Janne Lehtiö's Group

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Cancer proteomics to improve therapy. Proteomics is a collective name of several techniques and methods for global analysis of proteins. Proteins are vital for any living organism and have numerous important functions including: accelerating chemical reactions as enzymes, serving as hormonal signaling molecules, providing recognition sites for the immune system, and serving as building blocks responsible for maintaining the cell's form and structure.

The Cancer Proteomics Mass Spectrometry group headed by Professor Janne Lehtiö is continuously developing mass spectrometry based methods to improve proteome analysis. We use omics methods to obtain detailed pictures of the molecular phenotype of cancer cells and tumors. One fundamental question we currently address is how cancer genome aberrations influence the functional proteome. This emerging field is known as cancer proteogenomics (Branca, Nature methods 2014; Zhu, Mol. Cell. Prot. 2014; Boekel, Nature Biotechnology 2015). Another important focus of ours is to gain knowledge of how targeted cancer drugs affect the proteome and how this information can be used to select most effective treatment for an individual patient. Our efforts are currently focused on lung (Pernemalm, JPR 2013; Orre, Oncogene 2013) and breast cancer (Johansson, Nature Communications 2013; Johansson, Clinical Proteomics 2015). Additionally we have ongoing research on endocrine tumors, neuro endocrine tumors, melanoma, and childhood cancers. We are engaged in translational research within a large collaborative network whose members include preclinical and clinical researchers in Sweden as well as around the world.

Proteomics Methods

Team leader: Dr. Rui Branca

In our group, we are developing and applying mass spectrometry (MS)-based methods with an aim to improve human proteome analysis. We have developed a prefractionation method known as high-resolution peptide isoelectric focusing (HiRIEF) (Branca, Nature Methods 2014). This method allows reproducible fractionation leading to a reduction of sample complexity prior to MS-analysis. This improves analytical depth, increases peptide and protein sequence coverage, improves overlap of detected peptides and proteins between samples, and provides an additional data point to each peptide, the peptide isoelectric point, allowing novel applications. One such novel application is using MS-data in an unbiased genome-wide search for protein coding DNA sequences in the human genome (proteogenomics) (Branca, Nature Methods 2014; Boekel Nature Biotechnology 2015). We are also developing methods to improve tissue and plasma proteome analysis, detection and quantification of post-translational modifications (most notably phosphorylations, glycosylations, and redox related cysteine modifications), and targeted analysis of proteins. To interpret the vast amounts of information generated by proteomics technologies, we are actively working on development and use of bioinformatics tools. We have recently developed novel tools for splice variant specific quantification at protein level (Zhu, Mol. Cell. Prot. 2014), a galaxy based proteogenomics pipeline (Boekel, Nature Biotechnology), and methods to improve quantification accuracy in proteomics (Forshed, Mol. Cell. Prot. 2011; Hultin-Rosenberg Mol. Cell. Prot. 2013; Sandberg J. Proteomics 2014).

Lung cancer research

Team leader: Dr. Lukas Orre

Our general aim is to identify novel biomarkers and targets for cancer therapy to improve the treatment of non-small cell lung cancer (NSCLC). To accomplish this we are developing and applying advanced methods to study the effects of cancer therapy. We are specifically interested in therapies targeting receptor tyrosine kinases such as the epidermal growth factor receptor (EGFR). By determining the molecular response of cancer cells to different drugs we can identify the determinants of drug response or drug resistance, allowing us to suggest biomarkers and drug combinations for improved lung cancer therapy. 

The expression and functions of proteins in a cell determine its phenotype, i.e. the characteristics of the cell. For example the expression and functional activity of proteins in a cell will determine if a cell is cancerous or not, and whether it is sensitive to a specific type of treatment. Proteomics is the large-scale study of proteins in biological systems. The proteomics concept can be further developed into functional proteomics where the ambition is to also describe the functional state of proteins in large-scale experiments. For this purpose we are developing and applying proteomics methods that generate information about protein activity (e.g. phosphorylation), protein complex formation, and protein subcellular location.

All of this data is then used in concert to understand why cells from different tumors respond differently to cancer drugs, and what combinations of cancer therapies we should select for each patient. Ultimately this knowledge will help us tailor the best therapy for each lung cancer patient.

Breast cancer research

Team leader: Dr. Henrik Johansson

Our overall aim is to use MS based proteomics tools to increase our biological understanding of breast cancer, define clinical subgroups, and identify biomarkers for personalized therapy.

The estrogen and progesterone receptors are routinely used together with the tyrosine kinase receptor HER2 to stratify breast cancer patients into different clinical treatment regimens. Genomics efforts have further divided breast cancer into Luminal A, B, HER2, Basal and normal-like subtypes based on mRNA expression, with refinement based on mutational patterns. As part of the breast cancer research program, we address how the mutational and transcriptional landscapes affect the proteome and how this information can be used to stratify and define the subgroups for personalized therapy.

The drug tamoxifen has been one of the cornerstones of treatment of ER positive breast cancer for nearly 4 decades. Tamoxifen treatment has reduced the recurrence rate by 50%, but about one-third of these patients still relapse. 80% of all breast cancer patients are eligible for tamoxifen, which highlights the need to find biomarkers to guide treatment. In our efforts to address this question, we have identified a 13 protein panel and the nuclear receptor, RARA, as potential predictive markers of tamoxifen resistance (Johansson, Nature Communications 2013; Johansson, Clinical Proteomics 2015).

Translational cancer bioinformatics

Team leader: Dr. Erik Fredlund

The Translational Cancer Bioinformatics team is focused on functional analyses of omics-type cancer data. By integrating multiple layers of high-throughput data with a strong knowledge of tumor biology we build hypotheses that can be tested using molecular biology methods. We are continuously developing and improving methods for analysis of deregulated signaling pathways in cancer by merging mutational, copy-number, gene expression, and quantitative proteomics data. We also have extensive experience in developing tools and pipelines for the analysis of microarray and Next-Generation Sequencing transcriptome and genome data. We are applying our developed methods to study human breast cancer subtypes as well as tumor development in mouse models of human cancer. We also have a strong focus on developing methods for rational selection of treatment for cancer patients based on tumor omic and phenotypic features.

Infrastructure and funding

Mass spectrometry instrumentation

We use cutting edge mass spectrometry and chromatography instrumentation for our research. We have also developed the High Resolution Isoelectric Focusing (Branca et al., Nature Methods, 2014) for pre-fractionation to perform in-depth proteomics together with GE Healthcare (GE research unit at Uppsala, Sweden).


  • MS LTQ Orbitrap Velos Pro
  • MS Orbitrap Q Exactive
  • High resolution Q-TOF 6540
  • LC-Triple Q-MS 6410
  • LC-Triple Q-MS 6490, with iFUNNEL system
  • MS Orbitrap Fusion collaboration with David Lane, Sonia Lain
  • MS Q Exactive HF collaboration with Pär Nordlund
  • MS Q Exactive HF collaboration with Mathias Uhlén
  • MS Q Exactive collaboration with Pär Nordlund


Our methods and cancer research is made possible by grants from:

  • AstraZeneca
  • Bioinformatics infrastructures for proteomics (BILS support)
  • Cancer Research Foundations of Radiumhemmet
  • Cancer Society in Stockholm
  • COMPASS Bioinformatics
  • EU-FP7 (Glycoproteomics research)
  • KI-MDACC Collaborative Grant (STRAT-CAN)
  • KI Breast Cancer Theme Center (BRECT)
  • Karolinska Institutet
  • GE Healthcare
  • Novartis
  • Stockholm County Council
  • Swedish Cancer Society
  • Swedish Childhood Cancer Foundation
  • Swedish Research Council
  • Swedish Foundation for Strategic Research
  • Stiftelsen Sigurd och Elsa Goljes Minne

Mass spectrometry facility

We disseminate our latest methods research via the Clinical Proteomics Mass spectrometry facility. For facility function and services, please visit the facility home page.

Doctoral courses

We arrange two doctoral courses every year:

"Mass spectrometry based proteomics: When and How" (link to course syllabus) given in October every year. Course director: Maria Pernemalm

"Omics data analysis: From quantitative data to biological information" (link to course syllabus) given in November each year. Course director: AnnSofi Sandberg

Group members

Janne Lehtiö, Professor, Group Leader
Hillevi Andersson-Sand, MSc, Research Engineer
Jorrit Boekel, PhD, Scientific Programmer
Rui Branca, PhD, Assistant Professor
Helena Bäckvall, PhD, Research Coordinator
Jürgen Eirich, PhD, Postdoc
Jenny Forshed, PhD, Associate Professor
Erik Fredlund, PhD, Assistant Professor

Oliver Frings, PhD, Postdoc
Rozbeh Jafari, PhD, Postdoc
Henrik Johansson, PhD, Senior Scientist
Anna Lindahl, MSc, PhD student (also member of Anders Nordström group)
Gianluca Maddalo, PhD, Postdoc
Filip Mundt, PhD, Postdoc
Lukas Orre, PhD, Assistant Professor
Yanbo Pan, PhD, Postdoc
Elena Panizza, MSc, PhD-student
Maria Pernemalm, PhD, Assistant Professor
AnnSofi Sandberg, PhD, Postdoc
Hitoshi Takahashi, PhD, Visiting Researcher
Davide Tamburro, PhD, Postdoc
Jessie Thorslund, MSc, Research Engineer
Nathaniel Vacanti, PhD, Postdoc
Mattias Vesterlund, PhD, Postdoc
Yan Zhou, MSc, PhD-student
Yafeng Zhu, MSc, PhD-student

Affiliated members

Claudia Fredolini, PhD

Selected publications

Multi-omic data analysis using Galaxy.
Boekel J, Chilton J, Cooke I, Horvatovich P, Jagtap P, Käll L, et al
Nat. Biotechnol. 2015 Feb;33(2):137-9

SpliceVista, a tool for splice variant identification and visualization in shotgun proteomics data.
Zhu Y, Hultin-Rosenberg L, Forshed J, Branca R, Orre L, Lehtiö J
Mol. Cell Proteomics 2014 Jun;13(6):1552-62

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics.
Branca R, Orre L, Johansson H, Granholm V, Huss M, Pérez-Bercoff , et al
Nat. Methods 2014 Jan;11(1):59-62

Quantitative proteomics profiling of primary lung adenocarcinoma tumors reveals functional perturbations in tumor metabolism.
Pernemalm M, De Petris L, Branca R, Forshed J, Kanter L, Soria J, et al
J. Proteome Res. 2013 Sep;12(9):3934-43

Retinoic acid receptor alpha is associated with tamoxifen resistance in breast cancer.
Johansson H, Sanchez B, Mundt F, Forshed J, Kovacs A, Panizza E, et al
Nat Commun 2013 ;4():2175

S100A4 interacts with p53 in the nucleus and promotes p53 degradation.
Orre L, Panizza E, Kaminskyy V, Vernet E, Gräslund T, Zhivotovsky B, et al
Oncogene 2013 Dec;32(49):5531-40

Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data.
Hultin-Rosenberg L, Forshed J, Branca R, Lehtiö J, Johansson H
Mol. Cell Proteomics 2013 Jul;12(7):2021-31

Full list of publications October 2015