboratory, Box 1031, 17121 Solna, Sweden