Janne Lehtiö's Group
Cancer proteomics to improve therapy.
Proteomics is a collective name of several techniques and methods for global analysis of proteins in biological samples such as cells and tissues. Proteins are vital for any living organism and are involved in virtually all biological functions from accelerating chemical reactions as enzymes to cellular signaling, and serv 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 elt al., Mol. Cell. Prot. 2014; Boekel et al., Nature Biotechnol 2015; Zhu et al., Nature Commun., 2018). With this method we can also detect cancer cell specific protein variants, which opens up the possibility to use proteogenomics to discover novel immunotherapy targets and to define predictive biomarkers for immunotherapy. Another 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 cancer (Pernemalm, JPR 2013; Orre, Oncogene 2013; Zhou, Oncogene 2017), breast cancer (Johansson, Nature Commun 2013; Nature Commun 2019; Johansson, Clinical Proteomics 2015) and leukemia (Yang, Nature Commun 2019). We are engaged in translational research within a large collaborative network whose members include preclinical and clinical researchers in Sweden and all over the world.
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 et al., 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 et al., Nature Methods 2014; Boekel et al., Nature Biotechnology 2015; Zhu et al., Nature Communications 2018). 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 have developed tools for splice variant specific quantification at protein level (Zhu et al., Mol. Cell. Prot. 2014), a galaxy based proteogenomics pipeline (Boekel et al., Nature Biotechnology 2015), and methods to improve quantification accuracy in proteomics (Forshed et al., Mol. Cell. Prot. 2011; Hultin-Rosenberg et al., Mol. Cell. Prot. 2013; Sandberg et al., J. Proteomics 2014). Recently we have started to implement immunopeptidomics and our proteogenomics methods to the discovery of tumor specific antigens in cancer samples.
Plasma proteomics research
Team leader: Dr Maria Pernemalm
Personalized medicine is dependent on biomarkers to guide therapeutic decisions, monitor response to treatment and aid in diagnosis. Being able to measure these biomarkers by a simple blood test is in many cases the ultimate goal.
The overall aim for the plasma proteomics team is to develop and apply accurate and sensitive methods for mass spectrometry-based analysis of the plasma proteome.
Due to the skewed protein concentration distribution and high patient to patient variability it has proved to be extremely difficult to detect protein biomarkers in plasma and many methods that are suitable for cellular and tissue proteomics performs poorly in plasma (Pernemalm et al., Exp. Rev. Proteomics. 2014; Pernemalm et al., Proteomics. 2009). We have recently developed HiRIEF based methodologies both for global proteome analysis and targeted analysis of the plasma proteome. In addition, we have also developed a strategy for taking advantage of the advances within the proteogenomics field and incorporating coding DNA and RNA sequence variants in our proteome analyses (Pernemalm et al., eLife 2019).
Many of our studies aim to discover diagnostic, prognostic and predictive biomarkers in plasma (Chandran et al., Clin Can Res 2019; Russel et al., Int J Cancer. 2016; Walker, EBioMedicine. 2015), but we also have experience in adapting protocols for specific plasma proteome applications such as analysing the protein corona of viruses and nanoparticles (Ezzat Ahmed et al., Nature Comm. 2019; Vogt et al., PlosOne. 2015) or quantifying proteins from immunocapture analysis in plasma (Neiman et al., Proteomics, 2013) as well as analysing other biological fluids such as pleural effusion (Pernemalm et al., Proteomics. 2009), cystic fluid (Dinets et al., PlosOne 2015), and cerebrospinal fluid.
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 analyzing 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 (Branca et al., Nature Methods 2014; Zhou et al., Oncogene 2017).
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. Panizza et al., Scientific Reports 2017, Wei et al., Nature Biotech 2018), protein complex formation, and protein subcellular location (Orre et al., Molecular Cell 2019).
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 increase the biological understanding of the breast tumor proteome to define clinical subgroups, identify therapeutic targets and biomarkers for personalized
To aid this goal, we take part in development of mass spectrometry based proteomics methods in the group to obtain deep proteome coverage and identify novel protein coding protein regions (Branca et al., Nature Methods, 2014; Zhu et al., Nature Commun 2018).
We are mainly interested in 1) understanding and defining markers of endocrine resistance and 2) how the proteome defines breast tumor subgroups that can guide therapy.
In our efforts to define antiestrogen responders from non-responders, we have identified a 13-protein panel and the nuclear receptor, RARA, as potential predictive markers of tamoxifen resistance (Johansson et al., Nature Commun 2013; Johansson et al., Clinical Proteomics 2015).
In a recent study, unbiased characterization of breast tumor proteomes recapitulates known BC subtypes and further subdivide by immune component infiltration, suggesting the current classification is incomplete (Johansson et al., Nature Commun 2019). The proteome profiles were used as a base to interpret multiple layers of data collected on the same tumors, including those of mRNA expression, genome copy-number alterations, single-nucleotide polymorphisms, phosphoprotein levels, and metabolite abundances. Proteins from part of the genome, not thought to be translated, were also identified. These novel proteins have the potential to function as tumor specific antigens in immunotherapy.
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.
- 1 MS Orbitrap Q Exactive, Thermo Scientific.
- 4 MS Orbitrap HF Q Exactive, Thermo Scientific
- 1 MS Orbitrap Fusion, Thermo Scientific
- 1 MS LTQ Orbitrap Velos Pro, Thermo Scientific
- 1 LC-Triple Q-MS 6490, with iFUNNEL system, Agilent.
Peptide separation technologies.
- HiRIEF (High Resolution Isoelectric Focusing)
- Liquid Chromatography (nanoUPLC/HPLC/FPLC)
Our method development and cancer research is made possible by grants from:
- Cancer Research Foundations of Radiumhemmet
- Cancer Society in Stockholm
- Dr Åke Olsson Foundation for Hematological Research
- EU H2020 AiPBAND
- EU H2020 Oncobiome
- Karolinska Institutet
- GE Healthcare
- Stockholm County Council
- Swedish Cancer Society
- Swedish Childhood Cancer Foundation
- Swedish Research Council
- Swedish Foundation for Strategic Research
- Stiftelsen Sigurd och Elsa Goljes Minne
- The Erling-Persson Family Foundation
Mass spectrometry facility
We disseminate our latest methods research through our core facilities:
For the different facility function and services, please visit the facility home pages.
We arrange two doctoral courses every year:
Janne Lehtiö, PhD, Professor, Group Leader
Eduardo Araujo, Research Engineer
Taner Arslan, MSc, PhD Student
Ghazaleh Assadi, PhD, Lab coordinator
Haris Babacic, MD, PhD student
Jorrit Boekel, PhD, Scientific programmer
Rui Branca, PhD, Senior scientist
Helena Bäckvall, PhD, Research coordinator
Xiaofang Cao, PhD, Research Engineer
Jenny Forshed, PhD, Associate Professor
Rozbeh Jafari, PhD, Assistant Professor
Henrik Johansson, PhD, Senior Scientist
Kaveh Moazemi-Goudarzi, PhD, Research Engineer
Georgios Mermelekas, PhD, Senior Research Engineer
Lukas Orre, PhD, Senior scientist
Yanbo Pan, PhD, Postdoc
Maria Pernemalm, PhD, Assistant Professor
Mohammad Pirmoradian, PhD, Research Engineer
Maan Rachid, PhD, Postdoc/bioinformatician
AnnSofi Sandberg, PhD, Bioinformatician
John Siavelis, MD, MSc, PhD student
Fabio Sociarelli, MD, PhD student
Matthias Stahl, PhD, Postdoc
David Tamborero, PhD, Scientist/bioinformatician
Husen Umer, PhD, Postdoc
Mattias Vesterlund, PhD, Postdoc
Yan Tran Zhou, MSc, PhD-student
Filip Mundt, PhD
DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.
Zhu Y, Orre LM, Zhou Tran Y, Mermelekas G, Johansson HJ, Malyutina A, et al
Mol. Cell Proteomics 2020 06;19(6):1047-1057
Breast cancer quantitative proteome and proteogenomic landscape.
Johansson HJ, Socciarelli F, Vacanti NM, Haugen MH, Zhu Y, Siavelis I, et al
Nat Commun 2019 04;10(1):1600
In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta.
Pernemalm M, Sandberg A, Zhu Y, Boekel J, Tamburro D, Schwenk JM, et al
Elife 2019 Apr;8():
Proteogenomics and Hi-C reveal transcriptional dysregulation in high hyperdiploid childhood acute lymphoblastic leukemia.
Yang M, Vesterlund M, Siavelis I, Moura-Castro LH, Castor A, Fioretos T, et al
Nat Commun 2019 04;10(1):1519
SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization.
Orre LM, Vesterlund M, Pan Y, Arslan T, Zhu Y, Fernandez Woodbridge A, et al
Mol. Cell 2019 Jan;73(1):166-182.e7
Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow.
Zhu Y, Orre L, Johansson H, Huss M, Boekel J, Vesterlund M, et al
Nat Commun 2018 03;9(1):903
Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis.
Zhu Y, Engström P, Tellgren-Roth C, Baudo C, Kennell J, Sun S, et al
Nucleic Acids Res. 2017 03;45(5):2629-2643
Isoelectric point-based fractionation by HiRIEF coupled to LC-MS allows for in-depth quantitative analysis of the phosphoproteome.
Panizza E, Branca R, Oliviusson P, Orre L, Lehtiö J
Sci Rep 2017 07;7(1):4513
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
Janne Lehtiö, Professor, Group Leader, Janne.Lehtio@ki.se
Helena Bäckvall, PhD, Research coordinator, Helena.Backvall@ki.se