I lead the Predictive Medicine research group at Department of Medical Epidemiology and Biostatistics.
Prior to my current position I was a research fellow (2009-2013) with a joint affiliation at the Department of Statistics (University of Oxford) and at the Wellcome Trust Centre for Human Genetics (University of Oxford) where I worked with Professor Chris Holmes and Dr. Cecilia Lindgren. I was awarded a MRC Centenary Early Career Award (2012-2013) and a Medical Research Council (MRC) Special Training Fellowship in Biomedical Informatics (2009-2012). I had a postdoctoral position working on the data analysis work package for the MolPAGE consortium in Prof Chris Holmes’ group at the Department of Statistics in Oxford (2008). I completed my PhD at Imperial College London where I developed novel multivariate pattern recognition methods with applications in metabonomics, working together with Professor Elaine Holmes and Professor Jeremy Nicholson. I have an undergraduate degree in Engineering Biology (combined BSc/MSc) from Umeå University in Sweden.
We develop and apply statistical and machine learning methodologies for predictive modelling in biomedical applications with a particular interest in precision medicine and cancer research.
Our mission is to enable quantitative approaches to precision medicine and to develop novel patient stratification models for prognostic and treatment predictive applications. To achieve this we develop methods and models that allow us to transform large biomedical data into clinically relevant predictions at the individual level.
Our research is based on large and high-dimensional datasets (big-data) across multiple modalities including comprehensive molecular profiling (e.g. DNA- and RNA-sequencing), clinical information and medical imaging data.
- Development of predictive models based on comprehensive molecular phenotyping
- Application of machine learning and deep learning for histopathology image analysis
- Single-cell sequencing in cancer
- Methods for robust and integrative prediction modelling across multiple data modalities
- Histopathology image epidemiology: development of diagnostic and prognostic models based on large-scale histopathology image studies in breast cancer, prostate cancer and colorectal cancer (www.chimestudy.se)
- Molecular-based diagnostics and patient stratification in breast cancer based on DNA- and RNA-sequencing
- Improving patient risk stratification in AML based on DNA- and RNA-sequencing
Single-cell molecular profiling
- Applications of single-cell RNA sequencing for characterisation intra-tumor heterogeneity
- Methods for analysis of single-cell RNAseq data
Current research grants (as PI)
My work is supported by Swedish Research Council (VR), Swedish Cancer Society and Karolinska Insitutet.
- Yinxi Wang (main supervisor)
- Peter Ström (co-supervisor)
Other group members
- Boxi Zhang (MSc student)
- Youcheng Zhang (MSc student)
- Abhinav Sharma (MSc student)
- Charlotte Von Heijne Widlund (visiting scientist)
- Balasz Acs (associated postdoc)
Former group members
- Mei Wang (Postdoc; currently Research Assistant Professor, School of life science, Peking University, China)
- Arvind Mer (Postdoc; currently Research fellow at Princess Margaret Bioinformatics and Computational Genomics Laboratory, Toronto, Canada)
- Nghia Vu (Postdoc; currently Assistant Professor Karolinska Institutet)
Wang, M., Lindberg, J., Klevebring, D., Nilsson, C., Lehmann, S., Grönberg, H., Rantalainen, M., Development and validation of a novel RNA sequencing-based prognostic score for acute myeloid leukemia, J Natl Cancer Inst, 2018 Mar 18
Vu, T.N., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Pawitan, Y., Rantalainen M., Isoform-level gene expression patterns in single-cell RNA-sequencing data, Bioinformatics, 2018 Feb 1
Stålhammar, G., Robertson, S., Wedlund, L., Lippert, M., Rantalainen, M., Bergh, J., Hartman, J. Digital image analysis of Ki67 in hot spots is superior to both manual Ki67 and mitotic counts in breast cancer. Histopathology. 2017 Dec 8.
Karthik, GM., Rantalainen, M., Stålhammar, G., Lövrot, J., Ullah, I., Alkodsi, A., Ma, R., Wedlund, L., Lindberg, J., Frisell, J., Bergh, J., Hartman, J. Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling. BMC cancer. 2017 Dec;17(1):802.
Rantalainen M. Application of single-cell sequencing in human cancer. Briefings in functional genomics. 2017 Nov 2.
Holm J., Eriksson L., Ploner A., Rantalainen M., Li J., Hall P., Czene K.. Assessment of breast cancer risk factors reveals subtype heterogeneity. Cancer Research. 2017 Jan 1:canres-2574.
Spjuth, O., Karlsson, A., Clements, M., Humphreys, K., Ivansson, E., Dowling, J., Eklund, M., Jauhiainen, A., Czene, K., Grönberg, H., Sparén, P., Wiklund, F., Cheddad, A., Pálsdóttir, þ., Rantalainen, M., Abrahamsson, L., Laure, E., Litton, J.E., Palmgren, ,J. E-Science technologies in a workflow for personalized medicine using cancer screening as a case study. J Am Med Inform Assoc 2017 ocx038.
Wang, M., Lindberg, J., Klevebring, D., Nilsson, C., Mer, A.S., Rantalainen, M.§, Lehmann, S.§, Grönberg, H§. Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling. Leukemia, 2017, Feb 7.
Rantalainen, M., Klevebring, K., Lindberg, J., Ivansson,E., Rosin, G., Kis, L., Celebioglu, F., Fredriksson, I., Czene, K., Frisell, J., Hartman, J., Bergh, J., Grönberg, H.; Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers. Scientific Reports, 2016, 6, 38037.
Li, J., Ivansson, E., Klevebring, D., Tobin, N.P., Lindström, L.S., Holm, J., Prochazka, G., Cristando, C., Palmgren, J., Törnberg, S., Humphreys, K., Hartman, J., Frisell, J., Rantalainen, M., Lindberg, J., Hall, P., Bergh, J., Grönberg, H., Czene, K.; Molecular differences between screen-detected and interval breast cancers are largely explained by PAM50 subtypes. American Association for Cancer Research, 2016, pp.clincanres-0967.
Georgoudaki, A.M., Prokopec, K.E., Boura, V.F., Hellqvist, E., Sohn, S., Östling, J., Dahan, R., Harris, R.A., Rantalainen, M., Klevebring, D. and Sund, M., Brage S.E., Fuxe J., Rolny C., Li F., Ravetch J.V., Karlsson M.C.; Reprogramming Tumor-Associated Macrophages by Antibody Targeting Inhibits Cancer Progression and Metastasis. Cell reports, 2016, 15(9), 2000-2011.
Mer, A.S., Klevebring, D., Grönberg, H., Rantalainen, M.; Study design requirements for RNA sequencing-based breast cancer diagnostics. Scientific reports, 2016, 6.
Wang, M., Klevebring, D., Lindberg, K., Czene, K., Grönberg, H., Rantalainen M.; Determining breast cancer histological grade from RNA sequencing data. Breast Cancer Research, 2016, 18(1).
Vu, N.T., Wills, Q.F., Kalari, K.R., Niu, N., Wang, L., Rantalainen M.§, Pawitan Y.§. Beta-Poisson model for single-cell RNA-seq data analyses. Bioinformatics, 2016, btw202.
Stålhammar, G., Martinez, N.F., Lippert M., Tobin, N.P., Mølholm, I., Kis, L., Rosin, G., Rantalainen, M., Pedersen, L, Bergh, J, Grunkin, M.; Digital image analysis outperforms manual biomarker assessment in breast cancer. Modern Pathology, 2016, Feb 26.
- Course director, "Multivariate prediction modelling with applications in precision medicine".
- Degree project coordinator (Medicine)