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Isolation, Detection and Functional Characterization of Circulating
Tumor Cells Using Microfluidic-based Technologies
by
Leyla Kermanshah
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Institute of Biomaterial and Biomedical Engineering
University of Toronto
© Copyright by Leyla Kermanshah 2018
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Isolation, Detection and Functional Characterization of Circulating
Tumor Cells Using Microfluidic-based Technologies
Leyla Kermanshah
Doctor of Philosophy
Institute of Biomaterial and Biomedical Engineering
University of Toronto
2018
Abstract
Primary tumors shed thousands of cells into blood circulation every day. These circulating
tumor cells (CTCs) play a key role in metastasis. The application of CTCs, regarded as a real-time,
non-invasive and cost-effective “liquid biopsy”, has drawn much attention in the last two decades.
However, their application in clinical practice has been limited due to their extreme rarity and
heterogeneity. Highly specialized technologies have been developed to address these challenges.
So far, the majority of technologies have focused on separating CTCs from a background of
millions of blood cells with high purity and sensitivity. Despite the technological advancement in
CTC enrichment, the clinical relevance of these cells is still controversial. In-depth
characterization is therefore needed to elucidate their functionality in the metastatic cascade.
The principal aim of this thesis is to characterize heterogeneous populations of CTCs,
sorting them into subpopulations and assessing the CTCs for aggressive phenotypes. In this thesis,
specialized microfluidic-based technologies are used for isolating CTCs and profiling their
phenotypes according to a surface marker expression. We describe a two-dimensional separation
approach that separates phenotypically-distinct subpopulations of cancer cells. Profiling CTCs
based on an epithelial marker enabled us to identify CTCs that have undergone the epithelial to
mesenchymal transition (EMT). The EMT-transformed cells exhibited greater invasive
phenotypes, as confirmed by an in vitro collagen uptake assay.
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Using magnetic ranking cytometry (MagRC), a new technology designed for profiling rare
cells, we successfully obtained phenotypic profiles from cancer cells and xenograft CTCs. To
investigate metastatic phenotypes of CTCs, CTCs from mice bearing prostate cancer xenografts
with different levels of aggressiveness were analysed by MagRC. Real-time monitoring of
dynamic changes in CTC phenotypes during cancer progression and a course of chemotherapy
gave us insights into tumor evolution and treatment efficiency. Metastatic xenografts showed a
heterogeneous population of CTCs with epithelial-mesenchymal plasticity. A decrease in
heterogeneity followed by a reduction in metastasis incidence was observed after a course of
chemotherapy administered to highly metastatic xenografts. Phenotypic profiling of CTCs can
potentially be used for cancer prognostic profiling and therapeutic selection.
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Dedication
I dedicate this thesis to my parents, Farah Sedaghat and Mohammad Kermanshah, for their
unconditional love, support and encouragement, and to my brothers Ali Kermanshah and
Amirhassan Kermanshah for their endless joy, humor and laughter.
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Acknowledgements
First, I would like to acknowledge my supervisor, Dr. Shana Kelley, for giving me the opportunity
to work in her group. I appreciate the support and the freedom she gave me to pursue my ideas. I
absolutely enjoyed working in her lab with a collaborative, supportive and friendly environment.
I was very fortunate to have Dr. Ted Sargent and Dr. Gang Zhang in my advisory committee. They
provided me with valuable feedback and challenged me to think critically. I would also like to
thank my thesis committee Dr. David Juncker, Dr. Edmond Young and Dr. Craig Simmons for
their insightful feedback and critique.
I am truly thankful to my master’s supervisor Dr. Manouchehr Vossoughi who changed my
perspective toward science.
This thesis would not have been possible without the constant help of my lab mates and their
guidance over the course of my PhD. I would like to thank Dr. Mahla Poudineh who provided me
with countless helpful suggestions and supported me from the first day of my PhD to the last. I
would like to thank Brenda Green for being a great lab mate and for her contribution to this thesis.
I would like to thank Dr. Sharif Ahmed, who worked closely with me throughout my PhD, for
offering insights into science and life in general. I would like to thank all the co-op students who
worked in the Kelley lab for their contribution to this thesis, in particular Matthew Nguyen,
Sanjana Srikant, and Rhema Makonnen.
I would like to express my sincere gratitude to Peter Aldridge, Brenda Green and Dr. Mahmoud
Labib for helping me tremendously during the revision of this thesis.
My special thanks goes to Barbara Alexander, Alex Zaragoza, Dr. Mark Pereira, Dr. Jagotamoy
Das, Dr. Yi-Ge Zhou, Dr. Tina Saberi Safaei, Dr. Laili Mahmoudian, and Dr. Reza Mohamadi and
the rest of the group for their contributions to this thesis.
I have been blessed to be surrounded by caring and loving friends and family. I would like to thank
(alphabetically ordered) Shahed Abbasi Soha, Maliheh Aramoon, Atefeh Ebrahimian, Leila
Forozanfard, Fariba Ghaderinezhad, Ghazaleh Hajimiri, Zahra Hosseinnia, Fahimeh Kermanshah,
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Mohadeseh Mehrabian, Mahdieh Meratian, Maryam Naghdiani, Marzieh Nili, Vahid Noormofidi,
Roshana Pakzad, Majid Raeis, Sabereh Rezaei, Asma Raoufizadeh, Ali Saeidi, Maryam Saeidi,
Armin Taheri and many more!
Finally, I would like to thank my close family. My deepest gratitude goes to my mom, Farah, who
has always been my greatest inspiration with her endless enthusiasm for learning new things. I will
always be grateful to my dad, Prof. Mohammad Kermanshah, for his tremendous support at every
stage of my life. His strong belief in me gave me the confidence to pursue my PhD studies. I feel
extremely blessed to have two loving big brothers, Ali and Amirhassan, who made me believe that
there is no shame in failure and who have always kept me motivated.
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Table of Contents
Dedication ...................................................................................................................................... iv
Acknowledgements ..........................................................................................................................v
Table of Contents .......................................................................................................................... vii
List of Tables ................................................................................................................................. xi
List of Figures ............................................................................................................................... xii
List of Abbreviations .................................................................................................................. xvii
Introduction .................................................................................................................................1
1.1 Cancer ..................................................................................................................................2
1.1.1 Metastasis .................................................................................................................2
1.1.2 Circulating tumor cells (CTCs) ................................................................................3
1.1.3 Epithelial to mesenchymal transition (EMT) ...........................................................5
1.1.4 EMT and hypoxia ....................................................................................................5
1.1.5 Cancer diagnosis ......................................................................................................7
1.2 Liquid biopsy .......................................................................................................................7
1.2.1 Extracellular vesicles ...............................................................................................8
1.2.2 Circulating free DNA ...............................................................................................8
1.2.3 Circulating tumor cells .............................................................................................9
1.3 Enrichment and identification of CTCs .............................................................................10
1.4 Characterization of CTCs ..................................................................................................12
1.5 Microfluidics: a powerful tool for CTC analysis ...............................................................13
1.6 Magnetic separation of CTCs ............................................................................................14
1.6.1 Bulk magnetic separation .......................................................................................14
1.6.2 Microchip-based magnetic separation ...................................................................15
1.6.3 Magnetic ranking of CTCs.....................................................................................17
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1.7 Thesis objectives and overview .........................................................................................21
1.7.1 Chapter 2: Phenotypic characterization of cancer cells .........................................22
1.7.2 Chapter 3: Magnetic ranking cytometry of cancer cells ........................................22
1.7.3 Chapter 4: Real-time monitoring of dynamic CTC phenotypes in prostate
cancer models.........................................................................................................22
1.8 References ..........................................................................................................................23
Phenotypic Characterization of Cancer Cells ...........................................................................32
2.1 Introduction ........................................................................................................................33
2.2 Material and Methods ........................................................................................................35
2.2.1 Cell culture .............................................................................................................35
2.2.2 Hypoxic induction of SKBR3 cells........................................................................35
2.2.3 Western immunoblotting .......................................................................................35
2.2.4 In vitro wound healing assay .................................................................................36
2.2.5 RNA extraction, cDNA synthesis, and real-time PCR ..........................................36
2.2.6 Flow cytometry ......................................................................................................37
2.2.7 Chip fabrication .....................................................................................................37
2.2.8 Cell enrichment using anti-EpCAM magnetic nanoparticles ................................38
2.2.9 Microfluidic profiling of breast cancer cells spiked in blood ................................39
2.2.10 Collagen uptake assay ............................................................................................39
2.2.11 Immunocytochemistry ...........................................................................................40
2.2.12 Statistics .................................................................................................................40
2.3 Results and Discussion ......................................................................................................40
2.3.1 Hypoxia-driven model of EMT .............................................................................41
2.3.2 Nanoparticle-mediated separation of cell subpopulations .....................................44
2.3.3 Collagen uptake as a measure of invasiveness ......................................................45
2.4 Conclusion .........................................................................................................................49
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2.5 References ..........................................................................................................................50
Magnetic Ranking Cytometry of Cancer Cells .........................................................................52
3.1 Introduction ........................................................................................................................53
3.2 Material and Methods ........................................................................................................57
3.2.1 Next generation magnetic ranking chip fabrication ...............................................57
3.2.2 Cell culture .............................................................................................................57
3.2.3 Flow cytometry ......................................................................................................57
3.2.4 EpCAM profiling in cancer cells ...........................................................................58
3.2.5 Profiling of spiked samples ....................................................................................58
3.2.6 CTC imaging and analysis .....................................................................................58
3.3 Results and Discussion ......................................................................................................59
3.3.1 Next generation magnetic ranking cytometry ........................................................59
3.3.2 Performance assessment of the device using cultured cells ...................................62
3.3.3 Profiling prostate cancer cell lines with different levels of aggressiveness ...........64
3.3.4 Analysis of spiked samples ....................................................................................65
3.4 Conclusion .........................................................................................................................66
3.5 References ..........................................................................................................................67
Real-time Monitoring of Dynamic CTC Phenotypes in Prostate Cancer Models ....................70
4.1 Introduction ........................................................................................................................71
4.1.1 CTC analysis in prostate cancer xenograft models ................................................71
4.1.2 Monitoring treatment response in prostate cancer .................................................73
4.2 Material and Methods ........................................................................................................74
4.2.1 Orthotopic xenograft mouse models for prostate cancer .......................................74
4.2.2 Docetaxel treatment ...............................................................................................74
4.2.3 Histological analysis of xenografts ........................................................................74
4.2.4 Profiling of mouse CTCs and immunostaining .....................................................75
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4.2.5 MTT assay .............................................................................................................75
4.2.6 Flow cytometry ......................................................................................................75
4.3 Results and Discussion ......................................................................................................76
4.3.1 Generating prostate cancer mouse xenografts .......................................................76
4.3.2 Profiling CTCs in tumor-bearing mouse xenografts ..............................................77
4.3.1 In vitro treatment of PC-3M cells with docetaxel ..................................................82
4.3.2 Monitoring CTC dynamic phenotypes in metastatic xenografts during a course
of chemotherapy.....................................................................................................84
4.4 Conclusion .........................................................................................................................87
4.5 References ...........................................................................................................................87
Conclusions and Future Outlook ...............................................................................................91
5.1 Summary of research .........................................................................................................92
5.2 Limitations and future directions .......................................................................................94
5.3 Final remarks .....................................................................................................................95
5.4 References ..........................................................................................................................96
Appendices ................................................................................................................................97
6.1 Supporting information for Chapter 3................................................................................97
6.2 Supporting information for Chapter 4..............................................................................101
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List of Tables
Table 2.1 Sequence of primers used in the gene expression analysis of SKBR3 cells ................. 37
Table 4.1 Number of metastatic lesions in xenograft models ....................................................... 80
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List of Figures
Figure 1.1 Schematic representation of the multiple steps of the metastatic cascade. During
metastasis, cancer cells exit the primary site and enter blood circulation. Cancer cells have to
survive in circulation in order to reach distant organs that are suitable growth sites. Once they enter
a secondary site, they start to proliferate again and form metastatic lesions [9]. ........................... 3
Figure 1.2 CTC biology in metastatic cascade. CTCs enter the bloodstream either by passive
intravasation or active invasion by undergoing EMT. At a distant site, CTCs extravasate to initiate
metastatic lesion [20]. ..................................................................................................................... 4
Figure 1.3 Mechanism of regulation of the HIF-1α in normal and hypoxic conditions. In
normal conditions, HIF-1α is degraded. While under hypoxia, HIF-1α is stabilized and activates
transcription of hypoxia responsive genes [27]. ............................................................................. 6
Figure 1.4 CTC enrichment strategies. CTC enrichment based on either their physical features
or biological phenotypes [64]. ...................................................................................................... 11
Figure 1.5 Characterization of CTCs. Several assays are used to characterize CTCs such as: (A)
immunocytological techniques using antibodies specific to different proteins; (B) molecular assays
(RT-qPCR); (C) functional assays that assess CTC role in the metastatic cascade [59]. ............. 13
Figure 1.6 Schematic of CTC-iChip. After removing cells of 30 µm sizes, the
remaining cells (mostly CTCs and WBCs) are separated using immunomagnetic separation
technique. Here, CTCs are labelled with magnetic beads and selectively recovered at the outlet
[93]. ............................................................................................................................................... 16
Figure 1.7 CTC detection using a µ-Hall sensor. (A) The µHall senor detects CTCs that are
labelled with MNPs. (B) Once a CTC passes over a µHall senor it induces a voltage proportional
to the number of MNPs bound to its surface. (C) MNPs with different sizes can be used for
multiplexed protein analysis [93].................................................................................................. 17
Figure 1.8 Capture and sorting CTCs based on their EpCAM expression in velocity valley
microfluidic device. (A) Cells labeled with magnetic anti-EpCAM antibodies are isolated in a
microfluidic channel. In this device CTCs are sorted based on their surface marker expression into
four different subgroups (four zones). Cells with high EpCAM expression are captured in the
earlier zones, while low EpCAM-expressing cells are captured in later zones (B) Each zone
contains an array of X-shaped structures to create areas with lower linear velocities known as
velocity valleys. (C) Two arrays of NdFeB magnets are placed on top and bottom of the chip to
generate a magnetic field in the channel [84]. .............................................................................. 19
Figure 1.9 Magnetic ranking cytometry (MagRC) approach for profiling rare cells. (A)
MagRC contains 100 distinct zones with varied magnetic forces. Circular nickel micromagnets
with varying sizes are patterned within the channel to enhance the externally applied magnetic
field. (B) Two arrays of NdFeB magnets are placed on top and bottom of the chip to generate the
external magnetic field. (C) Nickel micromagnets are used to amplify magnetic field gradients
[88]. ............................................................................................................................................... 20
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Figure 1.10 Two-dimensional CTC sorting approach based on velocity valley device. In first
step, CTCs are sorted in a velocity valley device according to a surface marker expression. Isolated
CTCs are then subjected to a second sorting step based on a new marker expression [100]. ..... 21
Figure 2.1 Phenotypic profiling of cancer cell subpopulations. Schematic showing the
separation of cancer cells into four zones of the microfluidic device in the presence of an external
magnetic field. Cells are incubated with magnetic nanoparticles labelled with EpCAM. Cells that
have high levels of EpCAM and subsequently high number of magnetic nanoparticles are captured
in zone 1 and 2, whereas cells with low levels of EpCAM, and low number of magnetic
nanoparticles, are captured in zone 3 and 4. The linear velocity in the device decreases in a stepwise
manner in each zone, to increase the probability of cell capture in the apex of the X-structures.
Viable cells are released from each zone and assessed using a fluorescent collagen uptake assay.
Low-EpCAM cells have increased collagen uptake relative to high-EpCAM cells. Scale bar is 5
µm. ................................................................................................................................................ 34
Figure 2.2 Confirmation of EMT induction in SKBR3 cells after treatment with CoCl2. (A)
Morphological changes of SKBR3 cells are observed after 72 hour treatment with 150 µM of
CoCl2. Scale bars are 20 µm. (B) Western blot analysis of SKBR3 cells. HIF-1α expression in
SKBR3 cells that were treated with CoCl2 for 24, 48 and 72 hours. Over-expression of HIF-1α is
observed 24 hours after the treatment. (C) In vitro wound healing assay of SKBR3 cells. Scratch
closure is monitored in SKBR3 cells that were treated with CoCl2 for 24, 48 and 72 hours. An
increased closure of the scratch is observed in the CoCl2-treated cells. Scale bars are 20 µm. ... 42
Figure 2.3 Expression of EMT markers in SKBR3 cells after CoCl2 treatment. (A) EMT gene
expression profiles of SKBR3 cells using real-time PCR. SKBR3 cells were treated with CoCl2 for
24, 48 and 72 hours. Downregulation of epithelial markers (EpCAM, Cytokeratin 7 and
Cytokeratin 8) and upregulation of mesenchymal markers (Snail1, Slug and vimentin) are observed
in SKBR3 cells after the treatment with CoCl2 for 24, 48 and 72 hours. Standard errors of the mean
are shown (we wish to acknowledge Laili Mahmoudian for doing gene expression analysis). (B)
Protein expression analysis of SKBR3 cells using flow cytometry. SKBR3 cells were treated with
CoCl2 for 72 hours. Downregulation of epithelial markers (E-Cadherin, EpCAM, and PAN
cytokeratin) and upregulation of mesenchymal marker (N-Cadherin) are observed in SKBR3 cells
after 72 hours of CoCl2 treatment. ................................................................................................ 43
Figure 2.4 Microfluidic profiling of breast cancer cells. Cells were labelled with anti-EpCAM
magnetic nanoparticles and captured in the microfluidic device. (A) Cell sorting profile of
MCF-7 and MDA-MB-231 cells; (B) Cell sorting profile of SKBR3 and SKBR3-EMT cells.
SKBR3-EMT cells were treated with CoCl2 for 72 hours. (C) Flow cytometric analysis of EpCAM
levels in MDA-MB-231, SKBR3, SKBR3-EMT and MCF-7 cells. (D) Cell sorting profile of low
numbers of MCF-7 and MDA-MB-231 cells spiked in whole blood. Cells were captured and then
stained with cytokeratin-APC, DAPI and CD45-FITC. Cancer cells were identified as
CK+/DAPI+/CD45-. Experiments were repeated in triplicate. Standard errors of the mean are
shown. Statistics are performed with one-way ANOVA followed by the Tukey multiple
comparisons (p
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Figure 2.6 Collagen uptake assay. (A) Representative images of breast cancer cells that have
ingested collagen. Cells were stained with DAPI, cytokeratin-APC, and FITC collagen. Scale bar
represents 5 µm. (B and C) Collagen uptake in MCF-7, MDA-MB-231, SKBR3 and SKBR3-EMT
cells. Flow cytometry median relative fluorescent intensities are shown normalized to the
unstained control (Collagen uptake assay is performed by Brenda Green). ................................. 48
Figure 2.7 2-D sorting of phenotypically-distinct CTC subpopulations. SKBR3 and SKBR3-
EMT cell subpopulations were released from the microfluidic device and analyzed using flow
cytometry for ingested collagen. Median fluorescent intensities are shown relative to the unstained
control. Experiments were repeated in triplicate. Standard errors of the mean are shown. Statistics
were performed with one-way ANOVA followed by the Tukey multiple comparisons (p
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231. Each profile represents the data collected from at least three trials. Cells were suspended in
PBS buffer and stained with DAPI (nuclear stain). Insets, phenotypic profiles obtained from 100-
zone MagRC device and EpCAM expression levels measured by flow cytometry; (Data is obtained
by (B) Capture efficiencies of VCaP, SKBR3 and MDA-MB-231cells; (C) Sensitivity analysis of
the device: the microfluidic device was challenged with low number of cells. Error bars represent
data from three trials. .................................................................................................................... 63
Figure 3.7 Profiling prostate cancer cell lines using new MagRC device. (A) EpCAM
expression profiles in three prostate cancer cell lines with different phenotypes: LNCaP, PC-3 and
PC-3M. Each profile represents the data collected from three trials. Cells were suspended in PBS
buffer and stained with DAPI (nuclear stain). (B) Capture efficiencies of LNCaP, PC-3 and PC-
3M cells. ........................................................................................................................................ 65
Figure 3.8 Analysis of spiked samples. (A) Representative images of a PC-3M cell and a mouse
blood cell stained with antibodies specific to cytokeratins, vimentin and mouse CD45. (B) Capture
profiles obtained from LNCaP, PC-3 and PC-3M cells spiked in mouse whole blood. Subsequent
to on-chip cell capture, cells were stained with cytokeratins, vimentin specific antibodies and
DAPI. Mouse blood cells were stained with mouse anti-CD45 to eliminate false positives. ....... 66
Figure 4.1 Monitoring dynamic CTC phenotypes in mice bearing human prostate cancer
xenografts. Three xenograft models with varying aggressiveness are generated by orthotopic
implantation of prostate cancer cell lines into the prostate of immunodeficient mice. CTCs from
these mice are analyzed with the next generation of MagRC device. The correlation between CTC
phenotypic profiles and their metastatic potential is then investigated. ....................................... 72
Figure 4.2 Generating prostate cancer xenograft models. (A) The tumor xenografts generated
in athymic nude mice are shown 2 and 4 weeks after implantation of LNCaP, PC-3 and PC-3M
cells. (B) Tumor growth in LNCaP, PC-3 and PC-3M xenografts (n=3). ................................... 77
Figure 4.3 CTC analysis in prostate cancer xenografts. (A) Representative images of CTCs
from LNCaP, PC-3 and PC-3M xenografts. CTCs were stained for cytokeratins and vimentin, and
mouse cells for mouse anti-CD45. DAPI was used to stain nuclei. (B) Total CTC counts in the
xenografts at different time points. CTC counts in LNCaP and PC-3 xenografts are plotted on the
left axis and PC-3M CTC counts are plotted on the right axis. .................................................... 78
Figure 4.4 Phenotypic profiling of CTCs in prostate cancer xenografts. EpCAM-based CTC
distributions in (A) LNCaP, (B) PC-3 and (C) PC-3M xenograft mouse models (each profile
represents data from a single mouse). ........................................................................................... 79
Figure 4.5 Metastasis incidence in PC-3 and PC-3M xenografts. Representative
immunohistochemistry (IHC) images of lymph nodes and lung metastasis in PC-3M and PC-3
mice, at 4 and 12 weeks post-injection, respectively. ................................................................... 80
Figure 4.6 Changes in CTC phenotypic profiles during disease progression. Comparing
EpCAM-based CTC profiles at early and end time points in (A) LNCaP, (B) PC-3 xenograft mice
(each profile represents data from a single mouse). ..................................................................... 81
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Figure 4.7 The correlation between phenotypic profiles of CTCs and their metastasis-
initiating potential. Comparing EpCAM-based CTC profiles at week 4 and week 8 post-injection
in (A) LNCaP, (B) PC-3 xenograft mice (each profile represents data from a single mouse). .... 82
Figure 4.8 MTT assay. Cell viability is measured using the MTT Assay. PC-3M cells were
treated with increasing doses of docetaxel (0.5-200 nM) for 24 hours. Data shown is representative
of six trials..................................................................................................................................... 83
Figure 4.9 Flow cytometric analysis of EpCAM expression after treatment with docetaxel.
EpCAM expression in PC-3M cells treated with different concentrations of docetaxel after (A) 24
hours and (B) 72 hours of treatment compared to untreated (control) PC-3M cells. ................... 84
Figure 4.10 Docetaxel treatment of PC-3M mouse xenografts. (A) Tumor growth rate in PC-
3M xenografts before and after treatment with 10 mg/kg docetaxel; Comparing CTC phenotypic
profiles in PC-3M mice from control and docetaxel-treated cohorts at (A) week 2 and (B) week 4
post-injection (each profile represents data from a single mouse). .............................................. 86
Figure S.1 The next generation of MagRC design and modeling. (A) Schematic of the next
generation of MagRC. (B) Calculation of the capture zone radius versus zone number. ........... 100
Figure S.2 Characterization of prostate cancer cell lines based on their protein expression.
Expression levels of epithelial and mesenchymal markers are measured in LNCaP, PC-3 and PC-
3M cells. The results display differences in EMT phenotype in these cells. .............................. 101
Figure S.3 Changes in body weight of mice bearing PC-3M tumors during a course of
treatment with docetaxel. PC-3M xenografts were injected at a dose of 10 mg docetaxel/kg body
weight for 2 doses every 10 days post-implantation. Control mice received saline injection. All the
PC-3M mice from both control and treated group were weighted twice weekly. ...................... 102
Figure S.4 Measuring toxicity of docetaxel in athymic nude mice. Tumor-free 6-8 week old
male athymic nude (nu/nu) mice were treated with docetaxel via intravenous administration for
two doses with 10 day interval. Weight of these mice was monitored for one month and compared
to healthy mice injected with saline. ........................................................................................... 103
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List of Abbreviations
BSA – Bovine serum albumin
CGH– Comparative genomic hybridization
CK – Cytokeratin
CTC – Circulating tumor cell
DAPI – 4',6-diamidino-2-phenylindole
DNA – Deoxyribonucleic acid
EMT – Epithelial to mesenchymal transition
EpCAM – Epithelial cell adhesion molecule
FACS – Fluorescence-activated cell sorting
FISH – Fluorescent in situ hybridization
GAPDH – Glyceraldehyde-3-phosphate dehydrogenase
H&E – Hematoxylin and eosin
HIF – Hypoxia-inducible factor
iCTC – Invasive circulating tumor cell
MagRC – Magnetic ranking cytometry
MNP – Magnetic nanoparticle
MTT – 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
PBS – Phosphate buffered saline
PDMS – Polydimethylsiloxane
PHD – Prolyl hydroxylase enzyme
qPCR – Quantitative polymerase chain reaction
RNA – Ribonucleic acid
VHL – Von Hippel–Lind
http://en.wikipedia.org/wiki/Hypoxia-inducible_factors
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Chapter 1
Introduction
Cancer cells constantly adapt to their ever-changing environment. The level of adaptation
is not the same for all of the cells within a tumor, resulting in an extensive heterogeneity. In a
primary tumor, based on the level of access to oxygen and nutrients and exposure to
chemotherapeutic agents, cancer cells undergo dynamic genetic and phenotypic changes. This
intratumor heterogeneity has a significant impact on tumor progression and treatment efficacy, and
cannot be fully deconvoluted using current diagnostic tools.
Tissue biopsy is considered to be the gold standard for cancer diagnosis; however, it is
susceptible to sampling bias. Also, this invasive technique is not an effective tool for real-time
assessment of patient health status, particularly over a course of treatment. The emergence of liquid
biopsy has opened new possibilities to address this shortcoming. This minimally invasive diagnostic
tool can be used for monitoring tumor progression. In addition, liquid biopsies can provide critical
information needed for treatment decisions.
Circulating tumor cells (CTCs) are cells shed from primary tumors into blood circulation
as viable or apoptotic cells. CTCs are often referred to as the missing link between a tumor and
metastasis. These cells carry molecular signatures of primary tumors into the bloodstream.
Application of CTCs as a liquid biopsy biomarker enables us to characterize tumors in a real-time
and non-invasive manner, especially during a course of treatment. The rarity and heterogeneity of
CTCs represent the main obstacles toward their integration into routine clinical medicine.
Advanced technologies have emerged to address these challenges. Microfluidics offers
solutions to these problems given the ability to process large volumes of blood while isolating
single cells. Many microfluidic platforms have been developed to isolate, detect and characterize
CTCs. Significant advances have now been made towards enrichment of CTCs with high purity
and efficiency. Unfortunately, the heterogeneity of CTCs usually complicates their
characterization and subsequent translation to clinical applications. This creates a demand for new
strategies to characterize these tumor surrogates and investigate their metastatic phenotypes in
order to point out their clinical significance.
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1.1 Cancer
Cancer is a disease caused by genetic and epigenetic alterations leading to abnormal growth
of cells [1]. Cancer has been recognized as the second leading cause of death globally, accounting
for 8.8 million deaths in 2015 [2]. More than 100 different types of cancer have been recognized
and can be classified into five main groups: leukemia, lymphoma, melanoma, sarcoma and
carcinoma [3]. In leukemia, abnormal numbers of white blood cells (leucocytes) are produced in
bone marrow [4]. Uncontrolled production of lymphocytes (a type of leucocytes) by the spleen
and lymph nodes causes lymphomas. Melanoma relates to the melanocytes, which are the cells
responsible for production of pigments in the skin [5]. Sarcoma is a highly malignant and rare type
of cancer which arises from connective tissues, bone, muscle, fat and cartilage [6]. Finally,
carcinomas are the most commonly diagnosed cancers, and they occur in epithelial tissues such as
glands, breasts and linings of many organs [3].
During carcinogenesis, two groups of genes are altered: oncogenes and tumor suppressors
[7]. Activation of oncogenes and inactivation of tumor suppressor genes affect key cellular
processes such as metabolism, proliferation and death [8]. Accumulation of multiple genetic
mutations results in deregulation of signaling pathways that control cell growth and death.
Therefore, cells start to proliferate in an uncontrolled manner and form local tumors. Not all tumors
are life-threatening. In fact, many tumors, such as moles and freckles, are benign and do not spread
to other parts of the body. However, malignant tumors continuously grow and spread throughout
the body via vascular and lymphatic systems and initiate secondary tumors through a process
called “metastasis” [9].
1.1.1 Metastasis
Metastasis is the main cause of cancer-related deaths [9]. When a primary tumor
metastasizes, cancer cells undergo a series of alterations that leads to their detachment from the
primary site and invasion of adjacent tissues. From there, they enter the vascular system in order
to implant a secondary tumor at a distant site. In general, metastasis includes the following steps:
tumor cell motility and invasion through extracellular matrix, intravasation into the blood
circulation, survival in the circulation, arrest at a distant organ site, extravasation, formation of
metastatic lesions in distinct organs and metastatic outgrowth in secondary site (Figure 1.1).
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Figure 1.1 Schematic representation of the multiple steps of the metastatic cascade. During metastasis, cancer
cells exit the primary site and enter blood circulation. Cancer cells have to survive in circulation in order to reach
distant organs that are suitable growth sites. Once they enter a secondary site, they start to proliferate again and form
metastatic lesions [9].
Entry of cancer cells into the bloodstream is the most critical step in the metastatic cascade
[10]. During this step, tumor cells become more motile and move through extracellular matrix
(ECM). Downregulation of protease inhibitors and upregulation of matrix metalloproteinases
(MMPs) that degrade ECM facilitates this process [11]. In general, cancer cells migrate in two
forms: 1) epithelial tissues with cell-cell junctions, and 2) individual cells that detach from the
primary tumor, known as circulating tumor cells (CTCs) [12].
1.1.2 Circulating tumor cells (CTCs)
Circulating tumor cells (CTCs) are a sub-population of tumor cells that detach from the
primary site and enter blood circulation to seed metastasis. CTCs are highly heterogeneous, and
their phenotypes change dynamically throughout their journey, from the primary tumor site to the
blood circulation, and then to the metastatic niche [13]. Little is known about how these cells break
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free from the primary site. However, two scenarios have been postulated for this process: 1) active
invasion or 2) passive shedding of cells into the blood circulation (Figure 1.2) [14].
In active invasion, cancer cells undergo a process called epithelial to mesenchymal
transition (EMT) in which they lose their epithelial phenotypes and acquire mesenchymal
characteristics. This transition enables cells to invade ECM and enter the bloodstream (see the next
section for further details) [15]. In the alternative scenario, which is passive shedding of CTCs in
the blood, clumps of cells may detach from the tumor and enter the blood circulation [16]. These
cell clusters, which contain approximately 2-50 cells, may get stuck in capillaries and start
proliferating. Both theories are supported with experimental evidence. However, the presence of
EMT markers in tumor tissues and the prevalence of CTCs that express mesenchymal markers
lend more credibility to the first scenario [17][18]. Despite the importance of EMT in the metastatic
cascade, the interplay between CTCs, EMT and metastasis is still unclear [19].
Figure 1.2 CTC biology in metastatic cascade. CTCs enter the bloodstream either by passive intravasation or active
invasion by undergoing EMT. At a distant site, CTCs extravasate to initiate metastatic lesion [20].
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1.1.3 Epithelial to mesenchymal transition (EMT)
EMT was originally detected during embryogenesis, where cells migrate to form different
tissues and organs [20]. The same process is adopted in wound healing and tissue regeneration
[21]. To enter the vascular system, tumor cells have to become motile, so they use an EMT process
resembling cell movements in embryo development. EMT enables cancer cells to change their
behavior by acquiring new characteristics that allow them to invade blood vessels and survive in
the hostile environment of the blood [19]. Cells undergoing EMT lose their cell polarity, the cell-
cell adhesion and apical-basal polarity, and become more motile. Acquiring mesenchymal
phenotypes allows CTCs to invade adjacent tissues as spindle-shaped cells and intravasate blood
circulation to initiate metastasis. CTCs which have gone through EMT are thought to be more
aggressive and invasive, exhibiting stem cell-like and non-apoptotic phenotypes [22]. Upon arrival
at a suitable niche, CTCs revert to an epithelial state through a reverse process called mesenchymal
to epithelial transition (MET) [23]. In this process, CTCs regain their ability to proliferate rapidly
and initiate a secondary tumor or metastatic lesion.
1.1.4 EMT and hypoxia
EMT is a complex molecular network influenced by a wide range of molecules. These
molecules mainly fall into three groups: EMT effectors, EMT regulators and EMT inducers [23].
EMT effectors are proteins that define the epithelial or mesenchymal state of a cell. During EMT,
these molecules undergo changes in their expression. Epithelial markers such as E-cadherin and
cytokeratins are mainly down-regulated, while mesenchymal markers like N-cadherin and
vimentin are up-regulated; all together, these help the cell to acquire new motile and invasive
characteristics [24]. EMT regulators, on the other hand, orchestrate the EMT by means of
transcription factors. This group of transcription factors, such as Snail, Slug, and Twist, regulates
the transcription of genes that are involved in mesenchymal differentiation of a cell. Finally, EMT
inducers are extracellular signals that promote EMT in a cell. Various signaling pathways, such as
TGF-β, Wnt, and Notch, are shown to induce EMT in cells [25]. In addition to these signaling
pathways, special conditions in the tumor microenvironment, such as hypoxia, can result in
induction of EMT.
During carcinogenesis, cancer cells grow rapidly in an avascular environment; therefore,
oxygen becomes scarce in the inner layers of the cells. This condition where oxygen pressure is
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less than 5–10 mmHg is called hypoxia [19]. A substantial body of evidence indicates that hypoxic
tumor microenvironment plays a pivotal role in the induction of EMT and, consequently, the
emergence of CTCs [19]. In line with these findings, several studies have shown that patients with
hypoxic tumors have poor prognosis and decreased overall survival [26].
Cancer cells adapt to hypoxic conditions by regulation of a family of transcription factors
called hypoxia-inducible factor (HIF) (Figure 1.3) [27]. HIF-1, which is the most important family
member due to its crucial role in tumorigenesis, is a heterodimer consisting of stable β subunits
and unstable α subunits. In the presence of oxygen, HIF-1α subunits are constantly synthesized
and rapidly degraded through a multistep process catalyzed by prolyl hydroxylase enzymes
(PHDs) and the Von Hippel–Lindau (VHL) tumor suppressor protein, while in the absence of
oxygen HIF-1α accumulates in the cell [28]. Stabilized HIF-1α dimerizes with HIF-1β and form a
complex that consequently activates the transcription of many genes. A great number of the genes
are regulated by activation of HIF-1, and many of these are involved in metastatic cascade,
especially the ones that are involved in EMT [27]. Note that the transcription factor HIF-1 is
activated not only by the absence of oxygen, but also by any factor that disturbs or interferes with
the process of HIF-1α degradation.
Figure 1.3 Mechanism of regulation of the HIF-1α in normal and hypoxic conditions. In normal conditions, HIF-
1α is degraded. While under hypoxia, HIF-1α is stabilized and activates transcription of hypoxia responsive genes
[27].
http://en.wikipedia.org/wiki/Hypoxia-inducible_factors
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1.1.5 Cancer diagnosis
Cancer is typically diagnosed through the emergence of clinical symptoms in patients.
After diagnosis, a variety of techniques are used to detect the tumor and determine the stage of the
disease. Imaging methods such as X-ray, computed tomography (CT) scans, magnetic
resonance imaging (MRI), ultrasound scans and positron emission tomography (PET) are used to
monitor the tumor growth, cancer progression and relapse [29]. These tools are also utilized to
locate the tumor for biopsies and surgery. Biopsy is a clinical test through which cancer cells or
tumor tissues are extracted and analyzed to identify gene mutations and cancer stage [30]. Based
on the location of the tumor and the suspected type of cancer, different kinds of biopsies exist,
including excisional, incisional and needle-aspiration biopsy [31]. In excisional biopsy, the entire
suspicious area is removed, while in incisional biopsy, only a small sample is taken for analysis.
Needle aspiration can be used to take out very small pieces of the tumor [32]. Once the biopsy is
taken, the cells or tissues are subjected to histopathological analysis by an expert pathologist.
Although tissue biopsy is the gold standard for cancer diagnosis, it is susceptible to
sampling bias. Besides, this invasive technique is not an effective tool for real-time assessment of
patient health status, particularly over a course of treatment [33]. Moreover, after tumor resection,
it is difficult to monitor tumor progression and relapse. The idea of liquid biopsy has emerged to
address these shortcomings [34].
1.2 Liquid biopsy
Liquid biopsy has gained lots of attention in the last few years [35]. During cancer
progression, tumors release a wide range of biological factors into the bloodstream, including
extracellular vesicles (such as exosomes), cell-free DNA (cfDNA) and circulating tumor cells
(CTCs) [36]. Other body fluids, such as urine, saliva and cerebrospinal fluid have been shown to
contain such tumor-derived materials as well [37]. Analysis of these body fluids is acknowledged
as liquid biopsy. This minimally invasive diagnostic tool can be used for monitoring tumor
progression and relapse. In addition, it can provide critical information needed for treatment
decisions [38]. Herein, we discuss three main types of cancer biomarkers that have been studied
for cancer detection and monitoring: 1) extracellular vesicles (EVs), 2) cell-free DNA (cfDNA)
and 3) circulating tumor cells (CTCs).
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1.2.1 Extracellular vesicles
Many types of mammalian cells release extracellular vesicles (EVs) to facilitate
communication with other cells [39]. Exosomes (40-120 nm in diameter) and microsomes (100-
1000 nm in diameter) are two main types of EVs that contain a wide range of biomolecules such
as proteins, lipids, DNA and RNA. Released EVs circulate in body fluids and reach distant sites
where they are taken up by other cells. Similar to normal cells, cancer cells secrete EVs for their
intercellular communication and, potentially, initiation of metastasis at a secondary site. The role
of EVs, especially exosomes, in forming metastatic lesions, angiogenesis, tumor motility, and
immune escape has been demonstrated in several studies and therefore, they are proposed as a
potential biomarker for cancer diagnosis [40][41]. However, their small size and low concentration
have hindered their analysis and application in clinical practices [42].
The most common technique for enrichment of EVs is differential ultracentrifugation,
which is often accompanied by multi-step filtration. High speed centrifugation (up to 200,000 g)
for more than 10 hours is needed for enrichment of exosomes, since they are much smaller than
other EVs [43]. Characterization of EVs have been done based on their physical properties, such
as size and morphology, their protein expression, and their nucleic acid content [44]. Optical and
non-optical techniques such as scattering and electron microscopy have been used for analyzing
EVs based on their physical characteristics [45]. Affinity-based purification, western blot, and
enzyme-linked immunosorbent assay (ELISA) have been utilized to characterize EVs based on
their protein expression [46]. Polymerase chain reaction (PCR) and electrophoresis are used to
analyze EVs based on their nucleic acid content [47]. In the Kelley group, a nanoparticle-mediated
electrochemical sensor has been developed for rapid detection of exosomes with clinically-relevant
levels of sensitivity [48].
1.2.2 Circulating free DNA
The presence of circulating free DNA (cfDNA) was first reported in 1948 [49]. Thirty years
later, cancer patients were found to have elevated levels of cfDNA in their blood. Direct
sequencing of these circulating tumor DNA (ctDNA) showed the same genetic alterations as the
tumor cells [37]. Indeed, they share various types of neoplastic genomic alterations, such as
mutations in oncogenes and/or tumor-suppressor genes and epigenetic changes. Based on these
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findings, analysis of ctDNA could potentially reflect the spatial and temporal heterogeneity in solid
tumors [50].
The exact mechanism by which cfDNA is released into circulation is not fully understood.
However, some possible mechanisms have been proposed, such as passive release from apoptotic
and necrotic cells, or active secretion from non-proliferating cells [37]. Similarly, ctDNA might
be actively shed by cancer cells or passively released from lysed tumor cells, dying CTCs, or
tumor-derived exosomes [50]. In normal conditions, cfDNA is cleared from blood by DNase I and
DNase II enzymes except for a small amount which remains in circulation [51]. However, these
enzymes are inhibited during the metastatic cascade, resulting in an increase of cfDNA
concentration in the blood [52]. Normally, less than 10 ng/ml of cfDNA exists in the plasma of
healthy individuals [50]. This amount typically increases by 0.1% to 10% in cancer patients. The
exact amount of ctDNA varies in different cancer type and stage.
The use of ctDNA as a cancer biomarker requires isolation of cfDNA and detection of
ctDNA with high sensitivity and specificity [53]. Detection of minute amounts of ctDNA in a vast
background of cfDNA is very challenging. Around 5×107 cells are needed to produce a measurable
amount of ctDNA [54]. Typically, the size of ctDNA ranges between 80 bp to 260 bp with the
dominant fraction being less than 150 bp. Thus, the traditional approaches for DNA analysis are
not adequately sensitive for detection of ctDNA. Advanced technologies have emerged to address
this challenge, including digital PCR and next-generation sequencing (NGS) [37].
Despite the advances in ctDNA isolation and detection, it is still difficult to distinguish
ctDNA from cfDNAs that are released due to other medical conditions that cause elevation of
cfDNA, such as tissue damage or autoimmune diseases [55].
1.2.3 Circulating tumor cells
CTCs are currently employed as a diagnostic biomarker for investigating cancer biology
and tumor metastasis [56]. The number of CTCs in blood was first proposed as an index for tumor
progression and invasiveness [57]. Their extreme rarity, with concentration of 1 CTC per billion
of blood cells in a human cancer patient, called for specialized enrichment technologies [58]. As a
result, various strategies have been developed to detect and enumerate CTCs (see the next section
for further details) [59]. The prognostic value of CTCs has been validated in many cancers, such
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as breast, colon, and prostate cancer [60][61][62] . Data collected from cancer patients showed a
correlation between CTC count and adverse outcomes and, in many cases, decreased progression-
free survival and overall survival [38]. These clinical data prompted attempts to explore CTC
potential use for drug screening and therapeutic decisions. Many ongoing clinical trials are now
focusing on treatment regimens based on data obtained from CTC counts and protein expression
[38]. Nevertheless, further validations are needed before translation of CTC analysis into clinical
practice.
1.3 Enrichment and identification of CTCs
Over the past two decades, a variety of techniques have been developed to identify and
enumerate CTCs [1][63][64]. CTC enrichment is the first step in all these techniques, through
which concentration of CTCs increases by several log units relative to levels of normal blood cells
[20]. This step facilitates subsequent detection of single CTCs in the presence of a background of
millions of blood cells. Subsequent to the enrichment step, CTCs are distinguished from non-target
cells by immunostaining or reverse transcription polymerase chain reaction (RT-PCR) [1][64].
CTC enrichment is mainly carried out based on either the physical properties or biological
phenotypes of CTCs [64]. In the first group of assays, CTCs are separated from surrounding
peripheral blood mononuclear cells (PBMC) according to the differences between their size,
deformability and density [65][66]. Although phenotypically heterogeneous populations of CTCs
can be isolated through these assays, many of them suffer from a lack of sensitivity and selectivity
[67]. On the other hand, biological-based assays take advantage of cell surface protein expression
in CTCs (Figure 1.4) [68]. A variety of surface markers have been proposed for this purpose [59].
Epithelial cell adhesion molecule (EpCAM), is the most commonly used surface antigen for CTC
analysis [69][70]. Employing epithelial markers has always been a matter of controversy as CTCs
lose their epithelial features during EMT [70]. However, a large body of research has shown that
CTCs that have partially undergone EMT are more malignant than fully mesenchymal CTCs
[71][72][64]. In other words, CTCs should retain some epithelial-like traits to be able to proliferate
and form metastatic tumors [20]. Another technique for CTC enrichment is negative selection of
white blood cells (WBC) using antibodies specific to CD45 [20]. This method avoids the bias of
selecting CTCs which express a particular marker. However, cells isolated by negative selection
might be a mixture of different cell types, such as normal blood vessel or stromal cells. To
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overcome this issue and increase sample purity, negative selection is usually accompanied by a
positive enrichment step [67].
One of the most widely used technologies for CTC isolation and detection is CellSearch [73].
A combination of immunomagnetic separation and immunofluorescence staining of CTCs is used
in this system. CTCs are magnetically labeled with anti-EpCAM coated magnetic particles and
subsequently captured by an external magnet. CellSearch is the only FDA-cleared technology for
CTC analysis and has been used in numerous clinical trials [74][75]. Despite its widespread utility,
CellSearch has limitations, such as its inability to capture low-EpCAM expressing CTCs, leading
to low capture efficiency [76][67].
Figure 1.4 CTC enrichment strategies. CTC enrichment based on either their physical features or biological
phenotypes [64].
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1.4 Characterization of CTCs
At present, the need for enrichment and enumeration of CTCs has been largely fulfilled;
however, the clinical utility of CTCs as a criterion to mark the initiation and progression of a tumor
as well as treatment efficacy is yet to be fully demonstrated [1]. CTCs are heterogeneous and only
a small fraction of them have the aggressive phenotypes required to reach the final stage of the
metastatic cascade [57]. The phenotypic properties of this metastatic subpopulation are not
completely understood. This creates an urgent need for specialized technologies to unravel the
complex properties of these cells.
Characterization of CTCs can involve immunostaining, real-time quantitative polymerase
chain reaction (RT-qPCR), and fluorescence in situ hybridization (FISH) [33][59]. The
heterogeneity of single CTCs has been assessed using whole genome amplification, RNA-
sequencing, and comparative genome hybridization (CGH) [77]. Advances in single cell genomics
have provided insight into the mutation spectra of CTCs. All these techniques require cell fixation
or permeabilization. However, for functional characterization of CTCs, tumor cells should be in a
viable state [78]. Live-cell functional assays are still a relatively unexplored area, and has the
potential to advance CTC characterization. Existing functional assays include: detection of specific
proteins secreted during the in vitro culture of CTCs, fluorescent collagen adhesion assay and in
vivo transplantation of patient-derived CTCs into immunodeficient mice (Figure 1.5) [79][80].
These approaches are limited by the low yield of CTCs from patients, but have the ability to detect
metastasis-initiating cells.
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Figure 1.5 Characterization of CTCs. Several assays are used to characterize CTCs such as: (A) immunocytological
techniques using antibodies specific to different proteins; (B) molecular assays (RT-qPCR); (C) functional assays that
assess CTC role in the metastatic cascade [59].
1.5 Microfluidics: a powerful tool for CTC analysis
All the aforementioned CTC enrichment and detection methods can be applied at both the
macro- and microscale. At the micro-scale, the emergence of microfluidics has opened up new
avenues for isolation and characterization of rare cells by offering unique features such as: high
capture efficiency, high system throughput, and high selectivity while requiring only small sample-
volume [81]. Microfluidics enables us to manipulate small volumes of fluid in a controlled
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environment with high volumetric throughput. This technology has shown a great potential in
isolating CTCs from patient samples and characterizing them at a single-cell level [81].
A variety of cell separation mechanisms have been applied in microfluidic-based systems.
Examples include immunomagnetic capture, size/deformability-based isolation, and
dielectrophpresis, to name a few [82][83]. In addition to CTC isolation, several studies have
reported on-chip strategies for characterizing CTCs [84]–[89]. Finely engineered microfluidic
devices enable cell sorting based on CTCs’ biological and functional phenotypes, such as surface
protein expression, collagen uptake, and their migration potential [86] [89].
1.6 Magnetic separation of CTCs
Magnetic separation is one of the most widely used techniques for CTC isolation [90]. In
this method, CTCs are labelled with magnetic beads conjugated to target-specific antibodies such
as EpCAM [91]. CTCs bound to magnetic beads are subsequently isolated by an external magnet.
This technique, also known as immunomagnetic isolation of CTCs, allows us to isolate and
characterize CTCs based on their biological phenotype. A variety of magnetic particles with
different sizes, shapes and compositions have been utilized for this purpose [92]. The particles
used for CTC isolation are either microbeads or magnetic nanoparticles (MNPs). Microbeads with
>0.5µm diameter consist of two components: a polymeric matrix and a magnetic material, such as
iron, cobalt or nickel, which is embedded in the matrix. Magnetic nanoparticles are much smaller
than microbeads (5-200 nm) with higher cellular binding capability and stability, especially in
complex media such as whole blood. For multiplexed detection, different magnetic beads with
varying sizes and detection tags can be used [93]. Among all CTC enrichment techniques,
magnetic separation is acknowledged as a relatively easy approach with high capture efficiency
and specificity. Upon removal of the external magnetic field, captured cells can be recovered and
subjected to down-stream analysis.
1.6.1 Bulk magnetic separation
Magnetic separation has been applied at both the macro- and micro-scales. In bulk or
macro-scale separation, an external permanent magnet (usually neodymium-iron boron (NdFeB))
is used for isolation of CTCs tagged with magnetic nanoparticles (MNPs) under a stationary
condition [92]. The magnetic force acting on a cell is proportional to the number of magnetic
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particles bound to the surface of the cell. CellSearch takes advantage of bulk magnetic separation
using 120-200 nm Fe nanoparticles (ferrofluid) conjugated to anti-EpCAM antibodies [74].
AdnaTest is another technology that employs similar principles; a mixture of 4.5 µm
superparamagnetic Dynabeads labelled with anti-EpCAM and a cancer specific marker such as
MUC-1 and HER-2 is used as the capture agent [94]. Post-capture, RT-PCR is used for detection
of CTCs. Compared to CellSearch, AdnaTest enriches different subpopulations of CTCs, including
EpCAM negative cells. Despite this advantage, the large size of the beads might affect their capture
efficiency [95]. Instead of manipulating the magnetic particles, some platforms have changed
external magnets. For instance, MagSweeper captures CTCs bound to EpCAM Dynabeads by
using moving, rod-shaped neodymium magnets [92].
Platforms that magnetize and isolate CTCs directly are known as positive enrichment.
Although positive enrichment boasts simplicity and purity, some CTCs with low marker
expression may be missed by this approach. An alternative is negative depletion, where excess
blood cells are removed using a similar immunomagnetic approach. First, red blood cells are lysed,
and then white blood cells are magnetically removed by the means of MNPs bound to anti-CD45
[96]. This method can eliminate blood cells by 100-fold, but lacks purity. Additionally, the process
of lysing red blood cells can affect the viability of CTCs [97].
1.6.2 Microchip-based magnetic separation
During the last decade, microfluidics has offered new possibilities for analyzing CTCs. As
was mentioned earlier, many microfluidic technologies have been developed for isolation and
detection of CTCs based on their specific properties [93]. Immunomagnetic separation is one of
the most commonly used methods in these platforms. In many cases, a magnetic field is applied
by placing permanent magnets under the microfluidic chip. However, CTC samples with high
purity are obtained by placing the magnets on top of the channel, as the effect of blood cell
sedimentation is minimized [92].
Intricate designs in microfluidic channels have been employed to enrich CTCs with high
efficiency and purity. The CTC-iChip is one of the early technologies with the ability of analyzing
whole blood without preprocessing (Figure 1.6) [98]. Blood cells smaller than 8 µm (RBCs and
platelets) and larger than 30 µm are first eliminated by deterministic lateral displacement. Later a
magnetic field is applied to separate labelled cells. CTC-iChip can operate in both positive- or
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negative-selection, based on the antibody used for labelling cells. In iChippos, anti-EpCAM is used
for isolation of CTCs while in iChipneg, WBC specific antibodies, such as anti-CD45, anti-CD15
and anti-CD66, are used for WBC depletion. Up to 107 cells/s can be processed by CTC-iChip
with a high recovery rate (97%).
Figure 1.6 Schematic of CTC-iChip. After removing cells of 30 µm sizes, the remaining cells (mostly
CTCs and WBCs) are separated using the immunomagnetic separation technique. Here, CTCs are labelled with
magnetic beads and selectively recovered at the outlet [93].
In addition to CTC enrichment, immunomagnetic techniques have been used for CTC
detection. A micro-Hall detector (µHD) is designed to identify magnetically-labelled CTCs based
on Hall effect (Figure 1.7.A) [99]. In this platform, the signal intensity reports on the number of
MNPs on the surface of CTCs and so the level of marker expression (Figure 1.7.B). Multiplexed
analysis of different protein marker is performed using MNPs with different sizes (Figure 1.7.C).
This platform has been used for analyzing samples from patients with ovarian cancer and
benchmarked against CellSearch. Despite its advantage over fluorescence-based detection
methods, it faces limitations for analyzing samples with low marker expression; each CTC should
have at least 106 MNPs to generate a measurable signal.
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Figure 1.7 CTC detection using a µ-Hall sensor. (A) The µHall senor detects CTCs that are labelled with MNPs.
(B) Once a CTC passes over a µHall senor it induces a voltage proportional to the number of MNPs bound to its
surface. (C) MNPs with different sizes can be used for multiplexed protein analysis [93].
1.6.3 Magnetic ranking of CTCs
The next step in CTC analysis is characterizing CTCs and investigating their metastatic
phenotypes. Phenotypic profiling of CTCs during disease progression provides us with insightful
information on their metastatic potential. The Kelley group has taken advantage of
immunomagnetic separation to not only isolate CTCs, but also sort them based on their surface
marker expression [84][85][88]. Velocity valley and magnetic ranking cytometry (MagRC) are
two principal technologies that can isolate magnetically-labelled CTCs in different capture zones
according to expression levels of biomarkers, such as EpCAM, HER2, and N-cadherin.
Unprocessed blood samples are incubated with antibody-coated magnetic beads prior to running
through the microfluidic channel at a flow rate of 0.6 ml/h [84]. Labelled CTCs are captured by an
external magnetic field applied by two arrays of NdFeB magnets on the top and bottom of the chip.
Non-target blood cells are washed away to increase the purity of the captured CTCs. Finally,
isolated CTCs are identified by immunofluorescence staining.
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In these platforms, CTCs are captured under the flow; therefore, the capture efficiency is
influenced by the ratio of magnetic force and drag force acting on a cell in each zone [85]. At low
Reynolds numbers, the drag force acting on a cell is governed by Stokes’ law (Equation 1):
𝑭𝒅 = −6𝜋𝜂𝑟𝑣
where Fd [N] is the drag force, η [Pa.s] is the dynamic viscosity of the medium, r [m] is the cell radius,
and v [m.s−1] is the relative velocity of the cell compared to the surrounding fluid.
The magnetic force acting on a cell is (Equation 2):
�⃗⃗� 𝑚 = 𝑁𝑏𝑉𝑚Δ𝜒𝑏𝑒𝑎𝑑
𝜇0(�⃗⃗� ∙ 𝛻)�⃗⃗�
where Nb is the average number of nanoparticles per cell, Vm [m3] is the nanoparticle volume,
Δχbead [unitless] is the difference between the magnetic susceptibility of the nanoparticle and the
medium, μ0 [H m−1] is the permeability of free space, and �⃗⃗� [T] is the applied magnetic field. The
value of Nb depends on the expression level of the protein in the cell and the affinity constant of the
antibody. Cells bound to magnetic particles are isolated whenever the magnetic force exceeds the
drag force (Equation 3):
�⃗⃗� 𝑚> �⃗⃗� 𝒅
In the velocity valley chip, CTCs are selectively isolated by manipulation of the drag force
acting on them [84]. Each chip consists of 4 compartments, known as zones, which have different
widths and therefore different linear velocities (Figure 1.8). The first zone has the narrowest width
and so the highest linear velocity. For other zones, the width increases stepwise by a factor of two.
An array of X-shaped structures are fabricated throughout the channel to facilitate cell capture by
creating areas of locally-low velocities, which are called velocity valleys (Figure 1.8.B) [85].
According to Equation 1, changes in linear velocity results in alteration of the drag force. Since
the magnetic field gradient is similar in all zones, the magnetic force acting on a cell mainly
depends on the number of magnetic beads bound to the surface of the cell (Figure 1.8.C). Based
on these principles, cells with high EpCAM expression and subsequently more magnetic beads on
their surface are captured in the first zone where the drag force is the highest, whereas cells with
lower EpCAM expression are captured in the later zones, where they experience lower drag force.
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Figure 1.8 Capturing and sorting CTCs based on their EpCAM expression in the velocity valley microfluidic
device. (A) Cells labeled with magnetic anti-EpCAM antibodies are isolated in a microfluidic channel. In this device
CTCs are sorted based on their surface marker expression into four different subgroups (four zones). Cells with high
EpCAM expression are captured in the earlier zones, while low EpCAM-expressing cells are captured in later zones
(B) Each zone contains an array of X-shaped structures to create areas with lower linear velocities known as velocity
valleys. (C) Two arrays of NdFeB magnets are placed on top and bottom of the chip to generate a magnetic field in
the channel [84].
MagRC is the next technology for sorting CTCs. This device gives us higher sorting
resolution by having 100 capture zones. Similar principles to the velocity valley chip have been
adopted in this device [88]. However, instead of manipulating the drag force, the magnetic force
is manipulated by altering magnetic field gradients (Equation 2). Circular nickel micromagnets of
varying size are patterned beneath X-shaped structures in a flow channel with a fixed width
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(constant linear velocity and therefore constant drag force) (Figure 1.9.A). The flow channel is
sandwiched between two arrays of NdFeB magnets that generate a magnetic field gradient inside
the chip (Figure 1.9.B). Increasing the size of the micromagnets along the channel creates areas
with very high magnetic field gradients, leading to efficient ranking of CTCs with different levels
of marker expression (Figure 1.9.C). Cancer cells with varying numbers of immunomagnetic beads
bound to their surface are sorted into 100 distinct zones based on their magnetic loading.
Figure 1.9 Magnetic ranking cytometry (MagRC) approach for profiling rare cells. (A) MagRC contains 100
distinct zones with varied magnetic forces. Circular nickel micromagnets with varying sizes are patterned within the
channel to enhance the externally applied magnetic field. (B) Two arrays of NdFeB magnets are placed on the top and
bottom of the chip to generate the external magnetic field. (C) Nickel micromagnets are used to amplify magnetic
field gradients [88].
Both the velocity valley and MagRC technologies can recover at least 90% of cell lines
with different EpCAM expression levels with high specificity. Prostate cancer clinical samples
analyzed by the velocity valley and MagRC devices show superior capture efficiency compared to
paired CellSearch tests. In further studies, downstream functional analyses of CTCs released from
different zones of the velocity valley chip have identified subpopulations of CTCs with higher cell
migration ability and greater invasive potential (see the next chapter for further details)[86][89].
Also, in a two-dimensional sorting technology, zonally extracted CTCs from velocity valley are
subjected to a second sorting step, yielding 16 phenotypically different subpopulations of cells
[100]. MagRC is employed to explore metastasis-initiating potential of CTCs by real-time
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monitoring of their dynamic phenotypes throughout cancer progression in a breast cancer
xenograft model.
Figure 1.10 Two-dimensional CTC sorting approach based on velocity valley device. In first step, CTCs are sorted
in a velocity valley device according to a surface marker expression. Isolated CTCs are then subjected to a second
sorting step based on a new marker expression [100].
1.7 Thesis objectives and overview
The main objective of this thesis is to explore new methodologies for characterization of
CTCs and to study the phenotypes that render them more invasive and metastatic. We employ new
microfluidic platforms for enrichment, identification, and characterization of CTCs. These devices
allow us to sort phenotypically-distinct CTCs based on their surface marker expression with high
efficiency, sensitivity and selectivity. The impact of epithelial-mesenchymal plasticity in
metastatic cascade is explored by monitoring dynamic changes in CTC phenotypes during cancer
progression. The metastatic potential of CTCs from xenograft mouse models is assessed through
histopathological analysis of mice organs and its correlation to CTC dynamic phenotypes is
explored. Finally, the effect of a chemotherapeutic drug on CTC phenotypes and their malignant
potential is investigated.
These objectives will be discussed in the following chapters:
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1.7.1 Chapter 2: Phenotypic characterization of cancer cells
In this chapter, we seek to separate and characterize phenotypically-distinct subpopulations
within a heterogeneous population of cancer cells. To this end, a microfluidic-based separation
and characterization approach is described. First, cancer cells are enriched by magnetic
nanoparticles coated with EpCAM-specific antibodies. Concurrently, cells are sorted by velocity
valley device based on the levels of EpCAM expression, which enables the detection of EMT-
transformed cells. Subsequent to cell sorting, cell subpopulations are subjected to collagen uptake
assay to assess their level of aggressiveness. This approach facilitates isolation of functionally
distinct cell subpopulations and allows surface marker expression to be associated with
invasiveness.
1.7.2 Chapter 3: Magnetic ranking cytometry of cancer cells
In this chapter, we demonstrate a new microfluidic device that is capable of isolating CTCs
and sorting them according to their surface marker expression using an immunomagnetic
approach. The next generation of MagRC is a modified version of the original MagRC and consists
of ten zones, which makes the fabrication more cost- and time-effective.Cancer cell lines with
different levels of EpCAM expression are introduced into the device in order to assess its
performance. Unprocessed blood samples from mice are also analyzed by this platform. On-chip
immunostaining of cancer cells using fluorescent-labelled antibodies is carried out to distinguish
cancer cells from non-target blood cells. It can also provide us with valuable information on the
phenotype of the cells.
1.7.3 Chapter 4: Real-time monitoring of dynamic CTC phenotypes in
prostate cancer models
In this chapter, we generate prostate cancer xenograft mouse models by implantation of
human prostate cancer cells into the prostate of immunodeficient mice. Three cell lines with
varying EMT phenotypes are used. Their ability to form primary tumors, their dynamic changes
in phenotypes during cancer progression, and their metastatic potential are assessed and compared.
CTCs shed from these xenografted tumors are isolated and characterized at different time points.
We explore the role of CTC phenotypic profiles and their dissemination patterns in the metastatic
cascade. Additionally, we monitor the phenotypic profiles of CTCs in a metastatic mouse model
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during a course of chemotherapy. Mice bearing human prostate cancer tumors are treated with a
chemotherapeutic drug. Docetaxel is first-line chemotherapy for patients diagnosed with
metastatic castration-resistant prostate cancer (mCRPC). Highly metastatic xenografted mice are
treated with docetaxel over a period of one month. The size of the tumor and metastasis incidence
are monitored during the course of treatment. Also, CTCs collected from the mice are analyzed by
the next generation of MagRC. The effect of the chemotherapy drug is traced by comparing CTC
phenotypic profiles obtained from treated mice with CTC profiles from an untreated cohort of
mice.
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