By integrating whole-specimen confocal microscopy, vascular counterstaining, and an advanced computational pipeline combining convolutional neural networks and Hidden Markov Models, our newly developed method GlioTrace identifies distinct invasion modes, including dynamic morphological switching, vessel-guided migration, and immune cell interactions and quantifies patient-specific variations in invasion plasticity. Using our method, we demonstrate that targeted therapies can selectively modulate invasion phenotypes, revealing spatially and temporally distinct drug responses. This scalable platform provides an unprecedented window into glioblastoma progression and treatment response, offering a powerful tool for precision oncology and anti-invasion therapeutic development.
Our team is exploring how innovative analyses of big data sets can be used to discover new therapeutics. In one of our recent projects, we developed a new algorithm, TargetTranslator, which could identify new treatments for neuroblastoma (Almstedt et al, Nature Communications 2020). Our current work explores how data-driven methods can be used to target a broader range of pediatric cancer, including medulloblastoma and DIPG. Our team has invented several other tools for data-driven cancer research, such as CancerLandscapes, EPoC, and CoPIA.
Our team is pioneering the use of patient-derived cell biobanks as a tool to uncover growth and drug response in the brain tumor glioblastoma (Cell Reports 2020, NeuroOncology 2018, Science Translational Medicine 2015, EBioMedicine 2015,). In recent work, we used our in-house Uppsala-based biobank HGCC to report on pharmacological subtypes of GBM. We are currently using the HGCC models to uncover differences in brain tumor invasion, using several different approaches. A key goal is to uncover the genetic programs that mediate invasion along different anatomical routes, including single-cell /spatial methods, CRISPR, and computational modeling. The key directions are:
Our team is investigating how tumor heterogeneity and drug response depends on time-dependent changes in individual tumor cells. In a recent study (Molecular Systems Biology, 2021), we have proposed a new strategy to construct models of cellular hierarchies from barcoded single cell experiments. The models, which we call State Transition and Growth models, are fitted from experimental data, obtained using barcoded single-cell sequencing and optical reporters. STAG models may have several potential uses in tumor biology and drug development.
Sven Nelander is a cross-disciplinary scientist focused on understanding, modeling, and treating brain cancers. With a background in medicine and mathematics, his research applies mathematical and experimental techniques to identify mechanisms and therapeutic targets, emphasizing cellular reprogramming to suppress tumor growth and improve therapies. Originally from southern Sweden, Sven studied medicine and mathematics in Lund before earning a Ph.D. in developmental processes in Gothenburg, doing early work in decoding cell type-specific gene regulation. As a postdoc in Chris Sander’s lab and later as an independent investigator, he invented new ways of modeling how cancer cells integrate perturbations from mutation, treatment, and stress. Since joining Uppsala University and SciLifeLab in 2012, Sven has built a research program linking tumor biobanking and neuro-oncology with modern systems biology, with support from several major foundations. He also serves as the founding director of CNSx3, a national center advancing modeling approaches for brain diseases. Outside the lab, Sven enjoys time with family, running, mushroom foraging, and restoring his summer house. He’s also an avid watercolorist with a passion for capturing light and fleeting moments in pigments.
Irem is a PhD student in Nelander lab.
Anders Sundström is a bioinformatician working predominantly across the omics landscape along some ventures into system biology and image analysis. A native of Uppsala, he started studying social science and humanities but quickly changed to life science. After completing his Master of Science in Engineering with a focus on bioinformatics, he worked on developing software for comparative genomics. Since 2015, he has been part of the Neuro-Oncology program at the Department of Immunology, Genetics, and Pathology at Uppsala University. In his parallel personal journey, he spent a year at art school where he participated in a group exhibition at the Iziko National Gallery of South Africa, earned a green belt in capoeira and became a two-time champion in the international Gagarin Cup floorball tournament. More recently he is trying to master bouldering with very limited success.
Madeleine started as a computational PhD student in Nelander lab in 2023, after earning her M.Sc. in Biophysics from Uppsala University the same year. Her work is focused on leveraging machine learning and image analysis to investigate the morphological and transcriptional heterogeneity and plasticity in glioblastoma. Her most recent work involves developing a method of single-cell tracking for in vitro and ex vivo settings, with the goal of following fluctuations in cellular morphology and protein expression over time. Outside of work, Madeleine dedicates most of her time to catching up with family and friends, as well as taking the occasional forest bath.
Hitesh Mangukiya specializes in studying the complex interactions between tumor cells, vascular cells, and microglia, with a focus on cell dynamics and invasion in glioblastoma. His current research centers on investigating cell dynamics, characterizing patient-specific tumor-endothelial interactions, and enhancing our understanding of glioblastoma’s invasive nature.
Josi is a PhD student in Nelander lab.
Rebecka is a PhD student in Nelander lab.
Neuro Oncol. 2022 May 4;24(5):726-738.
Nat Commun. 2021 Oct 21;12(1):6130.
PLoS Comput Biol. 2022 Mar 3;18(3):e1009844.
Mol Syst Biol. 2021 Sep;17(9):e10105.
Elife. 2021 Aug 17;10:e64846.
Bioinformatics. 2021 May 12:btab367.
Brain Commun. 2020 Jan 11;2(1):fcaa002.
Cell Rep. 2020 Jul 14;32(2):107897.
Mol Cancer Res. 2020 Oct;18(10):1522-1533.
Mol Cancer Res. 2020 Jul;18(7):981-991. doi: 10.1158/1541-7786.MCR-19-0638.
PMID: 31900415; PMCID: PMC6941971.
Neuro Oncol. 2022 May 4;24(5):726-738.
PMID: 35490730; PMCID: PMC9009924.
Nat Commun. 2021 Oct 21;12(1):6130.
PMID: 34615747; PMCID: PMC8533345.
PLoS Comput Biol. 2022 Mar 3;18(3):e1009844.
PMID: 35275681; PMCID: PMC8915275.
Mol Syst Biol. 2021 Sep;17(9):e10105.
PMID: 34530610; PMCID: PMC8430723.
Elife. 2021 Aug 17;10:e64846.
PMID: 34421016; PMCID: PMC8352079.
Bioinformatics. 2021 May 12:btab367.
PMID: 33858493; PMCID: PMC8251454.
Brain Commun. 2020 Jan 11;2(1):fcaa002.
PMID: 32007595; PMCID: PMC6983439.
Cell Rep. 2020 Jul 14;32(2):107897.
PMID: 32608799; PMCID: PMC7388369.
Mol Cancer Res. 2020 Oct;18(10):1522-1533.
PMID: 32758616; PMCID: PMC7522241.
Mol Cancer Res. 2020 Jul;18(7):981-991.
PMID: 32004222; PMCID: PMC7522682.