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

ii

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.

iv

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.

v

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,

vi

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.

vii

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

viii

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

ix

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

x

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

xi

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

xii

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

xiv

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

xv

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

xvi

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

xvii

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

1

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.

2

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).

3

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

4

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].

5

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

6

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

7

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).

8

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

9

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

10

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

11

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].

12

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.

13

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

14

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

15

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

16

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.

17

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.

18

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.

19

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

20

(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

21

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:

22

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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