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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.
Phospho-proteomics
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
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.

Selected publications:

Brunner A, Li Q, Fisicaro S, Kourtesakis A, Viiliäinen J, Johansson HJ, Pandey V, Mayank AK, Lehtiö J, Wohlschlegel JA, Spruck C, Rantala JK,  

Taner Arslan, Yanbo Pan, Georgios Mermelekas, Mattias Vesterlund, Lukas M. Orre 

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 

Zhou Tran Y, Minozada R, Cao X, Johansson HJ, Branca RM, Seashore-Ludlow B and 

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 

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, Vesterlund M, Pan Y, Arslan T, Zhu Y, Fernandez Woodbridge A, Frings O, Fredlund E, Lehtiö J. SubCellBarCode: Proteome-wide Mapping of 

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. 

Zhu Y, Orre LM, Johansson HJ, Huss M, Boekel J, Vesterlund M, Fernandez-Woodbridge A, Branca RMM, Lehtiö J. Discovery of coding regions in 

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. 

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.