sCR
JiV MASARYK UNIVERSITY
MOLECULAR MECHANISMS OF METASTASIS:
INTEGRATED GENOMICS AND PROTEOMICS
APPROACHES
Ph.D. Dissertation
Monika Dvořáková
I FACULTY OF SCIENCE
DEPARTMENT OF BIOCHEMISTRY
Supervisor: Mgr. Pavel Bouchal, Ph.D. Brno, 2016
Bibliographie Entry
Author:
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Mgr. Monika Dvořáková
Faculty of Science, Masaryk University
Department of Biochemistry
Molecular mechanisms of metastasis: Integrated
genomics and proteomics approaches
Biochemistry
Biochemistry
Mgr. Pavel Bouchal, Ph.D.
Faculty of Science, Masaryk University
Department of Biochemistry
2016/2017
129
Metastasis, proteomics, biomarker, transgelin, LA-12
Bibliografický záznam
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Monika Dvořáková
Přírodovědecká fakulta, Masarykova univerzita
Ústav Biochemie
Molekulární mechanismy metastazování:
Integrované přístupy na bázi genomiky a
proteomiky
Biochemie
Biochemie
Mgr. Pavel Bouchal, Ph.D.
Přírodovědecká fakulta, Masarykova univerzita
Ústav Biochemie
2016/2017
129
Metastazování, biomarker, transgelin, LA-12
Abstract
The introduction section of the thesis deals with the metastasis of the tumors and the use of
biomarkers in oncology. The metastases are the most common cause of death of the
oncological patients. In the last decades, the researchers have been focusing on this process
in an effort to understand its causes and mechanisms and to find new biomarkers which
would specify risk of its occurrence in individual patients. The use of biomarkers in
oncology enables development of the personalized therapy aiming for appropriate
treatment for every patient. I mention the utility of biomarkers in oncology and show
examples of the biomarkers used in clinical practice nowadays. Moreover, I discuss
difficulties and limitations which are associated with the biomarker discovery and with the
application of biomarkers in clinical practice and utility of the proteomics in these fields.
The last chapter of the introduction summarizes information about protein transgelin,
which is supposed to be a potential cancer biomarker.
The result section of the thesis summarizes results of four original peer-reviewed articles
and of one manuscript in preparation for publication. In the first paper, we described the
correlation between the level of Retinol-binding protein (RBP4) and a new anticancer drug
LA-12 in rat plasma using SELDI-TOF MS. For identification of the protein peak from the
SELDI spectrum correlating with the LA-12 level, we used the approach developed by us,
which combined three-dimensional pre-separation of the proteins with their subsequent
MS/MS identification. In the second paper, we discuss approaches used for protein
identification in MALDI/SELDI-based studies and we present a new approach based on the
top-down RP-LC-FT-MSn
analysis. The third and fourth paper deal with the discovery of new
biomarkers of lymph node metastasis in low-grade breast cancer. Despite a generally good
prognosis of the patients with low-grade tumors, the low percentage of the patients within
this diagnosis soon develops lymph node metastases. Our results show differences between
the mechanism of lymph node metastasis in low and high-grade tumors. The mechanism of
low-grade tumors includes up-regulation of carboxypeptidase B l (CPB1), activation of
NFKB pathway and the changes in cell survival and in the cytoskeleton. Moreover, we
described up-regulation of transgelin in the cells of the tumor stroma as a biomarker of
lymph node metastasis in breast cancer. In the last paper, we functionally characterized
protein transgelin in two breast cancer cell lines. We described transgelin role in the
migration of the BT549 cells and in the cell death of the PMC42 cells.
Abstrakt
Úvodní část práce se zabývá metastazováním nádorů a použitím „biomarkerů" v onkológii.
Metastáze jsou nejčastější příčinou smrti onkologických pacientů. Onkologický výzkum se
proto v posledních desetiletích zaměřil právě na tento proces s cílem porozumění jeho
příčinám, popsání jeho mechanizmů a nalezení „biomarkerů", které by umožnily odhadnout
riziko jeho rozvoje u jednotlivých pacientů. Biomarkery jsou v onkológii využívány stále
častěji a umožňují zejména rozvoj tzv. personalizované terapie, jejímž cílem je, aby každý
pacient dostal léčbu odpovídající jeho přesné diagnóze. V práci je diskutován potenciál
biomarkerů v onkológii a jsou zde ukázány příklady dnes rutinně využívaných biomarkerů
v klinické praxi. Rozebírám zde také problémy a limitace, které sebou přináší hledání
nových biomarkerů a jejich zavádění do praxe a potenciál proteomiky v těchto oblastech.
Poslední kapitola úvodu podává stručný přehled o proteinu transgelinu, který byl nejen
námi popsán jako potenciální biomarker řady nádorových onemocnění.
Výsledková část práce shrnuje výsledky čtyř prací publikovaných v recenzovaných
časopisech a jednoho rukopisu v přípravě. V první práci jsme pomocí metody SELDI-TOF
MS popsali korelaci mezi hladinou proteinu retinol-binding protein 4 (RBP4) a cytostatikem
LA-12 v krevní plazmě potkanů. Pro identifikaci píku ze SELDI spektra korelujícího
s hladinou LA-12 byl použit námi navržený přístup kombinující trojrozměrnou separaci
proteinů s jejich následnou MS/MS identifikací. V druhé práci podrobně rozebíráme
přístupy používané k identifikaci proteinů ze SELDI studií a představujeme nový přístup
založený na „top-down" RP-LC-FT-MSn
analýze. Třetí a čtvrtá práce se zabývají hledáním
nových biomarkerů metastazování do lymfatických uzlin u dobře diferencovaných („lowgrade")
nádorů prsu. Navzdory obecně dobré prognóze pacientů s dobře diferencovanými
nádory, u malého procenta pacientů s touto diagnózou dochází velmi brzy k rozvoji
lymfatických metastáz. Naše výsledky ukazují, že mechanismus metastazování dobře
diferencovaných nádorů je odlišný od mechanismu metastazování u špatně
diferencovaných nádorů a že tento mechanismus zahrnuje zvýšenou expresi proteinu
carboxypeptidase BI (CPB1), aktivaci NFkB dráhy, změny cytoskeletonu a změny na úrovni
přežívání buněk. Jako potenciální biomarker metastazování do lymfatických uzlin jsme
popsali zvýšenou expresi transgelinu buňkami nádorového stromatu. V poslední práci se
zabýváme funkční charakterizací transgelinu u buněčných linií odvozených od karcinomu
prsu. Popsali jsme zde jeho úlohu v regulaci migrace u linie BT549 a v regulaci buněčné
smrti u linie PMC42.
© Monika Dvořáková, Masaryk University, 2016
Acknowledgements
Firstly, I would like to express my sincere gratitude to my supervisor Mgr. Pavel Bouchal,
Ph.D. for valuable guidance of my Ph.D. study, for the opportunity I was given to work on
interesting topics, for his motivation and knowledge. Many thanks to all friends and
members of the laboratory RECAMO for all their support and fun we had together. I would
like to thank my family for all their love and encouragement. This work was supported by
Czech Science Foundation (project No. 14-19250S), by MEYS -NPS I - L01413, and by MH
CZ -DRO (MMCI, 00209805).
Pledge
I hereby confirm that I have entirely worked out this thesis by myself, using techniques
and literature described herein.
Septembre 1th, 2016
Brno, Czech Republic Mgr. Monika Dvořáková
List of abbreviations
AFP - Alpha-fetoprotein
ASC - American Cancer Society
ASCO - American Society of Clinical Oncology
BM - Basal membrane
CAFs - Cancer-associate fibroblasts
CEA - Carcinoembryonic antigen
CPB1 - Carboxypeptidase B l
CRC - Colorectal cancer
DIA - Data-independent acquisition
ECM - Extracellular matrix
EGFR - Epidermal growth factor receptor
EMT - Epithelial-mesenchymal transition
ER - Estrogen receptor
hCG - Human choriogonadotropin
ITGB1 - Integrin beta-1
LA-12 - (0C-6-43)-bis(acetato)(l-adamantylamine) amminedichloroplatinum (IV]
LDH - Lactate dehydrogenase
MALDI - Matrix-assisted laser desorption/ionization
MMPs - Matrix metalloproteinases
MRTFs - Myocardin related transcription factors
NK - Natural killer
NPI - Nottingham Prognostic Index
PAcIFIC - Precursor acquisition-independent from ion count
PAI1 - Plasminogen activator inhibitor 1
PAM50 - Prediction of microarray 50 genes
PDLIM2 - PDZ and LIM domain protein 2
PR - Progesterone receptor
PSA - Prostatic specific antigen
RBP4 - Retinol binding protein 4
RELA - NF-KB transcription factor p65
ROS - Reactive oxygen species
RNF25 - Ring finger protein 25
SELDI-TOF MS - Surface-enhanced laser desorption-ionization time-of-flight mass
spectrometry
SMCs - Smooth muscle cells
SRF - Serum response factor
SRM - Selected reaction monitoring
STMN1 - Stathmin 1
SWATH-MS - Sequential window acquisition of all theoretical fragment ion spectra
TAM - Tumor-associated macrophage
TMSB10 - Thymosin beta 10
TPMT - Thiopurine methyltransferase
TRAF3IP2 - TRAF3 interacting protein 2
uPA - Urokinase plasminogen activator
VEGF - Vascular endothelial growth factor
Contents
Foreword 11
1 General introduction 12
1.1 Cancer metastasis 12
1.1.1 Metastatic cascade 13
1.1.1.1 Cancer cell invasion 13
1.1.1.2 Angiogenesis and intravasation of invading cancer cells 14
1.1.1.3 Cells surviving in circulation 15
1.1.1.4 Arresting in distant site and extravasation 15
1.1.1.5 Surviving and proliferation in the secondary organs 15
1.2 Biomarkers in oncology 17
1.2.1 Biomarker definition 17
1.2.2 Potential of biomarkers in cancer diagnosis and treatment 17
1.2.3 Cancer biomarkers translated into clinical practice 18
1.2.4 Breast cancer specific biomarkers 19
1.2.4.1 Clinically used factors: TNM, ER and HER2 19
1.2.4.2 Gene expression profiling as a complementary approach 19
1.2.4.3 Biomarkers in management of early breast cancer 20
1.2.5 Cancer biomarker issue 21
1.2.6 Oncoproteomics 21
1.3 Transgelin 24
1.3.1 Structure and homologs 24
1.3.2 Gene expression and its regulation 24
1.3.3 Function 25
1.3.4 Transgelin in cancer 26
1.3.4.1 Transgelin as proposed cancer biomarker 26
1.3.4.2 Transgelin function in cancer 27
2 Research aims 29
3 Materials and methods 30
4 Results 34
4.1 Research article 1 35
4.2 Research article II 37
4.3 Research article III 39
4.4 Research article IV 41
4.5 Research article V 44
5 Conclusions 47
6 References 48
9
7 List of publications b Z
8 List of contributions at conferences and symposia 63
Appendices 64
10
Foreword
Cancer research constantly deals with new questions and challenges. Development
of tumors is a complex process, which depends on the characteristics of the cancer
cells and their interaction with non-malignant cells of the tumor microenvironment.
The origin and development of the primary tumors are nowadays quite welldescribed
and many primary tumors are curable. The big challenge in the treatment
of cancer remains metastatic disease, which is the main cause of the death of the
oncological patients. During metastasis development, the cancer cells have to
surpass many barriers and only a small percentage of the cells is able to create
metastases in secondary organs. Hence, metastasis is a very ineffective process.
A big effort in the cancer research is dedicated to biomarker discovery. The cancer
biomarkers are applied in all phases of cancer diagnosis and treatment. Their
application to monitoring of patient respond to particular treatment or drug is just
one example (chapter "Research article I").
The biomarkers are routinely used in breast cancer. The breast cancer is a
heterogeneous disease, whose treatment strategy depends on molecular diagnosis
of the cancer subtype. Distinct breast cancer subtypes differ in their aggressiveness
and in response to therapeutic interventions. Early breast cancer (stage I and II)
require special attention. These tumors have generally very good prognosis
resulting in treatment by less aggressive adjuvant therapy and no chemotherapy.
However, low percentage of these patients forms early lymph node metastasis. Our
effort was to describe biomarkers which would select the patients with the higher
risk of metastasis formation, or would represent mechanisms targetable by more
personalized therapy (chapter "Research article III and IV).
The cytoskeleton plays an important role in cancer metastasis. Reconstructions of
actin cytoskeleton are characteristics of both the cancer cells and the cells of the
tumor stroma. Deregulation of cytoskeletal proteins can influence many cancerrelated
processes including metastasis. Transgelin is one of the cytoskeletonassociated
protein, whose change in expression was described in cancer. In relation
to metastasis, transgelin is up-regulated in cancer-associated fibroblasts and
changes in its expression influence migration of the cancer cells (chapter "Research
article IV and V).
11
1 General introduction
1.1 Cancer metastasis
Despite the progress in the treatment of the primary tumors, metastases still remain
incurable and thus the most common cause of death of the oncologic patients. In the case of
the breast cancer, which is one of the main topics of this thesis, up to 30% of the early
diagnosed patients will experience a metastatic disease later in their life (O'Shaughnessy,
2005). The knowledge of the metastasis has deeply broadened in the last decades.
Currently, the metastasis is perceived as a complex process depending on the gained
features of the cancer cells and also on the diverse interactions between cancer cells and
the cells of the tumor microenvironment (Hanahan and Weinberg 2011).
The metastases often resist the treatment, which cured the primary tumor. It is caused by
the fact that only a small cell population (often representing a minority of the cancer mass)
is able to metastasize. Moreover, cancer cells are exposed to a new microenvironment,
which often protects them against the effects of the treatment. The good prognosis of the
oncologic patients is mainly determined by the early diagnosis of cancer. However, the
metastatic disease appears later in the life of many patients with the early diagnosed
tumors. The fact is well demonstrated in the breast cancer, where despite the early
diagnosis (thanks to mammography screening) the risk of future metastasis remains
relatively high. An effort was made to define molecular markers, which would specify the
risk of metastasis development in particular patients (see chapter "Biomarkers in oncology"
for more details). The anti-metastatic treatment should aim not only cancer cells but also
the cells of the tumor microenvironment (the use of anti-VEGF antibodies is successful
example of such strategy). The aim of the treatment should be to decrease the metastatic
potential of cancer cells and to disrupt pro-metastatic interactions between the cancer cells
and the microenvironment.
The tumor is a heterogeneous cell population. The heterogeneity is based on the both
internal and external factors of cancer cells. The genetic instability of the cells generating
mutations is the main internal factor. The cell bearing mutations favorable to cell
proliferation and surviving can expand within the tumor (Nowell, 1976). The external
factors are based on the fact that a tumor is a mass of the cells, in which the cells are exposed
to different conditions in the different parts (e.g. hypoxia in the central part, which is badly
supplied by the blood). This results in a selection of the different cell populations within
different parts of the tumor. Then, the cell population able to metastasize can form only a
small, localized part of the tumor.
We still don't know when the metastases set up on the time axis of the tumor development.
In most cases, the metastases clinically manifest in the late stages of cancer. The cell has to
overcome many barriers (so-called metastatic cascade) to successfully develop secondary
tumors. In a classical model of malignant transformation of the tumors, the ability to
metastasize belongs to the most aggressive clone and the metastasis follows the
development of the primary tumors. However, it is possible that the metastases setup much
early and develop parallel to primary tumors as a result of clonal selection in secondary
organs (Klein, 2009). The earlier set up of the metastases is supported also by the results
made in breast cancer: (i) Genetic profiles of metastases and primary tumors can
significantly differ (Shah etal., 2009). (ii) Partially transformed cells are able to disseminate
from the tumor in pre-invasive stages of tumor progression of murine breast cancer
(Hiisemann et al., 2008).
12
1.1.1 Metastatic cascade
The metastasis can be divided into two main parts: the tumor cell dissemination from the
primary tumor into secondary organs and the adaptation of the cells to the new
microenvironment The metastasis is formed from the following processes: (i) local
invasion into surrounding tissue, (ii) intravasation into the bloodstream, (iii) surviving in
the blood circulation, (iv) arresting in the distant organs, (v) extravasation into the tissue of
the secondary organs, (vi) surviving in the new microenvironment and creating so-called
micrometastasis and (vii) proliferation into macroscopic metastasis (Figure 1).
Figure 1: The overview of the processes involved in the cancer metastasis. The cancer cells have to overcome many
barriers to create metastases in the secondary organs. (Cotran, 1999]
1.1.1.1 Cancer cell invasion
To invade into surrounding tissue, the tumor cells have to change their interactions with
other cells and with the extracellular matrix (ECM) and have to break a basal membrane
(BM). Three distinct ways of the invasion have been described. When the intracellular
junctions are partially preserved, the cells undergo a so-called collective migration
(typically for the cells of the squamous cell carcinoma). Full loss of intracellular junctions
leads to migration of individual cells. When their invasion is dependent on the interaction
with ECM they undergo the mesenchymal invasion, when it is independent of the interaction
they undergo the amoeboid invasion. None of the ways of the invasion is unique and the
tumor cells can switch between them in reaction to changes in the microenvironment The
epithelial-mesenchymal transition (EMT) is one of the ways how the tumor cells increase
their ability to invade. Physiologically, the EMT is connected with the embryonic
13
development and with the wound healing. EMT is transcriptionally regulated by several
factors (e.g. Snail, Slug Twist or Zeb Vz). These factors are strictly regulated during the
embryogenesis and the increase of their expression support metastasis in the cancer models
(Micalizzi et al., 2010; Schmalhofer et al., 2009; Taube et al., 2010). The cells undergoing
EMT gain characteristics of mesenchymal cells (expression of N-cadherin, vimentin), they
break their interactions with the surrounding cells, degrade BM and interact with the
stromal cells.
The heterogeneous interaction between tumor and stromal cells supports malignant
phenotype of the tumor cells and at the same time supports the development of reactive
tumor stroma (Karnoub and Weinberg, 2006). In connection with the EMT, Hlubek et al.
showed that the cells on the edges of the tumor interacting with the stromal cells undergo
EMT in colorectal cancer (CRC) (Hlubek et al., 2007). The tumor stroma contains a great
number of cell types (fibroblasts, pericytes, endothelial cells, immune cells) and its
composition changes during the progression of cancer. The factors produced by the tumor
cells activate present stromal cells and attract new cells (immune cells) from the circulation.
The tumor stroma thus reminds the activated microenvironment in the area of the wound
healing. The greatest population in the tumor stroma represents fibroblasts, which are
activated into so-called Cancer-associate fibroblasts (CAFs). The CAFs modify ECM. They
produce the components of the ECM (mainly collagen I and III, tenascin C) and thereby they
change its composition and stiffness. These changes support survival and migration of the
tumors cells through the activation of integrin-depending signaling (Hood and Cheresh,
2002; Stupack and Cheresh, 2002). The CAFs modulate ECM also by production of the
proteins degrading ECM (mainly matrix metalloproteinases - MMPs and cysteine
proteinases). The degradation supports the malignant behavior of the tumor cells directly
by the degradation of the BM or indirectly by the releasing of the bound growth factors and
producing pro-angiogenic and pro-inflammation fragments. Moreover, CAFs, the same as
tumor cells themselves, produce many chemokines and interleukins, which attract and
activate pro-inflammation cells. The chemokines CCL2 (MCP-1) and CCL5 (RANTES) have
the key role in the case of breast cancer (Soria and Ben-Baruch, 2008). The level of the both
chemokines is low in a normal breast, elevates together with the malignant transformation
of the breast tissue and is also detectable in the lymph nodes infiltrated by tumor cells and
in the metastases. Their increased level thus correlates with the worse prognosis of the
breast cancer patients (Chavey et al., 2007; Niwa etal., 2001; Ueno etal., 2000). The studies
done on mice show the ability of the chemokines to support metastasis and to shorten
survival of the mice (Nam et al., 2006; Salcedo et al., 2000). They support the invasion of
tumor cells in vitro (Karnoub and Weinberg, 2006; Nam etal., 2006; Prestetal., 1999) and
infiltration of monocytes into the tumor. Moreover, they stimulate these infiltrated
monocytes to production of pro-tumorigenic factors (Adler et al., 2003; Robinson et al.,
2003). The monocytes are precursors of tumor-associated macrophages (TAM). TAMs
produce factors (e.g. cathepsin proteases or epidermal growth factor in breast cancer)
which positively influence invasion and intravasation of tumor cells (Gocheva et al., 2010;
Wyckoff etal., 2007).
1.1.1.2 Angiogenesis and intravasation of invading cancer cells
Angiogenesis, which was proved already in non-malignant lesions, is an early event in the
tumorigenesis. It is a process of new vessel formation. It ensures the tumor nutrition and
thus enables growth of the tumor. Pro-angiogenic factors are produced by the tumor cells
themselves (after activation of oncogenes Ras and Myc), by the cells of the tumor stroma
(mainly the immune cells) and are also released from the ECM after its degradation. The
vascular endothelial growth factor (VEGF) has a key role in the angiogenic switch. The
angiogenesis also contributes to metastasis. The main reason is immaturity of the new
14
vasculature. The tumor vessels are considerably permeable, which enables the tumor cells
to invade into them.
1.1.1.3 Cells surviving in circulation
Once the tumor cells enter the blood vessels, they circulate through the bloodstream to
distant organs, where they are arrested. The cells leave the friendly environment of the
primary tumor and are faced with the new hostile factors. The main obstacles for the cells
in the circulation are the immune cells (the natural killers - NK cells) and anoikis, which is
switched on by the absence of the cell-cell or cell-ECM contacts. The important mechanism
for overcoming these barriers is the aggregation of the tumor cells with the thrombocytes
(Joyce and Pollard, 2009). The resistance to anoikis was described in the cells undergoing
EMT and in the cells with the constitutive activation of anti-apoptotic pathways (mainly
PI3K/Akt pathway) (Paoli etal., 2013). Moreover, it is supposed that majority ofthe cells is
arrested in the blood capillaries of the nearest organs (several minutes after intravasation).
Thus, the time, which the cells spend in the circulation, is too short to induce anoikis
(Valastyan and Weinberg, 2011).
1.1.1.4 Arresting in distant site and extravasation
It is known, that some cancers metastasize into certain organs. For instance, the breast
cancer creates metastases the most often in bones, brain and lungs. Already in 1889, on the
basis of the dates from more than 900 patients with a different cancer diagnosis, Stephen
Paget formulated a theory that the tumor cells create metastases only in the
microenvironment which is compatible with them ("Seed and soil" theory) (Paget, 1989).
He already supposed active interaction between tumor cells and the microenvironment of
the secondary organs. Paget's theory was however shortly doubted and it was believed that
tumor cells arrest in the secondary organs is a passive process, which is mainly influenced
by the direction of the blood flow, by the anatomy of the vasculature in the secondary organs
and by the size of the tumor cells. Currently, we assume the combination of the passive and
active processes. The majority of the tumor cells is really arrested already in the first organs
thanks to their size which is much bigger than the diameter of the capillaries. A typical
example is a colorectal carcinoma, which metastasizes into the liver; the organ directly
supplied by the blood from the gastrointestinal tract by the portal vein. However, the arrest
is not just a passive process. It can be facilitated by a specific interaction between tumor
cells and the endothelium of the organs. An interaction between the breast cancer cells
expressing a protein metadherin and the lung endothelium is a good example (Brown and
Ruoslahti, 2004). Afterward, the arrested cells penetrate the endothelial cells into the
parenchyma of the organs. The penetration can be passive (a burst of the capillary as a
reaction to the growth of the micrometastasis) or active (extravasation) process. Contrary
to intravasation which is eased by the abnormalities in the cancer vasculature, the
extravasation, which takes place in the health tissue, is much more difficult. However, the
tumor cells can produce some factors (e.g. angiopoietin-like-4, EREG, COX-2, MMP-1, MMP-
2) which increase the permeability of the endothelial cells and thus they make the
extravasation easier (Gupta et al., 2007; Padua et al., 2008).
1.1.1.5 Surviving and proliferation in the secondary organs
The tumor cells enter the parenchyma of the secondary organs, where they are exposed to
new conditions. Their survival there depends on their own characteristics, on the
characteristics of the microenvironment and in agreement with the Paget's theory on their
compatibility with the microenvironment. Some studies suggest that the factors released by
the primary tumors are able to change microenvironment of the distant organs even before
15
the tumor cell dissemination. According to this theory, the factors released by the primary
tumor activate fibroblast of distant organs to overproduce fibronectin (Erler et al., 2009).
The increased level of the fibronectin attracts VEGFR-1 positive hematopoietic progenitor
cells of the bone marrow. These cells then produce MMP-9 and thus make the
microenvironment more accessible for tumor cells (Erler et al., 2009). The expression
analysis done on the metastatic breast cancer cells suggest, that the tumor cells produce
factors, which support their metastasis into specific organs (Bos et al., 2009; Kang et al.,
2003; Minn etal., 2005; Tabaries etal., 2011). Such example is chemokine receptor CXCR4,
whose ligand CXCL12 is plentifully expressed by the cells of the organs with the highest
occurrence of the metastasis in breast cancer (lymph nodes, lungs, liver, bones) (Miiller et
al., 2001).
The number of the disseminated cells which are able to create macroscopic metastasis is
very low. The interaction between disseminated tumor cells and the microenvironment of
the secondary organs can be crucial for the full development of the metastatic disease. The
microenvironment of the secondary organs can (i) be hostile to tumor cells and cause their
apoptosis, (ii) be tolerant (the cells survive but do not create macroscopic metastasis) and
(iii) be supportive of the metastasis development The main limitations in the metastasis
formation in the secondary organs are the inability of the tumor cells to switch on the
angiogenesis, absence of the proliferative factors or a balance between proliferation and
apoptosis of the tumor cells. The tumor cells thus frequently persist in the form of "dormant
micrometastasis". The new mutations of the tumor cells or the changes in the
microenvironment (caused by e.g. disease, injury, age) can provoke the development of the
metastatic disease even years after the diagnosis or curing of the primary tumor.
16
1.2 Biomarkers in oncology
1.2.1 Biomarker definition
The biomarker is a characteristic which is objectively measured and evaluated as an
indicator of normal biological processes, pathological processes, or pharmacologic
responses to therapeutic intervention (definition by the National Institute of Health)
(Diamandis, 2010). The traditional conception of the biomarker is that it is a molecule (DNA,
mRNA, protein, metabolite) whose level in the blood, other body fluids or tissues changes
depending on the presence/absence/progress of the certain disease. However, the
biomarkers have also non-molecular nature, such example is a presence of the blood in the
stool as an indicator of colorectal cancer or a presence of cancer stem cells in the blood
circulation as an indicator of metastatic disease (Yang et al., 2015). The biomarkers can be
also processes such as angiogenesis, apoptosis or proliferation as markers of cancer
progression (Hayes etal., 1996). For its clinical application, biomarker has to be measurable
easily, reliably and cost-effectively by assay with high analytical sensitivity and specificity.
Moreover, its use must be associated with proven improvements in patient outcomes, such
as increased survival or enhanced quality of life (Hayes et al., 1996). The disease markers
are generally common in the medicine. Their application in oncology is however still limited
and only a few cancer markers are used in the clinical practice.
1.2.2 Potential of biomarkers in cancer diagnosis and
treatment
Currently, only a few cancer biomarkers are routinely used in a clinical practice. Their
potential is, however enormous (Figure 2). The cancer biomarkers are crucial for the
expansion of the personalized therapy (Duffy and Crown, 2008). Currently, the cancer
therapies are still chosen according to diagnostic category - typically based on the tumor
type and stage of the disease. This approach leads to the situation when all the patients
within the given category receive the same type of treatment despite the biological
heterogeneity known to exists from patient to patient (Dalton and Friend, 2006). The aim
of the personalized therapy is giving the right drug at the right dose to the right patient
(Duffy and Crown, 2008). To achieve this, strong and independent predictive markers are
needed to determine the aggressiveness of cancer. These markers enable to separate
patients with indolent disease from those with aggressive forms. One group of the patients
then can avoid the useless toxicity of the adjuvant treatment whereas the other group can
profit from it We also need markers to prospectively predict response or resistance to
specific therapies and to identify patients who are likely to develop severe toxic side effects
from specific treatments (Duffy and Crown, 2008). The predictive markers are important
especially when the biological treatment (aiming specific molecule or process) is available.
Thanks to these markers the right patient receives the right drug. Nowadays the markers
used in clinical practice are especially markers monitoring therapy in advanced disease. The
great effort to discover new biomarkers for screening for early malignancy, aiding cancer
diagnosis, determining prognosis and many others was made. The discovery of such
markers and their subsequent putting into the practice is demanding process, whose
difficulties are discussed in the subsequent chapters.
17
USES OF BIOMARKERS IN CANCER MEDICINE
Risk
Assesment
Diagnosis
Do 1 have
cancer? What
type of cancer
do 1 have?
Prognosis Predicting Pharmaco- Monitoring
Treatment kinetics Treatment
Response Response
Recurrence
Am 1 at
increased risk
for cancer?
Diagnosis
Do 1 have
cancer? What
type of cancer
do 1 have?
What is the Will my Should 1 How is my
expected cancer receive a cancer
course of respond to normal or responding
my cancer? this drug? lower dose to this
or no dose? treatment?
Will
my cancer
come back?
Figure 2: The potential of the biomarkers in cancer medicine. The cancer biomarkers are utile in management of
the cancer patients and enable progress of so-called personalized therapy.
1.2.3 Cancer biomarkers translated into clinical practice
Despite all the efforts, only a few markers are recommended by the guidelines of the
European and American oncological societies. The most known test, which was successively
implemented into clinical practice and became a part of a preventive health care in many
countries, is the occult blood testing for the screening of colorectal cancer. The decrease in
CRC mortality thanks to the regular screening of the health population over 50 was proved
in several randomized prospective studies done in Europe and North America (Hewitson et
al., 2008).
Another successful application of the markers in cancer is the use of alpha-fetoprotein
(AFP), human choriogonadotropin (hCG) and lactate dehydrogenase (LDH) in the
determination of the prognosis in the metastatic testis cancer (Milose et al., 2011). The
assessment of these markers in combination with the traditional clinical parameters (the
localization of the primary tumor and metastasis) became the basis for a new prognostic
classification system, which serves as a guideline for the treatment selection (Milose et al.,
2011).
The serological markers carcinoembryonic antigen (CEA), CA-125 and CA 19-9 are used in
the management of colorectal cancer (CEA), ovarian cancer (CA-125) and pancreatic cancer
(CA 19-9). None of these markers is, however, recommended for cancer screening (Henry
and Hayes, 2012). Their main application is in surveillance following curative surgery and
for the monitoring therapy in advanced disease. The levels of the markers are determined
in the patients at regular intervals. The changes in their levels indicate the
regression/progression of the disease in reaction to the therapy or the recurrence of the
disease and the metastasis formation in the patients after curative surgery.
Two markers are currently available for identifying drug-induced adverse reactions:
thiopurine methyltransferase (TPMT) to predict toxicity from thiopurines in the treatment
of acute lymphoblastic leukemia and urine diphosphate glucuronyltransferase to predict
toxicity from irinotecan in the treatment of colorectal cancer (Duffy and Crown, 2008).
The important predictive marker is a determination of K-RAS gene mutation in the
management of colorectal cancer. The mutation in K-RAS occurs in 30-40 % of all colorectal
cancer patients (Arrington et al., 2012). A retrospective study of the patients treated with
the inhibitors of epidermal growth factor receptor (EGFR) cetuximab and panitumumab
showed, that the treatment was effective only in the patients without the K-RAS gene
mutation (Amado et al., 2008; Karapetis et al., 2008). Subsequently, ASCO recommended
18
the determination of K-RAS mutation in all patients to predict response to EGFR inhibitors
therapy in metastatic colorectal cancer (Allegra et al., 2009).
A prostatic specific antigen (PSA) is an oncological marker used for the longest time in a
clinical practice. PSA was firstly described already in 1979 (Wang et al., 1979) and soon
after its discovery it was used in the management of the prostate cancer patients (Catalona
et al., 1991; Mettlin et al., 1991). PSA is currently the gold standard in the care for the
patients with prostate cancer. It is used mainly for monitoring of the patients after the
curable surgery and for determination of the disease recurrence. However, even PSA has its
limitations. The most important limitation is in its use for the cancer screening in the health
man population (Duffy, 2011). The ability of the PSA to detect cancer in the early phases in
asymptomatic individuals was described already at the beginning (Catalona et al., 1991).
The clinical importance of the PSA screening was also tested in two big randomized studies
in USA (Andriole et al., 2012) and in Europe (Schroder et al., 2009). The conclusions of the
studies are, however, ambiguous. The use of the PSA for the screening in the healthy
population is problematic because of its low specificity; higher levels of PSA are detected
also in a benign hyperplasia or in prostatitis. The number of false-positive results is thus
high, resulting in the expensive diagnostic evaluation and needless stress of the patients.
Another problem is a diagnosis of very small tumors, which would possibly never manifest.
For that reason, American cancer society (ASC) described PSA screening as non-effective
and recommended its application only for the patients with the life-expectancy more than
ten years (Wolf etal., 2010).
1.2.4 Breast cancer specific biomarkers
1.2.4.1 Clinically used factors: TNM, ER and HER2
The breast cancer is on histological and molecular level heterogeneous disease. The proper
classification of newly diagnosed tumors is essential for the determination of the prognosis
and the therapy selection. The classical histopathological classification includes
determination of the histological type, grade and stage (TNM system) (Singletary and
Connolly, 2006). According to this classification 70 - 80% of the tumors are classified into
the two most common types (invasive ductal or invasive lobular carcinoma) and their
prognosis and treatment management depends mainly on the stage of the disease. This
classification, however, does not reflect the biological and clinical heterogeneity of the
tumors. As a result, the treatment management of the individual patients based only on the
classical histopathological classification is not optimal. It is evident that the classification
based on the molecular markers better reflects a biological heterogeneity of the tumors.
Nowadays, the molecular markers are routinely used in the management of the breast
cancer. In combination with the classical parameters they determine the prognosis and
moreover, they predict response to relevant therapies. The gold standard in the
management of the breast cancer patients is the determination of the expression of the
hormonal (estrogen + progesterone) and HER2/neu receptor. The positive expression of
these markers is an essential requirement for the application of the adjuvant hormone
therapy and the therapy by the inhibitors of the HER2 receptor (Trastuzumab or Lapatinib),
respectively (Duffy etal., 2011).
1.2.4.2 Gene expression profiling as a complementary approach
The gene expression profiling enabled characterization of molecular subtypes of breast
cancer. These subtypes share some key pathological pathways, which can be subsequently
targeted by the specific therapies. Perou et al. realized the first gene expression profiling of
breast cancer in 2000 and on its basis five main molecular subtypes of breast cancer were
described (luminal A, luminal B, basal-like, Erb-B2+ and normal-breast-like) (Perou et al.,
19
2000). The particular subtypes are associated with the differences in overall survival of the
patients (S0rlie et al., 2001) and in the response to different therapies (Rody et al., 2007;
Rouzier et al., 2005; Wirapati et al., 2008). On the basis of the mentioned studies the
minimized set of fifty genes was developed (PAM50 - prediction of microarray 50 genes)
(Parker etal., 2009). The PAM50 distinguishes particular molecular subtypes and calculates
the risk of relapse score for individual patients (Parker et al., 2009). Its prognostic value
was tested in several retrospective studies (Ellis et al., 2011; Nielsen et al., 2010). The
technology of the gene profiling led to the development of other gene signatures with
prognostic and predictive value (Mammaprint, Oncotype DX). The limitation of the gene
expression profiling is the use of fresh tissue samples. This was overcome in some
predictors like PAM50 or OncotypeDX, which are RT-PCR based assays working with the
material extracted from the formalin-fixed tissue from paraffin block. However, a
widespread application of the gene profiling in a clinical practice is limited by its high cost
(Norum et al., 2014). The way how to implement molecular classification into clinical
practice is the use of immunohistochemistry of ER, PR and HER2 overexpression and
determination of proliferation activity (on the basis of IHC of Ki-67 or tumor grading
system) of the tumors (Hugh et al., 2009). Even though the agreement between the
molecular subtypes characterized by the immunohistochemistry (surrogate intrinsic tumor
phenotypes) and gene expression profiling is not 100%, the panelist of St Gallen consensus
2011 endorses the use of these markers for tumor classification with respect to
determination of prognosis and therapy selection (Gnantetal., 2011).
1.2.4.3 Biomarkers in management of early breast cancer
The use of tumor markers has a great potential in a management of the patients with early
breast cancer (stage I and stage II). After the primary therapy (surgery, radiation, used to
reduce or eliminate cancer), the adjuvant therapy is given to patients in order to increase
the chance of long-term disease-free survival by preventing a recurrence of the disease. The
decision on systemic adjuvant treatment should be based on the predicted sensitivity to
particular treatment types, the benefit from their use and individual risk of relapse
(Goldhirsch etal., 2013). For some patients, the adjuvant therapy brings no benefit and they
should avoid its toxicity.
Nowadays, the factors used for prediction of the recurrence risk and response to particular
treatment are ER/PR/HER2 expression, expression of proliferation markers, age, the
number of involved regional lymph nodes, tumor histology, size, grade and presence of
peritumoral vascular invasion. The clinical parameters have been integrated into several
scoring systems (Nottingham Prognostic Index - NPI, Adjuvant! Online or the PREDICT
score (Blarney et al., 2007; Ravdin et al., 2001; Wishart et al., 2011), which help with the
prediction. In recent years, immunohistochemically-defined surrogate intrinsic tumor
phenotypes have also been recommended for treatment individualization (Gnant et al.,
2011). According to this classification, for example, the luminal A tumors can be in most
cases avoided the adjuvant chemotherapy (Gnantetal., 2011; Senkus etal., 2015).
The prognosis of the patients is also evaluated on the basis of the metastatic potential of the
tumors. The two markers of tumor invasiveness urokinase plasminogen activator and
plasminogen activator inhibitor 1 (uPA and PAI1) have been validated in prospective
clinical trials as a prognostic markers for lymph node-negative breast cancer (Harbeck et
al., 2013) and are recommended in treatment decision making for early breast cancer (Duffy
et al., 2014). The commercial Elisa test (FEMTELLE, Sekisui Diagnostics) is available for its
determination. The test, however, is not extensively used, probably because of the
requirement for a substantial amount of fresh-frozen tissue (Harbeck et al., 2014).
The gene expression profiles may be used to gain additional prognostic and predictive
information to complement pathology assessment and to predict response to adjuvant
20
chemotherapy (Azim et al., 2013; Coates et al., 2015). Their use is mainly recommended in
challenging cases, such as luminal B HER2-negative and node-negative breast cancer. The
commercially available molecular signatures for ER-positive breast cancer are Oncotype DX
(Genomic Health, Redwood City, CA), EndoPredict (Myriad Genetics), Prosigna (Nanostring
technologies, Seattle, WA), and MammaPrint (Agendia, Amsterdam, the Netherlands) and
Genomic Grade Index (MapQuant Dx, Ipsogen, France) for all types of breast cancer (pNO-
1.2.5 Cancer biomarker issue
The enormous number of the studies of biomarkers in cancer is published every year.
However, the majority of the markers will never be approved for the use in the clinical care.
Nowadays, the "discovery" approach is frequently used in an identification of new
biomarkers. In this approach, new technologies such as a high-throughput sequencing, gene
expression arrays or mass spectrometry are used to identify individual or groups of
biomarkers that differ between cohorts. The discovery approach is very effective in
generating data but is also prone to false-positive results when the study design and/or the
data analysis is underestimated. One critical factor that can introduce bias is patient
selection. The selected population has to address the clinical question. The cases and the
controls have to be as similar in their clinicopathological characteristics as possible, except
for the disease of interest (Henry and Hayes, 2012).
When discovered, the new potential biomarker has to surpass many hurdles before its
application in practice. The new markers have to undergo rigorous evaluation, including
analytical validation, clinical validation and assessment of the clinical utility (Teutsch et al.,
2009). The assay for their determination has to be developed and its technical aspects
(sensitivity, specificity, robustness or reproducibility) have to be evaluated. The analytical
part of the validation should also address pre-analytical issues, which refers to the handling
of the samples (e.g. storage time and conditions).
The clinical validity should evaluate the ability of the biomarker to divide the overall
population into the groups according to the probability, that they will suffer certain event
(Henry and Hayes, 2012). The clinical validity has to be reproduced on the set of samples
which is completely independent of the set used in the discovery phase. Before its
implementation into clinical practice, the biomarkers have to prove their clinical utility
(Henry and Hayes, 2012). It has to be shown, that the biomarker provides information in
addition to currently used decision-making factors (more favorable clinical outcome for the
patient, including increased overall survival, increased disease-free survival, improvement
in quality of life, or reduction in cost of care) and the guidance for its use in the management
of the patients should be available to the clinicians.
The proper reporting of the results of the studies is a key component of the evaluating of a
new biomarker (Henry and Hayes, 2012). The reports have to contain sufficient information
to independently validate the results. Several guidelines have been developed for reporting
results of biomarker studies (e.g. The Biospecimen Reporting for Improved Study Quality BRISQ,
and REporting recommendations for tumor MARKer - REMARK). The clinical utility
of the biomarkers is evaluated using the systems, which place cancer biomarker results into
various levels of evidence and grades of recommendation.
1.2.6 Oncoproteomics
Proteomics-based strategies are able to describe dynamic and complex changes in the
proteome of the cancer cells. Proteins (and not transcripts) are the functional products of
the genes. The levels of the proteins in the cells, however, do not fully correspond to the
level of the gene expression and the protein activity/function can be further modified in the
21
cells via post-transcriptional modifications resulting in no- or abnormally functional
molecules. The changes of the cellular proteome reflect the malignant character of the cells.
The proteome is thus a source of valuable cancer biomarkers. The analysis of the cell
proteome has its limitations. These are mainly a vast dynamic range of the protein
concentrations and the fact that the signaling molecules, which are crucial for the cell
function, represent a minority of the cell proteome. The finding of the protein cancer
biomarkers is moreover complicated by the known heterogeneity of the tumors.
Historically, the key event in the analysis of the proteins by mass spectrometry has been
describing of the Matrix-assisted laser desorption/ionization (MALDI) MS, which enabled a
soft ionization of the proteins (Karas and Hillenkamp, 1988). At the beginning the MS
identification of the proteins was combined mainly with the gel-based methods for the
protein separation. The mainly used two-dimensional gel electrophoresis (2-DE) separated
proteins according to their molecular weight and isoelectric point Moreover, its
modifications enabled simultaneous analysis of the several samples in the same gel using
fluorescent labeling (2-D DIGE) (Unlii etal., 1997). One of the modifications of the MALDI is
a surface-enhanced laser-desorption-ionization time-of-flight mass spectrometry (SELDITOF
MS), which uses a specific surface of the chip for protein pre-separation. The method
enables a low-cost analysis of the intact proteome and its use led to the identification of the
proteome profiles specific for various types of cancer. (Engwegenetal., 2006). SELDI works
in the MS mode and thus does not provide information about the identity of the proteins.
Despite the hopes neither the SELDI method nor the SELDI-based proteomic signatures
have never been used in the clinical practice. The method suffered from low number of
identified proteins and unavailability of identity of the quantified peaks (Baggerly et al.,
2005; Diamandis, 2004). Currently, the MALDI can be replaced by electrospray ionization
of the proteins/peptides, which is combined with the chromatographic separation methods.
Proteomics has developed rapidly in the last two decades and the current high-throughput
methods are able to analyze the proteome of the cells. Two groups of proteomics methods
are recognized: discovery and targeted. The discovery methods analyze the whole cell
proteome, generate a large amount of the data and thus they are suitable mainly for the
discovery phase of the biomarker research. They involve two main approaches: bottom-up
(protein digestion and subsequent peptides separation and identification) and top-down
(separation of the whole cell proteome and subsequent protein fragmentation in the mass
spectrometer). The protein quantification in discovery proteomics is done using different
labeling methods (iTRAQ, TMT, SILAC) and also using the label-free approaches. Targeted
proteomics is suitable for the later phases of the biomarker research (verification of the
biomarkers). It is represented by selected reaction monitoring (SRM) method. SRM enables
selective quantification of the specific proteins within the complex protein samples using
the knowledge of the peptide-specific transitions (combination of the m/z of the specific
precursor ions and its specific fragment ions). A novel approach in targeted proteomics is
data-independent acquisition (DIA) represented by SWATH-MS (sequential window
acquisition of all theoretical fragment ion spectra) or PAcIFIC (precursor acquisitionindependent
from ion count) which analyzes the whole proteome creating digital proteome
maps. DIA data are structurally similar to SRM data in general and the targeted data can be
easily extracted from them resulting in possible quantification of all detectable proteins in
large sample sets. The methods of targeted proteomics have a potential to replace
immunochemical methods (ELISA, IHC) in the research and potentially in clinical practice.
Their advantage over the classical immunochemical methods is their rapid and costeffective
development and easy transferability of the analyzed data. The utility of the
methods of targeted proteomics prove the studies in which the results of SRM well
correlated with the results of the standardized ELISA in the quantification of known
markers (e.g prostatic specific antigen (Fortin et al., 2009), epidermal growth factor
receptor (Hembrough et al., 2012)). Targeted proteomics is also successfully used in
validation of the new biomarkers (Mustafa et al., 2013; Pan et al., 2012) and in monitoring
22
of the level of specific proteins in model system (e.g. SRM assay for 22 proteins involved in
the Wnt/P-catenin signaling pathway (Chen et al., 2010] or SRM assay for monitoring of
S100 family proteins in colorectal cell lines (Martinez-Aguilar and Molloy, 2013].
23
1.3 Transgelin
Transgelin is a cytoplasmic protein. Its name derives from its important characteristics, the
lost expression in viral-transformed cells and the ability to gel actin filaments (Shapland et
al., 1993). It is also known as a protein SM2 2 (an abundant 22-kDa protein of chicken gizzard
smooth muscle (Lees-Miller et al., 1987)) and a protein WS3-10 (a protein upregulated in
the senescent fibroblasts of the patients with the M/erner syndrome (Murano et al., 1991)).
1.3.1 Structure and homologs
Transgelin is evolutionarily conserved as far back as yeast (Shapland et al., 1988). Its
structural homologs are known across species, for example, p27 in mouse (Almendral et al.,
1989) or Scpl in yeast (Goodman etal., 2003). Transgelin structurally belongs to acalponin
protein family, which is characterized by the presence of a single N-terminal calponin
homology (CH) domain and by one C-terminal calponin-like repeat (Li et al., 2008). In
humans, two of its homologs, transgelin-2 and transgelin-3 were described with 64% and
67% amino acid sequence homology to transgelin, respectively. All the transgelins are the
cytoplasmic proteins associated with the actin filaments (Mori et al., 2004; Zhang et al.,
2002). They differ in cell type expression specificity. Transgelin is abundant in SMCs and
fibroblasts, transgelin-2 co-express with transgelin in arterial and venous SMCs and,
moreover, is expressed at high level in epithelial cells (Zhang etal., 2002). Transgelin-3 is a
neuron-specific protein (Ren etal., 1994).
1.3.2 Gene expression and its regulation
Transgelin encoding gene [TAGLN) was localized to chromosome llq23.2. It is composed
of five exons, includes a large first intron and three short introns (Camoretti-Mercado et al.,
1998). As already mentioned the expression of transgelin is the highest in the SMCs and the
fibroblasts but has also been described in epithelial cells (Figure 3). Interestingly, its cell
expression can significantly change. For instance, during the embryogenesis, transgelin is
expressed by all three muscle lineage cells (smooth, cardiac, skeletal), but its expression is
restricted to smooth muscle lineage in adulthood (Li et al., 1996). Moreover, its expression
in SMCs depends on the phenotype of the cells (described further). Fibroblasts lose
transgelin after oncogenic transformation and by switching to non-adherent cultivation
conditions (Shapland et al., 1988). On the contrary transgelin expression increases in
senescent fibroblasts (Murano etal., 1991) and in myofibroblasts (Untergasser etal., 2005).
In epithelial cells, the high expression of transgelin was described in myoepithelial cells in
breast tissue (Page et al., 1999) and was also connected with the tissue injury and healing
processes in kidney and lung (Marshall et al., 2011; Yu et al., 2008). Some processes which
are associated with the changes in transgelin expression are atherosclerosis (Feil et al.,
2004), pulmonary hypertension (Zhang et al., 2009), fibrosis (Yu et al., 2008), senescence
(Murano etal., 1991) and cancer (see chapter "Transgelin in cancer" for more detail).
24
vi-,
Figure 3: Transgelin expression in breast cancer. Transgelin is expressed mainly by the tumor stroma cells in the
breast cancer tissue. It is expressed also by several breast cancer cell lines. It is localized in the cytoplasm of the cells
and it is associated with the actin cytoskeleton in BT 549 cells.
Regulation of TAGLN expression was studied mainly in connection with the regulation of
SMC differentiation (Mack, 2011; Owens et al., 2004). Serum response factor (SRF) and
cytokine TGF-|3 play key roles in this process and they cooperate in TAGLN promoter
activation. TGF-|3 activates transgelin promoter via both classical Smad-dependent and
alternative Smad-independent (mainly via GTPase RhoA) pathway (Chen etal., 2003, 2006;
Liu et al., 2003; Mack et al., 2001). SRF is a transcription factor which binds to transgelin
promoter as a dimer. This binding is relatively weak and is enhanced by SRF interaction
with additional transcriptional factors and co-activators (e.g. myocardin and myocardinrelated
transcription factors (MRTFs) (Wang etal., 2001, 2002; Yoshida et al., 2003). TAGLN
transcription is also activated by Wnt/|3-catenin pathway (Shafer and Towler, 2009). The
main negative regulator of TAGLN transcription is the growth factor PDGF-BB, which
triggers Ras/Raf/MEK/ERKkinase cascade (Kaplan-Albuquerque etal., 2003).
Transgelin function is closely connected with its actin binding activity. Sequences
responsible for transgelin binding to actin are localized within the C-terminus of the protein
(Fu et al., 2000; Goodman et al., 2003). CH domain, which is an actin-binding site of many
cytoskeletal and signaling proteins, acts as a 'locator' domain for actin binding in case of
transgelin.
Smooth muscle cells exhibit a considerable plasticity of the phenotype. In adulthood, SMCs
are differentiated and are characterized by a low proliferation, by a low ECM protein
synthesis and by the expression of the molecule important for contractile functions.
However, during reparative processes in adults like wound healing and during the early
embryonic development, SMCs change their phenotype, dedifferentiate, increase their
proliferative potential and are characterized by high expression of ECM components.
Transgelin is a marker of differentiated SMCs, whose expression decreases during
phenotype modulation (Yamamura et al., 1997). Transgelin stabilizes differentiated SMCs
by the assembly of actin filaments into bundles (Han et al., 2009) and thus prevents their
phenotypic modulation (Feil etal., 2004). Studies on transgelin-knockout mice showed, that
transgelin is directly involved in the Ca2 +
-independent vascular SMCs contraction (Je and
Sohn, 2007). In SMCs transgelin was described to be involved in the formation of podosomes
(Gimona et al., 2003; Kaverina et al., 2003) and in the migration of the cells (Gimona et al.,
2003; Kaverina etal., 2003; Zhang etal., 2009).
1.3.3 Function
25
Transgelin is associated with the formation of stress fibers in non-muscle cells (Shapland et
al., 1988). These actomyosin bundles are responsible for retraction of the cell tail during cell
migration and for static contraction (Pellegrin and Mellor, 2007). The contraction of stress
fibers of the myoepithelial cells squeezes the duct to expel milk in breast tissue and their
contraction in myofibroblasts actively contributes to wound healing (Tomasek et al., 2002).
1.3.4 Transgelin in cancer
1.3.4.1 Transgelin as proposed cancer biomarker
In recent years, owing to the development of proteomics and progress in the field of
biomarker discovery, there is a growing number of studies describing altered expression of
transgelin in tumor samples indicating that transgelin contributes to the development of
many types of cancers. However, the results of these studies are often contradictory, even
regarding the same tumor type. Decreased level of transgelin has been considered as a
marker of colorectal (Li et al., 2010; Peng et al., 2009; Shields et al., 2002; Yeo et al., 2006,
2010; Zhao et al., 2009), esophageal (Zhang et al., 2011a), pancreatic (Sitek et al., 2005),
prostate (Prasad et al., 2010), breast (Sayar et al., 2015; Shields et al., 2002; Wulfkuhle et
al., 2002), bladder (Chen et al., 2011; Kawakami et al., 2006), endometrial (DeSouza et al.,
2005) and lung (Li et al., 2010) cancer, whilst its increased level was described as a poor
prognostic marker in gastric (Huang etal., 2008; Lietal., 2007; Ryu etal., 2003), esophageal
(Harada et al., 2007; Qi et al., 2005), pancreatic (Mikuriya et al., 2007), renal (Klade et al.,
2001) and lung (Rho et al., 2009) cancer. Thus, we can see for example in the esophageal,
pancreatic and lung cancer that the studies connect both transgelin up- and downregulation
with the oncogenic process.
This discrepancy is partly explained by the differences between transgelin expression in the
cancer cells and the cells of the tumor stroma. It seems that determined up-regulation of
transgelin in certain types of tumors is a consequence of its overexpression by the cells of
tumor stroma rather than by the tumor cells themselves. A discrepancy in the results of
transgelin expression in pancreatic cancer (Mikuriya et al., 2007; Sitek et al., 2005) may
serve as a good example: Sitec et al. showed a down-regulation of transgelin in pancreatic
tumor samples (Sitek et al., 2005), Miruriya et al. described its up-regulation in tumors
(Mikuriya et al., 2007). These conflicted results might be a consequence of differences in the
composition of samples that were in fact analyzed: Although Sitek et al. worked with the
microdissected tumor cells (Sitek et al., 2005), Mikuriya et al. used whole tissue samples
(Mikuriya et al., 2007). Thus, the samples of Mikuriya et al. contained both the tumor cells
and the cells of tumor stroma. With the subsequent use of IHC, transgelin expression was
observed just in the cells of tumor stroma and not in the tumor cells (Mikuriya et al., 2007).
The same was concluded in the studies determining upregulation of transgelin in gastric (Li
et al., 2007), renal (Klade et al., 2001) and lung (Rho et al., 2009) cancer. In these studies,
transgelin was not expressed in malignant cells but in the cells of tumor stroma.
In the majority of the studies, the loss of transgelin expression in the malignant epithelial
cells was shown. However, there are studies, which show, that transgelin re-expression in
the cancer cells is connected with the high grade and more aggressive tumors (Kim et al.,
2012a; Rao et al., 2015; Zhou et al., 2013). Moreover, some studies show an association
between transgelin upregulation in cancer cells and their metastatic potential (Ami et al.,
2005; Lee etal., 2010; Lin etal., 2009; Zhang etal., 2011b; Zhou etal., 2015, 2013). Lin etal.
analyzed 24 microdissected human colorectal specimens differing in their status of lymph
node metastasis (Lin et al., 2009). Using 2D difference gel electrophoresis method and
following IHC on the larger set of samples, they showed a positive correlation between
transgelin level and lymph node metastasis. Zhang et al. observed an increased level of
transgelin in a rat model of submucosal invasive CRC in comparison with submucosal non-
26
invasive CRC (Zhang etal., 2011b). In in vitro models, transgelin was upregulated in CD133+
cells, which are considered as cancer stem cells, in comparison with CD133- cells isolated
from the human hepatocellular carcinoma cell line Huh7 (Lee et al., 2010) and was
upregulated in renal cell carcinoma cell lines derived from metastatic lesions when
compared with those derived from the primary tumor (Ami et al., 2005). Recently Zhou et
al. showed that transgelin overexpression or attenuation in CRC cell lines increased or
decreased metastatic potential of the cells, respectively (Zhou et al., 2015). Moreover,
metastasis of cancer cells is supported by transgelin overexpression by stromal cells.
Transgelin is upregulated in the stromal cells, especially when tumor cells invade into these
layers (Li et al., 2007) and is able to support the production of metalloproteinase-2 by
cancer-associated fibroblasts and thus support metastasis of cancer cells (Yu et al., 2013).
1.3.4.2 Transgelin function in cancer
Transgelin function is associated with the actin cytoskeleton, namely with F-actin and the
stress fibers, in the cells. Its absence disrupts the normal actin organization and fibroblasts
without transgelin show less bundled, less organized actin cytoskeleton (Thompson et al.,
2012a). In the normal cells, the cytoskeleton is involved in the crucial cell processes such as
cell movement, polarity, organization, signaling division, and survival. Actin is an essential
building block of the cytoskeleton and more than 100 hundred proteins participate in the
constant modulation of the actin cytoskeleton. Changes in the expression of the actinassociated
proteins lead to dysfunctions in the actin cytoskeleton and these changes
contribute to cancer progression. The signaling of the cells depends on the cytoskeleton
which serves as a mechanosensor and scaffold for the signaling molecules, ensures
connection with the extracellular environment and maintains cell polarity, whose
disruption breaks cell-cell junctions and induces epithelial-to-mesenchymal transition.
Recently Zhou et al. showed that transgelin overexpression in the CRC cell lines alters
expression of approximately 250 genes (Zhou et al., 2015). The disrupted cell polarity,
interactions with the other cells and the extracellular matrix can all support the
proliferation of the cells. Moreover, the actin cytoskeleton is involved in the regulation of
cell cycle progression; it undergoes drastic changes and remodeling during cell division
(Heng and Koh, 2010). Several studies showed that transgelin overexpression inhibits the
proliferation of cancer cells and induces retardation of in vivo tumor growth in a xenograft
model (Yang et al., 2007; Yeo et al., 2006). Yang et al. described the mechanism in prostate
cancer cells. They showed that transgelin inhibits ARA54-enhanced androgen receptor (AR)
transactivation via the interruption of ARA54 homodimerization and AR-ARA54
heterodimerization (Yangetal., 2007).
Actin cytoskeleton, namely polymerization of actin monomers into polarized filaments
drives the motility of the cells (Olson and Sahai, 2009). The interaction of additional
proteins with polymerized actin filaments modulates the geometry and function of the actin
structures (Olson and Sahai, 2009). The reconstruction of actin cytoskeleton leads to the
formation of protrusions at the leading edge of the cell and to the induction of actomyosin
contractions, which promote the locomotion of the cells (Vega and Ridley, 2008). Transgelin
is involved in the formation of podosomes (Gimona et al., 2003; Kaverina et al., 2003) and
the cell contractility (Zeidan et al., 2004) in the SMCs and in the TGF-|3-induced migration of
the epithelial cells in lung fibrosis (Yu et al., 2008). The direct impact of transgelin in the
promotion of the migration and invasion of the cancer cells was described in the cell lines
derived from CRC (Lin etal., 2009; Zhou etal., 2015), pancreatic (Zhou etal., 2013) and lung
cancer (Wu etal., 2014). Moreover, transgelin overexpression enhanced the invasiveness of
cancer stem cells isolated from the human hepatocellular carcinoma cell line (Lee et al.,
2010). However, the positive role of transgelin in cell migration and invasion is challenged
by the results of the studies done by Yeo etal. aNair etal. (Nair etal., 2006; Yeo etal., 2010).
27
Regulation of cellular senescence and apoptosis of the cells is accompanied by the dynamic
changes of actin cytoskeleton and transgelin participates in both processes. Already first
studies showed transgelin association with the senescence in the fibroblasts of the patients
with the Werner syndrome and several studies connect transgelin with survival and
apoptosis of the cells (Gourlay et al., 2004a; Kato et al., 2007a; Thompson et al., 2012a).
Transgelin influences these processes by the deregulation of the level of reactive oxygen
species (ROS) in the cells. A depletion of the yeast homolog of transgelin, Scpl, enhances the
dynamics of the actin cytoskeleton, resulting in the changes in the mitochondrial membrane
potential and the lower ROS release from the mitochondria (Gourlay et al., 2004a). The
lower level of ROS in the cells increases survival of the cells, contrary its high level causes
senescence or cell death (Gourlay et al., 2004a; Yang et al., 2014). Thus, a loss of transgelin
expression by the cells supports the longevity of the cells by lower ROS release from
mitochondria; this mechanism was described in transgelin-depleted fibroblasts under
oxidative stress caused by serum starvation (Thompson et al., 2012a). In contrast,
overexpression of transgelin increases ROS release from mitochondria, resulting in cell
death or induction of cell senescence. The overexpression of the transgelin was shown to be
a mechanism of the cell death resistance to some cytotoxic agents and radiation (Kim et al.,
2009, 2010, 2012b), which was triggered by the induction of cellular senescence (Kim etal.,
2010).
28
2 Research aims
• To identify proteins whose expression respond to administration of selected anticancer
drug using proteomics techniques.
• To propose new biomarkers of metastatic potential in low grade breast cancer on
the basis of the analysis of data from expression profiling at transcript and protein
level.
• To functionally characterize selected proteins differentially expressed in metastatic
tumors using genomic and proteomic approaches.
29
3 Materials and methods
Chemicals, animals and dosing
LA-12 was synthesized by Pliva-Lachema. The animals (male albino Wistar-Hahn rats) were
kept under conditions described in Appendix 1. All animal protocols were approved by the
Institute's Animal Experimental Ethics Committee and animals were treated according to
OECD guidelines. LA-12 was administered in four different doses: 37.5 - 75 -150- and 300
mg LA-12/kg body weight each. Blood samples were collected in 6 intervals (pre-dose and
at 2, 8, 24, 48 and 72 h after dosage). Each combination of dosage and blood sampling time
was represented by three animals. Plasma was prepared and stored under conditions
described in Appendix 1.
Tissue specimens
The cohort of the patients used for determination of RBP4 level in human plasma samples
consisted of randomly selected patients with the solid tumors undergoing Phase I clinical
trials of LA-12. The procedures of blood collection and plasma preparation are described in
Appendix 1.
Breast cancer tissue specimens were obtained from the Masaryk Memorial Cancer Institute,
Brno. Patient informed consent, tissue procurement procedures and storage conditions are
described in Appendix 3 and 4. For each patient sample frozen tissue and paraffin block of
formalin fixed tissue were available. Three cohorts of samples were used. The cohort of 12
samples was used in the 2-DE experiment (Appendix 4). The cohort of 96 samples was used
in proteomics analysis (iTRAQ - Appendix 3, SRM - Appendix 4), transcriptomics analysis
(TaqMan Low density arrays - Appendix 3, quantitative Real-time PCR - Appendix 4) and
in immunohistochemical analysis (Appendix 3 + 4). The patient characteristics are
described in details in Appendix 3. The last cohort of 64 samples was used for IHC validation
of CPB1 protein levels using IHC (Appendix 3).
Cell lines
The breast cancer cell lines MCF7, T47D, CAMA-1, SK-BR-3, PMC42, ZR75.1, BT20, BT474,
BT549, MDA-MB-157, MDA-MB-231 and MDA-MB-468 were purchased from American
Type Culture Collection (ATCC) and were maintained according to their protocols. Details
of cultivation are described in Appendix 5.
Platinum concentration measurement
The platinum concentration in the rat plasma samples and human plasma samples was
measured by a validated method as described previously (Sova et al., 2011). The platinum
concentrations in response to LA-12 dose and time since drug administration were
statistically evaluated using Kruskal-Wallis test
SELDI-TOF MS of rat plasma samples
A detailed protocol is described in Appendix 1. Briefly, rat plasma denaturated samples
were mixed with IMAC binding buffer and were loaded on IMAC-Cu SELDI chips. Sample
protein composition was analyzed using SELDI-TOF MS in PBS lie Protein Chip Reader (BioRad,
USA). Peak clustering was performed with Biomarker Wizard software (Bio-Rad, USA).
The average intensities of all protein peak clusters across the whole SELDI-TOF MS
experiment were correlated with a platinum concentration in corresponding samples using
Spearman rank correlation coefficient All analysis were performed using Statistica 9
software (StatSoft, Inc., USA).
Identification of proteins from SELDI-based studies
"Bottom up" approach - proteins correlating with LA-12 level
30
The detailed protocol is described in Appendix 1. Rat plasma samples were pre-separated
using IMAC Spin Columns (Bio-Rad, USA) according to manufacturer's instructions. Proteins
were further fractionated using reverse phase-liquid chromatography on an Agilent HP
1100 HPLC system (Agilent Technologies, Santa Clara, CA, USA) using a Discovery Bio Wide
Pore C18 column. Sixty collected fractions were analyzed using SELDI-TOF MS on NP-20
chips to determine their protein profile. The fractions containing proteins of interest were
analyzed using tricine SDS-PAGE on PROTEAN II XL apparatus (Bio-Rad, USA) according to
Schagger (Schagger, 2006). The gel was stained using colloidal Coomassie Blue (Matsui et
al., 1999). The bands with appropriate molecular weight were cut out and digested with
trypsin as described previously (Havlasova et al., 2005). Protein identification was done
using a 4800 MALDI-TOF/TOF™ mass spectrometer (Applied Biosystems, Framingham,
MA, USA).
"Top-down" approach
This approach was used for identification of a peak with m/z = 3357 from a large-scale study
on breast cancer tissues (Brozkova et al., 2008). The samples were analyzed by IMAC Spin
columns, reverse phase-liquid chromatography and SELDI-TOF MS the same as in the
"bottom-up" approach. LC fractions containing peaks of interests were then analyzed by
top-down LC-MS/MS on a LTQ Orbitrap Velos from Thermo Electron (San Jose, CA, USA)
equipped with a NanoAcquity system from Waters. A l protocols are available online in Supl.
File 1 of the article by Bouchal et al. (Bouchal etal., 2013).
Electrophoresis of complex protein lysates
The experiment was performed to show that protein profiles are strongly dependent on
separation principle. The unfractionated breast cancer tissue lysate was analyzed using
different separation approaches (SELDI-TOF MS, tricine SDS-PAGE and MOPS SDS-PAGE)
and the resulted protein profiles were compared. All the protocols are available online in
Supl. File 1 of the article by Bouchal et al. (Bouchal etal., 2013).
Determination ofRBP4 level in human plasma samples
A l the protocols are presented in Appendix 1. The samples were separated by SDS-PAGE
and transferred onto nitrocellulose membranes. RBP4 was detected using anti-RBP4
specific antibody (Anti-RBP4, Sigma-Aldrich, USA, cat No. HP001641). Band intensities
were quantified using QuantityOne 4.6.1 software (Bio-Rad, USA).
RNA isolation, RNA integrity control and reverse transcription
RNA was isolated using RNeasy mini kit (Qiagen, Germany) following the manufacturer's
protocol. RNA was quantified at 260 nm using NanoDrop ND-1000 (Thermo Fisher
Scientific, USA) and its quality was checked by RNA integrity number (RIN) measurement
on Agilent 2010 Bioanalyzer (Agilent, USA). cDNA was synthesized using H Minus M-MuLV
Reverse Transcriptase and random hexamer primers (both Fermentas Life Sciences,
Canada) according to manufacturer's protocol. The cDNA was analyzed by Taqman Low
density arrays (Appendix 3) and by quantitative real-time PCR (Appendix 4).
qRT-PCR gene expression analysis using TaqMan Low density arrays
Expression of 95 genes was analyzed in the set of 96 primary breast cancer samples using
Low Density Arrays (Micro Fluidic Card System, 384 qRT-PCR reactions/card) on 7900HT
Fast Real-Time PCR system (Applied Biosystems, Foster City, CA) (Appendix 3). 200 ng of
cDNA was used in each loading reservoir. A l analysis parameters were provided by the
manufacturer. Gene expression was evaluated by the comparative C t method of relative
quantification using Manager 1.2 software (Applied Biosystems) with the use of 18S rRNA
as an endogenous control. The statistical analysis of the qRT-PCR data is described in
Appendix 3. The qRT-PCR data were used for hierarchical clustering analysis as described
in Appendix 3.
31
Quantitative Real-Time PCR
Transgelin mRNA level was analyzed in the set of 96 primary breast cancer samples using
real-time PCR on 7900HT Fast Real-Time PCR system (Applied Biosystems, Foster City, CA).
The protocol is presented in Appendix 4. Absolute quantification was done using the
standard curve method. Statistical analysis was performed using STATISTICA 7.1 software
(StatSoft, USA). Mann-Whitney U-test was used to determine statistically significant
changes.
Proteomics analysis of the 96 sample set
Set of 96 primary breast cancer samples was analyzed at the protein level by the methods
of non-targeted proteomics (iTRAQ-2DLC-MS/MS, Appendix 3) and targeted proteomics
(SRM, Appendix 4). The isolation of the proteins from the tissue is described in Appendix 3.
iTRAQ-2DLC-MS/MS
To match the sample set size with the sample capacity of iTRAQ-2DLC-MS/MS approach,
always four lysates of the samples with the same clinicopathological characteristics were
pooled together (details in Appendix 3). Protocols for sample preparation and eight-plex
iTRAQ labeling are presented in Appendix 3. The samples in each eight-plex were then
mixed and peptides were fractionated at the first dimension by ZIC-pHILIC chromatography
and in the second dimension by nanoscale reversed phase liquid chromatography coupled
online to an LTQ-orbitrap Velos mass spectrometer (Thermo Fisher Scientific). Detailed
protocols are presented in Appendix 3. Protein identification and quantification was
performed with Proteome Discoverer™ version 1.1 software (Thermo Fisher Scientific,
Bremen, Germany) using the Mascot database search algorithm. Statistical analysis of
iTRAQ proteomics data and criteria for selection of differentially expressed proteins are
described in Appendix 3.
Selected reaction monitoring
Unlike iTRAQ analysis, individual samples were measured by SRM. Protocols for sample
preparation and mTRAQ labeling are presented in Appendix 4. The unreacted mTRAQ labels
were removed using POROS® 50 HS cartridge (Applied Biosystems, USA) and the samples
were then desalted using C18 columns (300 A, MicroSpin™ Column, The Nest Group, USA)
as described earlier (Bouchal et al., 2009). SRM transitions were designed in Skyline
software version 1.4 and higher (MacCPoss Lab, University of Washington, Seattle, WA)
(MacLean et al., 2010). Peptide samples were measured on Tempo nano MDLC capLC
System coupled to a 4000 QTRAP mass spectrometer (both AB Sciex) working in SRM mode
using the developed mTRAQ-SRM assays. Details of the MS analysis are described in
Appendix 4. MS/MS spectra were evaluated using MASCOT search engine, quantification
was done using Skyline software and statistical significance was tested by Mann-Whitney
U-test (details in Appendix 4).
Two-dimensional gel electrophoresis
Protein lysates of 12 primary breast cancer samples were prepared as described in
Appendix 4. Protein concentration was measured by the RC-DC Protein Assay (Bio-Rad,
USA). The proteins were separated by isoelectric focusing (IEF) in the 1s t
dimension using
IPG Ready Strips (pH 3-10 NL, 18 cm, Bio-Rad, USA) and by SDS-PAGE in the 2n d
dimension.
Protocols of IEF and SDS-PAGE are presented in Appendix 4. Protein staining was
performed as described previously (Bouchal etal., 2010). The gels were scanned by GS-800
densitometer or Pharos FX fluoroimager and analyzed by PDQUEST software version 8.0
(all Bio-Rad, USA). The spots with the highest fold change between lymph node positive and
negative group were identified by MS as described in Appendix 1.
Transfection of cells and gene silencing
The cells were transfected using AMAXA Nucleofector technology (Lonza, Switzerland)
according to optimized protocols provided by the company. Transgelin expression in the
32
cells was silenced using specific siRNA (ON-TARGETplus siRNA, Thermo Scientific
Dharmacon, USA).
Cell lysis, SDS-PAGE, Western blot
Western blot analysis of cell lysates was used to determine transgelin level in the panel of
breast cancer cell lines and to evaluate the efficiency of transgelin silencing in the cells. The
protocols of the procedures are presented in Appendix 5.
Monitoring of cell migration
Cell migration was monitored using xCELLigence® DP system and CIM-16 plates (ACEA
Bioscience, Inc., USA). The protocols of the experiment are presented in Appendix 5.
Quantitative study of proteome changes after transgelin silencing in the cells
The cells were harvested 72 hours after transfection and the protein lysates were prepared
as described in Appendix 5. Protocols for sample preparation and iTRAQ labeling are
presented in Appendix 5. Labeled samples were analyzed by LC-MS/MS using nanoscale
reversed phase liquid chromatography coupled on-line to Orbitrap Elite mass spectrometer
(Thermo Fisher Scientific) as described in Appendix 5. Protein identification and
quantification was performed with MaxQuant 1.3.0.5 using Andromeda database search
algorithm (Appendix 5). Proteins up- or down-regulated more than 1.15 or less than 0.85 in
transgelin positive versus negative samples were subjected to functional analysis using IPA
(Ingenuity Pathway Analysis) software.
Immunohistochemistry
IHC was done on the set of 96 breast cancer samples using Tissue Microarrays (TMAs).
TMAs preparation is described in Appendix 3. Antibodies and protocols of the staining are
presented in Appendix 3 and 4. Associations between staining intensities of each selected
protein and clinicopathological characteristics were assessed by Wilcoxon rank sum test or
by Pearson's Chi-squared.
Analysis of Gene Expression and connection of gene expression with patient survival in
independent published sample sets
Association between expression of selected genes and lymph node status within luminal A
samples was tested using publicly available gene expression dataset SUPERTAM_HGU133A.
Association between the expression of selected genes and patient survival was tested using
Kaplan-Meier Plotter (http://kmplot.org) involving a microarray data set from 4142 breast
cancer tissues (Gyôrffy etal., 2010). Details of both analyses are described in Appendix 3.
33
4 Results
The result section is composed of four original peer-reviewed articles (Research article IIV).
Research article V is summarizing data and results under preparation for publication.
I. Bouchal, P., Jarkovsky, J., Hrazdilova, K., Dvořákova, M., Struharova, I., Hernychova,
L., Damborsky, J., Sova, P., and Vojtesek, B. (2011). The new platinum-based
anticancer agent LA-12 induces retinol binding protein 4 in vivo. Proteome Sci. 9,
68.
II. Bouchal, P., Dvořákova, M., Scherl, A , Garbis, S.D., Nenutil, R, and Vojtesek, B. (2013).
Intact protein profiling in breast cancer biomarker discovery: protein identification
issue and the solutions based on 3D protein separation, bottom-up and top-down
mass spectrometry. Proteomics 13,1053-1058.
III. Bouchal, P., Dvořáková, M., Roumeliotis, T., Bortlíček, Z., Ihnatová, I., Procházková, I.,
Ho, J.T.C., Maryáš, J., Imrichová, H., Budinská, E., etal. (2015). Combined Proteomics
and Transcriptomics Identifies Carboxypeptidase B l and Nuclear Factor KB (NF-KB)
Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast
Cancer. Mol. Cell. Proteomics MCP 14,1814-1830.
IV. Dvořáková, M., Jeřábkova, J., Procházková, I., Lenčo, J., Nenutil, R, and Bouchal, P.
(2016). Transgelin is upregulated in stromal cells of lymph node positive breast
cancer. J. Proteomics 132,103-111.
V. Dvořáková, M., Potěšil, D., Bouchal, P. Transgelin silencing influences different
processes in PMC 42 and BT 549 breast cancer cells, (in preparation)
Contribution to the papers:
I. Protein identification of the peak from the SELDI spectrum correlating with the LA-
12 level in the rat plasma (pre-separation on IMAC Spin Columns, reverse phaseliquid
chromatography fractionation, SELDI-TOF MS profiling of RP-LC fractions,
tricine SDS-PAGE of selected RP-LC fractions, comparison of the electropherogram
with the SELDI spectrum, band excision from the gel and trypsin digestion of the
proteins).
II. "Top-down" protein identification of the peak from the SELDI spectrum (preseparation
on IMAC Spin Columns, reverse phase-liquid chromatography
fractionation, SELDI-TOF MS profiling of RP-LC fractions), comparison of the protein
profiles obtained using different separation principles applied on the same sample
(sample analysis and data interpretation).
III. Analysis of the gene expression at the transcript level (RNA isolation, reverse
transcription, sample measurement using Microfluidic cards, preparation of the
data for statistical analysis), immunohistochemistry (antibody testing participation
on the data evaluation).
IV. Study design, analysis of the cohort II (RNA isolation, reverse transcription, realtime
PCR, IHC), data interpretation, writing of the manuscript.
V. Design of the experiments, measurement of transgelin expression in breast cancer
cell lines using western blot, silencing of the gene expression using siRNA, analysis
of the cell migration using xCELLigence, quantification of the proteome changes
after transgelin silencing using iTRAQ, functional characterization of the
deregulated proteins using IPA, data interpretation, writing of the manuscript
34
4.1 Research article I
The aim of the study was to describe serum markers of the activity of the new anticancer
drug (0C-6-43)-bis(acetato)(l-adamantylamine) amminedichloroplatinum (IV) (LA-12).
These markers could serve as markers of LA-12 treatment, response and therapy
monitoring. LA-12 is a platinum(IV)-based complex, which was developed to overcome the
limitations of the clinically used platinum-based drugs (e.g. cisplatin - severe side effects
and intrinsic or acquired resistance). The study complement previously published
pharmacokinetic data on LA-12 (Sova et al., 2011).
We performed proteomic profiling of the rat plasma samples in response to LA-12 using
surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDITOF
MS). In total, we analyzed 72 samples stratified according to the administrated dose of
the LA-12 (37.5 - 75 - 150 - 300 mg LA-12/kg body weight) and the time of the sample
collection ( 0 - 2 - 8 - 2 4 - 4 8 - 7 2 h after LA-12 dosage). Each sample was available in three
biological replicates. We measured each sample in two analytical replicates, thus, we
obtained 144 MS spectra. In total, 92 protein/peptides were detected and quantified in each
MS spectrum across the experiment.
Using Spearman correlation statistical analysis, we identified the protein/peptide peaks
with intensities that correlated with platinum concentration (corresponding to LA-12 level)
in the samples. Only protein peak No.75 (m/z = 22684) exhibited a statistically significant
positive correlation with the platinum level in rat plasma.
To reveal the identity of the protein peak, we developed a method based on the threedimensional
separation of the proteins followed by MALDI-MS/MS identification. The
separation consisted of the following steps: (i) Pre-separation of the plasma proteins on
IMAC Spin columns to obtain the same SELDI-TOF MS protein composition as measured
using analytical IMAC 30 chips, (ii) The reduction of the sample complexity using liquid
chromatography with reverse phase column (RP-LC). (iii) Separation of an RP-LC fraction
N o . l l (containing the protein peak of interest with m/z = 22684) by tricine SDS-PAGE. To
find this fraction, we analyzed all the RP-LC fractions using SELDI-TOF MS (normal phase
NP-20 chip surface). We matched protein profiles of the fraction N o . l l obtained by tricine
SDS-PAGE and SELDI-TOF MS and compared molecular weights of intact proteins in SELDI
spectrum and SDS-PAGE electropherogram (Figure 4). The gel band corresponding to SELDI
peak No. 75 was excised from the gel, analyzed using mass spectrometry and identified as
Retinol-binding protein 4 (RBP4, UniProt accession No. P04916). RBP4 is a plasma protein
which serves as a transport protein for retinol produced in hepatocytes (Kanai et al., 1968).
Retinoids are involved in cellular differentiation pathways and it is thought that the lack of
RBP4 is involved in maintaining the undifferentiated nature of cancer cells (Tang and Gudas,
2011).
35
R B P 4
Figure 4: The process of identification ofSELDI-TOF MS peak No. 75. After two-dimensional protein separation, the
RP-LCfraction No.ll containing protein corresponding with peak No. 75 was separated on tricine SDS-PAGE. The
corresponding gel band was identified by comparison of molecular masses as well as by comparison ofSELDI TOF
MS spectrum with the electrophoregram. The band was then excised from the gel, analyzed using mass
spectrometry and identified as RBP4 protein (UniProt accession No. P04916J. (Bouchal etal, 2011]
We further investigated the cause of the correlation between RBP4 and LA-12 level in the
plasma. We worked with two hypotheses: (i) LA-12 binds to retinol binding site of RBP4,
which works as LA-12 transport protein analogously to the retinol or (ii) LA-12 inducts
RBP4 expression. The second hypothesis is supported by induction of RBP4 expression by
amantadine, a drug bearing an adamantine group closely related to that present in LA-12
(Wolfgang CurtD. etal.). We performed molecular modeling of the RBP4/LA-12 complex to
clarify the first hypothesis and we showed that the central cavity of RBP4 is too small for
LA-12 binding and that the protein would have to undertake a significant conformation
change to form a cavity capable of LA-12 binding.
To independently verify the identity of the platinum-correlated peak in the rat plasma
samples and to investigate RBP4 induction by LA-12 in humans, we analyzed plasma
samples using western blotting with an RBP4 specific antibody. We demonstrated that
circulating RBP4 levels correlated well with platinum levels in human plasma of 12
randomly selected patients involved in Phase I clinical trials of LA-12. (Bouchal etal., 2011)
36
4.2 Research article II
MALDI-TOF MS and its modification SELDI-TOF MS (using a chemically modified array chip)
have been widely used in cancer biomarker discovery studies. The methods enable highthroughput
analysis of proteomic profiles. They, however, suffer from a number of
limitations such as low resolution, low sensitivity and that no knowledge is gained on the
identity of the respective proteins in the discovery mode. The result of a typical
MALDI/SELDI-TOF MS protein profiling is a set of peaks of unknown origin. To gain
additional information on the identity of the protein peak, the analysts isolate proteins
corresponding to the mass determined with the MALDI/SELDI-TOF MS approach and they
subsequently analyze them with high-resolution MS/MS methods.
In the paper, we focus on a topic of accurate and correct protein peak identification in
MALDI/SELDI-TOF MS-based studies. We compare the available separation and MS
techniques used for this purpose and we discuss their limitations and the difficulties which
have to be considered during the process of the identification. Moreover, we propose the
optimal workflow to overcome the protein identification uncertainly and demonstrate its
effectiveness by the application to research samples.
We highlight two major difficulties: (i) In a typical scenario of the protein peak
identification, the proteins are separated by ID SDS-PAGE. The resulting electropherogram
is compared with the MALDI/SELDI-TOF MS spectrum to identify the protein band of
interest, which is then excised, in-gel trypsin digested, and identify by the MS/MS. However,
the proteins profiles obtained by these two approaches can differ and the identification of
the corresponding proteins in the gels based just on their comparison might be confusing.
These differences are demonstrated in Figure 5 and they come mainly from the differences
in ionization behavior between the individual proteins represented as peaks in the MALDITOF
MS spectrum relative to the different proteins staining in a gel and they also come from
the occurrence of the multiple ions per given SELDI-TOF MS peak, (ii) The MALDI/SELDITOF
MS measure the intact mass of the proteins, which are highly post-translationally
modified in the human samples. This leads to a difference between the experimental protein
mass versus its theoretical mass recorded in protein databases. This means that the
identification based on the simple comparison of the experimental mass with the theoretical
mass can lead to false-positive results.
37
Figure 5: Comparison of [A] SELDI-TOF MS spectrum of unfractionated breast cancer tissue lysate (displayed as
both MS spectrum and its "gel view"] with the electrophoregrams of(B] tricine SDS-PAGE and [C] MOPS SDS-PAGE
of the lysates originated from the same tissue. It is evident that protein profiles are strongly dependent on
separation principle (including buffer system and gel composition]. As a result, the identification of the
corresponding proteins in these gels by simple comparison of the experimental molecular weights obtained [i] from
MALDI/SELDI TOFMS spectra and [ii] the SDS PAGE electrophoregrams might become a confusing exercise. For
this reason, more precise separation approaches are warranted to ensure reliable protein identification. (Bouchal
et ah, 2013]
To overcome the above-mentioned limitations, a procedure that encompasses both the
determination of the intact mass of the protein and its MS/MS analysis, without recourse to
a comparison of the intact mass and its theoretical mass, is the only reliable approach. We
have used such an approach based on the 3D protein separation followed by the MS/MS
identification (bottom-up approach) for the identification of RBP4 in rat plasma (Research
article I) and of Annexin V and Hsp27 in breast cancer tissues (Brozkova et al., 2008).
Moreover, we present there a more accurate, complementary approach that encompasses
top-down RP-LC-FT-MSn
analysis. The first steps of the approach are identical with the
above-mentioned bottom-up approach (pre-separation on a spin column, protein
fractionation by reverse phase LC, MALDI/SELDI-TOF MS of the RP-LC fractions). Resulting
target fractions are then subjected to top-down LC-MS/MS analysis, e.g. using an FT-based
high-resolution platform. The advantage of the top-down approach is that both the
measurement of the intact m/z and protein/peptide identification are done in a single step.
Using the top-down approach, we have identified a peak with m/z = 3357 from a large-scale
study on breast cancer tissues previously published (Brozkova et al., 2008) as a C-terminal
native peptide of Heterogeneous nuclear ribonucleoprotein A2/B1. (Bouchal etal., 2013)
38
4.3 Research article III
The aim of the study was to find new biomarkers of lymph node metastasis in low-grade
breast cancer that could be easily incorporated into the clinical practice. These markers
would serve for better risk-discrimination in patients with this diagnosis resulting in
appropriate treatment selection. The prognosis of the patients with low-grade breast
tumors is generally very favorable, resulting in treatment by less aggressive adjuvant
therapy and no chemotherapy. However, low percentage of these patients forms early
lymph node metastasis and would benefit from more intensive follow-up. The mechanism
of the metastasis in low-grade breast cancer is so far unknown and current clinical practice
lacks markers for predicting its occurrence.
To identify metastasis-related proteins in low-grade breast cancer, we employed iTRAQ-
2DLC-MS/MS proteomics to a set of 48 clinicopathologically well characterized small
primary grade 1 luminal A (ER+, PR+, HER2-) breast tumors; 24 lymph node positive and
24 lymph node negative. Moreover, together with low-grade tumors, we analyzed matched
set of 48 high-grade tumors. Doing like this we were able to see whether the mechanism of
metastasis differs in early and late stages of cancer and in different breast cancer subtypes.
A total of 4405 proteins were identified based on at least one tryptic peptide (FDR < 0.05).
We selected 42 proteins whose levels correlated with lymph node metastasis and 23
proteins which exhibited dysregulation in another parameter under comparison (grade, ER
status, HER2 status).
We analyzed gene expression of these 65 proteins and of additional 30 proteins (connected
to metastasis according to literature or internally validating the sample set design) at
transcript level using the technology of Microfluidic card. We performed transcriptomics to
verify shotgun proteomics data. Moreover, the combining of protein and transcript level
profiles allowed us to interrogate independent large patient data sets for validation and
their impact on survival. Only the targets which exhibited statistically significant changes at
both protein and transcript level in lymph node positive versus negative grade 1 tumors
were then selected. This group of proteins included carboxypeptidase B l (CPB1), P D Z and
L I M domain protein 2 (PDLIM2), ring finger protein 25 (RNF25), NF-KB transcription factor
p65 (RELA), 14-3-3T] (YWHAH), stathmin 1 ( S T M N 1 ) and thymosin beta 1 0 (TMSB10).
T R A F 3 interacting protein 2 (TRAF3IP2) was up-regulated in lymph node positive versus
negative tumors regardless of grade and integrin beta-1 (ITGB1) was up-regulated in grade
3 but not grade 1 tumors with metastasis.
Using hierarchical clustering, we identified three clusters of gene products in cold maps
related to lymph node metastasis in grade 1 tumors: Cluster 1 containing CPB1, PDLIM2 and
RNF25 (associated with lymph node metastasis of grade 1 tumors and had low levels in
grade 3 tumors), cluster 2 containing STMN1 and TMSB10 and other metastasis-related
genes EPCAM, KISS1 and MTA1 (associated with lymph node metastasis of grade 1 tumors
and had high levels in grade 3 tumors) and cluster 3 containing RELA and YWHAH and other
metastasis associated genes SERBP1 and gelsolin (associated with lymph node metastasis
of grade 1 tumors with no significant differences between low and high grade tumors).
We performed IHC staining for the top targets for which IHC compatible specific antibodies
were available (CPB1, RNF25, STMN1, ITGB1, and YWHAH). The data confirmed key trends
observed in proteomics and transcriptomics data: (i) up-regulation of CPB1 in lymph node
positive versus negative grade 1 tumors and (ii) up-regulation of STMN1 in grade 3 versus
grade 1 tumors. We tested the most promising target CPB1 in an independent set of grade
1 luminal A tumors (n=64) and we observed a similar trend, but without the statistical
significance.
39
Combining of proteomics, transcriptomics, and IHC data confirmed selectivity of proteins
CPB1, RNF25, PDLIM2, STMN1, TMSB10, RELA and YWHAH for lymph node metastasis in
low grade breast cancer. Protein TRAF3IP2 was upregulated in lymph node positive
samples regardless grade.
We confirmed up-regulation of CPB1, PDLIM2, and RELA in lymph node positive luminal A
tumors on an independent, previously published data set SUPERTAM of gene expression
data. Moreover, the analysis of the publically searchable microarray database (Gyorffy etal.,
2010) showed statistically significant connection between relapse-free survival and
expression of the CPB1, PDLIM2, RNF25, STMN1, and TMSB10 in luminal A breast cancer
patients. (Bouchaletal., 2015)
40
4.4 Research article IV
Similarly as in the previous paper (Research article III), here, we aimed to identify new
targets related to lymph node metastasis in low-grade breast cancer.
In the pilot study, we analyzed twelve low-grade breast cancer tissues (cohort I, a half with
and a half without metastasis in lymph nodes) using two-dimensional electrophoresis and
we identified transgelin as a protein with the highest up-regulation in metastatic versus nonmetastatic
tumors (6.7 fold change). To our knowledge, transgelin up-regulation in
metastatic breast cancer was not previously described but was observed in metastatic
colorectal (Lin et al., 2009), gastric (Yu et al., 2013) and pancreatic (Zhou et al., 2013)
cancer.
We subsequently clinically validated transgelin expression in a larger independent cohort
of 48 low grade tumors and 48 high grade tumors (the same cohort as in the Research article
III). We analyzed transgelin expression at both protein and transcript level. We performed
data analysis of the previously published dataset acquired by iTRAQ-2DLC-MS/MS
technique (the Research article III). Since the results of the analysis were supportive to 2DE
results, we developed a mTRAQ-SRM assay for transgelin and analyzed its protein level
using targeted proteomics. We measured transgelin gene expression also at mRNA level by
real-time PCR with absolute quantification and we analyzed its cell type specific expression
in the breast tissue by immunohistochemistry on the tissue microarrays (TMAs, the samples
of the cohort II). At the protein level (iTRAQ, SRM, IHC) we analyzed also an expression of
transgelin-2 (transgelin homologous protein and putative cancer biomarker (Hill et al.,
2011; Shi et al., 2005; Zhang et al., 2010) and compared its expression with the expression
of transgelin.
We observed the trend of higher protein and transcript level of transgelin in lymph node
positive samples within the group of 48 low-grade tumors and also within the group of 48
high-grade tumors. This trend was statistically significant when all 96 samples (regardless
tumor grade) were analyzed (Figure 6a, 6b). Further validation involving a larger set of
patients would be necessary to close the question of the specificity of transgelin as a marker
of lymph node metastasis in low-grade tumors.
a) Proteomics b) Transcriptomics c) IHC - stromal cells
fold change N1/N0
iTRAQ 1.09*
S R M 1.02*
NO N1 I Non-Outlier Range 5 20 40 60 80
nodes •> Outliers intensity (%)
" p < 0.00 by Mann Whitney U-test
Figure 6: Transgelin is up-regulated in stromal cells of lymph node positive tumors. Expression analysis showed
statistically significant up-regulation of transgelin in lymph node positive versus lymph node negative samples at
both protein [a] and transcript (b) level Immunohistochemical analysis showed that the higher intensity of staining
by anti-transgelin antibody was morefrequent in stromal cells of lymph node positive samples (c).
41
In addition, we observed a strong relationship between transgelin protein/transcript level
and histological grade of the tumors. Transgelin was significantly (p<0.002) downregulated
in high versus low-grade tumors, as determined by all methods (Figure 7a).
a
) Expression analysis, comparison of grade 3 and grade 1 tumors
ProteomicsTranscriptomics
6.4
fold change G3/G1
iTRAQ
S R M
grade
a Median
• 25%-75%
I Non-Outlier Range
° Outliers
0.68**
0.84**
** p < 0.002 by Mann Whitney U-test
b) Comparison of % of stromal cells
in grade 3 vs. grade 1 tumors
100
90
80
~o 70
o
70
60
e
o
50
40
o 30
20
10
0
**
—
•
G3
grade
C)
G1
• Median
• 25%-75%
~J_ Non-Outlier Range
o Outliers
p < 0.0001 by Mann Whitney U-test
Correlation (SRM and % of stromal cells)
or
CD
O
. o R °
o u u i 0
o O \1
- - 8 - C T a
@ I" I] 1 ! 8
% of stromal cells (TMA)
R = 0.341, p< 0.001
Figure 7: Transgelin is down-regulated in high grade versus low-grade tumors, which reflects lower content of
stromal cells in high grade versus low-grade tumors. Expression analysis of high grade versus low-grade tumors
showed statistically significant down-regulation of transgelin in high-grade tumors (a). Transgelin is expressed
mainly by stromal cells of the tumors. We showed that high-grade tumors had statistically significantly lower
content of stromal cells in comparison to low grade tumors (h). Moreover, transgelin expression at the protein level
(SRM data) positively correlated with the % of stromal cells in the samples (determined from the TMA samples) (cj.
Using IHC we localized transgelin expression mainly in the cells of tumor stroma
(fibroblasts + endothelial cells). In normal tissue, transgelin was highly expressed by
myoepithelial cells, which reflect the high content of the stress fibers in this cell type. We
also compared the IHC staining intensity of transgelin with the clinicopathological
parameters. This comparison showed that: (i) transgelin upregulation in lymph node
positive tumors is restricted to its up-regulation in the stromal cells of the tumor (Figure
6c) and that (ii) transgelin determined down-regulation in the high-grade tumors reflects a
lower content of the stromal cells (which mainly express transgelin) in the tissue of the
high-grade tumors (Figure 7b, 7c). This shows how the cellular composition of the sample
can influence results of the expression analysis when samples of whole tumor tissue are
analyzed and it could partially explain often opposite results published on the topic of
transgelin expression in cancer (Dvorakovaetal., 2014). The connection between transgelin
42
up-regulation and metastasis is in accordance with the results published by Yu et al. who
showed that tumor fibroblasts over-expressing transgelin supported metastasis of gastric
tumor cells via increased production of matrix metalloproteinase-2 (Yu et al., 2013). A
Higher level of transgelin in fibroblasts of metastatic tumors can be associated with
activation of fibroblasts into cancer-associated fibroblasts (CAFs), which promote
metastasis of cancer cells in many ways (Cirri and Chiarugi, 2011). The connection between
CAFs and transgelin was already described in gastric (Li et al., 2007) and lung (Rho et al.,
2009) cancer.
Transgelin mRNA level slightly differed between the samples of distinct breast cancer
molecular tissues (grade 3 tumors). These differences, however, were not connected with
the expression of molecular markers (ER, HER2), as we observed no association of
transgelin expression with the expression of these markers. Transgelin expression
significantly differed between luminal A (LA, the highest expression) and triple negative
(TN, the lowest expression) tumors, which reflect the highest/lowest content of stromal
cells in the LA/TN samples. Two studies dealt with the transgelin expression in different
breast cancer subtypes and their results are contradictory. In accordance with our results,
Sayar et al. described low transgelin expression in TN tumors and no association between
transgelin expression and molecular markers (ER, HER2) (Sayar etal., 2015). Contrary Rao
et al. described the high expression of transgelin mainly in TN tumors (Rao et al., 2015).
Transgelin-2 was contrary to transgelin expressed by both stromal and epithelial cancer
cells (as determined by IHC), with the higher staining intensities in the cancer cells. The
same as transgelin, transgelin-2 was up-regulated in the samples of the lymph node positive
tumors. Contrary to the transgelin results, its expression was higher in high-grade in
comparison to low grade tumors. (Dvořáková et al., 2016)
43
4.5 Research article V
In the study, we focused on the expression and function of transgelin in the breast cancer
cell lines. Generally, the question of transgelin expression by epithelial cancer cells is
controversial. There are both the studies describing up- and down-regulation of transgelin
in tumors, even within the same tumor type (reviewed in (Dvorakova et al., 2014)).
Moreover, some authors questioned transgelin expression by the cancer cells, when they
localized transgelin expression just into the stromal cells of the tumor. In our previous
study, we detected transgelin expression in cancer cells only in a small number of the breast
cancer samples (17/96, Research article IV). The majority of these samples were poorly
differentiated high grade tumors (14/17, Research article IV).
Since transgelin is associated with the stress fibers in non-muscle cells and these contractile
structures are involved in the migration of the cells, we decided to study the influence of
transgelin silencing on the migration of breast cancer cell lines.
Using western blot we detected transgelin expression in the three breast cancer cell lines
from the panel of eleven tested. The cell lines BT549 and PMC42, which had the highest
endogenous level of transgelin, were chosen for the further analysis. Interestingly, these
two cell lines are completely different: BT 549 is a triple-negative cell line, which is
characterized by high migration and invasiveness; PMC 42 is an unusual cell line, which
retains progenitor pluripotency allowing its differentiation into different morphological
types, which have characteristics of both secretory and myoepithelial cells (Whitehead et
al., 1983) and which is similar in mRNA and miRNA expression profile to a normal breast
tissue (Git etal., 2008).
We silenced transgelin expression in the cells using specific siRNAs and subsequently we
monitored the effect of the silencing on the migration of the cells using xCELLigence system.
We came to opposite results in the two studied cell lines: transgelin silencing impaired
migration of BT 549 cells and enhanced migration of PMC 42 cells (Figure 8) These
contradictory results are in accordance with the literature, where both positive and
negative role of transgelin in migration and invasion of different cancer cells was described
(Lin et al., 2009; Yeo etal., 2010).
44
a) PMC 42
TAGLN + - +
A M A X A - + +
aciin- — — —
transgelin — —
1.2E+00
1.0E+00
8.0E-01
B-6.0E-01
W
4.0E-01
2.0E-01
O.OE-01 I
P M C 42
•TAGLN+ TAGLN-
1
E
o
30 , I 1 1 1 lj
25
20
15
10
5
o
-5
o
-5
0 't Ii 12 16 20 2
TAGLN+
time (hours)
TAGLN- • TAGLN+ S F TAGLN- S F
b) BT 549
TAGLN + - +
A M A X A - + +
actin
transgelin mm —
6.0E-02
5.0E-02
4.0E-02
CD
§• 3.0E-02
W
2.0E-02
1.0E-02
0.0E-02 BT 549
•TAGLN+ TAGLN- TAGLN+
12
time (hours)
TAGLN- • TAGLN+ S F
24
TAGLN- S F
Figure 8: Effect of transgelin silencing on cell migration. We transfected PMC 42 (a] and BT 549 (b) cells with either
transgelin-specific or control siRNA using AMAXA technology. The efficiency of the silencing was checked by western
blot (left part of the Figure]. We subsequently compared migration of the cells with or without expression of
transgelin using xCELLigence system. The Figure shows real-time records of the migration (right part] and slopes
of the migration curves depicted as bar graphs (middle part]. The silencing of transgelin influenced migration in
both studied cell lines. The effect was, however opposite; the silencing supported and disturbed migration of PMC
42 and BT549 cells, respectively.
With regard to the controversy in the results of migration experiment and limitations of
xCELLigence (changes in other properties than migration potential of the cells, such as the
adhesion strength of the cells or their sensitivity to apoptosis, may influence results of the
experiment), we decided to further elucidate changes happening in the cells after transgelin
silencing.
We observed the changes at the proteome level using the method of quantitative proteomics
(iTRAQ). We identified 74 (56/18, up/down) and 59 (44/15, up/down) proteins with
changed level after transgelin silencing in PMC 42 and BT 549 cells, respectively. Functional
characterization of the deregulated proteins after transgelin silencing using IPA (Ingenuity
Pathway Analysis, http://www.ingenuity.com) confirmed transgelin role in the migration
of BT 549 cells and suggested its role in cellular death and survival and in the biochemistry
of biomolecules in PMC 42 cells (Figure 9). The results of the migratory experiment in
PMC42 cells can be thus distorted by the changed sensitivity of the cells to apoptosis. The
pro-apoptotic function of transgelin was described in several studies. Transgelin homolog
in yeasts (Sep 1) regulates dynamics of the actin cytoskeleton, which influence the release
of reactive oxygen species (ROS) from mitochondria and apoptosis of the cells (Gourlay et
al., 2004b). Association between transgelin and ROS-induced apoptosis was described also
in HMV-I melanoma cell line and REF52 fibroblast cells (Kato etal., 2007b; Thompson etal.,
2012b).
45
BT 549
Organization of cytoskeleton
and cytoplasm
Formation of cellular -
protrusions
Migration of cells
Invasion of tumor cell lines
SMARCE1 TMED2 AKAP12
ITGB4 LDLR SLIT3 I
SLIT2 Cd82 APP j
SYN M PLAUR FNDC3B
CTSZ COL4A2 Sp100
SYNE2 NT5E
PMC 42
Small molecule biochemistry
Cellular assembly and -
organization
Cell death and survival
POFUT1
CMAS
HSPBP1
PNP
AMPD2
TXN2
CEBPB
PFN2
GCSH
IVD
NUDT5
FABP5
/ALDOB MUT
STARD3NL SORBS2
SNRPD1 SPTBN2
CEBPZ MRPL15
MARCKSL1
MBTPS1
ACTG1
PFN2
SERPINE2
ITM2B
DDX20 CÍB1
CEBPB
NDUFS3
EIF3F
SAP30BP
CDK6
Figure 9: Functional analysis of proteins deregulated after transgelin silencing in BT 549 and PMC 42 cells. Proteins
associated with cellular assembly and organization were deregulated in both cell lines. The results confirmed
transgelin role in cell migration in BT 549 cells and suggested transgelin role in cellular death and survival and
small molecule biochemistry in PMC 42 cells.
46
5 Conclusions
Biomarkers have a great potential in oncology. Their use varies from the screening of the
health population to surveillance of the patients after curative treatment. The method
suitable for the screening has to enable cheap, high-throughput analysis of the
noninvasively obtained samples (blood plasma, urine). SELDI-TOF MS method fitted these
criteria and thus it is not surprising that soon after its invention SELDI proteome profiles
specific for various types of cancer were described. The method suffers from various
limitations and thus the SELDI-based proteomic signatures have never been used in the
clinical practice. Our work aimed to overcome one of the limitations: the method does not
provide information on the identity of the proteins in the spectrum. We developed two
approaches which enabled relatively accurate and correct protein peak identification.
Moreover, our "top-down" approach detects the intact proteins with their posttranslational
modifications and bypasses the need for proteolysis, which increases the sample complexity
and measurement nonreproducibility.
Metastasis is a life-threatening complication of cancer. The mechanisms of metastasis are
still poorly understood. It is generally accepted that the higher stage of cancer the higher
risk of metastasis. However, this is not valid implicitly. For instance, a low percentage of
patients with early breast cancer forms early lymph node metastasis, which does not
respond to classical chemotherapeutic treatment. Our results show that the mechanisms of
the metastasis in low-grade breast cancer differ from the mechanisms in high-grade tumors.
The treatment of the low-grade tumors thus requires different therapeutic approaches. Our
results suggest that NF-KB signaling pathway could be a new target of anti-metastatic
treatment in low great breast cancer. Our set of proteins has a potential to predict patients
within the group of rare low-grade luminal A tumors with the high risk of the metastatic
formation. These patients can profit from more intensive follow-up and more targeted
therapy.
We described protein transgelin as another potential marker of lymph node metastasis in
breast cancer. Its selectivity for low-grade breast cancer for any potential clinical
application has to be further investigated. Transgelin was up-regulated in the stromal cells
of the tumor tissue. Deregulated expression of transgelin was recently described as a
marker of a wide range of cancers. Moreover, some studies described transgelin role in
cancer and metastasis-related processes. Our work brings new knowledge of transgelin
expression and function in breast cancer. Our results show that the tumor stroma can be a
source of valuable biomarkers and that the changes in the stromal cells can support cancer
metastasis. Moreover, our results obtained in breast cancer cell lines show that transgelin
can also directly influence migration of the cancer cells and thus support their metastasis.
We worked with two very distinctive cell lines (BT 549 and PMC 42). While the cell line
BT549 represents the most aggressive TN tumors, the PMC42 cell line has some
characteristics of the cells of the normal breast tissue. The results of the functional
experiments in these two cell lines suggest that transgelin function can change from tumor
suppressive in the early stages to tumor supportive in later stages of cancer which could
partially explain contradictory results of the studies published on this topic.
47
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7 List of publications
Dvořáková, M., Jeřábkova, J., Procházková, I., Lenčo, J., Nenutil, R., and Bouchal, P. (2016).
Transgelin is upregulated in stromal cells of lymph node positive breast cancer. J.
Proteomics 132,103-111. (IF = 3.867)
Bouchal, P., Dvořáková, M., Roumeliotis, T., Bortlíček, Z., Ihnatová, I., Procházková, I., Ho,
J.T.C., Maryáš, J., Imrichová, H., Budinská, E., et al. (2015). Combined Proteomics and
Transcriptomics Identifies Carboxypeptidase B l and Nuclear Factor KB (NF-KB) Associated
Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer. Mol. Cell.
Proteomics MCP 14,1814-1830. (IF = 5.912)
Maryáš, J., Faktor, J., Dvořáková, M., Struhárová, I., Grell, P., and Bouchal, P. (2014).
Proteomics in investigation of cancer metastasis: functional and clinical consequences and
methodological challenges. Proteomics 14, 426-440. (IF = 4.079)
Dvořákova, M., Nenutil, R, and Bouchal, P. (2014). Transgelins, cytoskeletal proteins
implicated in different aspects of cancer development Expert Rev. Proteomics 11,149-165.
(IF = 2.896)
Bouchal, P., Dvořákova, M., Scherl, A , Garbis, S.D., Nenutil, R, and Vojtesek, B. (2013). Intact
protein profiling in breast cancer biomarker discovery: protein identification issue and the
solutions based on 3D protein separation, bottom-up and top-down mass spectrometry.
Proteomics 13,1053-1058. (IF = 4.079)
Faktor, J., Dvořákova, M., Maryas, J., Struharova, I., and Bouchal, P. (2012). Identification
and characterisation of pro-metastatic targets, pathways and molecular complexes using a
toolbox of proteomic technologies. Klin. Onkol. Cas. České Slov. Onkol. Společnosti 25 Suppl
2, 2S70-77.
62
8 List of contributions at conferences and
symposia
Oral presentations
Dvořáková, M., Potěšil, D., Lenčo, J., Nenutil, R., Vojtěšek, B., Bouchal, P. Multiple roles
of cytoskeletal proteins transgelins in breast cancer development and metastasis.
4th RECAMO joint meeting, 30.9.-3.10.2013. Brno, Czech Republic
Dvořáková, M., Bouchal, P., Nenutil, R., Vojtěšek, B. What is the role of actin
cytoskeleton and its associated proteins transgelins in breast cancer. IntegRECAMO
Summer school 2013,18.-23.8.2013. Litohoř, Czech Republic
Dvořáková, M., Bouchal, P., Miller, P., Scherl, A., Nenutil, R., Vojtěšek, B. Transgelin, an
actin associated protein implicated in breast cancer. 3r d
RECAMO joint meeting, 24.-
27.9.2012. Brno, Czech Republic
Posters
Dvořáková, M., Potěšil, D., Vojtěšek, B., Bouchal, P. Úloha transgelinu v migraci a
apoptóze buněk. XXI. Biologické dny, 4.-5.9.2014. Brno, Czech Republic
Dvořáková, M., Nenutil, R., Vojtěšek, B., Bouchal, P. Transgelin function in metastatic
behavior of breast cancer cells. 5t h
RECAMO joint meeting 12.-15.5.2014. Brno,
Czech Republic
Dvořáková, M., Bouchal, P., Miller, P., Scherl, A., Nenutil, R., Vojtěšek, B. Transgelin, a
protein associated with lymph node metastasis in breast cancer. 6th European
Summer School, 19.-25.8.2012. Brixen, Italy
Dvořáková, M., Bouchal, P., Miller, P., Scherl, A., Nenutil, R., Vojtěšek, B. How is
transgelin involved in the mechanism of breast cancer metastasis. 2nd RECAMO joint
meet;n,g.l0.-13.10.2011. Brno, Czech Republic
Dvořáková, M., Miiler, P., Hernychová, L., Garbis, S.D., Nenutil, R, Vojtěšek, B.,
Bouchal, P. Identification and functional studies on proteins involved in in low-grade
breast cancer metastasis. Computional Mass Spectrometry-Based proteomics. 22.-
27.5.2011. Munich, Germany
63
Appendices
64
1. RESEARCH ARTICLE I
The new platinum-based anticancer agent LA-
12 induces retinol binding protein 4 in vivo
Bouchal, P., Jarkovsky, J., Hrazdilova, K., Dvořákova, M.,
Struharova, I., Hernychova, L., Damborsky, J., Sova, P., and
Vojtesek, B. (2011). Proteome Sci. 9, 68.
2. RESEARCH ARTICLE II
Intact protein profiling in breast cancer
biomarker discovery: protein identification
issue and the solutions based on 3D protein
separation, bottom-up and top-down mass
spectrometry
Bouchal, P., Dvořákova, M., Scherl, A., Garbis, S.D., Nenutil, R., and
Vojtesek, B. (2013). Proteomics 13,1053-1058.
3. RESEARCH ARTICLE III
Combined Proteomics and Transcriptomics
Identifies Carboxypeptidase B l and Nuclear
Factor kB (NF-kB) Associated Proteins as
Putative Biomarkers of Metastasis in Low Grade
Breast Cancer
Bouchal, P., Dvořáková, M., Roumeliotis, T., Bortlíček, Z., Ihnatová, I.,
Procházková, I., Ho, J.T.C., Maryáš, J., Imrichová, H., Budinská, E., et al.
(2015). Mol. Cell. Proteomics MCP 14,1814-1830.
4. RESEARCH ARTICLE IV
Transgelin is upregulated in stromal cells of
lymph node positive breast cancer
Dvořáková, M., Jeřábkova, J., Procházková, I., Lenčo, J., Nenutil, R., and
Bouchal, P. (2016). J. Proteomics 132,103-111.
5. RESEARCH ARTICLE V
Transgelin silencing influences different
processes in PMC 42 and BT 549 breast cancer
cells
Dvořáková, M., Potěšil, D., B., Bouchal, P. (in preparation]