Janne Lehtiö's Group
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; Zhu; Nature Comm., 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), breast cancer (Johansson, Nature Communications 2013; Johansson, Clinical Proteomics 2015) and leukemia. 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, 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; Zhu, 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, Mol. Cell. Prot. 2014), a galaxy based proteogenomics pipeline (Boekel, Nature Biotechnology 2015), and methods to improve quantification accuracy in proteomics (Forshed, Mol. Cell. Prot. 2011; Hultin-Rosenberg, Mol. Cell. Prot. 2013; Sandberg, 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 an accurate and sensitive pipeline 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, Exp. Rev. Proteomics. 2014; Pernemalm, Proteomics. 2009). Currently we are developing HiRIEF based methodologies both for global proteome analysis and targeted MRM-analysis of the plasma proteome. We are also taking advantage of the advances within the proteogenomics field and incorporating data on single amino acid variants in our analyses.
Many of our studies aim to discover diagnostic, prognostic and predictive biomarkers in plasma (Russel, Int J Cancer. 2016; Walker, EBioMedicine. 2015; Pernemalm, Proteomics. 2009), but we also have experience in other applications such as analysing the protein corona of nanoparticles (Vogt, PlosOne. 2015) or quantifying proteins from immunocapture analysis in plasma (Neiman, Proteomics, 2013) as well as analysing other biological fluids such as pleural effusion (Pernemalm, Proteomics. 2009), cystic fluid (Dinets, 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 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 (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.
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).
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.
- MS Orbitrap Q Exactive, Thermo Scientific.
- MS Orbitrap HF Q Exactive, Thermo Scientific
- MS Orbitrap Fusion, Thermo Scientific
- MS LTQ Orbitrap Velos Pro, Thermo Scientific
- MS LTQ Orbitrap Elite, Thermo Scientific
- LC-Triple Q-MS 6490, with iFUNNEL system, Agilent.
Peptide separation technologies
- HiRIEF (High Resolution Isoelectric Focusing)
- Liquid Chromatography (nanoUPLC/HPLC/FPLC)
Our methods 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
- 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
Hillevi Andersson-Sand, MSc, Research Engineer
Taner Arslan, MSc, PhD student
Ghazaleh Assadi, PhD, Research Engineer
Jorrit Boekel, PhD, Scientific programmer
Rui Branca, PhD, Senior scientist
Helena Bäckvall, PhD, Research coordinator
Xiaofang Cao, PhD, Research Engineer
Jürgen Eirich, PhD, Postdoc
Jenny Forshed, PhD, Associate Professor
Rozbeh Jafari, PhD, Postdoc
Henrik Johansson, PhD, Senior Scientist
Elena Kunold, PhD, Postdoc
Georgios Mermelekas, PhD, Research Engineer
Lukas Orre, PhD, Senior scientist
Yanbo Pan, PhD, Postdoc
Maria Pernemalm, PhD, Assistant Professor
Maan Rachid, PhD, Postdoc/bioinformatician
AnnSofi Sandberg, PhD, Postdoc
John Siavelis, MD, MSc, PhD-student
Fabio Socciarelli, MD, PhD student
Matthias Stahl, PhD, Postdoc
David Tamborero, PhD, Scientist/bioinformatician
Lingjie Tao, MSc, Research Engineer
Husen Umer, PhD, Postdoc
Nathaniel Vacanti, PhD, Postdoc
Mattias Vesterlund, PhD, Postdoc
Yan Zhou Tran, MSc, PhD-student
Yafeng Zhu, MSc, PhD-student
Filip Mundt, PhD