Kasper Karlsson

Kasper Karlsson

Assistant Professor
Visiting address: SciLifeLab Alfa 5 Tomtebodavägen 23 B, 17165 Solna
Postal address: K7 Onkologi-Patologi, K7 Forskning Kallioniemi Karlsson, 171 77 Stockholm

About me

  • We develop new experimental and computational models to study tumor evolution
    at single cell and single subclone level, in order to better predict drug
    response for pediatric cancers
    Dr Karlsson received his PhD at Karolinska Institutet under the supervision
    of professor Sten Linnarsson, where he was trained in the fields of molecular
    diagnostics and single-cell technologies. Specifically, he developed methods
    to sequence cell-free DNA without amplification in order to make more
    accurate predictions for non-invasive prenatal testing, and examined isoform
    expression in single cells.
    During his postdoctoral work, his focus shifted to studies of tumor biology.
    In the laboratories of professors Christina Curtis and Calvin Kuo at Stanford
    University, Dr. Karlsson developed an organoid based experimental model to
    study early tumor evolution. Here he also co-developed several tools,
    including an expressed cellular barcoding system and a microwell platform to
    better quantify tumor subclone dynamics during evolution and under drug
    Dr Karlsson became an Assistant professor at Karolinska Institutet in March
    2020 Swedish Childhood Cancer Foundation, Project Grant
    2020 Swedish Childhood Cancer Foundation, Research Assistant Grant
    2018 Stanford Center for Cancer Systems Biology (CCSB) Pilot project
    2018 Postdocs at the Interface, Stanford University, ChEM-H
    2018 Swedish Research Council, International Postdoc Grant
    2017 Stanford School of Medicine Dean’s Postdoctoral Fellowship
    2003 - 2009, Master of Science, Industrial Engineering, the Royal Institute
    of Technology, Sweden
    2011 - 2016, PhD in Medicine, Karolinska Instiutet, Sweden
    2016 - 2021, Postdoc, Stanford University, USA


  • I am an systems biologist with expertise in developing new models to study
    tumor evolution at single cell and single tumor subclone level. Specifically,
    my research aims at developing new experimental and computational tools for
    solid pediatric cancers to predict drug response that take intra-tumoral
    heterogeneity into account.
    It is well known that intratumoral heterogeneity is important for cancer
    evolution and the development of drug resistance. It has been shown that even
    cells located in close spatial proximity in the tumor, can exhibit large
    variation in drug response. Thus, to achieve a lasting therapeutic response,
    each tumor subclone needs to be addressed adequately. However, this
    fundamental aspect of tumor biology is often overlooked when new therapies
    are being developed, because it is difficult to model and address
    intratumoral heterogeneity. For example, most drug response studies use cell
    line models, which are known to be homogenous, and bulk sequencing data that
    fails to distinguish between different tumor subclones. We believe this is an
    important reason for the high failure rate of new compounds entering clinical
    trials, and why many new therapies only show a modest extension of life
    compared to standard of care.
    To study intratumoral heterogeneity we apply state-of-the-art organoid 3D
    models, that better preserve tumor heterogeneity compared to 2D cell lines.
    Furthermore, we have developed several tools, including an expressed cellular
    barcode system that marks the genome if individual cells with a unique DNA
    barcode. Daughter cells inherit the barcode, and this can be used for lineage
    tracing. With this system we can track thousands of unique tumor subclones
    and also assess their phenotype by single cell sequencing. We have also
    developed a microwell system to track thousands of single cell derived
    organoids over time, and methods to extract e.g. drug resistant organoids for
    further characterization. The lab collaborates closely with experts in
    computational modeling of tumor evolution and AI/machine learning labs to
    develop quantitative models of drug prediction that takes intra-tumoral
    heterogeneity into account. We also work closely with clinicians to
    facilitate clinical implementation of identified combinatorial therapies for
    pediatric cancer.


All other publications



  • Assistant Professor, Department of Oncology-Pathology, Karolinska Institutet, 2021-2025

Degrees and Education

  • Degree Of Doctor Of Philosophy, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 2016

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