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 perturbations.
Dr Karlsson became an Assistant professor at Karolinska Institutet in March 2021.
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.
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
Academic honours, awards and prizes
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