Lung Cancer and Personalized Medicine
Lung cancer is by far the deadliest form of cancer and in many cases there are no available therapeutic options for patients with this disease. Targeted therapies that inhibit growth factor signaling in lung cancer raised hope for improved patient survival, but ten years after approval it is evident that monotherapy with these agents will only delay disease progression by a few months and in addition only in a small subset of patients.
It is becoming increasingly appreciated that the reasons for this lack of efficacy is that cancer is a heterogeneous disease. This is displayed in two ways. First, the molecular paths to lung cancer are not the same in different patients. This means that different patients will have different molecular drivers of their cancer and consequently they should be treated in different ways i.e. we should personalize the therapy. Second, the tumor cells that together build up a tumor in a patient are also heterogeneous i.e. each tumor is composed of several different clones of cells that can differ in mutation spectrum and cell type. These different cells within a single tumor will also respond differently to cancer therapy. A drug that efficiently kills one type of cells in the tumor can still leave other cancer cells unaffected. To efficiently kill all cancer cells within a tumor and ultimately cure the patient we need to combine several types of therapy.
In order to personalize cancer therapy and identify efficient therapy combinations we need biomarkers that can be used to predict if a specific type of therapy will be efficient in a specific patient. Once identified, the presence of these biomarkers can be measured in the tumor of a patient, and this information can then be used for rational selection of therapy.
The general aim of my research is to identify novel biomarkers and targets for cancer therapy to improve the treatment of lung cancer. To accomplish this I am developing and applying advanced methods as described below to study the effects of cancer therapy on cancer cells. By reading out the molecular response of cancer cells to different drugs in great detail we can identify the determinants of drug response or drug resistance, and use this information to suggest biomarkers and drug combinations for improved lung cancer therapy.
Functional Proteomics and Systems Biology
The expression, and functions of proteins in a cell is what determines the 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 a cancer cell is sensitive for a specific type of treatment. Proteomics is the large-scale study of proteins in biological systems, or in other words methods that aim to comprehensively describe the protein landscape of a biological sample. 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.
In depth mass spectrometry based proteomics
In our lab we use high-resolution orbitrap mass spectrometers to identify and quantify thousands of proteins in biological samples. More specifically, we have developed a method, HiRIEF-LC-MS (Nature Methods 2014) that allows us to study more than 10 000 unique proteins and their regulation in response to treatment in a single experiment. This analytical depth gives us the possibility to describe the molecular response of cancer cells to cancer therapy in great detail, which is essential to understand the effects of the treatment.
The activity of a protein is not only determined by the abundance of the protein. Since it is important for a cell to tightly regulate all cellular processes, the proteins are equipped with “molecular switches” that turn on or off certain functions of the proteins. This regulation is performed by adding or removing functional groups to the proteins in processes referred to as protein post-translational modification (PTM). The most well characterized type of PTM is protein phosphorylation and the phosphorylation pattern of proteins in cancer cells are commonly disturbed, which can result in for example increased cell proliferation or decreased apoptosis (cell death). Many targeted cancer therapies are aiming to restore the phosphorylation pattern in cancer cells to stop the uncontrolled cell growth and induce cell death. To fully understand the consequences of targeted therapies, it is therefor important to study changes in protein phosphorylation in response to treatment. In our lab we are setting up methods to specifically study protein phosphorylation, and these methods are then applied to analyze the effects of treatment on the phospho-proteome.
Protein subcellular localization and relocalization
The functions and activities of proteins are also determined by their specific localization in the cell. A protein can have one type of function when localized in the nucleus, and a completely different function when localized in the cytoplasm. More common maybe is that a protein is active when it is residing in one subcellular compartment and inactive when sequestered in another. Treatment of cells with different types of drugs will alter the subcellular localization of proteins, i.e. induce protein relocalization, which will alter the function of affected proteins. Currently, large-scale methods to study protein localization and relocalization are lacking. We are developing methods to comprehensively study the localization of proteins in cells, and these methods will be used to understand how proteins shuttle between different subcellular compartments in response to cancer therapy, and ultimately how this affects the drug sensitivity.
Systems biology is the combination of different levels of biological information in order to see the full picture. In practice this means that we merge the information generated by several different types of experiments used to study the same biological question. The data included in our systems biology analysis is generated by the proteomics methods described above, but we also include data describing the transcriptional activity of cancer cells in response to treatment (mRNA-level analysis) as well as the genomic background of the cells (DNA-level analysis). All this data is then used in concert to understand why the cancer cells from different tumors respond differently to cancer drugs. Ultimately this knowledge will help us tailor the best cancer therapy to each lung cancer patient with the ambition to cure him or her from the disease.
Janne Lehtiö, Taner Arslan, Ioannis Siavelis, Yanbo Pan, Fabio Socciarelli, Olena Berkovska, Husen M. Umer, Georgios Mermelekas, Mohammad Pirmoradian, Mats Jönsson, Hans Brunnström, Odd Terje Brustugun, Krishna Pinganksha Purohit, Richard Cunningham, Hassan Foroughi Asl, Sofi Isaksson, Elsa Arbajian, Mattias Aine, Anna Karlsson, Marija Kotevska, Carsten Gram Hansen, Vilde Drageset Haakensen, Åslaug Helland, David Tamborero, Henrik J. Johansson, Rui M. Branca, Maria Planck, Johan Staaf, and Lukas M. Orre. Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms. Nature Cancer. 2021 Nov 22 (2), 1224–1242.
Veerman RE, Teeuwen L, Czarnewski P, Güclüler Akpinar G, Sandberg A, Cao X, Pernemalm M, Orre LM, Gabrielsson S, Eldh M. Molecular evaluation of five different isolation methods for extracellular vesicles reveals different clinical applicability and subcellular origin. J Extracell Vesicles. 2021 Jul;10(9), Epub 2021 Jul 22. PMID: 34322205
Zhou Tran Y, Minozada R, Cao X, Johansson HJ, Branca RM, Seashore-Ludlow B and Orre LM. Immediate Adaptation Analysis Implicates BCL6 as an EGFR-TKI Combination Therapy Target in NSCLC. Mol Cell Proteomics. 2020 Jun;19(6):928-943. PMID: 32234966
Fotouhi O, Kjellin H, Juhlin CC, Pan Y, Vesterlund M, Ghaderi M, Yousef A, Andersson-Sand H, Kharaziha P, Caramuta S, Kjellman M, Zedenius J, Larsson C and Orre LM. Proteomics identifies neddylation as a potential therapy target in small intestinal neuroendocrine tumors. Oncogene. 2019 Oct;38(43):6881-6897. PMID: 31406256
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; Consortia Oslo Breast Cancer Research Consortium (OSBREAC), Luders T, Borgen E, Kristensen VN, Russnes HG, Lingjærde OC, Mills GB, Sahlberg KK, Børresen-Dale AL, Lehtiö J. Breast cancer quantitative proteome and proteogenomic landscape. Nature Communications. 2019 Apr 8;10(1):1600. PMID: 30962452
Orre LM, Vesterlund M, Pan Y, Arslan T, Zhu Y, Fernandez Woodbridge A, Frings O, Fredlund E, Lehtiö J. SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization. Molecular Cell. 2019 Jan 3;73(1):166-182.e7. PMID: 30609389
Wei B, Jolma A, Sahu B, Orre LM, Zhong F, Zhu F, Kivioja T, Sur I, Lehtiö J, Taipale M, Taipale J. A protein activity assay to measure global transcription factor activity reveals determinants of chromatin accessibility. Nature Biotechnology. 2018 Jul;36(6):521-529. PMID: 29786094
Zhu Y, Orre LM, Johansson HJ, Huss M, Boekel J, Vesterlund M, Fernandez-Woodbridge A, Branca RMM, Lehtiö J. Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nature Communications. 2018 Mar 2;9(1):903. PMID: 29500430
Zhou Y, Frings O, Branca RM, Boekel J, le Sage C, Fredlund E, Agami R and Orre LM. microRNAs with AAGUGC seed motif constitute an integral part of an oncogenic signaling network. Oncogene. 2017 Feb 9;36(6):731-745. PMID: 27477696
Branca RM, Orre LM, Johansson HJ, Granholm V, Huss M, Pérez-Bercoff Å, Forshed J, Käll L, Lehtiö J. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature Methods. 2014 Jan; 11 (1): 59-62. PMID: 24240322