Jean Hausser
About me
I am scientist interested in engineering effective cancer immunotherapies. I enjoy most working on important and challenging problems, where data are plentiful but where the amount of data makes it difficult to know where to start. I address this challenge by formulating hypotheses, turning these hypotheses into mathematical equations, and implementing these equations into new data science approaches that make best use of the data to solve the problem at hand.
I initially trained in bioinformatics and data science for Masters and PhD, then as a mathematical theorist and finally as a cancer experimentalist during my postdoc, before starting my lab at Karolinska Institute.
Main research achievements include finding mathematical order in the complexity of the fundamentals of gene regulation, and in the coordination of gene programs in tumors. I have attained these achievements by integrating bioinformatics and data science, mathematical modeling, and experimental approaches. I have published 30+ articles which have been cited more than 10000 times, and acquired 4 million euros in research funding.
Research
The lab researches mathematical rules in the molecular tricks that cancer cells use to escape destruction by immune cells. We seek to articulate the molecular chat between immune and cancer cells into equations, to serve as the foundation to engineer personalized cancer immunotherapy. We combine single-cell and spatial tumor profiling experiments, AI & data science, and physics-style mathematical modeling.
Teaching
- Mathematical modeling in biomedicine. Karolinska Institute (2022-)
- Systems biology. Teaching assistant in Uri Alon's class at Weizmann
Institute (2014) - General biology. Teaching assistant in Michael Hall's class at Uni Basel
(2010)
Articles
- Article: SCIENTIFIC REPORTS. 2025;15(1):3811Cougnoux A; Mahmoud L; Johnsson PA; Eroglu A; Gsell L; Rosenbauer J; Sandberg R; Hausser J
- Article: NATURE COMMUNICATIONS. 2024;15(1):3226Watson SS; Duc B; Kang Z; de Tonnac A; Eling N; Font L; Whitmarsh T; Massara M; Hausser J; Bodenmiller B; Joyce JA
- Article: NATURE COMMUNICATIONS. 2023;14(1):7182El Marrahi A; Lipreri F; Kang Z; Gsell L; Eroglu A; Alber D; Hausser J
- Article: EXPERIMENTAL CELL RESEARCH. 2023;425(2):113527Mahmoud L; Cougnoux A; Bekiari C; Teba PARDC; El Marrahi A; Panneau G; Gsell L; Hausser J
- Journal article: MOLECULAR BIOLOGY AND EVOLUTION. 2022;39(1):msab297Adler M; Tendler A; Hausser J; Korem Y; Szekely P; Bossel N; Hart Y; Karin O; Mayo A; Alon U
- Article: NATURE COMMUNICATIONS. 2019;10(1):5423Hausser J; Szekely P; Bar N; Zimmer A; Sheftel H; Caldas C; Alon U
- Article: CELL. 2019;179(5):1207-1221.e22Laks E; McPherson A; Zahn H; Lai D; Steif A; Brimhall J; Biele J; Wang B; Masud T; Ting J; Grewal D; Nielsen C; Leung S; Bojilova V; Smith M; Golovko O; Poon S; Eirew P; Kabeer F; de Algara TR; Lee SR; Taghiyar MJ; Huebner C; Ngo J; Chan T; Vatrt-Watts S; Walters P; Abrar N; Chan S; Wiens M; Martin L; Scott RW; Underhill TM; Chavez E; Steidl C; Da Costa D; Ma Y; Coope RJN; Corbett R; Pleasance S; Moore R; Mungall AJ; Mar C; Cafferty F; Gelmon K; Chia S; Marra MA; Hansen C; Shah SP; Aparicio S
- Article: ENVIRONMENTAL MICROBIOLOGY. 2019;21(3):1068-1085Bucher T; Keren-Paz A; Hausser J; Olender T; Cytryn E; Kolodkin-Gal I
- Article: NATURE COMMUNICATIONS. 2019;10(1):68Hausser J; Mayo A; Keren L; Alon U
- Article: CELL. 2016;166(5):1282-1294.e18Keren L; Hausser J; Lotan-Pompan M; Slutskin IV; Alisar H; Kaminski S; Weinberger A; Alon U; Milo R; Segal E
- Article: JOURNAL OF BIOLOGICAL CHEMISTRY. 2015;290(33):20284-20294Tattikota SG; Rathjen T; Hausser J; Khedkar A; Kabra UD; Pandey V; Sury M; Wessels H-H; Mollet IG; Eliasson L; Selbach M; Zinzen RP; Zavolan M; Kadener S; Tschoep MH; Jastroch M; Friedlaender MR; Poy MN
- Article: PLOS COMPUTATIONAL BIOLOGY. 2015;11(7):e1004224Korem Y; Szekely P; Hart Y; Sheftel H; Hausser J; Mayo A; Rothenberg ME; Kalisky T; Alon U
- Article: NATURE METHODS. 2015;12(3):233-235Hart Y; Sheftel H; Hausser J; Szekely P; Ben-Moshe NB; Korem Y; Tendler A; Mayo AE; Alon U
- Journal article: NATURE REVIEWS GENETICS. 2014;15(10):702Hausser J; Zavolan M
- Article: JOURNAL OF CLINICAL INVESTIGATION. 2014;124(6):2722-2735Latreille M; Hausser J; Stuetzer I; Zhang Q; Hastoy B; Gargani S; Kerr-Conte J; Pattou F; Zavolan M; Esguerra JLS; Eliasson L; Ruelicke T; Rorsman P; Stoffel M
- Article: BIOESSAYS. 2014;36(6):617-626Bruemmer A; Hausser J
- Article: PLOS COMPUTATIONAL BIOLOGY. 2014;10(5):e1003602Rothschild D; Dekel E; Hausser J; Bren A; Aidelberg G; Szekely P; Alon U
- Article: CELL METABOLISM. 2014;19(1):122-134Tattikota SG; Rathjen T; McAnulty SJ; Wessels H-H; Akerman I; van de Bunt M; Hausser J; Esguerra JLS; Musahl A; Pandey AK; You X; Chen W; Herrera PL; Johnson PR; O'Carroll D; Eliasson L; Zavolan M; Gloyn AL; Ferrer J; Shalom-Feuerstein R; Aberdam D; Poy MN
- Article: MOLECULAR SYSTEMS BIOLOGY. 2013;9:711Hausser J; Syed AP; Selevsek N; van Nimwegen E; Jaskiewicz L; Aebersold R; Zavolan M
- Article: GENOME RESEARCH. 2013;23(4):604-615Hausser J; Syed AP; Bilen B; Zayolanl M
- Article: NATURE METHODS. 2013;10(3):253-255Khorshid M; Hausser J; Zavolan M; van Nimwegen E
- Article: METHODS. 2012;58(2):106-112Jaskiewicz L; Bilen B; Hausser J; Zavolan M
- Article: HEPATOLOGY. 2012;55(1):98-107Kruetzfeldt J; Roesch N; Hausser J; Manoharan M; Zavolan M; Stoffel M
- Article: PLOS PATHOGENS. 2011;7(12):e1002405Suffert G; Malterer G; Hausser J; Viiliainen J; Fender A; Contrant M; Ivacevic T; Benes V; Gros F; Voinnet O; Zavolan M; Ojala PM; Haas JG; Pfeffer S
- Article: NATURE. 2011;474(7353):649-653Trajkovski M; Hausser J; Soutschek J; Bhat B; Akin A; Zavolan M; Heim MH; Stoffel M
- Article: NATURE METHODS. 2011;8(7):559-564Kishore S; Jaskiewicz L; Burger L; Hausser J; Khorshid M; Zavolan M
- Journal article: JOVE-JOURNAL OF VISUALIZED EXPERIMENTS. 2010;(41)Hafner M; Landthaler M; Burger L; Khorshid M; Hausser J; Berninger P; Rothballer A; Ascano M; Jungkamp A-C; Munschauer M; Ulrich A; Wardle GS; Dewell S; Zavolan M; Tuschl T
- Article: JOVE-JOURNAL OF VISUALIZED EXPERIMENTS. 2010;(41):2034Hafner M; Landthaler M; Burger L; Khorshid M; Hausser J; Berninger P; Rothballer A; Ascano M; Jungkamp A-C; Munschauer M; Ulrich A; Wardle GS; Dewell S; Zavolan M; Tuschl T
- Article: CELL. 2010;141(1):129-141Hafner M; Landthaler M; Burger L; Khorshid M; Hausser J; Berninger P; Rothballer A; Ascano MJ; Jungkamp A-C; Munschauer M; Ulrich A; Wardle GS; Dewell S; Zavolan M; Tuschl T
- Article: GENOME RESEARCH. 2009;19(11):2009-2020Hausser J; Landthaler M; Jaskiewicz L; Gaidatzis D; Zavolan M
- Article: JOURNAL OF MACHINE LEARNING RESEARCH. 2009;10:1469-1484Hausser J; Strimmer K
- Article: NUCLEIC ACIDS RESEARCH. 2009;37(Web Server issue):W266-W272Hausser J; Berninger P; Rodak C; Jantscher Y; Wirth S; Zavolan M
- Article: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. 2009;106(14):5813-5818Poy MN; Hausser J; Trajkovski M; Braun M; Collins S; Rorsman P; Zavolan M; Stoffel M
- Journal article: BMC BIOINFORMATICS. 2007;8:248Gaidatzis D; van Nimwegen E; Hausser J; Zavolan M
- Article: BMC BIOINFORMATICS. 2007;8:69Gaidatzis D; van Nimwegen E; Hausser J; Zavolan M
- Show more
All other publications
- Preprint: BIORXIV. 2025Hermet L; Laoubi L; Scavino M; Doffin A-C; Gazeu A; Berthet J; Pillat B; Tissot S; Rusakiewicz S; Michallet M-C; Bendriss-Vermare N; Valladeau-Guilemond J; Hausser J; Caux C; Hubert M
- Review: NATURE REVIEWS CANCER. 2020;20(4):247-257Hausser J; Alon U
- Preprint: BIORXIV. 2018Matalon O; Steinberg A; Sass E; Hausser J; Levy ED
- Review: NATURE REVIEWS GENETICS. 2014;15(9):599-612Hausser J; Zavolan M
- Book chapter: HANDBOOK OF RNA BIOCHEMISTRY. 2014;p. 833-860Hausser J; Zavolan M
Grants
- Swedish Research Council1 January 2025 - 31 December 2027Breast Cancer (BC) is a major public health concern. Triple-negative BC (TNBC) have long been challenging to treat. Pembrolizumab (anti-PD1) with neoadjuvant chemotherapy has recently become the standard of care in early TNBC, yet ~30% of patients resist and have poor outcome. More effective immunotherapy (IT) strategies are needed.To address this, we will 1) validate biomarkers to identify patients that will not benefit from treatment, and 2) discover/validate alternative targets.The novelty and feasibility of the program reside in i) unique TNBC cohorts and already generated data with clinical informationii) integrating innovative methodologies (scRNAseq, advanced spatial and computational biology) to identify biomarkers and resistance mechanism to IT and discover innate immune surveillance mechanisms that are overridden at preneoplastic stageiii) validating novel targets and companion biomarkers, evaluating their impact on clinical outcome and exploring their biology in vivo/in vitro, iv) developing therapeutic antibodies against the validated targets, and v) involving clinicians and patients to validate the unmet medical need and facilitate transfer to care and acceptability of knowledge.The project relies on multidisciplinary and inter-sectorial collaborations between key opinion leaders, partners with expertise in TNBC clinical management, immuno-oncology, computational biology and clinical bioinformatics, drug development and patients and lay public interactions.
- Swedish Cancer Society1 January 2022Tumors are not just pockets of cancer cells: you also find healthy cells from the tissue where the tumor grows and blood vessel cells. Tumors also contain immune cells whose task is to kill cancer cells, but which in some cases cannot intervene. The growth of a tumor or its rejection by the immune system depends on how these different cells organize themselves in space. But their organization is overwhelmingly complex: a tumor has millions to billions of cells that can perform dozens of different jobs in the tumor. Understanding tumor organization is like assembling an Ikea piece of furniture made of millions of parts of dozens of different types without building instructions. The aim of this project is to analyze many tumors to discover their common building plan. There is good reason to believe that there is a blueprint: healthy cells in tumors have evolved to cooperate, so there must be hidden order in the seemingly arbitrary chaos of tumor architecture. To discover this hidden order, we will develop a new microscopy method to obtain data on how cells are organized in tumors. We then analyze the data using mathematical techniques from ecology because ecology has a long tradition of studying how different species organize themselves in their environment. With this project, we hope to identify the basic building blocks of tumors and how these building blocks are assembled piece by piece to build the entire tumor. We hope to discover new important building blocks that could not be discovered without our new microscopy technique and mathematical method. Vii also hopes that our innovations in microscopy and mathematical methods for deciphering the blueprint of tumors will help other cancer researchers understand tumor architecture as well as help doctors repurpose successful treatments of tumors to other tumors with similar blueprints.
- Swedish Research Council1 January 2019 - 31 December 2022
- Swiss National Science Foundation1 June 2018 - 28 February 2019
- Prediction of cancer cell adaptations to drugsSwedish Cancer Society1 January 2018Cancer is still the second most common cause of death in many countries. This is largely due to the cancer's resistance to therapy. Resistance to therapy occurs because the millions or billions of cancer cells that make up the tumor differ slightly from each other. For example, they carry different mutations in the DNA. If we fail to eliminate 1% of the cancer cells, these cells will grow and divide. As a result, we transition to the second treatment line and another minority of cells begin to grow. The scenario is repeated until we have not long left any treatment options. To solve this problem, it would be helpful if we could predict how cancer cells will adapt to a given treatment. If we knew how cancer could most likely be adapted, we could combine a treatment targeting 99% of the cancer cells, with a second treatment targeting 1% of the cancer cells likely to adapt to the first treatment. With this combined therapy, we can reduce the risk of a minority of cancer cells surviving the treatment. In this project we will examine the potential of this strategy. We should first ask what is the main reason why cancer cells differ from each other before treatment. Secondly, we should treat cancer cells derived from 25 different breast tumors with tamoxifen and observe how they adapt to this drug. Finally, we will use these observations to develop a mathematical model to predict how our cancer cells adapt to the treatment and test whether the treatment of these adjustments reduces the risk of resistance. If this succeeds, this project will form the basis for incorporating these ideas into preclinical models and other cancer drugs.
- Swiss National Science Foundation1 August 2015 - 31 July 2017
- Swiss National Science Foundation1 April 2013 - 31 August 2013