Janne Lehtiö's Group

Our research group works on cancer proteogenomics for new biological knowledge and implementation of precision medicine.

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, tumors and plasma proteome on systems level. 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 open 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).

Our group are encouraging team science and embraces diversity. We are engaged in translational research within our group and via large collaborative networks, in Sweden and globally, from tech development to implementation of omics in clinical use (Tamborero D, Nature Medicine 2020).

Proteomics and Proteogenomics 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 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

Precision medicine is dependent on biomarkers to guide therapeutic decisions, monitor response to treatment and aid in diagnosis.  Blood based biomarkers has the advantage of being minimally invasive, routinely performed in the clinic and allows repetitive sampling.   

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, focusing in particular on precision medicine in oncology. 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 (Babacic et al., JITC 2020, 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 characterizing the protein component of circulating extracellular vesicle (Chandran et al., Clin Can Res 2019), 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

The Lung Cancer proteomics team has a strong focus on the application of proteomics methods in lung cancer research. Our general aim is to identify novel biomarkers and targets for cancer therapy to improve the treatment of non-small cell lung cancer (NSCLC). For state-of-the-art analytical depth, quantitative accuracy and proteome specific information we rely on in house developed methods (Branca et al., Nature Methods 2014, Panizza et al., Scientific Reports 2017, Zhu et al., Nature Commun. 2018, Wei et al., Nature Biotech 2018, Orre et al., Molecular Cell 2019, Zhu et al. Mol. Cell. Proteomics 2020).

In our clinical lung cancer proteomics projects primary tissue/biopsies from lung cancer are analysed in order to identify and characterize molecular subgroups of lung cancer. The gained knowledge of lung cancer subgroups is subsequently used for hypothesis driven projects where cancer drivers and corresponding therapeutic strategies are investigated in model systems. A current focus is also to understand the immune landscape of lung cancer in order to better target cancer cell escape from the immune system. In addition, translational projects are performed where discoveries from clinical samples are further investigated ex-vivo in primary cancer cells, as well as in model systems.

Further, pre-clinical research is performed where model systems are used to understand the molecular response to cancer therapy, with the aim to identify potential treatment predictive biomarkers and combination therapies (Orre et al., Oncogene 2013, Zhou et al., Oncogene 2017, Fotouhi et al., Oncogene 2019, Zhou Tran et al., Mol Cell Proteomics 2020). We are specifically interested in therapies targeting oncogenic drivers 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. Primary methods used are MS-based proteomics, transcriptomics, functional genomics, high-throughput drug screening as well as a range of classical molecular biology techniques.

The knowledge generated in different projects is 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 therapy.

To aid this goal, we take part in development of MS-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, 2) how the proteome defines breast tumor subgroups that can guide therapy and 3) how neoantigens shape the tumor and its response to 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.

Translational cancer bioinformatics

Team leader. David Tamborero, PhD

Our objective is to support the clinical decision-making based on the interpretation of the molecular profile of each patient’s tumor. Precision oncology relies on the accurate identification and interpretation of molecular alterations developed during the onset and progression of the disease, and vast efforts are ongoing to better understand their relevance for diagnosis, prognosis and therapy selection. To help in this task, we have developed the Molecular Tumor Board (MTB) portal (Tamborero D et al, Nature Med 2020), a clinical decision support tool that interprets tumor sequencing data and provides the infrastructure to harness the results. The MTB portal employs state-of-the-art biological, clinical and bioinformatics knowledge to annotate the functional significance of the molecular alterations observed in an individual tumor and their association with approved drugs and investigational therapies. The MTB portal also facilitates the discovery of new molecular biomarkers based on a systems biology approach and automates the detection of other events of clinical interest, such as allocation to available clinical trials and genetic counseling alerts, which support a more integrated care of the patients and their families. The MTB portal is currently used by the seven leading European comprehensive cancer centers forming the Cancer Core Europe.

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.

Instrumentation:

Mass spectrometers.

  • 1 MS Orbitrap Exploris 480, Thermo Scientific.
  • 1 TimsTOF Pro, Bruker Daltonics Inc
  • 4 MS Orbitrap HF Q Exactive, Thermo Scientific
  • 1 MS Orbitrap Fusion, Thermo Scientific

Peptide separation technologies.

  • HiRIEF (High Resolution Isoelectric Focusing)
  • Liquid Chromatography (nanoUPLC/HPLC/FPLC)

Sample preparations technologies

  • Sample prep robotics - modified Xantus by LAT

Funding

Our method development and cancer research are made possible by grants from:

  • AstraZeneca
  • Cancer Research Foundations of Radiumhemmet
  • Cancer Society in Stockholm
  • Dr Åke Olsson Foundation for Hematological Research
  • EU H2020 AiPBAND
  • EU H2020 Oncobiome
  • EU H2020 RESCUER
  • EU H2020 CCE-DART
  • Karolinska Institutet
  • Knut and Alice Wallenberg Foundation
  • GE Healthcare
  • Novartis
  • Roche
  • 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
  • VINNOVA
  • Vårdalstiftelsen

Mass spectrometry facility

We disseminate our latest methods research through our core facilities:

www.bioms.se and https://www.scilifelab.se/facilities/proteogenomics

For the different facility function and services, please visit the facility home pages.

Doctoral courses

We arrange two doctoral courses every year:

"Mass spectrometry based proteomics: When and How" (link to course syllabus) given in November. Course responsible: Henrik Johansson, henrik.johansson@ki.se 

"Omics data analysis: From quantitative data to biological information" (link to course syllabus) given in October. Course responsible: Mattias Vesterlund, mattias.vesterlund@ki.se

Group members

Group members in Janne Lehtiös research group.

 

Janne Lehtiö, PhD, Professor, Group Leader
Eduardo Araújo, PhD, Research Engineer
Taner Arslan, MSc, PhD Student
Ghazaleh Assadi, PhD, Lab coordinator
Luay Aswad, PhD, Postdoc
Haris Babacic, MD, PhD student
Olena Berkovska, MSc, 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
Sebastian Hansen, MD
Rozbeh Jafari, PhD, Researcher
Henrik Johansson, PhD, Senior Scientist
Markus Jonsson, PhD, Software Engineer
Isabelle Leo, BSc, PhD student
Georgios Mermelekas, PhD, Senior Research Engineer
Lukas Orre, PhD, Senior scientist
Yanbo Pan, PhD, Postdoc
Maria Pernemalm, PhD, Senior Scientist
Maan Rachid, PhD, Postdoc/bioinformatician
Ali Razzak, MSc, Bioinformatician
AnnSofi Sandberg, PhD, Bioinformatician
Ioannis Siavelis, MD, MSc, PhD student
Fabio Sociarelli, MD, PhD student
David Tamborero, PhD, Scientist/bioinformatician
Husen Umer, PhD, Postdoc
Mattias Vesterlund, PhD, Assistant Professor

 

Affiliated members

Filip Mundt, PhD
Nidhi Sharma, PhD, Postdoc
Frederik Post, MSc student
Noora Sissala, MSc student
Sebastian Kapell, Bioinformatician
Mohammad Tanvir Ahamed, PhD student

Selected publications

Support systems to guide clinical decision-making in precision oncology: The Cancer Core Europe Molecular Tumor Board Portal.
Tamborero D, Dienstmann R, Rachid MH, Boekel J, Baird R, Braña I, De Petris L, Yachnin J, Massard C, Opdam FL, Schlenk R, Vernieri C, Garralda E, Masucci M, Villalobos X, Chavarria E, , Calvo F, Fröhling S, Eggermont A, Apolone G, Voest EE, Caldas C, Tabernero J, Ernberg I, Rodon J, Lehtiö J
Nat Med 2020 07;26(7):992-994

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, Anders S, Lehtiö J
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, Fernandez-Woodbridge A, Aure MR, Sennblad B, Vesterlund M, Branca RM, Orre LM, Huss M, Fredlund E, Beraki E, Garred Ø, Boekel J, Sauer T, Zhao W, Nord S, Höglander EK, Jans DC, Brismar H, Haukaas TH, Bathen TF, Schlichting E, Naume B, , Luders T, Borgen E, Kristensen VN, Russnes HG, Lingjærde OC, Mills GB, Sahlberg KK, Børresen-Dale AL, Lehtiö J
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, Björk A, Wahren-Herlenius M, Åmark H, Östenson CG, Westgren M, Lehtiö J
Elife 2019 04;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, Jafari R, Lilljebjörn H, Odom DT, Olsson L, Ravi N, Woodward EL, Harewood L, Lehtiö J, Paulsson K
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, Frings O, Fredlund E, Lehtiö J
Mol Cell 2019 01;73(1):166-182.e7

Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow.
Zhu Y, Orre LM, Johansson HJ, Huss M, Boekel J, Vesterlund M, Fernandez-Woodbridge A, Branca RMM, Lehtiö J
Nat Commun 2018 03;9(1):903

Multi-omic data analysis using Galaxy.
Boekel J, Chilton JM, Cooke IR, Horvatovich PL, Jagtap PD, Käll L, Lehtiö J, Lukasse P, Moerland PD, Griffin TJ
Nat Biotechnol 2015 Feb;33(2):137-9

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics.
Branca RM, Orre LM, Johansson HJ, Granholm V, Huss M, Pérez-Bercoff Å, Forshed J, Käll L, Lehtiö J
Nat Methods 2014 Jan;11(1):59-62

Retinoic acid receptor alpha is associated with tamoxifen resistance in breast cancer.
Johansson HJ, Sanchez BC, Mundt F, Forshed J, Kovacs A, Panizza E, Hultin-Rosenberg L, Lundgren B, Martens U, Máthé G, Yakhini Z, Helou K, Krawiec K, Kanter L, Hjerpe A, Stål O, Linderholm BK, Lehtiö J
Nat Commun 2013 ;4():2175

Full list of publications (updated May 2021)

Contact

Janne Lehtiö, Professor, Group Leader, Janne.Lehtio@ki.se

Helena Bäckvall, PhD, Research coordinator, Helena.Backvall@ki.se