The Epithelial-To-Mesenchymal Transition Regulates the E-Selectin Ligand Activities of Breast Cancer Cells a Dissertation Prese

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The Epithelial-to-Mesenchymal Transition Regulates the E-selectin Ligand Activities of

Breast Cancer Cells

A dissertation presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Grady E. Carlson

December 2016

This dissertation titled

The Epithelial-to-Mesenchymal Transition Regulates the E-selectin Ligand Activities of

Breast Cancer Cells

GRADY E. CARLSON

has been approved for

the Department of Chemical and Biomolecular Engineering

and the Russ College of Engineering and Technology by

Monica M. Burdick

Associate Professor of Chemical and Biomolecular Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology 3

ABSTRACT

CARLSON, GRADY, E., Ph.D., December 2016, Chemical Engineering

The Epithelial-to-Mesenchymal Transition Regulates The E-selectin Ligand Activities of

Breast Cancer Cells

Director of Dissertation: Monica M. Burdick

During metastasis the epithelial-to-mesenchymal transition (EMT) increases the motility and invasiveness of breast cancer cells (BCs). However, it has yet to be determined whether the

EMT affects the trafficking of BCs by regulating the expression of ligands that mediate adhesion with E-selectin presented by endothelial cells lining the blood vessel walls. Thus, the first aim of this investigation was to determine whether the capacity for BCs to mediate adhesion via E- selectin/ligand interactions is modified by the EMT. BCs within tissues from 110 cases of breast cancer were assayed for EMT biomarkers using immunohistochemistry and evaluated for E- selectin ligand activity using E-selectin microsphere adhesion assays. A significantly greater percentage of E-selectin microspheres adhered to BCs with an epithelial phenotype compared to

BCs with a mesenchymal phenotype, indicating that the EMT is inversely correlated with E- selectin ligand activity. Similarly, BCs from cell lines with epithelial or mesenchymal phenotypes were assayed for E-selectin ligand activity using shear flow assays, in which significantly greater numbers of BCs with an epithelial phenotype adhered to E-selectin. Finally, the EMT was found to downregulate the E-selectin ligand activities of MDA-MB-468 cells and HMLE cells that underwent the EMT via ectopic expression of Snail or Twist, because in shear flow detachment assays greater percentages of cells with ectopic expression of Snail or Twist detached from E- selectin relative to the vector controls. Additionally, Hs578T cells that underwent the MET via shRNA knockdown of Snail or Twist demonstrated higher E-selectin ligand activities than the

Hs578T shcontrol cells in detachment assays, as significantly greater percentages of Hs578T shSnail cells and Hs578T shTwist cells adhered to E-selectin compared to the Hs578T shcontrol 4 cells. Interestingly, the adhesion of protease-treated cells to E-selectin was not significantly decreased by the EMT, indicating that the E-selectin ligand activities of glycoproteins and glycolipids were differentially affected by the EMT. Furthermore, qRT-PCR revealed that relative changes in gene expression for fucosyltransferases (FT) FT3 and FT6, which functionalize E-selectin ligands via the addition of fucose, paralleled the EMT or MET induced changes in the E-selectin ligand activities of BCs.

The second aim of this investigation was to determine how the constitutive expression of

E-selectin by bone marrow endothelial cells may affect the growth of BCs that metastasize to the bone marrow. Stem-like BCs with a mesenchymal phenotype, i.e., Hs578T cells, formed mammosphere-like structures on E-selectin-coated plates and activated HUVEC. In contrast, non- stem-like BCs with an epithelial phenotype were unaffected by E-selectin. RT2 PCR-profiler arrays and annexin V / propidium iodide staining indicated that Hs578T cells cultured on E- selectin pursued necrotic cell death via signaling mechanisms in which BCL-2 genes and TNF- receptor superfamily genes were expressed. Furthermore, RT2 PCR-profiler arrays implicated

Notch and Wnt signaling in regulating the behaviors of Hs578T cells grown on E-selectin.

The third aim of this study was to evaluate whether the apparent cellular mechanical properties of BCs are affected by the MET. A microfluidic device was used to deform the BCs and the apparent mechanical properties of BCs were calculated using a modified-power-law model. Compared to the Hs578T shcontrol cells, Hs578T shTwist cells were found to be more viscous, indicating that the MET increased the mechanical strength of BCs. In summation, this study provides a mechanism for the regulation of the E-selectin ligand activities of BCs via the

EMT and MET, reveals how E-selectin may affect the growth of BCs in the bone marrow, and shows that the apparent cellular mechanical properties of BCs are strengthened by the MET.

Approved: 5

DEDICATION

In memory of my father Neil Earl Carlson (10/17/1955 – 09/23/2015),

and for my family.

ACKNOWLEDGMENTS

Thank you to my advisor Monica M. Burdick for guidance, instruction, wisdom, kindness, support, and patience. Thank you to my thesis committee for guidance and insight.

Thank you to Aaron Burdette for instruction on microfluidic fabrication and friendship. Thank you to Eric Martin for being you.

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 5 Acknowledgments...... 6 List of Tables ...... 10 List of Figures ...... 11 Chapter 1: Introduction ...... 14 Breast Cancer Classifications and Treatment Strategies ...... 14 Generation of Intratumor Heterogeneity ...... 15 The Tumor Microenvironment and the Epithelial-to-Mesenchymal Transition ...... 16 Mechanisms of Breast Cancer Metastasis ...... 18 Selectins and Selectin Ligands ...... 20 Cellular Mechanics and Breast Cancer Metastasis ...... 22 Methods for Measuring Mechanical Properties of Single Cells ...... 23 Modeling Cellular Mechanical Properties of Single Cells ...... 24 The Cortical-Shell Newtonian Liquid Core Model ...... 25 The Maxwell Liquid Drop Model ...... 26 The Compound Newtonian Liquid Drop Model ...... 27 Viscoelastic Models ...... 28 Hypothesis and Specific Aims ...... 30 Chapter 2: Characterizing the E-selectin Ligand Activities of Breast Cancer Cells ...... 31 Introduction ...... 31 Materials and Methods ...... 33 Antibodies and Recombinant Proteins ...... 33 Tissue Sample Preparation and Cell Culture ...... 34 Flow Cytometry...... 35 Plasmid Isolation ...... 36 Retroviral Transduction and RNA Interference ...... 36 Reverse Transcription and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) ...... 37 Parallel Plate Flow Chamber Shear Flow Assays ...... 38 Dynamic Biochemical Tissue Analysis and E-Selectin Microsphere Attachment Assays ... 39 8

Immunohistochemistry ...... 41 E-Selectin Immunoprecipitation ...... 42 SDS-PAGE and Western Blotting ...... 44 Statistics ...... 44 Results ...... 45 The E-selectin Ligand Activities of Breast Cancer Cells in Human Tissue Samples are Inversely Correlated with Markers of the EMT ...... 45 Breast Cancer Cell Lines with Epithelial Phenotypes Have Greater Functional E-selectin Ligand Activities than Breast Cancer Cell Lines with Mesenchymal Phenotypes ...... 46 The Epithelial-to-Mesenchymal Transition Reduces the E-selectin Ligand Activities of Breast Cancer Cells and Human Mammary Epithelial Cells ...... 48 The Mesenchymal-to-Epithelial Transition (MET) Increases the E-selectin Ligand Activities of Breast Cancer Cells ...... 54 Stem-Like Traits of Breast Cancer Cells may be Relinquished through the MET and are Localized within Regions of the EMT Spectrum ...... 57 Discussion ...... 61 Chapter 3: Snail and Twist Transcription Factors Differentially Regulate the Expression of Glycosphingolipids in Breast Cancer Cells during the EMT and MET ...... 67 Introduction ...... 67 Materials and Methods ...... 68 Flow Cytometry...... 68 Shear Flow Detachment Assays ...... 68 Reverse Transcription and qRT-PCR ...... 68 Lipid Extraction ...... 68 Thin Layer Chromatography ...... 69 Results ...... 70 Breast Cancer Cell Lines that Undergo the EMT or MET Express Protease Resistant E- selectin Ligands ...... 70 Snail and Twist Differentially Regulate the Expression of Glycosphingolipids Presented by Breast Cancer Cells ...... 72 Discussion ...... 77 Chapter 4: Adhesion to E-selectin Alters the Behaviors of Stem-like Breast Cancer Cells ...... 81 Introduction ...... 81 Material and Methods ...... 82 Proteins and Antibodies ...... 82 Cell Culture ...... 83 9

Breast Cancer Cell Culture on Protein Coated Plates ...... 83 Immunofluorescence Microscopy ...... 83 Annexin V and Propidium Iodide Staining ...... 84 HUVEC E-selectin Expression Assays ...... 84 Results ...... 86 Adhesion to E-selectin Induces Atypical Growth Behaviors in Stem-like Breast Cancer Cells ...... 86 Discussion ...... 100 Chapter 5: Investigating the Effects of the MET on the Apparent Cellular Mechanical Properties of Breast Cancer Cells ...... 106 Introduction ...... 106 Methods and Materials ...... 108 Microfluidic Fabrication ...... 108 Cell Aspiration and Deformation ...... 109 Damped Power-Law Model ...... 112 Results ...... 124 The Mesenchymal-to-Epithelial Transition Generates Breast Cancer Cells that are Resistant to Deformation ...... 124 Discussion ...... 129 Chapter 6: Concluding Remarks and Recommended Future Work ...... 133 References ...... 137 Appendix A: Primers and RT2-PCR Profiler Arrays ...... 157 Appendix B: Mathematical Modeling...... 164

LIST OF TABLES

Page

Table A1 Forward primers used for qRT-PCR...... 157 Table A2 Reverse primers used for qRT-PCR...... 158 Table A3 RT2 Profiler PCR array for apoptosis signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are representative of n = 2 independent experiments...... 159 Table A4 RT2 Profiler PCR array for Wnt signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment...... 160 Table A5 RT2 Profiler PCR array for Hedgehog signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment...... 161 Table A6 RT2 Profiler PCR array for Notch signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment...... 162 Table A7 RT2 Profiler PCR array for extracellular signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment...... 163

LIST OF FIGURES

Page

Figure 1. Incidence of breast cancer subtypes. Adapted from Polyak and Filho (2012) (24)...... 15 Figure 2. Mechanisms of breast cancer metastasis ...... 19 Figure 3. Molecular structure of E-selectin and the sialofucosylated carbohydrates that decorate selectin ligands...... 22 Figure 4. Microspheres were prepared for DBTA and analyzed in flow cytometric analysis...... 39 Figure 5. Experimental set-up and workflow for dynamic biochemical tissue analysis (DBTA). 40 Figure 6. DBTA detected functional E-selectin ligands presented on breast cancer tissues...... 41 Figure 7. Breast cancer cells with an epithelial phenotype demonstrate greater levels of E-selectin ligand activity than breast cancer cells with a mesenchymal phenotype...... 46 Figure 8. Breast cancer cell lines with an epithelial phenotype have greater functional E-selectin ligand activities than breast cancer cells with a mesenchymal phenotype...... 48 Figure 9. The ectopic expression of Snail or Twist transcription factors in the MDA-MB-468 and HMLE cell lines resulted in the epithelial-to-mesenchymal transition (EMT) and reduced the functional E-selectin ligand activities of MDA-MB-468 cells and HMLE cells...... 52 Figure 10. Breast cancer cell lines and HMLE cells adhered to E-selectin via calcium-dependent E-selectin/ligand interactions...... 53 Figure 11. The EMT downregulated the expression of E-selectin ligands on MDA-MB-468 cells...... 54 Figure 12. Knockdown of Snail or Twist gene expression in the Hs578T cells resulted in the mesenchymal-to-epithelial transition (MET) and increased the functional E-selectin ligand activities of Hs578T shSnail and Hs578T shTwist cells...... 56 Figure 13. Hs578T cells adhered to E-selectin via calcium-dependent E-selectin/ligand interactions...... 57 Figure 14. HMLE cells reduced the expression of CD24 via the EMT...... 58 Figure 15. Breast cancer cell lines that underwent the EMT or MET were assayed for stem-like breast cancer cell biomarkers including CD24, CD44, and ALDH using multiplex staining and flow cytometry ...... 60 Figure 16. Preliminary analysis of CD44 gene expression levels in breast cancer cells...... 63 Figure 17. Flow cytometric analysis of breast cancer cell lines treated with bromelain protease. 74 Figure 18. The expression of protease-resistant E-selectin ligands on breast cancer cells was differentially modified by Snail and Twist transcription factors that facilitate the EMT and MET...... 75 Figure 19. Thin layer chromatography of glycolipids extracted from breast cancer cell lines. .... 76 Figure 20. Pathways for the synthesis of sialylated glycosphingolipids (gangliosides) by sialyltransferases and galactosyltransferases...... 77 12

Figure 21. SpectraMax M2 signal range of detection for AlexaFluor 488...... 85 Figure 22. Breast cancer cell lines were classified as stem-like or non-stem-like based on the expression of CD24 and CD44...... 87 Figure 23A. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner...... 89 Figure 23B. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner...... 90 Figure 23C. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner...... 91 Figure 24. E-selectin-induced formation of mammosphere-like structures of Hs578T cells occurred in a concentration dependent manner...... 92 Figure 25. Stem-like breast cancer cells with mesenchymal phenotypes exhibited atypical culture morphologies on E-selectin substrates...... 93 Figure 26. A matrix presentation of E-selectin on tissue culture plates induced the formation of mammosphere-like structures in stem-like mammary epithelial cells with mesenchymal phenotypes, yet non-stem-like HMLE cells with epithelial phenotypes appeared to be unaffected by E-selectin...... 93 Figure 27. Hs578T cells with shRNA knockdown of A) Twist or B) Snail formed mammosphere- like structures on E-selectin treated tissue culture plates...... 94 Figure 28. Hs578T cells had a spherical morphology when cultured on E-selectin-coated regions of tissue culture plates, yet the Hs578T cells maintained typical morphologies on adjacent fibronectin-coated regions of tissue culture plates...... 95 Figure 29. A matrix presentation of E-selectin but not soluble E-selectin induced Hs578T cells to form mammosphere-like structures...... 95 Figure 30. Hs578T cells cultured on E-selectin treated tissue culture plates underwent cell death...... 96 Figure 31. Site densities of E-selectin expression on HUVEC were quantified in terms of E- selectin incubation concentrations...... 97 Figure 32. Breast cancer cells were cultured on HUVEC monolayers...... 98 Figure 33. Equivalent concentrations of recombinant human or murine E-selectin-hFc protein produced in CHO cells (rhE) or NSO cells (chimeric protein rhE/Fc and rmE/Fc) elicited a differential response in Hs578T cells...... 104 Figure 34. Optical measurement of the channel height in the microfluidic device. Channel height was measured by optically focusing along the z (optical) axis of a DMI6000B inverted microscope using SimplePCI software...... 109 Figure 35. Schematic of the equipment setup for breast cancer cell deformation experiments conducted using a microfluidic device. Objects in the figure are not to scale...... 111 13

Figure 36. Aspiration pressures in the microfluidic device were generated using a 30 ml syringe pump and were calculated from A) data published by Rodriguez et al (315) using B) a linear regression...... 111 Figure 37. Axis symmetric entry of an Hs578T shcontrol cell into the microfluidic channel measuring 10μm in width and 20μm in height...... 113 Figure 38. Diagram depicting the entry of a cell into the microfluidic channel using modeled geometries and a defined constant pressure...... 116 Figure 39. Cell entry into the microfluidic channel...... 122 Figure 40. Flow through a narrow slit modeling the channel of the microfluidic device utilized in this study...... 123 Figure 41. Experimental observations of the time-course entry of a breast cancer cell into the microfluidic device and the time-course cell entry calculated by numerical simulations...... 126 Figure 42. Experimental observations of cell entry time into the channel of the microfluidic device were compared to entry times calculated using the numerical simulation for Hs578T shTwist cells and Hs578T shcontrol cells...... 127 Figure 43. The apparent cellular reference viscosities of breast cancer cells were calculated as a function of the cell radius using the damped power-law numerical simulation...... 127 Figure 44. Apparent cellular viscosities of breast cancer cells were calculated as a function of empirical observations of cell radius and entry time using a damped-power law model...... 128 Figure 45. Apparent cellular viscosities of Hs578T shTwist cells were significantly greater than apparent cellular viscosities of Hs578T shcontrol cells for cells that entered the microfluidic device with 5 < te < 100...... 128 Figure 46. The nuclei of Hs578T shTwist cells were approximately 75% as large as the entire breast cancer cell...... 132 Figure 47. Postulated mechanism of breast cancer metastasis demonstrating the impact of EMT- regulated E-selectin ligand activity...... 136 Figure A1. Numerical simulation of the time-course entry of a cell into a microfluidic channel...... 167

CHAPTER 1: INTRODUCTION

Breast Cancer Classifications and Treatment Strategies

The dysregulation of proto-oncogenes or tumor suppressor genes, e.g., BRCA1 and

BRCA2, can cause breast cancer cells to become cancerous (1-4). Common characteristics of cancer cells include uncontrolled proliferation, uncontrolled growth, and an inability to execute mechanisms for programmed cell death (5). A diverse population of breast cancer cells with distinct characteristics and drug sensitivities can be produced to form a tumor (6-10). To manage this diversity, breast cancer cells are categorized into subtypes using molecular profiles that are therapeutically relevant.

Typically breast cancers are classified as hormone receptor positive, growth receptor positive, or triple negative breast cancers (11). The growth and proliferation of hormone receptor positive breast cancer cells that express receptors for estrogen (ER) and progesterone (PR), is accelerated in the presence of estrogen and progesterone (12). Due to their hormone-dependent growth characteristics, endocrine therapy and chemotherapy can be used to eliminate hormone positive breast cancer cells (12-14). Additionally, breast cancer cells that express the human epidermal growth factor receptor 2 (HER2) protein and the corresponding ERBB2 proto- oncogene are classified as HER2 positive breast cancers. HER2 positive cancers can be treated with chemotherapy adjuvant with a monoclonal antibody (mAb) targeting HER2 (trastuzumab), or a tyrosine kinase (TRK) inhibitor (lapatinib) that inhibits the TRK activity linked to ERBB2 and epidermal growth factor receptor (EGFR) proto-oncogenes (15-18). Breast cancers that do not express hormone receptors or HER2 are termed triple negative breast cancers (TNBC) (19-

22). These cancers typically occur in younger women and propagate and spread more rapidly than hormone or HER2 positive cancers (23). Hormone and mAb therapies are typically less effective for triple negative cancers, thus treatment options are limited to chemotherapy, radiation, and surgery (Figure 1). 15

Figure 1. Incidence of breast cancer subtypes. Adapted from Polyak and Filho (2012) (24).

Generation of Intratumor Heterogeneity

Diversity amongst breast cancer cells within a tumor is termed intratumor heterogeneity.

Intratumor heterogeneity poses challenges for the treatment of breast cancer, because TNBC, luminal, and HER2 subtypes have different sensitivities to hormone and growth factor receptor therapies (25). Mechanisms for the generation of intratumor heterogeneity are founded in two central hypotheses, the clonal evolution hypothesis (26) and the cancer stem cell hypothesis (27).

In the clonal evolution hypothesis, intratumor heterogeneity is developed as cancer cells undergo genetic mutations and genetic drift such that aggressive and proliferative clones of cancer cells are naturally selected to form the tumor (25, 26, 28). In contrast, the cancer stem cell hypothesis proposes that intratumor heterogeneity is developed as cancer stem cells differentiate and give rise to a heterogeneous population of cancer cells (29, 30). Combined, the clonal evolution and cancer stem cell hypotheses are the cornerstone for modern theories of how intratumor heterogeneity is generated (28, 31). 16

The Tumor Microenvironment and the Epithelial-to-Mesenchymal Transition

The generation of intratumor heterogeneity can be explained by models of clonal evolution and cancer stem cell differentiation, yet over the last two decades many studies have demonstrated that immunogenic cytokines produced by the tumor microenvironment can also contribute to intratumor heterogeneity by causing cancer cells to undergo phenotypic transitions

(32). The microenvironment of breast cancer tumors is composed of cancerous and non- cancerous cells of the breast, the stroma, the endothelium, and immune cells that are drawn to the tumor by inflammatory signaling molecules, e.g., cytokines (32-38). Immunogenic cytokines produced by the growing tumor and/or the tumor microenvironment, e.g., transforming growth factor beta (TGF-β), can signal for epithelial breast cancer cells to adopt a mesenchymal phenotype through a process termed the epithelial-to-mesenchymal transition (EMT) (39, 40).

The origins of the EMT are rooted in non-cancerous biological processes, such as embryogenesis, placentation, and wound healing, which allow for the migration of cells of epithelial origins (41, 42). The EMT provokes cytoskeletal reorganization and increases the invasiveness and motility of breast cancer cells, thereby enhancing the ability of breast cancer cells to disseminate (metastatic potential) (43, 44). Cytoskeletal reorganization events brought about by the EMT are facilitated by transcription factors, such as, Snail and Twist, which are expressed in response to stimulation from immunogenic cytokines (44, 45). Upregulation of Snail or Twist transcription factors results in decreased expression of E-cadherin, which is the predominant cell-cell adhesion molecule in epithelial cells, decreased expression of cytokeratin intermediate filaments, increased expression of N-cadherin cell-cell adhesion molecules, and increased expression of vimentin intermediate filaments (43, 46-49). This cytoskeletal reorganization that is characteristic of the EMT causes epithelial cancer cells to abandon their distinctive apical-basal polarity and adopt an anterior-posterior polarity that is observed in cells of a mesenchymal phenotype (43, 50-52). 17

Through the EMT breast cancer cells can also acquire stem-like characteristics including the capacity to self-renew and differentiate (53-56). Stem-like cancer cells pose a challenge for cancer treatment because they can be resistant to chemotherapy and radiotherapy and promote intratumor heterogeneity by differentiating into different types of breast cancer cells (25, 57-60).

Recently, distinct subtypes of stem-like breast cancer cells were discovered to be a result of the

EMT or the reverse phenotypic transition termed, the mesenchymal-to-epithelial transition (MET) through which cells of a mesenchymal phenotype transition to an epithelial phenotype (61). More specifically, Liu et al. (2014) demonstrated that epithelial-like and mesenchymal-like stem-like breast cancer cells resembled the naturally occurring luminal and basal stem cells of the breast that could be identified by the expression of CD24 and CD44, as well as, aldehyde dehydrogenase (ALDH) activity (61-63). Mesenchymal-stem-like cells generated via the EMT were CD24lowCD44highALDHlow, quiescent, and invasive compared to the

CD24highCD44highALDHhigh epithelial-stem-like cells that were produced by the MET, which proliferated more rapidly than mesenchymal-stem-like cells (61).

The study that revealed distinct types of stem-like breast cancer cells are produced by the

EMT and MET (61) was undertaken in-part to explain inconsistencies within the literature regarding whether stem-like cells were generated via the EMT or if the acquisition of stem-like traits was due to a separate phenomenon (61, 64). However, discrepancies between reports detailing phenotypic changes that are resultant of the EMT may also be attributable to intermediate transition states within the EMT (42, 65-67). A number of investigators have demonstrated that during the EMT cells may achieve hybrid or intermediate-transition states, in which cells begin to lose cell-cell junctions and reorganize the cytoskeleton (42, 66, 67).

In intermediate or hybrid states of the EMT, cancer cells have been shown to exhibit characteristics that are distinct from the properties of cells with terminal transition states of mesenchymal or epithelial phenotypes. For example, Schneider et al. (2013) found that the 18 plasma membranes of murine epithelial cells had the greatest tensile strength when the cells were in an intermediate-EMT state, despite the fact that when the cells achieved a terminal mesenchymal phenotype (characterized by a near-complete loss of cell-cell junctions) the tensile strength of the plasma membranes were reduced relative to the tensile strength of plasma membranes in cells that did not undergo the EMT (68). Lu et al. (2013) also recognized that cancer cells in the EMT-hybrid state exhibit distinct characteristics and proposed a micro-RNA- based model for EMT-state switching that demonstrates how the epithelial, mesenchymal, and hybrid states of the EMT can be achieved through micro-RNA regulation (65).

In effort to describe the different behaviors that cancer cells of different origins exhibit during the EMT, Tan et al. (2014) developed an EMT spectrum scoring system based on curated

EMT signatures and genomic analyses (67). In this publication, the survival rate of breast cancer patients with tumors that were scored as mesenchymal-like had better survival rates than breast cancer patients with tumors that scored as epithelial-like (67), which contrasts with the findings of many other works (69-73). Nonetheless, these studies cumulatively show that the EMT of breast cancer cells is not simply an “on/off” switch for epithelial and mesenchymal phenotypes but rather a gradual process by which cancer cells progressively shift between global phenotypic states that bound intermediate EMT/MET phenotypes.

Mechanisms of Breast Cancer Metastasis

The negative impact that metastasis has on the health of breast cancer patients is reflected in survival statistics, which show that 98.8% of breast cancer patients that are diagnosed with a localized tumor are estimated to survive for at least five years after diagnosis, but only 26.3% of breast cancer patients that are diagnosed with cancers that have metastasized are estimated to survive for at least five years (23, 74). During hematogenous metastasis, cancer cells (or groups/clusters of cancer cells) that are made invasive and motile via the EMT (43, 44, 52, 53, 66, 19

72, 75, 76), break away from the primary tumor and intravasate into the blood stream as circulating tumor cells (CTCs, Figure 2A) (32, 77-80).

Trafficking of CTCs to the secondary site is postulated to resemble leukocyte extravasation, in which the initial tethering of CTCs onto the endothelium that lines the blood vessel walls at the secondary site is facilitated by cell-surface sialofucosylated glycoconjugates, which bind E-selectin molecules presented by activated endothelial cells (Figure 2B) (81-89).

After tethering CTCs to the endothelium E-selectin/ligand interactions mediate rolling adhesion, which occurs as E-selectin/ligand bonds are rapidly formed and broken as the adhesion of CTCs to the endothelium is challenged by forces generated by hemodynamic flow (90-92). While selectin ligands have been implicated as mediators of CTC trafficking during cancer metastasis

(84, 85, 93), it remains unclear whether the EMT affects the expression or presentation, e.g., clustering, of selectin ligands on the surface of breast cancer cells.

Figure 2. Mechanisms of breast cancer metastasis. A) During the initial stages of breast cancer metastasis cells in the primary tumor undergo the EMT, break away from the primary tumor, and intravasate into the blood stream as circulating tumor cells (CTCs). B) Trafficking of CTCs to the secondary site is postulated to resemble leukocyte extravasation as the initial adhesion of CTCs to the endothelium lining the blood vessel wall occurs as selectin ligands expressed on CTCs bind to E-selectin presented by activated endothelial cells.

Selectins and Selectin Ligands

Selectins are transmembrane glycoproteins that facilitate tethering and rolling adhesion by forming calcium-dependent bonds with ligands decorated with sialofucosylated carbohydrates

(94-100). There are three members of the selectin family, E-selectin, P-selectin, and L-selectin

(101-107). E-selectin, also known as ELAM-1 (CD62E), is presented by activated endothelial cells, i.e., endothelial cells that are stimulated by inflammatory cytokines such as interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α) (108). Additionally, in the vascular niche of the bone marrow, E-selectin is constitutively expressed by bone marrow endothelial cells (109).

E-selectin is the most permissive selectin, because it binds more readily to a larger population of selectin ligands than P-selectin or L-selectin, such as sialofucosylated glycoproteins and glycolipids that are often highly expressed by breast cancer cells (88, 89, 110, 111). P-selectin is presented on activated platelets, but is also presented on activated endothelial cells, albeit for a shorter duration than E-selectin (104). Additionally, L-selectin is presented on leukocytes, yet unlike E-selectin/ligand interactions, L-selectin/ligand interactions require a threshold level of shear force provided by hemodynamic flow (112-119). The three selectins share similar domain structures consisting of a cytosolic domain, a transmembrane domain, complement-regulatory

(consensus) repeat units (L-selectin = 2, E-selectin = 4, and P-selectin = 9), an EGF-like domain, and a C-type lectin domain (120, 121). E-, P-, and L-selectin bind to glycoconjugates decorated with sialofucosylated carbohydrate moieties such as sialyl Lewis X [sLeX,

(NeuAcα(2,3)Galβ(1,4))[Fucα(1,3)]GlcNAc)], yet only P- and E-selectin have been shown to bind to the sLeX-stereoisomer sialyl Lewis A [sLeA, NeuAcα(2,3)Galβ(1,3))[Fucα(1,4)]GlcNAc]

(Figure 3) (94, 97, 121-123). Selectins presented on endothelial cells mediate rolling adhesion with selectin-ligand bearing cells through selectin/ligand interactions, which engender tensile forces (92). These forces are, in-part, a result of the catch-to-slip bond kinetics characteristic of selectin/ligand binding under shear flow (124-126). Catch bonds sustain an increase in bond 21 lifetime when the external force on the bond is increased (127). In contrast, slip bonds sustain a decrease in bond lifetime as the force on the bond is increased (127). The catch-to-slip kinetics of selectin/ligand interactions allow selectin-ligand bearing cells to roll on endothelial cells at velocities that are 10-100 fold reduced from the bulk hemodynamic velocity, when forces are applied to the selectin/ligand bonds (117, 124, 125). Additional factors that affect selectin/ligand mediated rolling adhesion include the shear stress imparted on the cell by hemodynamic flow, the size of the cell, and the quantity and orientation, i.e., clustering, of selectin ligands (125, 128-

130).

Selectin ligands presented by breast cancer cells are membrane-bound glycolipids and glycoproteins decorated with sialofucosylated carbohydrates such as sLeX and (for P-and E- selectin) sLeA (88, 95, 96). However, E-selectin can also bind ligands decorated with sulfated carbohydrates that are similar to sLeX but have sulfate groups instead of sialic acid (131).

Additionally, E-selectin also binds to fucosylated ligands lacking sialic acid, e.g., fucosylated

Lewis antigen X, yet to achieve a similar E-selectin/ligand binding intensity as sialylated ligands these fucosylated Lewis antigens must be present in greater concentrations (at least one order of magnitude) (131, 132). Terminal fucosylation of E-selectin ligands is requisite for E- selectin/ligand interaction (133). The glycoform sLeX is fucosylated by α-(1,3) fucosyltransferases, which are encoded by FUT3, FUT4, FUT5, FUT6, and FUT7 genes, and sLeA is fucosylated by α-(1,4) fucosyltransferases, which are encoded by FUT3 and FUT5 genes

(84). Note that FUT3 and FUT5 are transcribed to produce α-(1,3/4) fucosyltransferases that can synthesize both sLeX and sLeA (133). Another feature of selectin ligands is that the linkage by which selectin-ligand carbohydrates decorate a scaffolding structure, i.e., serine/threonine (O- linked) or arginine (N-linked), is generally believed to be lineage-specific as leukocytes and leukemic cells express N- and O-linked selectin ligands (90, 97, 134), colon cancer cells highly express O-linked selectin ligands (87), and breast cancer cells express both N-linked selectin 22 ligands, i.e., CD44 variants (89), and O-linked selectin ligands, i.e., MUC-1 (135). Although the drivers that regulate the expression of sialofucosylated glycoconjugates on cancer cells have yet to be discovered, the over-expression of sialofucosylated glycoconjugates is associated with poor patient outcomes (100). This prognostic correlation may be due to the fact that CTCs can use selectin ligands to facilitate CTC trafficking to the endothelium during distal metastasis (78, 85,

136).

Figure 3. Molecular structure of E-selectin and the sialofucosylated carbohydrates that decorate selectin ligands.

Cellular Mechanics and Breast Cancer Metastasis

Common sites of breast cancer metastasis are the lung and the bone marrow (5, 137-139).

Each of these organs supports a labyrinth of blood vessels and capillaries through which CTCs

(diameter = 15 - 20μm), may traverse during metastasis (75, 78, 79, 140). Capillaries are characterized with diameters ranging from 2μm - 8μm; consequently, successful capillary 23 invasion may be achieved only by CTCs with mechanical and rheological properties that tolerate transit through this confined space (141, 142). Additionally, recent works by Stroka et al. (2014) have elucidated that osmotic pressures generated between CTCs and the surrounding fluid can propel the CTCs through tight capillaries (143).

The cellular mechanical properties of breast cancer cells have been shown to change after the EMT, because cytoskeletal reorganization events that facilitate changes in cell morphology also affect the mechanical and rheological properties of the cell (49, 144-146). Specifically, cells with a mesenchymal phenotype are typically characterized as softer or less stiff (lower elastic modulus) than their epithelial counterparts, and softer cells have been shown to be more invasive than comparatively stiff cells (49, 144-149). Although studies have shown that the elastic moduli of cells may be reduced via the EMT, few studies have examined whether cellular mechanical properties are modified as cells transition to an epithelial phenotype via the MET.

Methods for Measuring Mechanical Properties of Single Cells

The mechanical properties of single cells can be measured using several experimental techniques, including optical tweezers (150), atomic force microscopy (AFM) (151), micropipette aspiration (152-156), shear flow assays (157, 158), and microfluidic devices that cause a cellular deformation (143, 159, 160). One of the distinguishing factors between these techniques is the scale or magnitude of measurable force that is generated using each technique (161). Optical tweezers and AFM allow investigators to generate and measure forces on the scale of picoNewtons, for measurement of cellular and sub-cellular mechanical properties. For example, recently Liu et al. (2015) used AFM and applied a scanning force of 0.8-1nN to measure how changes in vimentin expression, which occurred during the EMT, influenced cell stiffness (144).

Additionally, Bambardekar et al. (2015) used optical tweezers to make high-precision measurements on the order of 100pN, to determine how changes in cortical tension at cell-cell interfaces influence cell morphology (162). 24

In contrast to the optical tweezer/trap and AFM experiments that can use picoNewton- scale forces to characterize the mechanical properties of cells without changing the geometry of the cell, micropipette aspiration and microfluidic experiments that examine single-cell mechanical properties typically employ forces that result in cell deformation (143, 155, 163).

Micropipette aspiration experiments have been used extensively to characterize the cellular mechanical properties, e.g., cortical tension and viscosity, of white blood cells (153, 155, 156,

163). Additionally, Mohmmadalipour and Tees (2016), used micropipette aspiration experiments to measure the mechanical properties of stem-like and non-stem-like breast cancer cell lines and determined that non-stem-like breast cancer cells are stiff compared to stem-like breast cancer cells, and this finding is consistent with other works (164). Over the last decade many investigations have employed microfluidic devices for the capture and characterization of cancer cells (77-80, 165, 166). For example, recently Byun et al. (2013) employed a microfluidic device equipped with a suspended microchannel resonator to characterize the deformability and surface friction of cancer cells (160). Additionally, Stroka et al. (2014) used microfluidic channels to reveal that water permeation contributes to cell migration in confined spaces (143).

Modeling Cellular Mechanical Properties of Single Cells

To-date several different approaches have been taken to model the mechanical properties of cells at the single cell level. In general these approaches either (1) treat the cell as a system of interconnected solid components and balance forces or energies exerted on the components within the single cell system to calculate cellular mechanical properties, or (2) consider the cell as an entity composed of a deformable state of matter that deforms according to the material properties that it is assigned (161). For studies that aim to characterize apparent cellular mechanical properties of the whole cell, e.g., apparent viscosity, cells are often modeled as a deformable state of matter (161). 25

Models that consider the cell to be a deformable state of matter include two main parameters, the material properties of the cell and the extent to which cellular deformation may occur. Therefore, the validity or appropriateness of each model is dependent on the actual physical properties of the cell that is modeled and the mechanism of deformation. As with any model the utility of the model, ease of use, implementation, and reproducibility in similar but non-identical experiments need to be carefully considered. For these reasons, a brief review of classical models that have been used to characterize the mechanical properties of mammalian cells as deformable states of matter is provided.

The Cortical-Shell Newtonian Liquid Core Model

In 1984 Evans and Kukan conducted micropipette aspiration experiments in which granulocytes that were largely deformed during aspiration recovered a spherical morphology after a period of recuperation outside of the micropipette (167). This work set the premise for the cortical-shell liquid core model that was developed by Yeung and Evans in 1989 (155). The cortical-shell liquid core model assumes that the cell is composed of a Newtonian fluid with a defined apparent viscosity that is contained within a capsule composed of anisotropic contractile liquid (the cortical-shell). In this model the rate at which the cell is aspirated into the micropipette is proportional to the aspiration pressure, once sufficient pressure (i.e., a threshold pressure) has been applied such that stresses on the capsule overcome the tension and the viscosity of the capsule (155). The viscous properties of the capsule were deemed distinct from those of the enclosed Newtonian fluid based on ultrastructural evidence, which demonstrated that resistance to capsule deformation occurs only after the folds of the lipid bilayer (a feature of leukocytes) composing the capsule are pulled smooth (155, 168). Once the membrane was pulled taught, changes in the surface density of the capsule were resisted by tensile forces that collectively provided the cortical tension, which allowed the granulocyte to recover its spherical shape after exiting the micropipette (155). Using the liquid drop model Evans and Yeung concluded that the 26 rate at which the cell was aspirated into the pipet was inversely proportional to the viscosity of the Newtonian fluid that modeled the inside of the cell (155).

The cortical-shell Newtonian liquid drop is not without its limitations. For example, it failed to explain the rapid elastic recoil that was observed in cells that were held within the micropipette for less than five seconds (155, 167, 169, 170). Additionally, modeling the entirety of cell interior as a Newtonian liquid with a single apparent viscosity oversimplified the cell interior, because in subsequent studies the apparent viscosity and stiffness of the cell interior was shown to vary continuously with deformation (170, 171). Nonetheless, the cortical-shell

Newtonian liquid core model provides a framework for calculation of the mechanical properties of cells that endure large deformations, and because of its simplistic design the liquid core model is still used in contemporary investigations (155, 160).

The Maxwell Liquid Drop Model

The Maxwell liquid drop model was proposed by Dong et al. in 1988 and explained the elastic behavior that cells exhibited as they were initially aspirated into a micropipette or ejected from a micropipette after no more than five seconds of deformation (169). The Maxwell liquid drop model functions on the premise that the interior of the cell is modeled by a Maxwell fluid and surrounded by a pre-stressed cortical shell (169). A Maxwell fluid is one that has both viscous and elastic properties that can be envisioned as fluid elements whose mechanical interactions are equivalent to a linear spring and a linear viscous dashpot in series that act to provide an additive fluid strain (161). Thus it follows that the equations governing the Maxwell liquid drop model include terms for both an elastic and a viscous constant. Initially, the model allowed only for small deformation of spherical cells (169). However, later works that advanced the Maxwell model and allowed for large deformation demonstrated that in order for the model to fit experimental data the constants for elasticity and viscosity must be increased continuously throughout deformation (172). The need for continuous growth of the viscous and elastic terms 27 during large deformations that were held constant for modeling small deformations illustrated that small and large deformations of the cell are fundamentally different and thus require additional modeling considerations.

The Compound Newtonian Liquid Drop Model

Both the Newtonian liquid drop model and the Maxwell liquid drop model fall short of capturing the mechanical and rheological behaviors of a cell for both large and small deformations. In 1991 Dong et al., noted that differences in cell behavior for large and small deformations may be attributed to the inhomogeneous structure of a cell, which consists of a nucleus that has a greater stiffness than the cytoplasm (173, 174). Dong and Hochmuth suggested that the cell is better modeled in three layers including an outermost cortical shell that contains a

Newtonian liquid (modeling the cytoplasm) that surrounds a more viscous Newtonian liquid

(modeling the nucleus and other cytoskeletal components) (154, 171). Kan et al. (1999) added that the central Newtonian fluid should also be surrounded by a cortical shell to model the tension provided by the nuclear envelope (175-177).

In order for the compound Newtonian liquid drop model to model large cell deformations, the relationship governing deformation of the outer cortical layer and fluid was made comparable to the relationship between the cortical tension of the inner cortical shell and inner Newtonian fluid (177). By doing so, the model allows for the inner and out fluid to maintain similar properties for large deformations that were well modeled by the single liquid drop (155,

171, 177). Moreover, when the deformation ratio of the inner cortex and fluid is comparable to that of the outer cortex and fluid the compound liquid drop model can be used to explain the apparent elastic behavior of the cell, because with a short deformation period (less than five seconds) the inner cortex and fluid does not have time to deform and allows for the outer layer to rapidly recover its spherical form due to the flow field around the inner cortex (171, 175-177). 28

Although the compound drop model benefits from the intricacy of multiple layers that permit explanation of how a cell can appear to have the properties of a Newtonian fluid or an elastic substance, the complexity of the model results in an exponentially greater number of causes for observed cell behaviors. Thus Lim et al. (2006) suggested that separate investigations of both cytoplasmic and nucleic cellular components are needed in order to determine the properties of the inner and outer layers of the compound cell (161). Notably, studies on isolated nuclei and whole cells indicate that the nucleus is more viscous and has a greater stiffness (elastic modulus) than the cytosol. These studies also show that the nuclear envelope has a greater cortical tension than the plasma membrane (163, 178-180).

Viscoelastic Models

The foundation for viscoelastic-cell models includes simple linear elastic solutions, which have independently been used to model the mechanics of cells (181). However, linear elastic models are not dependent on time and thus do not allow for computation of rate changes that are commonplace in cell mechanics, e.g., loading rate (161). Thus, viscoelastic models are the focus of this section. In 1981 Schmid-Schönbein et al. introduced a viscoelastic model for passive leukocytes (i.e., inactivated leukocytes) in which the cells were modeled as a solid homogeneous viscoelastic entity (182). In this publication the authors noted that the model provides only an approximation of the mechanical properties of the cell, because the model demands highly idealized cellular characteristics including a smooth surface and homogeneous composition (182). The justification for assuming that the cell can be modeled as a homogeneous entity was based on the observation that when leukocytes were aspirated into micropipettes at different locations on the cell surface similar deformations were observed (182). Interestingly, another claim of the model was that neglecting the presence of the membrane does not introduce large errors since the leukocyte membrane was only stressed once the folds of the membrane were pulled smooth (182). Note that this claim is only valid for small deformations, and 29 application of this claim to large deformations directly conflicts with the Newtonian liquid drop model, which demonstrated that cortical tension allows leukocytes to recover a spherical geometry after large deformations (155, 182). Thus, like the Maxwell liquid drop model, the viscoelastic model of Schmid-Schönbein et al. (1981) is best suited to approximate small deformations of the cell but overestimated model parameters for large deformations (169, 182).

A subsequent viscoelastic model developed by Sato et al. (1996) built upon the foundation laid by the purely elastic model of Theret et al. (1988), in which the cell was modeled as a homogeneous and incompressible solid (181, 183). This model required the micropipette radius to be small in comparison to the cell radius, such that the cortical layer of cytoskeletal elements that was postulated to provide most of the resistance to deformation could be modeled as a plate with the same thickness as the cortical layer, rather than a spherical surface (181, 183).

Furthermore, this viscoelastic model assumed that stresses in the cortical layer decayed quickly below the cell surface such that stresses were deteriorated rapidly as the distance from the cell surface (moving toward the center of the cell) was increased.

By making the aforementioned assumptions Sato et al. (1996) approximated the geometry at the micropipette and cell interface (e.g., the cortical layer) as a half space, using the approach that was pioneered by Theret et al. (1988) for analysis of the simple linear elastic deformation of a cell. Using the half space model, aspiration of the cell into the micropipette was idealized as a process involving a tensile stress that was applied over a circular region (181, 183). This stress represented the reduced pressure within the micropipette that is in equilibrium with the stress distribution in an annular zone, i.e., the contact region between the cell and the micropipette

(181). The aspirated region of the cell was then modeled to be square ended and rigid based on the assumption that in the annual zone the normal vector component of displacement vanished.

As a consequence of this assumption, only tangential traction forces acted on the surface of the 30 cell, thus interactions between the cell and the wall of the micropipette were modeled as being perfectly lubricated (i.e., frictionless) (181, 183).

Interestingly, analyses of the experiments performed using the aforementioned models and subsequent investigations revealed that white blood cells are well-represented by the

Newtonian-liquid cortical-shell model (156, 163, 169, 173, 184), yet in comparison adherent cells, such as endothelial cells and fibroblasts are better-represented by viscoelastic models (85,

185-187). Cytoskeletal properties of white blood cells and adherent cells contribute to cellular mechanics and are inherently different, thus these differences in the cytoskeletal frameworks of adherent and non-adherent cells may underlie why different types of cells are better modeled as viscoelastic solids or Newtonian fluids (180, 188-192). Nonetheless, investigations that aim simply to compare the apparent cellular mechanical properties of cancer cells have demonstrated that comparisons between the mechanical properties of adherent cells can be made when the cells are modeled as a Newtonian-liquids encased within cortical shells (142, 160).

Hypothesis and Specific Aims

Hypothesis: The E-selectin ligand activities and mechanical properties of breast cancer cells are modified by the EMT and MET, respectively.

Specific Aim 1: Determine if breast cancer cells of a mesenchymal or epithelial phenotype have differential levels of E-selectin ligand activity, after enduring the EMT or MET

(Chapter 2 & 3).

Specific Aim 2: Investigate whether prolonged adhesion to E-selectin affects the growth behaviors of breast cancer cells that are commonly found in bone marrow metastasis, i.e., breast cancer cells that express the CD44highCD24low stem-like biomarker profile (Chapter 4).

Specific Aim 3: Determine if the mesenchymal-to-epithelial transition affects the apparent mechanical properties of breast cancer cells. (Chapter 5). 31

CHAPTER 2: CHARACTERIZING THE E-SELECTIN LIGAND ACTIVITIES OF BREAST

CANCER CELLS

Introduction

The metastasis of breast cancer precipitously reduces a patients chances for survival, especially if metastasis occurs prior to diagnosis (74). Breast cancer cells are primed to metastasize through the epithelial-to-mesenchymal transition (EMT) (44). During the EMT immunogenic cytokines in the tumor microenvironment signal for breast cancer cells to upregulate the expression of Snail and Twist transcription factors, resulting in decreased expression of cell-cell adhesion molecules such as E-cadherin and increased expression of vimentin and N-cadherin (39, 41, 44, 193).

Due to changes in E-cadherin, N-cadherin, and vimentin expression that occur during the

EMT, these proteins are often used as EMT biomarkers to distinguish between tissues of epithelial or mesenchymal phenotypes. For example, Stoyianni et al. (2012) used E-cadherin, N- cadherin, and vimentin as markers of the EMT in a study that employed IHC to elucidate that the

EMT is infrequently observed in cancers of unknown primary origin, but is associated with poor prognosis in cases that present markers of the EMT (194). Additionally Nijkamp et al. (2011) used immunohistochemical analysis of E-cadherin and vimentin to shown that low expression of

E-cadherin and elevated expression of vimentin (a mesenchymal EMT-phenotype) was correlative with a high risk of metastasis for head and neck squamous cell carcinoma (195).

Furthermore, immunohistochemical analysis of E-cadherin and vimentin expression was used by

Afrem (2014) in a study of oral cancer tissues, which revealed that elevated vimentin expression and reduced E-cadherin expression were associated with patterns of tumor invasion and increasing disease stages (196).

By modifying the protein expression of breast cancer cells, the EMT produces highly invasive and motile tumor cells with a mesenchymal phenotype (43, 44, 46). In addition, cells 32 that undergo the EMT can garner stem-like characteristics including the capacity to self-renew, differentiate, and resist cancer therapies (53, 197). Recently, distinct subtypes of stem-like cancer cells were discovered to be a result of the EMT and the mesenchymal-to-epithelial transition

(MET), which causes cells with a mesenchymal phenotype to acquire an epithelial phenotype

(61). These epithelial and mesenchymal stem-like breast cancer cells resembled the naturally occurring luminal and basal stem cells of the breast, and were distinguished by the expression of

CD24, CD44, and aldehyde dehydrogenase (ALDH) activity (61-63). More specifically, the EMT generated CD24lowCD44highALDHlow mesenchymal-stem-like cells that were quiescent and highly invasive compared to the CD24highCD44highALDHhigh epithelial-stem-like cells produced by the

MET, which proliferated more rapidly than the mesenchymal-stem-like cells (61).

Early breast cancer metastases in the bone marrow are populated with CD24lowCD44high stem-like breast cancer cells (198, 199). Although it is probable that metastatic stem-like cancer cells are generated via the EMT at the primary tumor, immunogenic cytokines, e.g., TGF-β, that are present in the bone marrow may trigger the EMT and cause non-stem-like metastatic cancer cells to become stem-like once they enter the bone marrow (200). Insight as to whether stem-like or non-stem-like breast cancer cells are trafficked to the bone marrow from the primary site in

EMT-mediated mechanisms of breast cancer metastasis may be gained by studying the intermediary steps of metastasis in which tumor cells disseminate to the secondary site.

Trafficking of CTCs to the secondary site is postulated to resemble leukocyte extravasation, in which the initial attachment of CTCs onto the endothelium is facilitated by cell- surface sialofucosylated glycoconjugates that bind E-selectin molecules on activated endothelial cells lining the blood vessel wall (81-89). E-selectin/ligand interactions tether CTCs to the endothelium and mediate rolling adhesion, in which E-selectin/ligand bonds are rapidly formed and broken as the CTCs are challenged to remain on the endothelium by forces generated from hemodynamic flow (90-92). Owing to their potential role in CTC trafficking, E-selectin ligands, 33 e.g., ligands decorated with sialyl Lewis X (sLeX) and sialyl Lewis A (sLeA), have been implicated as mediators of metastasis (85). However it has yet to be shown whether the EMT affects the expression of selectin ligands on the surface of breast cancer cells.

Thus herein the effects of the EMT on the E-selectin ligand activities of breast cancer cells were investigated. First, the functional E-selectin ligand activities of tissues from 110 cases of breast cancer that were characterized with an epithelial or mesenchymal phenotype using immunohistochemical analysis of EMT-biomarkers, i.e., E-cadherin, N-cadherin, and vimentin

(epithelial phenotype: E-cadherinhigh/ N-cadherinlow/ vimentinlow and mesenchymal phenotype: E- cadherinlow/ N-cadherinhigh/ vimentinhigh) were quantified using E-selectin microsphere adhesion assays. Next, the functional E-selectin ligand activities of breast cancer cell lines with epithelial

(BT-20 and MDA-MB-468) or mesenchymal (Hs578T and MDA-MB-231) phenotypes were determined using shear flow attachment assays. Finally, the functional E-selectin ligand activities of cells that pursued the EMT or MET via ectopic expression or shRNA interference of Snail or

Twist transcription factors were determined using shear flow detachment assays that engendered physiological flow conditions of post-capillary venules at the site of metastasis, e.g., bone marrow (140, 199, 201). Flow cytometry was used to characterize the expression of sialofucosylated glycans on breast cancer cells. Furthermore, qRT-PCR was employed to quantify the relative changes in gene expression for α-(1,3)-fucosyltransferases (FUT3, FUT4, FUT5,

FUT6, FUT7) that synthesize sLeX and α-(1,4)-fucosyltransferases (FUT3 and FUT5) that synthesize sLeA, in MDA-MB-468 cells or Hs578T cells, which were induced to undergo the

EMT or MET, respectively.

Materials and Methods

Antibodies and Recombinant Proteins

Monoclonal antibodies (mAbs) recognizing sLeA (KM231) or sLeX (KM93) were purchased from EMD Millipore (Billerica, MA). Monoclonal antibodies recognizing sLeX 34

(CSLEX1), GD2, phycoerythrin-conjugated streptavidin (PE-SA), PE-conjugated anti-human

CD62E (E-selectin), PE-conjugated anti-mIgG1 isotype control, SA-horse radish peroxidase

(HRP), mIgM, mIgG, and rIgM were purchased from BD Biosciences (San Jose, CA). Anti- vimentin mAb (V9) was purchased from ThermoFisher Scientific (Waltham, MA). Anti-E- cadherin mAb (G10) was obtained from Santa Cruz Biotechnology (Santa Cruz, CA), and anti-N- cadherin mAb was purchased from R&D Systems (Minneapolis, MN). Recombinant human or murine E-selectin hFc chimeric protein was purchased from R&D Systems. Human immunoglobulin-G from human placenta (hIgG) and biotinylated cholera toxin were purchased from Sigma (St. Louis, MO). Other secondary antibodies included goat anti-mouse IgM FITC and human adsorbed anti-mouse IgG-biotin (Southern Biotech, Birmingham, AL), goat anti-mouse

IgG AlexaFluor 488, and goat anti-rat IgM AlexaFluor 488 (Life Technologies, Carlsbad, CA).

Tissue Sample Preparation and Cell Culture

Anonymous de-identified tissue microarrays (TMAs) were purchased from USBiomax

(Rockville, MD). Tissues were deparaffinized and rehydrated as previously described (202, 203).

Briefly, TMAs were incubated at 60oC for 90 minutes. Next, the TMAs were immersed in a series of xylenes, graded alcohols, and DPBS without calcium or magnesium (DPBS-). Antigen retrieval was performed by submerging the TMAs in 10mM citrate buffer (pH 6.0) supplemented with

0.05% Tween 20 for 5 minutes at 95oC. While submerged in antigen retrieval solution, the TMAs were cooled at room temperature for 10 minutes and then placed on ice for 10 minutes. Finally, the TMAs were washed three times in DPBS- in preparation for tissue analysis (204, 205).

Cell lines including BT-20, MDA-MB-468, MDA-MB-231, and Hs578T triple negative breast cancer cell lines were purchased from the American Type Culture Collection (ATCC,

Manassas, VA). BT-20 cells were cultured in MEM/EBSS with L-glutamine (GE Healthcare Life

Sciences, Pittsburg, PA) supplemented with non-essential amino acids (Corning Life Sciences,

Tewksbury, MA), penicillin-streptomycin (pen-strep, GE Healthcare Life Sciences), and 10% 35 fetal bovine serum (FBS). MDA-MB-468, MDA-MB-231, and Hs578T breast cancer cells were cultured in DMEM (GE Healthcare Life Sciences) supplemented with pen-strep and 10% FBS.

Human mammary epithelial (HMLE) cells and HMLE cells with ectopic expression of Snail or

Twist transcription factors were a generous gift from Dr. Robert A. Weinberg (Massachusetts

Institute of Technology, Whitehead Institute, Cambridge, MA). HMLE cell lines were propagated in MEBM (Lonza, Allendale, NJ) supplemented with a MEGM Bulletkit (Lonza).

Flow Cytometry

Flow cytometry was performed as previously described (89). Briefly, cells were harvested in DPBS- supplemented with 5mM ethylenediaminetetraacetic acid (EDTA). Then the cells were pelleted (centrifuged) and washed in DPBS with calcium and magnesium (DPBS+).

Antibodies were prepared in 0.1% bovine serum albumin (BSA) DPBS+. 1 x 105 cells were incubated with 10μl of primary antibody for 30 minutes on ice. Cells were washed three times with 0.1% BSA DPBS+. Subsequently, cells were incubated in secondary antibody for 30 minutes on ice. Cells were then washed once in 0.1% BSA DPBS+ and washed twice in DPBS+ before being analyzed with a FACSAria Special Order Research Product (SORP) cytometer sorter.

ALDEFLUOR assays (Stem Cell Technologies, Cambridge, MA) were optimized using the manufacturer’s recommendations. Briefly, 4 x 105 cells were suspended in 1mL of

ALDEFLUOR assay buffer. Immediately after 5 μl of ALDEFLUOR reagent was added to the cells, 500 μl of the cell suspension was transferred to a microcentrifuge tube containing 5 μl of

N,N-diethylaminobenzaldehyde (DEAB, control). Cells were incubated at 37oC for 30 minutes.

For multiplex staining with CD24 and CD44, the ALDEFLUOR assay buffer was supplemented with 0.1% BSA. Subsequently, 5 μl of PE-anti-CD24, PE-mIgG2a, AlexaFluor700-antiCD44,

AlexaFluor700-antimouse-IgG2b, PE-anti-CD24/AlexaFluor-anti-CD44, or PE- mIgG2a/AlexaFluor700-antimouse-IgG2b were added to the cells that had been assayed with 36

ALDEFLUOR reagent or ALDEFLUOR reagent with DEAB. Cells were washed three times in

ALDEFLUOR buffer and analyzed on a FACSAria SORP cytometer sorter.

Plasmid Isolation

Plasmids for retroviral constructs were obtained through Addgene (Cambridge, MA).

Plasmids included the envelope plasmid pCMV-VSV-G (Addgene plasmid #8454, deposited by

R.A. Weinberg (206)), the packing plasmid pUMVC (Addgene plasmid # 8449, deposited by

R.A. Weinberg (206)), pBabe-puro-Snail (Addgene plasmid # 23347, deposited by R.A.

Weinberg (72)), pBabe-puro-mTwist (Addgene plasmid # 1783, deposited by R.A. Weinberg

(44)), and pBabe-puro (Addgene plasmid # 1764, deposited by Hartmut Land & Jay Morgenstern

& R.A. Weinberg (207)). LB broth Miller (granulated), kanamycin sulfate, and ampicillin sodium salt were purchased from Fisher Scientific (Waltham, MA). LB agar was purchased from

Invitrogen (Waltham, MA) and LB agar plates supplemented with ampicillin were purchased from Sigma. D5Hα2 competent Escherichia coli with ampicillin (pBabe and pCMV-VSV-G) or kanamycin (pUMVC) resistance were cultured for 18 hours at 37oC in an incubating orbital shaker. Plasmids were extracted using a GeneJet Maxiprep kit (ThermoFisher Scientific,

Waltham, MA) according to the manufacturer’s protocol.

Retroviral Transduction and RNA Interference

One day before transfection 1 x 106 HEK 293FT cells (ThermoFisher Scientific) were plated in 6 cm dishes using Opti-MEM media (ThermoFisher Scientific) and cultured overnight.

To generate the retrovirus 1 μg of retroviral pBabe-puro, pBabe-puro-Snail, or pBabe-puro- mTwist, and 1 μg of an 8:1 mixture of pUMVC and pCMV-VSV-G plasmid were added to Opti-

MEM media (206). Subsequently, 6 μl of FuGENE-6 (Roche, Indianapolis, IN) was mixed with the plasmids in polypropylene microcentrifuge tubes and the mixture was allowed to incubate for

30 minutes at room temperature (206). Next, the FuGENE mixture was added to the HEK 293FT cells, which were then incubated overnight. The following day media was removed from the HEK 37

293FT cells and replaced with fresh Opti-MEM media, and MDA-MB-468 cells were plated in 6- well plates. Retroviral constructs were collected from the HEK 293FT cells using a 10 ml syringe and filtered through a 0.45μm syringe filter (EMD Millipore). Media was aspirated off the target

MDA-MB-468 cells and replaced with Opti-MEM media containing retroviral constructs with 8

μg/ml polybrene (Sigma). The MDA-MB-468 cells were infected overnight, and the infection was repeated the next day. After 72 hours infected cells were selected using puromycin.

Short hairpin RNA (shRNA) for stable knockdown of Snail or Twist transcription factors and control pLOK.1 shRNA lentiviral particles were purchased from Sigma. Five clones for shRNA targeting SNAI1 and six different clones targeting TWIST1 were tested. The clone that resulted in SNAI1 knockdown was 5’-CCGGGCAAATACTGCAACAAGGAATCTCGAG

ATTCCTTGTTGCAGTATTTGCTTTTTTG-3’ (TRCN0000454083), and the clone that resulted in knockdown of TWIST1 was 5’-CCGGAGCTGAGCAAGATTCAGACCCCTCGAG

GGGTCTGAATCTTGCTCAGCTTTTTTG-3’ (TRCN0000367866). Hs578T cells were intolerant of polybrene and were infected with the shRNA lentiviral particles in Opti-MEM media using TransDux (System Biosciences, Palo Alto, California). After 72 hours infected cells were selected using puromycin.

Reverse Transcription and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

Total RNA was isolated from cells using an RNeasy plus mini kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions. Briefly, cells were lysed and genomic DNA was sequestered for disposal using a gDNA spin column. Subsequently, total RNA was captured and then eluted from an RNeasy mini spin column using RNase free water. RNA purity and concentration were quantified using a NanoVue Plus (GE Healthcare Biosciences, Piscataway,

NJ). Complementary DNA (cDNA) was transcribed from RNA using a high capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using a Step One Plus Real-Time PCR 38 instrument using cDNA, forward and reverse primers purchased from ThermoFisher Scientific

(Waltham, MA) or Integrated DNA Technologies (Coralville, IA), and SYBR green master mix

(Applied Biosystems).

Relative changes in gene expression were normalized to housekeeping genes including glyceraldehyde-3-phosphatedehydrogenase (GAPDH) and Ribosomal Protein L13a (RPL13A)

(208, 209). Other genes including β-actin (ACTB) and Pumilio RNA Binding Family Member 1

(PUM1) were evaluated as potential housekeeping genes but were not used in calculations of relative gene expression because gene expression was not consistent between cell lines or independent experiments. Primers used in this study are provided in Table A1 and Table A2.

Parallel Plate Flow Chamber Shear Flow Assays

Shear flow attachment and detachment assays were conducted using a parallel plate flow chamber (Glycotech, Gaithersburg, MD) as previously described (86, 87). Briefly, recombinant human E-selectin hFc chimeric protein was plated on 35mm tissue culture plates using flexiPerm micro 12 gaskets (Starstedt, Numbrecht, Germany) and allowed to incubate overnight in a humidified incubator at 37oC. On the day of the experiment, plates were blocked in 1%

BSA/DPBS+ for one hour on ice. Breast cancer cells were harvested using 5mM EDTA/DPBS-, pelleted, suspended in a reservoir at a concentration of 5x105 cells per ml, and perfused over substrates in 1% BSA DPBS+ for two minutes at room temperature.

Throughout the shear flow attachment assay, the number of cells that adhered to the E- selectin substrate was quantified. To determine whether cell adhesion occurred via E- selectin/ligand interactions, cells were perfused over E-selectin substrate in buffer supplemented with EDTA, because EDTA chelates divalent cations, e.g., calcium, that are necessary for E- selectin/ligand bond formation (121). For detachment assays cells were brought into the field of view and allowed to settle onto the plate for approximately 10 minutes. Subsequently, the number of cells that settled onto the plate was quantified. Next, flow was initiated and the wall shear 39 stress was increased every two minutes to detach cells using the set levels of wall shear stress.

After two minutes of flow the number of cells remaining on the plate were quantified for each shear stress.

Dynamic Biochemical Tissue Analysis and E-Selectin Microsphere Attachment Assays

Polystyrene microspheres measuring 10μm in diameter were purchased from Bangs

Laboratories (Fisher, IN). Microspheres were washed with Tris balanced saline (TBS, pH 4.0) and TBS (pH 8.2) three times. Subsequently, microspheres were incubated with recombinant murine E-selectin hFc chimera or hIgG in TBS (pH 8.2) for one hour at room temperature.

Polystyrene microspheres were incubated with recombinant murine E-selectin hFc chimera or hIgG in TBS (pH 8.2) for one hour at room temperature. Microspheres were then twice washed in

1% BSA DPBS+ and incubated in 1% BSA DPBS+ for one hour at room temperature. Flow cytometric analysis was used to determine that the microspheres were functionalized with E- selectin or hIgG (negative control, Figure 4).

Figure 4. Microspheres were prepared for DBTA and analyzed in flow cytometric analysis. A) E- selectin microspheres targeted with anti-E-selectin (solid line) or anti-mouse IgG (dashed and shaded), B) human IgG (hIgG) microspheres targeted with anti-hIgG (solid line) or anti-mouse IgG (dashed and shaded).

The functionality of the E-selectin microspheres, e.g., ability to mediate adhesion on tissues, was determined using dynamic biochemical tissue analysis (DBTA), which provides 40 highly tunable and well-controlled shear flow parameters for assaying tissues with functionalized particles that are observed with a microscope in real-time (Figure 5).

Figure 5. Experimental set-up and workflow for dynamic biochemical tissue analysis (DBTA). Microspheres are suspended in a reservoir of buffered solution and perfused over tissues in a parallel plate flow chamber. A rubber gasket elevates the flow chamber from mounted tissues sections and defines the flow channel height. The flow chamber is vacuum-sealed to prevent leakage of the microsphere suspension. A syringe pump draws the microsphere suspension from the reservoir at a user-defined flow rate. A video-microscopy system consisting of a camera- equipped inverted microscope and a computer is used to observe and record the interaction of microspheres with the tissue sections.

Briefly, deparaffinized and rehydrated tissues were subjected to antigen retrieval and blocked in 1% BSA/ 1% FBS / DPBS+ for one hour at room temperature. TMAs were assayed in

DBTA using a rectangular parallel plate flow chamber that was vacuum sealed to the slides.

Microspheres were suspended in a reservoir at a concentration of 5x105 microspheres/ml in 1%

BSA DPBS+. Next, the microspheres were perfused over tissue slides at 0.5 dyn/cm2 using a syringe pump. Real-time adhesion events were recorded using an inverted microscope and a CCD camera, and the number of microspheres that adhered to tissues was quantified after two minutes of flow. DBTA demonstrated that E-selectin microspheres adhered to breast cancer tissues 41 presenting E-selectin ligands in significantly greater numbers than the hIgG control microspheres

(Figure 6).

For microsphere attachment assays TMAs were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked in 1%FBS / 1% BSA / DPBS+ for one hour at room temperature.

Next, E-selectin and hIgG microspheres were separately allowed to settle onto the TMAs.

Subsequently, the TMAs were washed three times with 1% BSA / DPBS+ and the number of microspheres that remained adhered to the tissues was quantified.

Figure 6. DBTA detected functional E-selectin ligands presented on breast cancer tissues. ANOVA and post-hoc Tukey’s multiple comparison test. *P<0.05 relative to hIgG and #P<0.05 relative to the number of E-selectin microspheres that adhered to the tissue sections via rolling adhesion and firm adhesion. n = 2 technical replicates (serial sections) in 2 independent experiments. Data are mean ± SE. Wall shear stress = 0.5 dyn/cm2.

Immunohistochemistry

Deparaffinized and rehydrated tissues were subjected to antigen retrieval and then

- incubated with 3% H2O2 DPBS blocking reagent for 15 minutes. Subsequently, tissues were washed in DPBS- for five minutes and blocked with 1% BSA / DPBS+ for one hour. Next, tissues were incubated with primary antibodies overnight at 4oC in a humidified container. Samples were 42 then washed three times with 0.05% tween 20 / DPBS- for five minutes (each), followed by a five minute wash in DPBS-. Endogenous biotin was blocked using a streptavidin/biotin blocking kit

(Vector labs, Burlingame, CA). Tissues were then washed three times with DPBS-. Subsequently, the tissue slides were incubated in biotinylated secondary antibody at room temperature for 30 minutes. Again tissue slides were washed three times with DPBS- / 0.05% tween 20 for five minutes (each), followed by a five minute wash in DPBS-. Streptavidin-HRP constructs were applied to the tissue for 30 minutes at room temperature. Tissue slides were washed three times with DPBS- / 0.05% tween 20 for five minutes (each), followed by a five minute wash in DPBS-, and then the slides were incubated in DAB Quanto (Thermo Scientific) for a maximum of three minutes. Next, the slides were rinsed with distilled water and twice washed in distilled water for two minutes (each). The samples were then washed in tap water for one minute and submerged in hematoxylin (Ricca Chemical Company, Arlington, TX) for twenty seconds. Slides were then washed in tap water for five minutes and then in distilled water for one minute. The TMA slides were then dehydrated using graded alcohols and xylenes and mounted with Permount (Fisher

Scientific).

TMAs were imaged using a Nuance FX multispectral imaging camera (PerkinElmer,

Waltham, MA) using bright field settings. Images were analyzed using InForm cell analysis software (PerkinElmer). Threshold levels of detection of E-cadherin, N-cadherin, and vimentin were determined by scaling the sensitivity of detection to the maximum optical density detected for each targeted protein. Optical density measurements were determined from cytoplasmic staining to determine threshold levels of detection. Cytoplasmic regions were segmented from nuclei using optical density measurements and InForm cell segmentation analysis.

E-Selectin Immunoprecipitation

E-selectin ligands were immunoprecipitated from breast cancer cells as previously described (89). Briefly, breast cancer cells were harvested using 5mM EDTA/DPBS-, counted 43 using a hemocytometer, and washed three times in ice cold DPBS- (pH 8.0). Subsequently, cells were suspended at a concentration of 2.5x107 cells/ml in DPBS- (pH 8.0). Cells were centrifuged for two minutes and DPBS- was discarded. Next cells were suspended at a concentration 2.5x107 cells/ml in ice cold 10mM Sulfo-NHS-Biotin / DPBS- and incubated for 30 minutes at 4oC using constant end-over-end agitation. To stop the biotinylation reaction cells were washed three times with 100mM glycine / DPBS- (pH 8.0).

Biotinylated cells were lysed in whole cell lysis buffer formulated as an aqueous solution of 150mM NaCl (Fisher Scientific), 0.5mM Tris Base (Fisher Scientific), 0.02% sodium azide

(Sigma-Aldrich), 1% Triton-X (Fisher Scientific), protease inhibitor (Roche), but without EDTA.

Cells were lysed at a concentration of 1x107 cells/ml for no less than one hour at 4oC with constant end-over-end agitation and centrifuged at 104 x g for 30 minutes at 4oC. Solid undigested fragments of the whole cell lysate were not utilized for immunoprecipitation. ProMag protein G microspheres were purchased from Bangs Laboratories. ProMag protein G microspheres were washed twice in TBS (pH 4.0) and then TBS (8.0). Subsequently, the microspheres were blocked in 1% BSA / DPBS+ for 30 minutes at room temperature with end-over-end agitation. Then the blocking solution was removed and microspheres were incubated in 20 μg/ml of recombinant murine E-selectin hFc chimeric protein (rmE-selectin, R&D Systems) or hIgG isotype control

(Sigma) overnight at 4oC with constant end-over-end agitation. Next, microspheres were washed three times in whole cell lysis buffer.

In preparation for immunoprecipitation whole cell lysates were pre-cleared in an overnight incubation with hIgG microspheres at 4oC with constant end-over-end agitation.

Subsequently, hIgG microspheres were removed from the whole cell lysate, rmE-selectin microspheres were added to the whole cell lysate, and rmE-selectin microspheres were incubated in whole cell lysate overnight at 4oC with constant end-over-end agitation. After incubation in whole cell lysate rmE-selectin microspheres were washed three times in lysis buffer. E-selectin 44 ligands were eluted by adding 10mM EDTA to the lysis buffer solution for a 30 minute incubation at room temperature with end-over-end agitation.

SDS-PAGE and Western Blotting

Whole cell lysates were diluted in Laemmli reducing buffer, heated at 95oC for 5 minutes, resolved using SDS-PAGE with 4-15% Tris-HCl precast gels (BioRad Laboratories,

Hercules, CA), and blotted onto polyvinylidene difluoride membranes (PVDF, BioRad) using a

Trans-Blot Turbo Plus semi-dry transfer system (87, 111, 210). Prior to staining, membranes were blocked in FBS or 1%BSA / TBS overnight at 4oC with constant agitation. Detection of E- selectin ligands and other target proteins was achieved by optimizing the staining protocol for each target protein and secondary antibody. In general, membranes were subjected to subsequent incubations in primary and secondary antibodies for four hours at room temperature with constant agitation. Incubations in primary and secondary antibodies were preceded by washing the membrane three times in 0.1% Tween20 / TBS. Membranes stained with HRP-conjugated secondary antibody were developed using Lumi-light western blotting substrate (Roche), whereas membranes stained with AP-conjugated secondary antibodies were developed with Immune-Star

ECL western blotting substrate (Bio-Rad). Western blots were imaged with a ChemiDoc XRS imaging system using Quantity One software (Bio-Rad).

Statistics

Statistical significance (P < 0.05) between the controls and experiment samples were tested using a paired Student’s t-test, unless otherwise indicated. Significant differences for multiple samples were determined using ANOVA coupled with a post-hoc Tukey’s multiple comparison test (P < 0.05). 45

Results

The E-selectin Ligand Activities of Breast Cancer Cells in Human Tissue Samples are Inversely

Correlated with Markers of the EMT

Prior investigations have established that breast cancer cells decrease the expression of E- cadherin but increase the expression vimentin and N-cadherin, during the EMT (44, 53, 211). To investigate whether the EMT affects the functional E-selectin ligand activities of breast cancer cells, tissues from 110 cases of breast cancer were examined with E-selectin microsphere adhesion assays and characterized for the expression of EMT biomarkers including E-cadherin,

N-cadherin, and vimentin using immunohistochemistry. Quantification of E-selectin microsphere adhesion revealed that breast cancer cells with high expression of E-cadherin and low expression of N-cadherin and vimentin exhibited significantly greater levels of functional E-selectin ligand activity compared to breast cancer cells that highly expressed vimentin or N-cadherin but had low expression of E-cadherin (Figure 7). Additionally, significantly fewer hIgG microspheres

(negative control) adhered to the tissues compared to E-selectin microspheres (Figure 7). E- cadherin is highly expressed in cells with an epithelial phenotype, yet the expression of E- cadherin is reduced as cells undergo the EMT and increase the expression of vimentin and N- cadherin to transition to mesenchymal phenotype. Thus, the tissue analyses indicate that breast cancer cells with an epithelial phenotype possess significantly greater levels of E-selectin ligand activity than breast cancer cells with a mesenchymal phenotype, and show that the E-selectin ligand activities of breast cancer cells are inversely correlated with biomarkers of the EMT

(Figure 7).

Figure 7. Breast cancer cells with an epithelial phenotype demonstrate greater levels of E-selectin ligand activity than breast cancer cells with a mesenchymal phenotype. A) The percentage of E- selectin microspheres that adhered to breast cancer cells that expressed detectable levels of E- cadherin but not N-cadherin or vimentin, was significantly greater than the number of E-selectin microspheres that adhered to tissues that expressed detectable levels N-cadherin or vimentin but not E-cadherin. Immunohistochemical tissue assays of B) E-cadherin, C) vimentin, and D) N- cadherin were analyzed using InForm tissue analysis software. Green overlays indicate where optical densities of the assayed tissue met the threshold for detection, and blue overlays indicate regions in which optical densities were below the threshold of detection. For greater visibility, red overlays illustrate where E-selectin microspheres adhered to tissues. Tissues are representative of a data set that included 110 cases of breast cancer tissues. Scale bar = 100 microns. Data are mean ± SEM. ANOVA and post hoc Tukey’s test, *P<0.05.

Breast Cancer Cell Lines with Epithelial Phenotypes Have Greater Functional E-selectin Ligand

Activities than Breast Cancer Cell Lines with Mesenchymal Phenotypes

Compared to other subtypes of breast cancer (Figure 1), triple negative breast cancers

(TNBCs) are aggressive, invasive, difficult to treat, and have a high metastatic potential (19).

However, it has yet to be established whether the sialofucosylated carbohydrate sLeX that has been identified in some E-selectin ligands and promotes the metastasis of hormone dependent breast cancers (82) may be influential to the metastasis of TNBC. In order to investigate whether there is a relationship between the epithelial-to-mesenchymal transition (EMT) and the functional 47

E-selectin ligand activities of TNBCs, triple negative BT-20 and MDA-MB-468 TNBC cell lines that have an epithelial phenotype (89) and Hs578T and MDA-MB-231 TNBC cell lines that have a mesenchymal phenotype (20, 89, 212) were assayed for functional E-selectin ligand activity.

The functional E-selectin ligand activities of TNBCs with epithelial and mesenchymal phenotypes were characterized using shear flow adhesion assays. To model physiological fluid flow in post-capillary venules (213) during E-selectin/ligand mediated CTC trafficking, breast cancer cells were delivered to E-selectin substrates using a parallel-plate flow chamber at a wall shear stress of 0.50 dyn/cm2. Among all four of the TNBC cell lines, adhesion to E-selectin was significantly reduced in the presence of EDTA, which chelates calcium and thereby prevents calcium-dependent E-selectin/ligand mediated interactions (Figure 5A). Additionally, significantly greater numbers of BT-20 cells and MDA-MB-468 cells adhered to E-selectin compared to the number of MDA-MB-231 cells and Hs578T cells that adhered to E-selectin, further demonstrating that breast cancer cells with an epithelial phenotype have greater functional

E-selectin ligand activities than breast cancer cells with a mesenchymal phenotype (Figure 8A).

Flow cytometric analysis was employed to characterize the TNBC cells’ expression of sLeX, sLeA, and HECA-452 sialofucosylated carbohydrate decorations that have previously been identified in E-selectin ligands (95). Of the TNBC cell lines, the BT-20 and MDA-MB-468 breast cancer cell lines had the highest levels of expression sLeX and HECA-452 antigens, yet compared to the MDA-MB-468 and Hs578T cells, the BT-20 and MDA-MB-231 cells expressed greater levels of sLeA (Figure 8B). Collectively, the flow cytometry and functional E-selectin ligand activity assays show that sLeX and HECA-452 antigens may be potential drivers of E- selectin/ligand mediated CTC-trafficking during the metastasis of TNBC cells with an epithelial phenotype and to a far lesser extent TNBC cells with a mesenchymal phenotype. 48

Figure 8. Breast cancer cell lines with an epithelial phenotype have greater functional E-selectin ligand activities than breast cancer cells with a mesenchymal phenotype. A) The functional E- selectin ligand activities of breast cancer cell lines were characterized using parallel plate flow chamber shear flow attachment assays. The number of cells that adhered to E-selectin substrate after two minutes of flow at 0.50 dyn/cm2 was quantified. Significantly greater numbers of BT-20 and MDA-MB-468 cells with epithelial phenotypes adhered to E-selectin compared to the MDA- MB-231 and Hs578T cells with mesenchymal phenotypes. E-selectin mediated adhesion of breast cancer cells was significantly reduced in the presence of EDTA, which chelates calcium preventing calcium dependent E-selectin/ligand mediated interactions. Data are mean ± SE and from n > 3 independent experiments. ANOVA and Tukey’s multiple comparison test; #P<0.05 with respect to BT-20 and MDA-MB-468 cells and *P<0.05 with respect to BT-20, MDA-MB- 468, and MDA-MB-231 cells, †P<0.05 between EDTA and untreated (DPBS+) control. B) Flow cytometric analysis demonstrates that I) BT-20 cells and II) MDA-MB-468 cells with epithelial phenotypes express greater levels sLeX and HECA-452 antigens relative to III) MDA-MB-231 cells and IV) Hs578T breast cancer cells with mesenchymal phenotypes. Isotype controls are dashed lines and shaded. Data are representative of n > 3 independent experiments.

The Epithelial-to-Mesenchymal Transition Reduces the E-selectin Ligand Activities of Breast

Cancer Cells and Human Mammary Epithelial Cells

To investigate whether the EMT affects the functional E-selectin ligand activities of breast cancer cell lines, MDA-MB-468 TNBC were infected with retroviral expression constructs 49 containing Snail, Twist, or the empty vector plasmid (pBabe) to stimulate the ectopic expression of the Snail or Twist transcription factors that promote the EMT. Additionally, the functional E- selectin ligand activities of human mammary epithelial cells (HMLE cells) and HMLE cells with ectopic expression of Snail (HMLE Snail) or Twist (HMLE Twist) transcription factors were also investigated. Cell lines were characterized for markers of the EMT including Snail, Twist, vimentin, E-cadherin, and N-cadherin, using qRT-PCR. MDA-MB-468 cells and HMLE cells with ectopic expression of Snail or Twist had significantly lower levels of gene expression for E- cadherin and significantly greater gene expression for the respective transcription factors, vimentin, and N-cadherin, relative to the vector controls, indicating that the cells with ectopic expression of Snail or Twist underwent the EMT (Figure 9A).

The functional E-selectin ligand activities of the MDA-MB-468 vector, MDA-MB-468

Snail, MDA-MB-468 Twist, HMLE vector, HMLE Snail, HMLE Twist cell lines were quantified using parallel-plate flow chamber shear flow detachment assays. In view of the fact that bone marrow is frequently a site of breast cancer metastasis, cells were detached from E-selectin substrates using bone marrow vascular flow conditions (140). In shear flow detachment assays a significantly greater percentage of MDA-MB-468 vector cells remained attached to the E-selectin substrate compared to the MDA-MB-468 Snail and MDA-MB-468 Twist cells, demonstrating that through the EMT the MDA-MB-468 Snail and MDA-MB-468 Twist cells reduced their E- selectin ligand activities (Figure 9B). Additionally, the HMLE Twist cells displayed significantly lower levels of E-selectin ligand activity relative to the HMLE vector control at a wall shear stress of 2.0 dyn/cm2 (Figure 9B), and both HMLE Snail and HMLE Twist cells followed a similar trend as the MDA-MB-468 cells with ectopic expression of Snail or Twist, which had lower levels of E-selectin ligand activities relative to the vector control. Furthermore, significantly fewer cells remained adhered to E-selectin in the presence of EDTA which chelates 50 divalent cations, i.e., Ca2+, indicating that adhesion to E-selectin in shear flow detachment assays was due to calcium-dependent E-selectin/ligand interactions (Figure 10).

To determine whether the MDA-MB-468 cells or HMLE cells with ectopic expression of

Snail or Twist transcription factors and the vector controls presented differential levels of glycans bearing sialofucosylated carbohydrates that decorate E-selectin ligands, the cell lines were assayed for the expression of sLeX, sLeA, and HECA-452 antigens using flow cytometry. Cells with ectopic expression of Snail or Twist had reduced expression of sLeX, sLeA, and HECA-452 antigens compared to the vector controls (Figure 9C). Additionally, membrane-bound E-selectin ligands were immunoprecipitated from whole cell lysates (prepared with equivalent cell densities) and detected in western blots. Western blots revealed that bands corresponding to 72 kDa were more intense for the MDA-MB-468 vector cells compared to the MDA-MB-468 cells with ectopic expression of Snail or Twist transcription factors (Figure 11A), further demonstrating that through the EMT the E-selectin ligand activities of MDA-MB-468 cells were reduced. Similarly, western blots of whole cell lysates from MDA-MB-468 cells displayed bands for HECA-452 antigens at 170 kDa that were more intense for the MDA-MB-468 vector cells compared to the

MDA-MB-468 Snail or MDA-MB-468 Twist cells that underwent the EMT (Figure 11B).

Terminal fucosylation is necessary for ligands to bind E-selectin (133). The carbohydrate sLeX is fucosylated by α-(1,3) fucosyltransferase, which is encoded by FUT3, FUT4, FUT5,

FUT6, and FUT7 genes, and sLeA is fucosylated by α-(1,4) fucosyltransferase, which is encoded by FUT3 and FUT5 genes (84). QRT-PCR showed that MDA-MB-468 and HMLE cells with ectopic expression of Snail or Twist had significantly lower levels of expression of FUT3 RNA and FUT6 RNA compared to the vector controls (Figure 9D). Additionally, MDA-MB-468 Twist,

HMLE Twist, and HMLE Snail cells expressed lower levels of FUT7 RNA relative to the respective vector controls (Figure 9D). However, the MDA-MB-468 Snail cells had greater levels of expression of FUT7 RNA compared to the MDA-MB-468 vector control cells (Figure 9D). 51

Furthermore, the EMT caused the MDA-MB-468 Snail, MDA-MB-468 Twist, HMLE Snail, and

HMLE Twist cell lines to increase their expression of FUT4 and FUT5 relative to the respective vector controls (Figure 9D). Collectively, the results presented herein demonstrate a mechanism by which the EMT decreases the functional E-selectin ligand activities of breast cancer cell lines and modifies the gene expression of fucosyltransferases that catalyze the transfer of fucose onto the terminal residues of carbohydrate decorations that may decorate E-selectin ligands.

Figure 9. The ectopic expression of Snail or Twist transcription factors in the MDA-MB-468 and HMLE cell lines resulted in the epithelial-to-mesenchymal transition (EMT) and reduced the functional E-selectin ligand activities of MDA-MB-468 cells and HMLE cells. A) Quantitative RT-PCR was used to quantify the relative levels of gene expression for Snail and Twist transcription factors and markers of the EMT including E-cadherin (E-cad), N-cadherin (N-cad), and vimentin (Vim) between cells with ectopic expression of Snail or Twist and the vector controls. Data are mean fold change ± SE for n = 3 independent experiments. Paired student’s t- test, *P<0.05, #P<0.01, with respect to the vector controls. B) The EMT reduces the functional E- selectin ligand activities of MDA-MB-468 cells and HMLE cells. In shear flow detachment assays a significantly greater percentage of MDA-MB-468 vector cells remained attached to E- selectin substrates relative to MDA-MB-468 cells with ectopic expression of Snail or Twist. HMLE cells with ectopic expression of Snail and Twist demonstrated reduced levels of E-selectin ligand activities relative to the vector control. Significant differences in E-selectin ligand activity 53 were observed at a wall shear stress of 2.0 dyn/cm2. Data are mean ± SE. ANOVA and post-hoc Tukey’s multiple comparison test, *P<0.05 for n = 4 independent experiments. C) Flow cytometric analysis revealed that HMLE vector controls demonstrated a higher expression of sLeX, sLeA, and HECA-452 antigens compared to HMLE Snail cells and HMLE Twist cells. Similarly, the MDA-MB-468 vector cells demonstrated greater expression of sLeX and HECA- 452 antigens compared to MDA-MB-468 Snail cells and MDA-MB-468 Twist cells. Data are representative of n > 3 independent experiments. D) Relative to the vector controls the HMLE cells and MDA-MB-468 cells with ectopic expression of Snail or Twist that underwent the EMT downregulated the gene expression of FUT3 and FUT6. Data are mean fold change ± SE for n = 3 independent experiment. Paired student’s t-test, *P<0.05 respective to the vector controls.

Figure 10. Breast cancer cell lines and HMLE cells adhered to E-selectin via calcium-dependent E-selectin/ligand interactions. Significantly fewer cells remained attached to E-selectin in shear flow detachment assays in the presence of EDTA. Wall shear stress = 0.50 dyn/cm2. Data are mean ± SE for n = 3 independent experiments. Student’s t-test, *P<0.05.

Figure 11. The EMT downregulated the expression of E-selectin ligands on MDA-MB-468 cells. A) Western blots of membrane-bound E-selectin ligands immunoprecipitated from MDA-MB- 468 cells. Equivalent cell densities were used for loading controls. Data are representative of n = 3 independent experiments. B) Western blot of whole cell lysate from MDA-MB-468 vector, Snail, and Twist cells reveal that the MDA-MB-468 vector cells had elevated levels of HECA- 452 antigen expression compared to the MDA-MB-468 Snail or Twist cells. GAPDH was used as a loading control. Data are representative of n = 3 independent experiments.

The Mesenchymal-to-Epithelial Transition (MET) Increases the E-selectin Ligand Activities of

Breast Cancer Cells

To investigate whether the mesenchymal-to-epithelial transition (MET) modifies the functional E-selectin ligand activities of breast cancer cells, Hs578T cells were infected with lentiviral shRNA constructs targeting Snail or Twist gene expression to induce the MET.

Additionally, Hs578T cells were infected with the empty (pLKO.1) lentiviral shRNA constructs to generate the Hs578T shcontrol cells. Changes in the gene expression of Snail, Twist, E- cadherin, N-cadherin, and vimentin (which are inversely regulated by the EMT and the MET) were quantified using qRT-PCR. Quantitative RT-PCR revealed that both the Hs578T shSnail and the Hs578T shTwist cell lines significantly reduced the gene expression for the respective

Snail or Twist transcription factors compared to the Hs578T shcontrol (Figure 12A).

Additionally, N-cadherin and vimentin gene expression was significantly reduced in the Hs578T shTwist cells relative to the Hs578T shcontrol, yet there was no significant change in E-cadherin gene expression (Figure 12A). Similarly, Hs578T shSnail cells significantly reduced N-cadherin gene expression relative to the Hs578T shcontrol cells but did not significantly change the gene 55 expression of E-cadherin or vimentin (Figure 12A). Therefore, the quantitative gene analyses revealed that knockdown of Twist or Snail gene expression caused the Hs578T shTwist and

Hs578T shSnail cells to transition toward an epithelial phenotype, via the MET.

The functional E-selectin ligand activities of the Hs578T shcontrol, Hs578T shSnail, and

Hs578T shTwist cell lines were evaluated using shear flow detachment assays. During the detachment assays significantly greater percentages of Hs578T shSnail and Hs578T shTwist cells remained adhered to E-selectin substrates compared to the Hs578T shcontrol cells, indicating that the Hs578T shSnail and Hs578T shTwist cells had significantly greater levels of functional E- selectin ligand activities compared to the Hs578T shcontrol cells (Figure 12B). Additionally, in the presence of EDTA significantly fewer Hs578T cells remained adhered to E-selectin demonstrating that the cells adhered to E-selectin via calcium-dependent E-selectin/ligand interactions (Figure 13). To investigate whether the Hs578T cell lines increased the expression of sialofucosylated carbohydrate decorations via the MET, the Hs578T shcontrol, Hs578T shSnail, and Hs578T shTwist cells were assayed for sLeX, sLeA, and HECA-452 antigens using flow cytometry. However, Hs578T cells expressed sLeX, sLeA, and HECA-452 antigens at undetectable levels, because the mean fluorescence intensities (MFI) of cells labeled with primary antibodies (Figure 12C, solid line) were similar to the MFI of cells labeled with the isotype controls (Figure 12C, dashed line and shaded).

To determine if changes in fucosyltransferase gene expression complemented the elevated functional E-selectin ligand activities of Hs578T cells that underwent the MET, qRT-

PCR was used to quantify the relative changes in the expression of FUT3 – FUT7 genes between the Hs578T shcontrol cells and Hs578T cells with knockdown of Snail or Twist. Relative to the

Hs578T shcontrol cell line, Hs578T shSnail and Hs578T shTwist cells had significantly greater levels of expression of FUT3 and FUT6 genes and significantly lower levels of expression of

FUT4 (Figure 12D). Hs578T shTwist cells also significantly increased their expression of FUT5 56 and FUT7 genes relative to the Hs578T shcontrol cells, yet the gene expression of FUT5 and

FUT7 was similar between the Hs578T shSnail cells and the Hs578T shcontrol cells (Figure

12D). Thus, these data support a mechanism for breast cancer cells to enhance their E-selectin ligand activities through the MET by increasing the expression of FUT3 and FUT6 RNA that is translated into fucosyltransferases, which partake in the synthesis of E-selectin ligands.

Figure 12. Knockdown of Snail or Twist gene expression in the Hs578T cells resulted in the mesenchymal-to-epithelial transition (MET) and increased the functional E-selectin ligand activities of Hs578T shSnail and Hs578T shTwist cells. A) Changes in the gene expression of Hs578T shTwist and Hs578T shSnail cells were quantified relative to the Hs578T shcontrol cells using qRT-PCR. ShRNA knockdown of the Snail or Twist transcription factors resulted in significantly lower levels N-cadherin (N-cad) gene expression in both the Hs578T shTwist cells and Hs578T shSnail cells, relative to the control. The Hs578T shTwist cells reduced vimentin (Vim) gene expression relative to the Hs578T shcontrol cells, but changes in E-cadherin (E-cad) gene expression were insignificant. Data are mean fold change ± SE for n = 3 independent experiments. Paired student’s t-test, *P<0.05, #P<0.01, with respect to the Hs578T shcontrol cells. B) Significantly greater percentages of Hs578T shTwist and Hs578T shSnail cells 57 remained adhered to E-selectin in shear flow detachment assays compared to the Hs578T shcontrol cells. Wall shear stress = 0.5 dyn/cm2. Data are mean ± SE for n = 3 independent experiments. ANOVA and post-hoc Tukey’s test, *P<0.05 relative to Hs578T shTwist and Hs578T shSnail. C) Sialofucosylated carbohydrates were undetectable in flow cytometric analysis. Data are representative of n > 3 independent experiments. D) Hs578T cells with knockdown of Snail or Twist transcription factors had significantly greater levels of FUT3 and FUT6 gene expression relative to the Hs578T shcontrol, and FUT4 gene expression was significantly reduced in the Hs578T shSnail and Hs578T shTwist cells relative to the Hs578T shcontrol cells. Data are mean fold change ± SE for n = 3 independent experiments. Student’s t- test, *P<0.05 relative to the Hs578T shcontrol.

Figure 13. Hs578T cells adhered to E-selectin via calcium-dependent E-selectin/ligand interactions. Significantly fewer Hs578T shcontrol, Hs578T shTwist, and Hs578T shSnail cells remained attached to E-selectin when EDTA was added to the perfusion buffer (1% BSA/DPBS+). Wall shear stress = 0.50 dyn/cm2. Data are mean ± SE for n = 3 independent experiments. Students t-test, *P<0.05.

Stem-Like Traits of Breast Cancer Cells may be Relinquished through the MET and are Localized

within Regions of the EMT Spectrum

Previous investigations have demonstrated that breast cancer cells gain stem-like properties via the EMT (53). To determine whether the breast cancer cell lines that underwent the

EMT or MET in this study became more or less stem-like as they transitioned to mesenchymal or epithelial phenotypes, the cell lines were assayed for the expression of CD24, CD44, and ALDH 58 using flow cytometric analysis. Unlike the HMLE Snail and HMLE Twist cell lines that downregulated the expression of CD24 through the EMT (Figure 14), the MDA-MB-468 Snail cells and MDA-MB-468 Twist cells had similar levels of expression of CD24 and CD44 compared to the MDA-MB-468 vector controls (Figure 15A).

Figure 14. HMLE cells reduced the expression of CD24 via the EMT. A) HMLE vector cells had greater levels of CD24 expression compared to B) HMLE Snail cells and C) HMLE Twist cells. Data are representative of n = 4 independent experiments.

Additionally, MDA-MB-468 vector, MDA-MB-468 Snail, and MDA-MB-468 Twist cells were assayed for aldehyde dehydrogenase activity (ALDH) using the ALDEFLUOR assay and had similar levels of ALDH activity (Figure 15B). Thus, neither the MDA-MB-468 Snail cells nor the MDA-MB-468 Twist cells exhibited an increase in mesenchymal-stem-like or epithelial-stem-like biomarkers relative to the MDA-MB-468 vector control cell line. In contrast, the Hs578T shTwist and Hs578T shSnail cell lines had higher levels of expression of CD24 compared to the Hs578T shcontrol cell line (Figure 15A). This increase in the expression of

CD24 shows that the MET caused the Hs578T shTwist and Hs578T shSnail cells to lose mesenchymal-stem-like traits and demonstrates that, like the EMT, the MET may modify the stem-like properties of breast cancer cells. 59

To determine whether the Hs578T shSnail or Hs578T shTwist cell lines shifted from a mesenchymal-stem-like state to an epithelial-stem-like-state through the MET, the Hs578T shSnail, Hs578T shTwist, and Hs578T shcontrol cell lines were assayed for aldehyde dehydrogenase activity in flow cytometric analysis using the ALDEFLUOR assay. Flow cytometry revealed that all of the Hs578T cell lines demonstrated low levels of ALDH activity

(Figure 15B), thus differences in ALDH expression were not observed between the Hs578T shTwist cells or Hs578T shSnail cells and the Hs578T shcontrol cells. In summation, these data show that stem-like traits, which may be acquired through the EMT, e.g., low expression of

CD24, may be relinquished as breast cancer cells transition toward an epithelial phenotype through the MET, and that stem-like characteristics may be restricted to regions of the EMT spectrum so that breast cancer cells may transition to a mesenchymal phenotype without acquiring stem-like traits. 60

Figure 15. Breast cancer cell lines that underwent the EMT or MET were assayed for stem-like breast cancer cell biomarkers including CD24, CD44, and ALDH using multiplex staining and flow cytometry. A) Breast cancer cell lines that underwent the EMT or MET were assayed for the expression of CD24 and CD44. The expression of CD24 and CD44 on I) MDA-MB-468 vector cells was similar to that of II) MDA-MB-468 Snail cells and III) MDA-MB-468 Twist cells. Overlays of CD24 and CD44 expression between IV) MDA-MB-468 vector (black) and MDA- MB-468 Snail cells (aqua) and V) MDA-MB-468 vector (black) and MDA-MB-468 Twist cells (aqua) show that the MDA-MB-468 cell lines with ectopic expression of Snail or Twist did not modify the expression of CD24 or CD44 via the EMT. The I) Hs578T shcontrol cell line expressed lower levels of CD24 compared to the II) Hs578T shSnail cells and III) Hs578T shTwist cells. Overlays of CD24 and CD44 expression between IV) Hs578T shcontrol cells (black) and Hs578T shSnail cells (red) or V) Hs578T shcontrol (black) and Hs578T shTwist cells (red) show that the MET generated cells with increased expression of CD24. B) Breast cancer cells lines that underwent the EMT or MET were assayed for ALDH activity using the ALDEFLUOR assay. The I) MDA-MB-468 vector, II) MDA-MB-468 Snail, and III) MDA-MB- 468 Twist cell lines demonstrated similar levels of ALDH activity (green), negative control (DEAB, black). Overlays of flow cytometric analysis of ALDH expression between IV) MDA- MB-468 vector (magenta) and MDA-MB-468 Snail (green) cells and V) MDA-MB-468 vector 61

(magenta) and MDA-MB-468 Twist (green) cells show that the EMT did not cause the MDA- MB-468 cells to modify their ALDH activities. I) Hs578T shcontrol cells demonstrated similar levels of ALDH activity compared to the II) Hs578T shSnail and III) Hs578T shTwist cells. Negative controls (DEAB) are shown in black. Overlays of flow cytometric analysis for the ALDH activities of IV) Hs578T shcontrol (magenta) and Hs578T shSnail (blue) or V) Hs578T shcontrol (magenta) and Hs578T shTwist (blue) reveal that the MET did not change the ALDH activities of Hs578T cells. Data are representative of n = 4 independent experiments.

Discussion

Herein a mechanism for the regulation of the functional E-selectin ligand activities of breast cancer cells via the EMT and MET was revealed. In this mechanism, the functional E- selectin ligand activities of breast cancer cells were downregulated by the EMT (Figure 9) but upregulated by the MET (Figure 12). Furthermore, changes in the gene expression of FUT3 and

FUT6, which were previously implicated as the primary fucosyltransferases for synthesis of functional E-selectin ligands on breast cancer cells with an epithelial phenotype (89) paralleled the changes in the functional E-selectin ligand activities of breast cancer cells that were downregulated via the EMT (EMT≡↓FUT3 & ↓FUT6≡↓E-selectin ligand activity, Figure 9) and upregulated by the MET (MET≡↑FUT3 & ↑FUT6 ≡ ↑E-selectin ligand activity, Figure 12). These findings suggest that through the EMT and MET the expression of α-(1,3) fucosyltransferases

(FTs), including FT3 and FT6 (and FT7 for Twist-dependent EMT), govern E-selectin/ligand mediated breast cancer cell trafficking during metastasis.

Fucosyltransferases were established as potential regulators of metastasis for prostate cancer cells in the work of Barthel et al. (2013), which demonstrated that the expression of FT3,

FT6, and FT7 in prostate cancer cells enhanced the abilities of the cells to facilitate E- selectin/ligand-mediated adhesion thereby increasing the potential for prostate cancer cells to engage in E-selectin-mediated trafficking during metastasis (133). Additionally, Breiman et al.

(2016), found similar results to those reported herein and demonstrated that the expression of

FUT3 and fucosylated antigens, e.g., Lewis Y, are reduced by the EMT in breast cancer cell lines 62

(93). Although breast cancer cells and prostate cancer cells may share a similar mechanism for

EMT-regulation of functional E-selectin ligand activity, colon cancer cells have been shown to upregulate sLeX/A expression and E-selectin ligand activity via epidermal-growth-factor induction of the EMT (214). Thus, further investigation is required to determine whether the E-selectin ligand activities of other primary cancers, in which the over-expression of selectin ligands is associated with poor prognosis, e.g., pancreatic and head and neck cancer (84, 215, 216), are reduced by the EMT, or if different mechanisms for the EMT differentially modify the E-selectin ligand activities of cancer cells.

While changes in the gene expression of FUT3 and FUT6 paralleled changes in the functional E-selectin ligand activities of breast cancer cells, changes in the gene expression of

FUT4 were inversely correlative with changes the functional E-selectin ligand activities of breast cancer cells (EMT ≡ ↑FUT4 ≡ ↓E-selectin ligand activity, Figure 9). Notably, Shirure et al.

(2015) also found that FUT4 gene expression was higher in breast cancer cells with a mesenchymal phenotype (MDA-MB-231) compared to breast cancer cells with an epithelial phenotype (BT-20) (89). Additionally, Yang et al. (2013) observed that transfection of MDA-

MB-231 and MCF-7 cells with pcDNA3.1-FUT4 leads to overexpression of FT4 and caused the cells to upregulate the expression of EMT-biomarkers including vimentin, N-cadherin, Zeb1,

Twist and Snail and downregulate the expression of E-cadherin. Additionally, due to decreased levels of PI3K/Akt activity in the FUT4-siRNA transfected cells, Yang et al. (2013) implicated

FT4 as a novel regulator of the EMT that facilitates the EMT via PI3K/Akt activation (217).

Although FT4 may indeed be involved in a PI3K/Akt pathway that promotes the EMT, FT4 has yet to be established as a signaling molecule or receptor, thus it is unlikely to be the primary actionable agent of an EMT regulatory pathway in the same way that the TGF-β receptor is a primary agent of an EMT regulatory pathway. Furthermore, Brown et al. (2012) elucidated that 63

PI3K/Akt signaling during the EMT was mediated by CD44 standard (CD44s), thus signaling via

CD44s may have activated the PI3/Akt pathway in the study performed by Yang et al (2013).

Brown et al. (2012) found that the during the EMT the expression of CD44s was elevated on HMLE-Twist-ER cells due to the action of the ESRP1 splice factor that was expressed after tamoxifen treatment induced the EMT and facilitated isoform switching of CD44 variants (CD44v), thus CD44s was observed to participate in a positive feedback loop promoting the EMT (218). Shirure et al. (2015) established CD44v but not CD44s as E-selectin ligands on breast cancer cells, thus the finding that CD44v isoform switching to CD44s occurs during the

EMT follows the mechanism proposed herein, in which the EMT downregulated the E-selectin ligand activities of breast cancer cells. While CD44 isoform switching has been established in the

EMT mechanism, preliminary analysis of CD44 gene expression in cells that underwent the EMT or MET indicate that CD44 isoform switching is localized to a region of the EMT spectrum that was not attained by the MDA-MB-468 or Hs578T cells (Figure 16).

Figure 16. Preliminary analysis of CD44 gene expression levels in breast cancer cells. Analysis of gene expression in breast cancer cells that underwent the A) EMT or B) MET indicate that CD44 isoform switching is localized to a region of the EMT spectrum that was unobserved by the MDA-MB-468 and Hs578T cells. Data are mean fold change ± SD for n = 2 technical replicates. 64

In the study herein, flow cytometric analysis revealed that relative to the vector controls the MDA-MB-468 cells with ectopic expression of Snail or Twist reduced the expression of glycoconjugates decorated with sLeX carbohydrates and HECA-452 antigens, but neither the vector controls nor the cells with ectopic expression of Snail or Twist expressed a detectable level of ligands decorated with sLeA (Figure 9C). Thus, of the assayed carbohydrate decorations sLeX and HECA-452 antigens are most likely to promote the E-selectin/ligand mediated metastasis of

MDA-MB-468 TNBC cells with an epithelial phenotype.

While other studies support that the overexpression of sLeX is linked with poor prognosis and enhanced metastasis (82, 84, 87, 89, 100, 111, 133, 219, 220) E-selectin ligands other than sLeX, sLeA, or HECA-452 antigens may be downregulated by the EMT to reduce the functional

E-selectin ligand activities of the MDA-MB-468 Snail cells and MDA-MB-468 Twist cells relative to the vector control. For example, E-selectin has been reported to bind to fucosylated carbohydrates lacking sialic acid, as well as higher order fucosylated oligosaccharides other than sLeX and sLeA (131, 132, 221).

Additionally, through the MET Hs578T shSnail and Hs578T shTwist cells significantly increased their functional E-selectin ligand activities and expression of FUT3 and FUT6 genes relative to the Hs578T shcontrol cells (Figure 12), yet these increases in E-selectin ligand activity and FT gene expression were not accompanied by detectable changes in the expression of sLeX, sLeA, or HECA-452 antigens in flow cytometric analysis (Figure 12C). Similarly, Barthel et al.

(2013) observed elevated levels of E-selectin ligand activity in prostate cancer cells that increased the expression of FT3 without changing the expression of sLeX (133). Thus, further investigation is required to identify the carbohydrates decorations that may facilitate the MET induced upregulation of the E-selectin ligand activities of Hs578T shSnail and Hs578T shTwist cells. 65

Due to the existence of multiple glycoforms that have the potential to (but are not guaranteed to) function as E-selectin ligands, characterization of the functional E-selectin ligand activities of cell lines requires the use of functional assays, e.g., shear flow assays, AFM, and other applied force assays, that allow for observation of E-selectin/ligand mediated adhesion. In light of the fact that characterization of the E-selectin ligand activities of breast cancer cells and tissues requires a functional assay, dynamic biochemical tissue analysis was employed to establish that the E-selectin microspheres, which were delivered to tissues in E-selectin microsphere adhesion assays, demonstrated E-selectin/ligand mediated rolling adhesion, which is the hallmark adhesion event of E-selectin/ligand interaction (Figure 6).

Approximately 10% of the 110 tissues that were assayed for E-selectin ligand activity using E-selectin microsphere adhesion assays demonstrated functional E-selectin ligand activity

(100 cases of invasive ductal carcinoma: 9-E-selectin ligand positive, 4 cases adenosis: 0-E- selectin ligand positive, 6 cases of invasive ductal carcinoma with adjacent normal tissue: 2-E- selectin ligand positive). Within the breast cancer tissues that demonstrated E-selectin ligand activity, cancer cells with high expression of E-cadherin and low expression of N-cadherin and vimentin that were characterized as epithelial-like demonstrated significantly greater functional

E-selectin ligand activities than breast cancer cells with low expression of E-cadherin and high expression of N-cadherin or vimentin, which were characterized as mesenchymal-like (Figure 7).

The detection of functional E-selectin ligand activity requires the use of an assay that allows for observation of E-selectin mediated adhesion. Consequently, future therapeutics for breast cancer that target functional E-selectin ligands, such as those that are currently employed in clinical trials of acute myeloid leukemia (GMI-1271, Glycomimetics (139, 222)), may rely on diagnostics such as DBTA, as well as CTC capture devices, that are capable of characterizing the functional E-selectin ligand activities of breast cancer cells. Using a diagnostic such as DBTA 66 may allow clinicians to identify candidates for E-selectin-ligand-targeted treatments, which may inhibit E-selectin/ligand mediated trafficking during breast cancer metastasis.

In summary, this investigation demonstrates mechanisms in which the E-selectin ligand activities of breast cancer cells are downregulated through the EMT and upregulated by the MET.

Furthermore, changes in fucosyltransferase gene expression in cells that went through the EMT or

MET were shown to coincide with these changes in E-selectin ligand activity, suggesting that the

EMT and MET modify the activities or expression of fucosyltransferases that synthesize terminal sialofucosylated carbohydrate decorations of E-selectin ligands. Specifically, FUT3 and FUT6 were highly expressed in breast cancer cells that gained epithelial traits via the MET (Figure

12D), and were expressed at reduced levels in cells that underwent the EMT (Figure 9D), relative to the controls. Thus, FT3 and FT6 may regulate breast cancer cell trafficking during cancer metastasis via the EMT or MET by modifying the abilities of breast cancer cells to adhere to E- selectin presented by endothelial cells at the secondary site. Further investigation is required to determine whether the kinetics of the phenotypic transitions allow breast cancer cells to modify their E-selectin ligand activities whilst in the circulation, because the half-lives of CTCs from primary tumors are approximately 2 hours (223), yet studies that demonstrate EMT induction in the circulation utilized immortalized cell lines that survived in the murine circulatory system for longer periods of time (224).

CHAPTER 3: SNAIL AND TWIST TRANSCRIPTION FACTORS DIFFERENTIALLY

REGULATE THE EXPRESSION OF GLYCOSPHINGOLIPIDS IN BREAST CANCER

CELLS DURING THE EMT AND MET

Introduction

Within the plasma membrane sterols, ceramide derivatives (e.g., glycosphingolipids), and membrane-bound proteins (88, 225) mediate cellular functions including cell signaling, mechanotransduction, cell adhesion, cell growth, and cytoskeletal remodeling (226-229).

Furthermore, lipid rafts may collect and cluster glycolipid and glycoprotein E-selectin ligands and thereby enhance the intensity of multivalent E-selectin/ligand interactions that facilitate slow rolling adhesion and cell signaling for F-actin reconstruction (230-232). For example, polymorphonuclear neutrophils require PSGL-1 and L-selectin localization in lipid rafts to engage membrane-bound E-selectin ligands in E-selectin/ligand mediated rolling adhesion on activated endothelium (233). Additionally, in colon cancer, interaction between the E-selectin ligand death receptor-3 (DR-3) and E-selectin results in p38 and extracellular-receptor-kinase- mitogen-activated protein kinase activation and PI3K/Akt pathway signaling, to promote anti- apoptotic behaviors in HT29 colon cancer cells treated with curcumin (234, 235). Although DR-3 has not been established as an E-selectin ligand on breast cancer cells, sialylated glycosphingolipids (gangliosides) presented by BT-20 and MDA-MB-468 breast cancer cell lines are functional E-selectin ligands that may be localized in lipid rafts and potentially contribute to

E-selectin/ligand mediated trafficking during metastasis (88).

The expression of gangliosides and other glycosphingolipids on breast cancer cells has been linked to the EMT. For example, Guan et al. (2009) demonstrated that treatment with TGF-β or elimination of glycosphingolipid derivatives of glucosylceramide similarly caused breast cancer cells to decrease the expression of the epithelial-EMT-biomarker E-cadherin and increase the expression of the mesenchymal-EMT-biomarkers vimentin and fibronectin (236). 68

Additionally, breast cancer cells that were treated with TGF-β expressed a different composition of glycosphingolipids, e.g., lower levels of GM2, than untreated cells (236).

In subsequent studies, Liang et al. (2013) revealed that stem-like cells generated by the

EMT maintain a differential expression of glycosphingolipids compared to the non-stem-like controls (237). Furthermore, the ganglioside GD2 has been implicated as a stem-like biomarker for breast cancer cells, and the expression of GD2 on breast cancer cells was found to promote tumorigenesis (238). Herein the effects of the EMT or MET on the functional glycolipid-E- selectin-ligand activities of breast cancer cells were studied using bromelain protease treatment and shear flow detachment assays. To determine whether the EMT or MET modified the expression of glycosphingolipids on breast cancer cells, glycosphingolipid expression profiles were characterized using flow cytometry, thin layer chromatography, and qRT-PCR.

Materials and Methods

Flow Cytometry

Flow cytometric analysis was executed using the protocol described in Chapter 2. For bromelain treatment, breast cancer cell lines were treated with 0.1% bromelain protease (Sigma-

Aldrich) /DPBS- or the untreated control (DPBS-) for 45 minutes at 37oC. The cell lines were then washed three times in 1% BSA / DPBS+ and placed on ice in preparation for flow cytometric analysis or shear flow assays.

Shear Flow Detachment Assays

Shear flow detachment assays were performed as previously described in Chapter 2.

Reverse Transcription and qRT-PCR

Reverse transcription and qRT-PCR were carried out as described in Chapter 2.

Lipid Extraction

Lipids were extracted from breast cancer cell lines as previously described (239). Briefly, cells were pelleted via centrifugation for five minutes at 300 x g. Media was removed and cells 69 were washed in DPBS-. Cells were counted and 50% of the cell population was sequestered for protein analysis via a Bradford assay. Subsequently, cells were pelleted and homogenized in 2ml of methanol by vortexing. Next the mixture was supplemented with 1ml of chloroform and vortexed until the solution was monophasic. Then the mixture was centrifuged for 10 min at

1,700 x g at room temperature. Solid fragments were discarded and the supernatant was transferred to a new tube. Then 1ml chloroform and 1ml DI water were sequentially added to the mixture, which was then vortexed twice for 30 seconds. Phase separation was achieved by centrifugation at 1,700 x g for 5 minutes. The lower phase of the mixture was then transferred into a tube of known mass. Lipids were dried using nitrogen, massed, and stored at -20oC.

Thin Layer Chromatography

Thin layer chromatography (TLC) was performed as previously described (240). One day prior to running the TLC experiment, TLC chambers were loaded with a mixture of chloroform, methanol, and (0.25%, w/v) aqueous potassium chloride (CMW, 60:35:8). On the day of the experiment, dried lipids were brought to room temperature and reconstituted in chloroform at 1 mg/ml. Lipids were loaded onto the TLC plate in 5μl increments using a Hamilton syringe until

5μg of lipid were loaded onto the plate for each cell line or standard (cat. no. 1511, Matreya, State

College, PA). During TLC plate loading, the lipids were dried between each 5 μl application using a hair dryer set on low temperature. After loading the lipids, the TLC plate was placed into the TLC chamber containing the CMW mixture and the chamber was sealed using vacuum grease. The TLC plate was removed from the chamber once the CMW mixture had diffused to the top of the plate. A pencil was used to mark the extent to which the CMW mixture progressed up the plate. After the TLC plate was removed from the chamber, it was dried in the fume hood and placed in a TLC chamber filled with iodine, which binds to double bonds within the glycosphingolipids. After inspection, the TLC plate was removed from the iodine-filled TLC chamber and allowed to dry until the iodine staining was no longer visible. Subsequently, the 70

TLC plate was sprayed with resorcinol, covered with a glass plate using binder clips, and placed into an oven at 125oC for 20 minutes. The TLC plate was imaged on a ChemiDoc XRS imaging system using Quantity One software (Bio-Rad).

Results

Breast Cancer Cell Lines that Undergo the EMT or MET Express Protease Resistant E-selectin

Ligands

Sialofucosylated glycoproteins, e.g., CD44 variants, and sialofucosylated glycolipids, e.g., gangliosides, are differentially expressed on breast cancer cell lines with epithelial or mesenchymal phenotypes and have previously been established as E-selectin ligands (88, 89, 236,

237). To determine whether the EMT or MET regulated the expression of sialofucosylated glycosphingolipids on breast cancer cells, the MDA-MB-468 vector, MDA-MB-468 Snail, MDA-

MB-468 Twist, Hs578T shcontrol, Hs578T shSnail and Hs578T shTwist cell lines were analyzed for the expression of protease-insensitive ligands decorated with sialofucosylated carbohydrates, as well as GM1, and GD2 using flow cytometric analysis. Furthermore, the cell lines were evaluated for their bromelain-protease insensitive functional E-selectin ligand activities using shear flow detachment assays, and the glycosphingolipid expression of breast cancer cells was characterized using TLC and qRT-PCR.

First, flow cytometry was used to characterize the expression of bromelain-protease- insensitive molecules presented on the breast cancer cell lines using CD44 as a bromelain sensitive control (Figure 17). Interestingly, the expression of HECA-452 antigens and glycoconjugates decorated with sLeA appeared to be unchanged after the protease treatment of the assayed cell lines (Figure 17). Furthermore, sLeX was not detected on either the bromelain- protease treated or untreated Hs578T vector, Hs578T shSnail, or Hs578T shTwist cell lines using flow cytometry (Figure 17). 71

Next, to determine whether the functional E-selectin ligand activities of the MDA-MB-

468 vector, MDA-MB-468 Snail, MDA-MB-468 Twist, Hs578T shcontrol, Hs578T shSnail, or

Hs578T shTwist breast cancer cell lines were affected by bromelain-protease treatment, the functional E-selectin ligand activities of these breast cancer cell lines were quantified using shear flow detachment assays. The detachment assays revealed that the functional E-selectin ligand activities of breast cancer cell lines were not significantly reduced by bromelain-protease treatment (Figure 18A). In fact, bromelain-treated MDA-MB-468 Twist cells demonstrated a significant increase in functional E-selectin ligand activity relative to the untreated MDA-MB-

468 Twist cells (negative control). The increase was so large that the functional E-selectin ligand activity of the bromelain-protease treated MDA-MB-468 Twist cells were similar to the functional E-selectin ligand activity of the untreated or bromelain-protease treated MDA-MB-468 vector cells (Figure 18A). While the functional E-selectin ligand activities of bromelain-protease treated MDA-MB-468 Snail cells were comparable to the functional E-selectin ligand activity of the untreated MDA-MB-468 Snail cells (negative control), the functional E-selectin ligand activities of the bromelain-protease treated MDA-MB-468 Snail cells were increased such that they were similar to the functional E-selectin ligand activities of untreated or bromelain-treated

MDA-MB-468 vector cells (Figure 18A).

Furthermore, the functional E-selectin ligand activities of bromelain-protease-treated

Hs578T shcontrol cells were significantly greater than the functional E-selectin ligand activities of the untreated Hs578T shcontrol cells (Figure 18B). However, the functional E-selectin ligand activities of the bromelain-treated and untreated Hs578T shTwist cells were similar (Figure 18B).

Similarly, the functional E-selectin ligand activities of the Hs578T shSnail cells were not significantly different from the functional E-selectin ligand activities of bromelain-protease treated Hs578T shSnail cells (Figure 18B). The bromelain-protease treated and untreated Hs578T shSnail cells and Hs578T shTwist cells demonstrated significantly greater functional E-selectin 72 ligand activities than the untreated Hs578T shcontrol cells (Figure 18B). However, only the bromelain-treated Hs578T shTwist cells and untreated Hs578T shTwist cells demonstrated greater E-selectin ligand activities than the bromelain-protease treated Hs578T shcontrol cells, which had greater functional E-selectin ligand activities than the untreated Hs578T shcontrol

(Figure 18B). Collectively, these results suggest that changes in the functional E-selectin ligand activities of breast cancer cells that underwent the EMT or MET were primarily due to changes in the expression of protease-sensitive functional E-selectin ligands, e.g., glycoproteins.

Snail and Twist Differentially Regulate the Expression of Glycosphingolipids Presented by Breast

Cancer Cells

Breast cancer cell lines were characterized for the expression of glycosphingolipids GM1 and GD2 using flow cytometry. Flow cytometry revealed that MDA-MB-468 vector and MDA-

MB-468 Snail cell lines expressed similar levels of expression of GM1, yet the expression of

GM1 was moderately reduced on the MDA-MB-468 Twist cells (Figure 18C). Additionally, the

Hs578T shcontrol, Hs578T shSnail, and Hs578T shTwist cell lines all demonstrated similar levels of GM1 expression (Figure 18C). While GD2 was not detected on the MDA-MB-468 vector,

MDA-MB-468 Snail, or MDA-MB-468 cell lines, the Hs578T shcontrol, Hs578T shSnail, and

Hs578T shTwist cell lines expressed GD2, yet the Hs578T shTwist cells expressed lower levels of GD2 compared to the Hs578T shcontrol cells (Figure 18C).

Thin layer chromatography was used to characterize the glycosphingolipid expression profiles of the breast cancer cell lines. TLC bands for the MDA-MB-468 vector, Snail, and Twist cells appear similar with the exception that the summed intensities of TLC bands for MDA-MB-

468 vector cells were greater than those of the MDA-MB-468 Twist cells or MDA-MB-468 Snail cells (Figure 19). Additionally, TLC revealed that while both the Hs578T shTwist cells and

Hs578T shSnail cells had reduced expressed of GM3 relative to the Hs578T shcontrol, the

Hs578T shSnail cells had greater expression of GM3 than the Hs578T shTwist cells (Figure 19). 73

Galactosyltransferases and sialyltransferases synthesize glycosphingolipids expressed by breast cancer cells. Thus, to further study how the EMT or MET affected the glycosphingolipid expression of the breast cancer cell lines, qRT-PCR was used to quantify the relative changes in gene expression for galactosyltransferases and sialyltransferases between cells that underwent the

EMT or MET and the respective vector or shcontrol cell lines. The gene expression of ST3GAL1,

ST3GAL2, and ST3GAL5 was significantly greater in MDA-MB-468 Snail cells and MDA-MB-

468 Twist cells, compared to the vector control (Figure 18D). Additionally, relative to the vector control the MDA-MB-468 Twist cells upregulated the gene expression of B3GALT4, B3GALT5,

B4GALT3, ST8SIA5, UGCG, and to a lesser extent B4GALT1 (Figure 18D). Interestingly, relative to the vector control the MDA-MB-468 Snail cells significantly decreased the gene expression of B3GALT4, B3GALT5, B4GALT1, ST8SIA1, and ST8SIA5, yet did not express significantly different levels of expression of B4GALT3 or UGCG (Figure 18D). Furthermore,

B4GALNT1 gene expression was significantly was significantly lower in both the MDA-MB-468

Snail and MDA-MB-468 Twist cell lines relative to the vector control (Figure 18D).

In contrast to the MDA-MB-468 cells that underwent the EMT, the Hs578T shTwist cells and Hs578T shSnail cells that underwent the MET significantly decreased the gene expression of

ST3GAL2 and UGCG relative to the Hs578T shcontrol (Figure 18D). Additionally, relative to the

Hs578T shcontrol cells the Hs578T shSnail cells significantly decreased the gene expression of

B4GALT1, ST3GAL1, and ST8SIA1, yet significantly increased the gene expression of

B4GALT3, B4GALNT1, ST3GAL5, and ST8SIA5 (Figure 18D). Notably, the Hs578T shSnail cells also increased the gene expression of B3GALT4 and B3GALT5, however these changes in expression were not statistically significant (Figure 18D). The Hs578T shTwist cells significantly increased the gene expression of B4GALT3 and ST3GAL1, but significantly decreased the gene expression of B3GALT5, B4GALNT1, ST3GAL5, ST8SIA1, and ST8SIA5 (Figure 18D). The 74 combined decrease in the gene expression of B4GALNT1, ST3GAL5, and ST8SIA1 may be responsible for the relatively low expression of GD2 on the Hs578T shTwist cells (Figure 20).

Figure 17. Flow cytometric analysis of breast cancer cell lines treated with bromelain protease. Flow cytometry revealed that bromelain protease cleaved CD44 from the surface of the assayed breast cancer cells, but did not affect the expression of protease insensitive glycans decorated with sLeX/A or HECA-452 antigens presented on MDA-MB-468 vector, MDA-MB-468 Snail, or MDA-MB-468 Twist cell lines. Additionally, Hs578T shcontrol cells, Hs578T shSnail cells, and Hs578T shTwist cells did not present detectable levels of glycans decorated with sialofucosylated carbohydrates before or after treatment with bromelain protease. Data are representative of n = 3 independent experiments.

Figure 18. The expression of protease-resistant E-selectin ligands on breast cancer cells was differentially modified by Snail and Twist transcription factors that facilitate the EMT and MET. A) A significantly lower percentage of untreated MDA-MB-468 Snail cells remained attached to E-selectin substrates in shear flow detachment assays compared to the untreated MDA-MB-468 vector cells and bromelain treated MDA-MB-468 vector cells (MDA-MB-468 vectorBP) and MDA-MB-468 TwistBP cells. *P<0.05. Similarly, a significantly lower percentage of untreated MDA-MB-468 Twist cells remained attached to E-selectin compared to the percentage of MDA- MB-468 TwistBP cells, untreated MDA-MB-468 vector cells, and MDA-MB-468 vectorBP cells that remained attached to E-selectin. #P<0.05. B) A significantly lower percentage of the Hs578T 76 shcontrol cells remained attached to E-selectin during the detachment assay compared to treated and untreated Hs578T shSnail or Hs578T shTwist cells, yet compared to the untreated control a significantly greater percentage of Hs578T shcontrolBP cells remained attached to E- selectin,*P<0.05. Notably, a significantly greater percentage of Hs578T shTwistBP cells remained attached to E-selectin during the detachment assays compared to the Hs578T shcontrolBP cells or the Hs578T shSnailBP cells, #P<0.05. Data are mean ± SE. ANOVA and post-hoc Tukey’s test. Data are n = 3 independent experiments. C) Flow cytometric analysis was used to characterize the expression of GM1 and GD2 on breast cancer cell lines that underwent the EMT or MET. Compared to MDA-MB-468 vectors cells and MDA-MB-468 Snail cells the MDA-MB-468 Twist cells had a lower level of GM1 expression. The Hs578T shcontrol cells, Hs578T shSnail cells, and Hs578T shTwist cells expressed similar levels of GM1, and Hs578T shTwist cells expressed lower levels of GD2 compared to the Hs578T shcontrol cells and Hs578T shSnail cells. Data are n = 3 independent experiments. D) The gene expression of galactosyltransferases and sialyltransferases that are translated into enzymes that synthesize glycosphingolipids was quantified using qRT-PCR for MDA-MB-468 cells and Hs578T cells. Data are mean fold change ± SE for n = 3 independent experiments.

Figure 19. Thin layer chromatography of glycolipids extracted from breast cancer cell lines. Lanes are A) standards, or lipids extracted from B) MDA-MB-468 vector cells, C) MDA-MB- 468 Twist cells, D) MDA-MB-468 Snail cells, E) Hs578T shcontrol cells, F) Hs578T shTwist cells, and G) Hs578T shSnail cells. Quantified band intensities (red text) are shown in the right panel. Intensities enclosed within lanes for multiple bands show the summed band intensity for each lane and are displayed at the bottom of the lane. Data are representative of n = 3 independent experiments. 77

Figure 20. Pathways for the synthesis of sialylated glycosphingolipids (gangliosides) by sialyltransferases and galactosyltransferases. Enzymes involved in lipid synthesis are perpendicular to white arrows while reactants and products of enzyme synthesis are shown at the start and end (tip) of the white arrows. Adapted from Liang et al. (2013) (237).

Discussion

Recent studies have shown that breast cancer cells can modify their expression of glycosphingolipids via the EMT (53, 237). For example, stem-like breast cancer cells generated by the EMT have been shown to express a different glycosphingolipid profile than their non- stem-like counterparts (237). Additionally, other works have revealed that the disialoganglioside

GD2 is a possible biomarker for stem-like breast cancer and promotes tumorigenesis (238).

Furthermore, gangliosides (sialylated glycosphingolipids) have been identified as E-selectin ligands on breast cancer cells (88), thus glycosphingolipids expressed on breast cancer cells may enhance E-selectin/ligand mediated CTC trafficking. Herein the effect of the EMT and MET on the expression of glycosphingolipids and protease resistant E-selectin ligands, e.g., glycolipids, was studied. 78

The EMT reduced the glycoprotein-E-selectin-ligand activities of MDA-MB-468 cells to a greater extent than the glycolipid-E-selectin-ligand activities of MDA-MB-468 cells (Figure

18B). The EMT may differentially affect the functional glycoprotein- and glycolipid-E-selectin ligand activities of breast cancer cells by differentially regulating the expression of FTs that have dissimilar substrate specificities (Figure 9D) (241-244). For example, Huang et al. (2002) revealed that FT4 but not FT7 synthesizes carbohydrates resembling sLeX on glycolipids in

Chinese Hamster Ovary (CHO) cells (245). Thus, FT4 and FT5 (which has a ratio of sLeX/LeX synthesis that is 20X greater than FT4 (246)) may have allowed the MDA-MB-468 cells with ectopic expression of Snail or Twist to maintain levels of protease-insensitive E-selectin ligand activities that were similar to the vector control.

Additionally, it is plausible that FT6 fucosylated glycoproteins to a greater extent than glycolipids in Hs578T shSnail cells, because the Hs578T shSnail cells highly expressed FUT6 and had similar protease-resistant functional E-selectin ligand activities as the Hs578T shcontrol cells. However, the Hs578T shSnail cells had greater (total) functional E-selectin ligand activities than the Hs578T shcontrol cells (Figure 18B). Moreover, the Hs578T shSnail cells only moderately increased FUT3 expression and did not change the expression of FUT5 and FUT7 relative to the control (Figure 12D). Furthermore, Hs578T shTwist cells significantly increased the expression of FUT3, FUT5, FUT6, and FUT7 relative to the control (Figure 12D) and expressed significantly greater protease-insensitive E-selectin ligand activities than the Hs578T shcontrol cells (Figure 18B). Thus, FT3 and FT5 may have fucosylated glycolipids on Hs578T shTwist cells. However, further investigation is required to determine whether the specificities of fucosyltransferases in CHO cells are similar to those of breast cancer cells, especially because

Barthel et al. (2013) demonstrated that FT3, FT6 and FT7 participate in the synthesis of sLeX on glycolipids and glycolipid-E-selectin ligands in prostate cancer cells (247). 79

Sialyltransferases and galactosyltransferases synthesize glycosphingolipids on breast cancer cells (237), and quantification of the relative changes in the gene expression of sialyltransferases and galactosyltransferases between the MDA-MB-468 Twist cells and MDA-

MB-468 vector cells suggest that the MDA-MB-468 Twist cells expressed greater levels of GM3,

GD3, and GT3 but lower levels of GM2, GD2, and GT2 via the EMT (Figure 18D). While differences in GM3 expression were not observed in thin layer chromatography (Figure 19), lower levels of GM2 could explain the decreased expression of GM1 in the MDA-MB-468 Twist cells, relative to the vector control (Figure 18C). Furthermore, this decrease in GM1 expression may only be slight because gene expression for B3GALT4, which encodes for a galactosyltransferase that synthesizes GM1 from GD2 (Figure 20), was elevated in the MDA-

MB-468 Twist cells, relative to vector control (Figure 18D).

Interestingly, the relative changes in gene expression for sialyltransferases and galactosyltransferases in MDA-MB-468 Twist cells (Figure 18D) were similar to the changes in gene expression of HMLE-Twist-ER cells that underwent the EMT in the study published by

Liang et al. (2013), which demonstrated circulating breast cancer stem cells maintain a distinct profile of glycosphingolipids compared to non-stem-like breast cancer cells (237). In stark contrast, the relative changes in gene expression for sialyltransferases and galactosyltransferases in Hs578T shTwist cells (Figure 18D) were nearly opposite of what was reported by Liang et al.

(2013) (237) indicating that the MET may have caused the Hs578T cells, which are often used as a model stem-like breast cancer cell line (based on the expression of CD44/24 biomarkers) (53,

55, 61-63, 85, 248-251) to become non-stem-like.

In contrast to the MDA-MB-468 Twist cells, the relative changes in gene expression that were reported to describe the glycosphingolipid profile of breast cancer stem cells were not observed in the MDA-MB-468 Snail cells, nor was an opposite pattern of gene expression observed in Hs578T shSnail cells (Figure 18D). Yet, the EMT-induced changes in the gene 80 expression of sialyltransferases and galactosyltransferases that occurred in MDA-MB-468 Snail cells were nearly the opposite of the changes in gene expression in the Hs578T shSnail cells

(Figure 18D), indicating a link between the EMT and MET via glycosphingolipid regulation.

Since the glycosphingolipid profile of circulating breast cancer cells that was reported by Liang et al. (2013) was determined from HMLE-Twist-ER cells but not HMLE-Snail-ER cells, it is plausible that Snail and Twist differentially modify galactosyltransferase and sialyltransferase gene expression in breast cancer cells and thereby generate multiple glycosphingolipid profiles for stem-like cells. In view of these findings, glycosphingolipid profiles appear to be poor biomarkers for the identification of stem-like breast cancer cells, since multiple transcription factors can induce the EMT, i.e., Snail, Twist, Slug (SNAI2), and Zeb1, and each may cause breast cancer cells to express different glycosphingolipid profiles (41, 193, 252, 253).

The results shown herein further support that glycolipids on breast cancer cells are E- selectin ligands (88), and suggest that FT4 and/or FT5 catalyze the addition of fucose onto terminal carbohydrate residues decorating sphingolipids that function as E-selectin ligands.

Furthermore, regulation of the gene expression of sialyltransferases and galactosyltransferases in breast cancer cells appears to be transcription-factor dependent, because the induction of the EMT or MET via regulation of the gene expression of Snail or Twist transcription factors results in differential gene expression of sialyltransferases and galactosyltransferases. Consequently, further investigation is needed to determine whether a specific transcription-factor-dependent glycosphingolipid profile confers CTCs with stem-like characteristics or enhanced metastatic potential. 81

CHAPTER 4: ADHESION TO E-SELECTIN ALTERS THE BEHAVIORS OF STEM-LIKE

BREAST CANCER CELLS

Introduction

The bone marrow is a frequent site of breast cancer metastasis (254). Bone marrow consists of two major components, the stroma, which originates from mesenchymal cells that form the bone marrow matrix and parenchymal (hematopoietic) cells (255, 256). While hematopoietic cells are responsible for the generation of blood cells produced by the bone marrow, cells of the stroma, e.g., reticular cells (fibroblasts) and endothelial cells, participate in hematopoiesis by regulating the growth, proliferation, differentiation, and release of the hematopoietic cells that are housed in the bone marrow cavity (256-262). Cells are transported through the marrow as blood flows through a hierarchy of venous structures (262, 263). Arterial vessels feed into smaller arterioles and capillaries that are distributed throughout the bone marrow. From here blood is delivered to a network of interconnected bone marrow sinusoids that surround a central sinus drain. Sinusoids are distinct from other arterial components, because the monolayer of bone marrow endothelial cells that lines the sinusoid lacks a basement membrane of tissue, making sinusoids relatively permeable (256-263).

Near the arterial vessels hematopoietic stem cells are housed between sinusoids in regions collectively termed the stem-cell or vascular niche (260, 262, 264-268). In the vascular niche extracellular signals from stromal components influence the growth and differentiation of hematopoietic stem cells (258, 262, 263, 268). Recently, Winkler et al. (2012) discovered that the presentation of E-selectin in the vascular niche influences the cycling rate and proliferation of hematopoietic stem cells. In their investigation they revealed that the constitutive expression of E- selectin by endothelial cells promoted the differentiation and proliferation of hematopoietic stem cells, while hematopoietic stem cells in E-selectin deficient knockout mice had greater potential for self-renewal and enhanced quiescence (109, 269). 82

E-selectin can also affect the behavior of cancer cells. For example, Winkler et al. (2004) found that adhesion to E-selectin induced apoptosis and inhibited the growth of the KG1a leukemic cell line (270). Additionally, E-selectin has been shown confer survival advantages to

HT-29 and LoVo colon cancer cell lines by binding to death receptor 3 and engaging PI3K and

ERK dependent signaling pathways (234, 235). While similar E-selectin ligand activities have yet be observed in breast cancer cells, soluble E-selectin has been shown to enhance metastasis in breast cancer cells with elevated expression of CD44 (271).

Breast cancer metastases in the bone marrow have been found to be populated by cells with a CD24lowCD44high stem-like phenotype (199). With the knowledge that CD24lowCD44high stem-like breast cancer cells populate tumors in the bone marrow, the study herein examined how the constitutive expression of E-selectin by endothelial cells within the vascular niche of the bone marrow may affect stem-like breast cancer cells that metastasize to the bone marrow. To accomplish this breast cancer cells with a CD24lowCD44high stem-like biomarker profile and non- stem-like CD24highCD44high breast cancer cells were cultured with continuous exposure to a matrix presentation of E-selectin that modeled the constitutive expression of E-selectin on vascular niche endothelial cells.

Material and Methods

Proteins and Antibodies

Chinese Hamster Ovary (CHO) derived E-selectin (rhE), NSO-derived recombinant human E-selectin human Fc (hFc) chimera (rhE/Fc), NSO-derived recombinant murine E-selectin hFc chimera (rmE/Fc), NSO-derived recombinant human P-selectin hFc chimera (rhP/Fc), NSO- derived recombinant human L-selectin hFc chimera (rhL/Fc), hFc fragment, and anti-CD62E mAb (BBIG-E4) were purchased from R&D systems (Minneapolis, MN). F(ab')2 fragment goat anti-human IgG, hFcγ fragment specific allophycocyanin (APC) conjugated secondary antibody was purchased from Jackson Immunoresearch (West Grove, PA). 83

Cell Culture

Breast cancer cell lines were obtained from American Type Culture Collection (ATCC,

Manassas, VA). Hs578T, MDA-MB-231, and MCF-7 BC cell lines were cultured in Dulbecco’s

Modified Eagles Medium (DMEM, Thermo Scientific, Waltham, MA) with high glucose, L- glutamine, and sodium pyruvate supplemented with 10% fetal bovine serum (FBS) and 1x penicillin-streptomycin (Life Technologies, Carlsbad, CA). The BT-20 breast cancer cell line was cultured in Minimum Essential Medium (MEM, Thermo Scientific) supplemented with 10%

FBS, 1x penicillin-streptomycin, 0.1mM non-essential amino acids, and 1mM sodium pyruvate

(Life Technologies). Human umbilical vein endothelial cells (HUVEC) were obtained from

Lonza (Allendale, NJ) and were cultured in Medium 199 supplemented with an EGM bulletkit

(Lonza) and 10% FBS.

Breast Cancer Cell Culture on Protein Coated Plates

Ninety-six well flat bottom cell culture plates (BD Biosciences) or flexiPERM gaskets

(Greiner Bio-one, Monroe, NC) affixed to 35 mm cell culture dishes (Corning, Corning, NY) were incubated in 75µL of rhE, rhE/Fc, hFc, or fibronectin in DPBS+ at desired concentrations overnight at 37oC. Wells were washed twice with DPBS+. Breast cancer cell lines were plated at

8,000 cells/well in 75µL of cell culture media. In certain experiments cells were transduced with modified insect virus (baculovirus) containing actin-green fluorescent protein (actin-GFP,

CellLight Actin-GFP, BacMam 2.0, Life Technologies) or negative transduction control

(CellLight Null (control), BacMam 2.0, Life Technologies) using the manufacturer’s instructions.

Immunofluorescence Microscopy

Cells were imaged using 10x and 40x objectives under wide field fluorescence using a

Leica DMI 6000B inverted microscope (Leica Microsystems, Wetzlar, Germany) equipped with a motorized high precision stage and an automated optical filter cube wheel with appropriate excitation and emission filters as previously described (210). Background emissions were filtered 84 using a DC2-cube red-green filter (Photometrics, Tucson, AZ) (272) and a Nuance FX camera

(PerkinElmer, Waltham, MA). Images were acquired using Simple PCI software (Hamamatsu

Corporation, Sewickley, PA) or Nuance imaging software (PerkinElmer).

Annexin V and Propidium Iodide Staining

Annexin V and propidium iodide immunocytochemistry was performed according to the manufacturer’s protocol. Briefly, Hs578T cells were cultured in 96 well black-walled tissue culture plates (BD Biosciences) at a seeding density of 20,000 cells / well. Hs578T cells were cultured on E-selectin (rhE/Fc, experiment sample), human immunoglobulin fragment (hFc, experiment control), or in the presence of hydrogen peroxide (positive apoptosis control, 100μM, three hours incubation at 37oC in the incubator). Note that higher cell densities were used for the annexin V propidium iodide staining protocol than for most experiments, because some of the

Hs578T cells that formed mammosphere-like structures on E-selectin were lost during the washing steps of the assay.

Annexin-binding buffer was prepared by adding one part 5 x annexin binding buffer to four parts deionized water (sterile). A 100μg/ml working solution of propidium iodide was made by diluting 5μl of the 1mg/ml stock propidium iodide solution to 45μl annexin-binding buffer.

Next, cells were washed in DPBS. Subsequently, 20μl of annexin V conjugate was added to 50μl of annexin binding buffer and applied to the cells for 10 minutes at room temperature. Then 2μl of propidium iodide working solution was added to the cells, which were allowed to incubate for an additional 5 minutes at room temperature. The cells were then washed with annexin binding buffer and mounted with prolong gold mounting media.

HUVEC E-selectin Expression Assays

Site densities of E-selectin presented by human umbilical vein endothelial cells (HUVEC,

Lonza, Walkersville, MD) were calculated in terms of the concentration of E-selectin that was used to coat 96 well plates in E-selectin-substrate cell culture experiments. Prior to the assay the 85 signal range of the SpectraMax M2 multispectral plate reader was explored to determine whether potential assay concentrations of AlexaFluor 488 fluorophores conjugated to goat anti-mouse IgG secondary mAbs emitted saturating signals (high intensity) of fluorescent light. Fluorophores were excited at 485 nm and emissions were measured at 555 nm (550 cutoff). The chosen concentration of secondary for the assay was 8μg/ml and tested concentrations remained within a linear range of signal detection (Figure 21).

600

400

Intensity 200

0 0 5 10 15 20 Concentration (μg/ml)

Figure 21. SpectraMax M2 signal range of detection for AlexaFluor 488. Tested concentrations of AlexaFluor 488 secondary antibody remained in a linear range of signal detection. Data are mean ± SE from n = 3 replicate wells.

Two hours prior to HUVEC seeding, tissue culture plates were coated with fibronectin.

HUVEC were plated at a concentration of 20,000 cells/well in 96 well black-walled tissue cultured plates (BD Biosciences) and were allowed to culture overnight to achieve a confluent monolayer. At the time of seeding, empty wells were loaded with 75μl of desired concentrations of recombinant human E-selectin hFc chimeric protein or hFc proteins diluted in DPBS+ (for comparison between E-selectin site densities on HUVEC and protein coated plates). On the day of the experiment HUVEC were activated with 1ng/ml IL-β1 for 1 – 8 hours. Wells containing

HUVEC or proteins were then washed twice in DPBS+ and fixed in 2% paraformaldehyde / 86

DPBS-. Subsequently, wells were blocked with 5% FBS / DPBS+ for 30 minutes at room temperature. Wells were then washed twice with DPBS+ and incubated with mouse anti-human

E-selectin mAb (BBIG-E4, R&D Systems) or mIgG isotype control (BD Biosciences) for 30 minutes in 0.1% BSA / DPBS+ on ice. Wells were then washed twice and incubated with goat anti-mouse IgG AlexaFluor 488 secondary antibodies for 20 minutes on ice. Wells were then washed twice in DPBS+ and 100μl of DPBS+ was loaded into the wells before fluorescence intensity was measured with the plate reader.

Results

Adhesion to E-selectin Induces Atypical Growth Behaviors in Stem-like Breast Cancer Cells

The proliferation and cycling rate of hematopoietic stem cells in the vascular niche of the bone marrow are affected by exposure to E-selectin (109). To determine whether the growth behaviors of stem-like or non-stem-like breast cancer cells that metastasize to the vascular niche of the bone marrow may also be affected by exposure to E-selectin, breast cancer cells were cultured with continuous exposure to a matrix presentation of E-selectin modeling the constitutive expression of E-selectin on vascular niche bone marrow endothelial cells.

Breast cancer cells were determined to be stem-like or non-stem-like based on their presentation of stem cell biomarkers including CD24 and CD44. Breast cancer cells with a

CD24low CD44high biomarker profile were characterized as stem-like breast cancer cells, as previously described (53). Flow cytometric analysis was used to assay the cell lines for CD24 and

CD44 expression and revealed that BT-20, MDA-MB-468, and MCF-7 cells expressed a

CD24high CD44high non-stem-like biomarker profile whilst Hs578T and MDA-MB-231 cells presented a CD24low CD44high stem-like biomarker profile (Figure 22). Furthermore, the expression of CD24 on HMLE Snail and HMLE Twist cells that model the EMT was relatively low compared to the expression of CD24 on HMLE cells (Figure 14). In contrast, relative to the 87 level of CD24 expression that was detected on Hs578T shcontrol cells CD24 was presented at high levels on the Hs578T shTwist and Hs578T shSnail cells that modeled the MET (Figure 15).

Figure 22. Breast cancer cell lines were classified as stem-like or non-stem-like based on the expression of CD24 and CD44. Using flow cytometric analysis A) BT-20 cells, B) MDA-MB- 468 cells, and C) MCF-7 cells were detected to express a CD24highCD44high non-stem-like biomarker profile while D) Hs578T cells and E) MDA-MB-231 cells were detected to express a CD24lowCD44high stem-like biomarker profile. Results are representative of n > 3 independent experiments.

A matrix presentation of E-selectin was achieved by coating 96 well flat bottom tissue culture plates with recombinant human E-selectin hFc chimeric protein (rhE/Fc). To control for effects that might be attributable to the hFc region of the recombinant E-selectin protein, separate wells were coated with hFc protein fragments. Initially, the BT-20, MCF-7, MDA-MB-231, and

Hs578T cells were cultured on selectin or hFc coated plates that were coated using 1 μg/ml,

2 μg/ml, 5 μg/ml, 7.5 μg/ml, or 10 μg/ml of rhE/Fc or the molar equivalent of hFc protein fragment (Figure 23). Notably, for all of the tested concentrations BT-20 and MCF-7 cells had typical growth morphologies when grown on E-selectin or hFc substrates (Figure 23). In contrast, at concentrations above 5μg/ml the Hs578T cells formed mammosphere-like structures on E- 88 selectin substrates, but maintained their typical morphologies and growth behaviors on the hFc substrate (Figure 23 and Figure 24).

Figure 23A. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner. Selectins were plated at 2μg/ml. Data are representative of n = 4 independent experiments. Scale bars = 100 μm.

Figure 23B. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner. Selectins were plated at 5μg/ml. Data are representative of n = 4 independent experiments. Scale bars = 100 μm. 91

Figure 23C. Adhesion to E-selectin caused stem-like breast cancer cells with mesenchymal phenotypes, but not non-stem-like breast cancer cells with epithelial phenotypes to modify their growth behaviors in a concentration-dependent manner. Selectins were plated at 10μg/ml. Data are representative of n = 4 independent experiments. Scale bars = 100 μm.

Similar to the Hs578T cells, the stem-like MDA-MB-231 cells of a mesenchymal phenotype demonstrated atypical growth behaviors on E-selectin substrates prepared with 10

μg/ml of rhE/Fc, yet the formation of mammosphere-like structures was not observed. More specifically, the MDA-MB-231 cells did not spread out into their usual morphologies but remained spherical on the E-selectin substrate (Figure 25). Additionally on E-selectin substrates prepared with 10 μg/ml of rhE/Fc, HMLE cells maintained their typical morphologies, whilst the

HMLE Snail and HMLE Twist cells formed mammosphere-like structures (Figure 26).

Interestingly, the Hs578T shTwist and Hs578T shSnail cells also formed mammosphere-like structures on E-selectin substrates prepared with 10 μg/ml of E-selectin (Figure 27). Collectively, these data suggest that the constitutive expression of E-selectin presented by bone marrow endothelial cells influences the behavior of stem-like breast cancer cells, but not non-stem-like breast cancer cells.

Figure 24. E-selectin-induced formation of mammosphere-like structures of Hs578T cells occurred in a concentration dependent manner. Hs578T cells were cultured on substrates treated with rhE/Fc at concentrations ranging from 1-10 µg/ml or with equimolar concentrations of hFc. After 24 h Hs578T cells cultured on plates treated with 1 µg/ml of rhE/Fc maintained morphologies similar to the morphologies of Hs578T cells cultured on hFc or untreated tissue culture plates, yet some mammosphere formation was observed in Hs578T cells cultured on plates treated with 5 µg/ml and 7.5 µg/ml of rhE/Fc. Pronounced mammosphere-like structures were formed by Hs578T cells cultured on plates treated with 10 µg/ml of rhE/Fc. Scale bar = 100 µm. Data are representative of n = 5 independent experiments. 93

Figure 25. Stem-like breast cancer cells with mesenchymal phenotypes exhibited atypical culture morphologies on E-selectin substrates. Breast cancer cells lines were cultured on E-selectin coated tissue culture plates prepared using 10μg/ml of rhE/Fc or the molar equivalent of hFc. Stem-like breast cancer cells including Hs578T and MDA-MB-231 cell lines did not spread out on the E-selectin coated plate and demonstrated atypical morphologies, yet the non-stem-like BT- 20, MCF-7, and MDA-MB-468 cells did not appear morphologically distinct on hFc or E-selectin substrates. Scale bar = 100μm. Data are representative of n > 3 independent experiments.

Figure 26. A matrix presentation of E-selectin on tissue culture plates induced the formation of mammosphere-like structures in stem-like mammary epithelial cells with mesenchymal phenotypes, yet non-stem-like HMLE cells with epithelial phenotypes appeared to be unaffected by E-selectin. A) HMLE Twist cells form mammosphere-like structures on tissue culture plates treated with rhE/Fc, yet B) HMLE cells maintain their typical morphologies on tissue culture plates treated with rhE/Fc. Cells were assayed for the expression of integrin α6 (green), plakoglobin (red), HECA-452 (cyan), and DAPI (blue) using widefield microscopy. Data are representative of n = 3 independent experiments. Scale bars = 100μm.

Figure 27. Hs578T cells with shRNA knockdown of A) Twist or B) Snail formed mammosphere- like structures on E-selectin treated tissue culture plates. Data are representative of n = 3 independent experiments. Scale bar = 100μm.

The aberrant behavior of Hs578T cells cultured on E-selectin was further investigated by culturing the cells on plates coated with regions of E-selectin that were adjacent to regions coated with fibronectin. Interestingly, the Hs578T cells appeared to “avoid” the E-selectin coated regions

(Figure 28). Additionally, cells that cells that did not avoid the E-selectin region remained spherical in morphology, while Hs578T cells that grew on fibronectin-coated regions maintained typical morphologies (Figure 28).

To determine whether a matrix presentation of E-selectin was required to induce the formation of mammosphere-like structures in Hs578T cells or if soluble E-selectin was capable of inducing a similar response, the Hs578T breast cancer cells were grown on standard tissue culture plates with media supplemented with 10 μg/ml of rhE/Fc. The cells maintained normal morphologies under these conditions and on tissue culture plates coated with mAbs targeting

HECA-452 antigens that are not expressed by Hs578T cells (negative control, Figure 29). From this finding it was apparent that a matrix presentation of E-selectin was needed to induce the formation of mammosphere-like structures in Hs578T cells. 95

Figure 28. Hs578T cells had a spherical morphology when cultured on E-selectin-coated regions of tissue culture plates, yet the Hs578T cells maintained typical morphologies on adjacent fibronectin-coated regions of tissue culture plates. Adjacent areas of a tissue culture plate were treated with either rhE/Fc (10 µg/mL) or fibronectin (molar equivalent). Hs578T cells appeared to avoid rhE/Fc-treated areas of the tissue culture plate, on which the cells did not successfully spread out to assume a typical morphology. Scale bars = 100 µm. Data are representative of n = 4 experiments.

Figure 29. A matrix presentation of E-selectin but not soluble E-selectin induced Hs578T cells to form mammosphere-like structures. A) Hs578T cells that were cultured on tissue culture plates in media supplemented with 10 μg/ml of rhE/Fc maintained typical growth morphologies. B) Hs578T cells grown on tissue culture plates with a matrix presentation of E-selectin (10 μg/ml rhE/Fc) formed mammosphere-like structures. White arrows indicate cell debris. C) Hs578T cells maintained typical growth morphologies on tissue culture plates treated with 10 μg/ml of HECA- 452 mAb (negative control). Images were acquired after 24 hours in culture. Data are representative of n=4 independent experiments. Scale bar = 100 µm.

The mammosphere-like structures that Hs578T cells formed on E-selectin substrates appeared to be surrounded by cell debris (Figure 29B, white arrows). Thus, to elucidate whether the Hs578T cells pursued a programmed cell death pathway, e.g., apoptosis or necrosis, in response to culture on E-selectin, the Hs578T cells were assayed with annexin V and propidium 96 iodide. Signals for both annexin V (green) and propidium iodide (PI, red) were detected on the

Hs578T cells that were cultured on E-selectin substrates. In contrast, relatively little signal for annexin V was detected from the Hs578T cells cultured on the hFc control substrate, and signal for propidium iodide was not detected on cells that were cultured on the hFc control substrate

(Figure 30). PI is not permeable to live cells and apoptotic cells typically pursue cell death while maintaining membrane integrity such as not to permit the entry of PI. Thus, these data suggest that the Hs578T cells that were cultured on E-selectin underwent cell death via necrosis.

Typically during necrosis the integrity of the cell membrane is lost while the cell dies. The release of intracellular components that follows membrane failure is possibly the source of the cell debris that surrounded the Hs578T cells that were cultured on E-selectin (Figure 29B, white arrows).

Figure 30. Hs578T cells cultured on E-selectin treated tissue culture plates underwent cell death. A) Hs578T cells cultured on hFc treated tissue culture plates assumed their typical morphologies and express low levels of annexin V (green). In contrast, B) Hs578T cells cultured on rhE/Fc treated tissue culture plates (10μg/ml) formed mammosphere-like structures, expressed annexin V (green), and bound propidium iodide (red) indicating that the cells in mammosphere-like structures were pursuing cell death via necrosis after 24 hours. Data are representative of n=4 independent experiments. Scale bar = 50μm.

To determine whether the response of breast cancer cells to culture on E-selectin may be observed in the vascular niche of the bone marrow where endothelial cells constitutively express

E-selectin, the site density of E-selectin presented by activated HUVEC, which express similar levels of E-selectin compared to bone marrow endothelial cells (273), was quantified in terms of

E-selectin incubation concentrations. HUVEC presented E-selectin with a site density that was equivalent to incubating 96 well plates with 1 - 2 μg/ml of rhE/Fc (Figure 31).

Figure 31. Site densities of E-selectin expression on HUVEC were quantified in terms of E- selectin incubation concentrations. E-selectin constructs were targeted by monoclonal mAbs and fluorophore conjugated secondary antibodies, and site density intensities were quantified using a fluorescence plate reader for both A) recombinant human E-selectin hFc chimeric protein coated onto 96 well tissue culture plates using a range of incubation concentrations and B) HUVEC that were stimulated to express E-selectin using 1 ng/ml IL-β1 for set periods of time. Data are mean ± SE from n = 3 technical replicates and representative of patterns observed from n = 3 independent experiments.

To further investigate whether breast cancer cells may form mammosphere-like structures on endothelial cells expressing E-selectin, breast cancer cells were cultured on HUVEC stimulated to express E-selectin. Hs578T and BT-20 breast cancer cell lines were separately cultured on HUVEC or on HUVEC that were fixed in paraformaldehyde. Hs578T cells formed mammosphere-like structures on fixed and unfixed activated HUVEC, but not inactivated

HUVEC (lacking E-selectin). However, BT-20 cells displayed abnormal morphologies when 98 grown on fixed and unfixed HUVEC monolayers (Figure 32). Additionally, HUVEC co-cultured with BT-20 breast cancer cells had abnormal morphologies, demonstrating that the cell lines poorly tolerated co-culture (Figure 32). Collectively, these data show that the induction of cell death pathways in breast cancer cells via E-selectin stimulation may be achieved when E-selectin is presented in clusters with site densities that are equivalent to an E-selectin incubation concentration above 5μg/ml.

Figure 32. Breast cancer cells were cultured on HUVEC monolayers. Hs578T cells formed mammosphere-like structures when grown on monolayers of activated HUVEC that were A) fixed in paraformaldehyde or B) unfixed. BT-20 cells did not form mammosphere-like structures on monolayers of activated HUVEC that were C) fixed or D) unfixed. When Hs578T cells were grown on monolayers of E) fixed inactivated HUVEC or F) inactivated HUVEC the morphologies of the Hs578T cells resembled typical culture morphologies. BT20 cells exhibited atypical morphologies on monolayers of inactivated HUVEC that were G) fixed or H) unfixed. Images were acquired using brightfield or fluorescence microscopy using live cell Hoechst dye. Data are representative of n = 3 independent experiment. Scale bar = 100μm.

To gain insight into the signaling mechanism(s) that were activated as the Hs578T cells formed mammosphere-like structures on E-selectin coated tissue culture plates, the relative changes in gene expression between Hs578T cells grown on E-selectin (10μg/ml) or hFc (molar equivalent) were analyzed using qRT-PCR and RT2 Profiler PCR Arrays for apoptosis, Wnt,

Hedgehog, and Notch signaling pathways. Furthermore, to gain insight as to how the extracellular matrix may have been modified in Hs578T cells that were grown on E-selectin, the cells were assayed with an extracellular matrix RT2 Profiler PCR Array.

Analysis of changes in gene expression that were quantified using the apoptosis RT2 PCR

Profiler Array revealed that BCL-2 genes and genes for TNF alpha superfamily of receptors were upregulated in Hs578T cells that were cultured on E-selectin, relative to the negative controls

(Table A3). Additionally, in the Wnt signaling pathway there were differences in gene regulation, most noticeably in WNT2B, VANGL2, and FRAT1 genes, which were expressed at greater levels in the Hs578T cells that were cultured on E-selectin substrate, compared to the controls (Table

A4). Furthermore, the Hedgehog PCR array revealed that relative to the Hs578T cells grown on hFc substrate, the Hs578T cells that formed mammosphere-like structures on E-selectin highly expressed genes for BCL-2, DHH, SMO, and VEGFa (Table A5).

Differences in gene expression were greatest in the Notch signaling pathway in which genes including ABL1, BDNF, CLOCK, EFNA1, EGR3, FRZB, HBEGF, HEY2, ID4, JUN,

KRT14, NAMPT, NOTCH3, RUNX1, SNAI1, SNW1, and VEGFa were highly upregulated in the Hs578T cells that were cultured on E-selectin substrate, compared to the Hs578T cells that were cultured on the hFc control substrate. Genes for extracellular matrix proteins including collagen receptors such as COL15A1, COL16A1, and COL1A1 were highly upregulated in

Hs578T cells grown on E-selectin coated plates relative to Hs578T cells grown on hFc coated plates (Table A7). In addition, RNA expression of THBS2 that codes for a protein that binds collagen and fibronectin was highly upregulated, as well as ITGAM, which is a gene that codes 100 for the integrin alpha M chain that is upregulated for platelet signaling and aggregation. However, genes for metalomatrix proteases were not consistently upregulated or downregulated. For example while MMP1, MMP3, MMP8, MMP11, and MMP12 genes were downregulated, gene expression of MMP2, MMP7, and MMP14 were upregulated.

Discussion

Breast cancer metastases in the bone marrow are populated with breast cancer cells expressing a CD24lowCD44high stem-like biomarker profile (199). Treatment of dormant micro- metastasis via chemotherapy may not prove effective, because the mechanism of action of many chemotherapies is associated with cellular replication (274). However preventative measures, e.g., E-selectin-inhibition, for high risk patients may avert the formation of bone marrow micro- metastasis. For example, in a recent study Price et al. (2016) demonstrated that the presentation of

E-selectin in the vascular niche was necessary for successful recruitment of metastatic breast cancer cells to the bone marrow (275). Using a breast cancer xenograft model, Price et al. (2016) demonstrated that disseminated breast cancer cells are predominantly found in E-selectin rich regions of the bone marrow. Notably, the micro-metastasis in this study were largely dormant prior to chemokine interference, which promoted intravasation of the disseminated breast cancer cells into the blood stream (275). Thus it has been established that E-selectin plays a critical role in trafficking breast cancer cells to the bone marrow via the vascular niche, which is composed of sinusoids that are supported by endothelial cells with constitutive expression of E-selectin (275).

Published works have revealed that the constitutive presentation of E-selectin by bone marrow endothelial cells that line blood vessel and sinusoid walls in the vascular niche is not only influential to the recruitment of breast cancer cells but also plays a role in the maintenance and proliferation of hematopoietic stem cells (109, 275). For example, in E-selectin deficient mice hematopoietic stem cells were demonstrated to maintain slower cycling rates and were comparably quiescent relative to the wild type controls (109). Based on these findings Winkler et 101 al. (2012) proposed that E-selectin antagonists may promote the survival of hematopoietic stem cells in patients that undergo chemotherapy (109). Additionally, Chien et al. (2013) found that the expression of E-selectin in the vascular niche of the bone marrow promoted the survival of acute myeloid leukemia (AML) blasts, and that inhibition of E-selectin activity using an E-selectin antagonist reduced patient tumor burdens (222). E-selectin conferred survival advantages to the

AML cells by promoting the upregulation of constituents of the Wnt and sonic hedgehog pathways (222). Interestingly, the published works of Chien et al. (2013) are inconsistent with the findings of Winker et al. (2004) that show leukemic KG1a cells become apoptotic with E-selectin exposure (270). The discrepancy between the findings of these studies has been attributed to variations in experiment design, since the study by Chien et al. (2013) modeled bone marrow homing in mice and Winkler et al. (2004) observed the effects of E-selectin on KG1a cells in vitro (222, 270, 276).

E-selectin has also been shown to affect the behavior of cancer cells. For example, E- selectin was found to confer survival advantages to HT-29 and LoVo colon cancer cell lines by binding to death receptor 3 and engaging PI3K and ERK dependent signaling pathways (234,

235). Although E-selectin has been established as a potential mediator of CTC trafficking to the bone marrow during breast cancer metastasis (275), it remains unclear whether E-selectin confers survival advantages to breast cancer cells or if E-selectin plays a role in regulating the behaviors of dormant micro-metastases in bone marrow.

In the present work E-selectin was investigated as a potential modulator of growth behavior in breast cancers that occupy the vascular niche of the bone marrow using an in vitro system. Hs578T mesenchymal stem-like breast cancer cells formed mammosphere-like structures on E-selectin in a dose dependent response, such that E-selectin substrates prepared with less than

5μg/ml of E-selectin did not elicit the formation of mammosphere-like structures (Figure 24).

Interestingly, the Hs578T cells differentially responded to culture on equimolar concentrations of 102 recombinant human or murine E-selectin hFc chimeric protein produced from a mouse myeloma cell line (NS0-derived) and recombinant E-selectin (non-chimeric) produced in Chinese hamster ovary cells (Figure 33). Note that chimeric and non-chimeric forms of recombinant E-selectin are reported to share the same amino acid sequence (R&D systems) but are structurally different in that the rhE/Fc protein is divalent whereas the rhE protein is monovalent. The divalent rhE/Fc construct may have promoted E-selectin clustering to a greater extent than the rhE construct.

Similarly, clustering of E-selectin may explain why Hs578T cells cultured on activated HUVEC formed mammosphere-like structures in a site specific manner (Figure 32).

Recombinant human E-selectin hFc chimeric protein (rhE/Fc) was used throughout this investigation, and the non-chimeric form of the protein (rhE) was only used when indicated.

Additionally, although Hs578T cells demonstrated a stronger response to culture on rhE/Fc compared to rhE or rmE/Fc (murine) coated plates the Hs578T cells were hypothesized to demonstrate a stronger response (more readily form mammosphere-like structures) on murine E- selectin compared to human E-selectin, because murine E-selectin has been demonstrated to have a higher affinity for E-selectin ligands than human E-selectin (277) and more readily binds E- selectin ligands in static (no-flow) conditions (laboratory observations). Furthermore, other studies have reported differences in the affinities of recombinant E-selectin chimeric proteins of rat, mouse, and human species for CD4+ T-cells expressing HECA-452 antigens (278), thus these finding support the results shown herein, which demonstrate different species of E-selectin differentially affect the behavior of Hs578T cells (Figure 33).

Hs578T cells that formed mammosphere-like structures on E-selectin appear to undergo necrosis (Figure 30) due to BCL-2 family and TNF superfamily receptor gene expression (Table

A3). Additionally, compared to Hs578T cells cultured on hFc protein, CD40 and CD40LG genes were highly upregulated in Hs578T cells that formed mammosphere-like structures on E-selectin.

The CD40/CD40L signaling pathway has been linked to the expression of the 103 immunosuppressive cytokine IL-10 in breast cancer cells (279), and the IL-10 gene was upregulated in Hs578T mammosphere-like structures, relative to the control (Table A3). CD40 has been shown to induce cell death in SV80 and HeLa cells that were transfected with the CD40 gene (280). Furthermore, CD40 was shown to induce necroptosis-like cell death in ovarian cancer cells without caspase activation (281).

CD40/CD40L signaling has previously been shown to contribute to the induction of apoptosis in breast cancer cells (282) and may induce necrosis or necroptosis in Hs578T cells cultured on E-selectin, yet the expression of TRAF2 and TRAF3 RNA, which are translated into tumor-necrosis-factor-receptor-associated factors (TRAFs) that bind to the cytosolic domain of

CD40 to mediate signaling (283) was downregulated. Consequently, if a CD40/CD40L signaling mechanism did cause the Hs578T cells to pursue cell death on E-selectin it may either be dependent on TRAF5/6 activity or function via an alternative CD40-mediated signaling pathway

(284). Further investigation is required to reveal the signaling mechanism by which the Hs578T cells pursued cell death on E-selectin and whether cell death also occurs in vivo.

The Wnt, Hedgehog, and Notch signaling pathways are highly conserved and involved in embryonic development, stem cell differentiation, and apoptosis (285-287). These signaling pathways have also been linked implicated in molecular mechanisms of the EMT (288), and the maintenance of cancer stem cells (289). Additionally, aberrant Notch signaling has been shown to be involved in the development and progression of breast cancer tumors (290). Notch signaling has also been shown to inhibit anoikis and promote the formation of prostate cancer spheroids (291). The preliminary analyses provided by the RT2-PCR profiler arrays (Table A4 –

Table A6) indicate that Wnt, Hedgehog, and Notch signaling may play a role in the formation of

Hs578T-cell-mammosphere-like structures that were cultured on E-selectin. However, due to the many functions of these signaling pathways further investigation is needed to determine how they may contribute to an E-selectin-induced response in breast cancer cells. 104

In addition to Wnt, Notch, and Hedgehog signaling pathways, signaling and expression of extracellular matrix adhesion molecules contributes to cancer progression, cell motility, changes in cell morphology, and apoptosis (84, 292-298). In the extracellular matrix RT2-PCR profiler array (Table A7) gene expression of metalomatrix proteases (MMPs) varied, thus the role of MMPs in the formation or maintenance of the mammosphere-like structures remains unclear.

Interestingly, the Hs578T cells grown on E-selectin upregulated the expression of the THBS2 gene that gives rise to Thrombospondin-2, which has been implicated as an inhibitor of angiogenesis and tumor progression (299). Furthermore, collagen genes were highly upregulated indicating that the Hs578T cells may have upregulated genes coding for collagen in effort to better support their mammosphere-like morphology.

Figure 33. Equivalent concentrations of recombinant human or murine E-selectin-hFc protein produced in CHO cells (rhE) or NSO cells (chimeric protein rhE/Fc and rmE/Fc) elicited a differential response in Hs578T cells. A) Hs578T cells were transfected to express monomeric actin-GFP and cultured on coated tissue culture plates for 48 hours. Scale bars = 100μm. B) Intensity measures of actin-GFP were quantified using NUANCE cell analysis software. Data are mean ± SE. *, #P<0.05 ANOVA and post-hoc Tukey’s test. Data are n = 3 independent experiments. 105

To fully elucidate a potential mechanism for E-selectin-mediated cell death, survival, or quiescence of breast cancer cells, future works will benefit from the use of an in vivo model, especially since there are discrepancies between the effects that E-selectin has on the behavior of leukemic cell lines in vitro and in vivo (222, 270). Additionally, these studies may elucidate whether Hs578T cells suffered cell death via E-selectin ligand signaling, or if the cells perished due to a non-supportive mammosphere-culture environment, i.e., experimental conditions.

Nonetheless, the study performed herein demonstrates that the constitutive expression of E- selectin in the vascular niche of the bone marrow may incite metastatic breast cancer cells to modify their behavior, e.g., form mammosphere-like structures, via Wnt, Hedgehog, and/or Notch signaling pathways.

106

CHAPTER 5: INVESTIGATING THE EFFECTS OF THE MET ON THE APPARENT

CELLULAR MECHANICAL PROPERTIES OF BREAST CANCER CELLS

Introduction

During hematogenous breast cancer metastasis, cancer cells break away from the primary tumor and invade the blood stream by transmigrating through endothelial cells that protect the basement tissue from forces generated by blood flow (Figure 2) (32). The EMT has widely been incorporated into models of breast cancer metastasis (32, 44, 50, 52, 300-302), because as breast cancer cells transition toward a mesenchymal phenotype they become increasingly motile (303).

Changes in motility occur as the expression for proteins that function in cell-cell adhesion and cytoskeletal construction are modified by the EMT. For example, during the EMT epithelial breast cancer cells reduce the expression of cytokeratin intermediate filaments and E-cadherin, which facilitates cell-cell adhesion, but increase the expression of vimentin intermediate filaments and N-cadherin cell-cell adhesion molecules (41, 43-46).

Modifications in protein expression that occur during the EMT bring about changes in cellular mechanical properties. For example, the cytoskeletal structure and motility of epithelial cells is largely influenced by keratin intermediate filaments (49), yet as cells adopt a mesenchymal phenotype through the EMT the expression of cytokeratin is reduced as vimentin becomes a highly expressed intermediate filament in the cell (47). Recently, Seltmann et al.

(2013) used an optical stretcher and a modified power-law model to show that keratin provides a significant contribution to cell structure, and that by decreasing the keratin content in mammalian cells the cells were made more deformable, more invasive, and less stiff (49). Similarly,

Schneider et al. (2013) used force spectroscopy to demonstrate that the EMT of murine mammary epithelial cells reduces the apical tension of the plasma membrane (68). Notably, as the murine mammary cells achieved a transition state in the EMT spectrum their apical tension increased, yet as the cells achieved a mesenchymal-like state this apical tension was minimized (68). From this 107 observation and from other works it is apparent that the mechanical properties of mammalian cells are a function of the expression and organization of cellular proteins that have distinct mechanical properties and are regulated by the EMT (47, 49, 188, 190, 304-308).

Mathematically, one may view EMT-induced changes in cellular mechanical properties as a function of protein content and relative position within the cell using equation 1 in which t is time, M are mechanical properties of the cell, c is the concentration of k proteins located at x positions within the cell.

 2 M  2 c  k k = n types of proteins, i, j =1,2,3 coordinates. (1) 2  xt i xij

The nucleus also makes a significant contribution to the mechanical properties of cancer cells, thus the concentration and position of proteins relative to the nuclear envelope need to be accounted for as well (304, 309, 310) and equation 1 becomes equation 2 where the subscript v indicates cytosolic and nuclear mechanical properties of the cell. Since proteins, e.g., histones, nucleic acids, and ionic distributions throughout the nucleus affects the mechanical properties of the cell as well (174, 310, 311), allow k= n proteins and nucleic acids. Note that equations 1 and

2 do not account for the cortical tension of the cytoplasmic membrane or nuclear envelope which have been modeled extensively by Herant et al. (2005), Dahl et al. (2004), and others, and contribute to the overall mechanical properties of the cell (127, 156, 163, 174, 180, 312, 313).

 2 M  2c v  k (2) 2  xt i xij

Few studies have examined the effects of the MET on the mechanical properties of breast cancer cells. Herein the effects of the MET on the apparent cellular mechanical properties of breast cancer cells were studied by deforming cells in a microfluidic device and modeling cells as liquid drops of a power-law fluid contained in cortical shells of constant tension, using a numerical simulation and a modified power-law model adapted from the shear thinning liquid 108 drop model developed by Tsai et al. (1993) (153). Although more complex models for cellular deformation have been developed to describe how cellular constituents, e.g., the nucleus, contribute to the mechanical properties of cells (156, 163, 180) the present investigation merely aims to compare general apparent cellular mechanical properties of breast cancer cells, and recent works have shown that the power-law model allows for computation and comparison of the apparent cellular mechanical properties of cancer cells (160).

Methods and Materials

Microfluidic Fabrication

The microfluidic device described herein was fabricated using polydimethylsiloxane

(PDMS) formulated as a 10:1 ratio of SYLGARD 184 silicone elastomer base to SYLGARD 184 silicone elastomer curing agent provided in the SYLGARD 184 silicone elastomer kit

(SYLGARD, Sigma) as previously described (314). Briefly, PDMS was cast over a pre-fabricated

SU-8 2010 (Microchem, Westborough, MA) substrate base (314). Prior to curing the PDMS the elastomer mixture was mixed in a 50 ml conical tube (Fisher Scientific) and degassed for approximately one hour. Subsequently, the elastomer mixture was decanted into the mold, which was affixed over the SU-8 base and degassed for an additional one hour. Next the elastomer mixture was cured at 150oC for 40 minutes on a heating block. After the PDMS solidified, PDMS was cleared from the inlet (reservoir) and outlet (hose barb). Next the microfluidic device and microscope slides were cleaned. Microscope slides were incubated overnight in an aqueous solution of 5N hydrochloric acid (HCl), washed in deionized water (DI water), and dried.

The microfluidic device was thoroughly washed using a series of washes in acetone, 70% ethanol, and deionized water. Before attaching the microfluidic device to the microscope slide, the bottom of the device (the side in which the channel is fabricated) was rinsed in 5N HCl, then rinsed in DI water, and finally dried using compressed air. The microfluidic device was thermally attached to microscope slides by heating the device on the microscope slides for four hours at 109

80oC on a heating block. The width of the microfluidic channel was measured as 10±0.50μm using ImageJ video analysis software and the height of the channel (20±3μm) was measured along the optical axis of a DMI6000B inverted microscope using SimplePCI video analysis software (Hamamatsu, Sewickley, PA, Figure 34).

Figure 34. Optical measurement of the channel height in the microfluidic device. Channel height was measured by optically focusing along the z (optical) axis of a DMI6000B inverted microscope using SimplePCI software. A) Top of the microfluidic channel. B) Bottom of the microfluidic channel. Images were acquired using a 40X objective.

Cell Aspiration and Deformation

Prior to cell deformation experiments, the microfluidic channel was blocked in 1%

BSA/DPBS+ for one hour at room temperature to inhibit non-specific cell adhesion to the microscope slide or the PDMS microfluidic channel. First, 1 ml of 1% BSA/DPBS+ was 110 deposited in the reservoir (inlet). Subsequently, 1 ml of fluid was drawn into a 30ml syringe

(refill volume) to prime the channel using a Harvard PHD 2000 syringe pump (Harvard apparatus). Fluid was drawn into the syringe through tygon tubing that was attached to the outlet of the microfluidic device. Video of cell deformation experiments was recorded to a computer via

StreamPix software (Norpix, Montreal, Canada) using a CCD camera that was mounted on a

DMI6000B inverted microscope (Figure 35). Analyses of cell deformation experiments were conducted using ImageJ video analysis software.

During the cell deformation experiments cells were aspirated through the microfluidic channel at pressures that were generated by withdrawing fluid into a 30 ml syringe using a

Harvard PHD 2000 syringe pump. The aspiration pressure was approximated using a linear regression over pressures measured by Rodriguez et al. (2012) (Figure 36) (315). Hs578T shcontrol and Hs578T shTwist cells were cultured as described in the previous chapters. Cells were harvested using 5mM EDTA/DPBS- and incubated in 1% BSA/DPBS+ for one hour on ice.

Prior to the experiment the cell suspension was brought to room temperature. Cells were aspirated through the microfluidic channel in 1% BSA/DPBS+. Approximately 500,000 cells were loaded into the reservoir for each set of experiments. To prevent contamination of the cell lines the microfluidic device was disassembled (detached from the microscope slide) and reassembled between experiments.

111

Figure 35. Schematic of the equipment setup for breast cancer cell deformation experiments conducted using a microfluidic device. Objects in the figure are not to scale.

Figure 36. Aspiration pressures in the microfluidic device were generated using a 30 ml syringe pump and were calculated from A) data published by Rodriguez et al (315) using B) a linear regression.

112

Damped Power-Law Model

Herein a damped power-law model modified from the power-law model developed by

Tsai et al (153), was used to calculate the apparent cellular mechanical properties of the Hs578T shTwist and Hs578T shcontrol breast cancer cells. In this model the cross sectional area of the microfluidic device was assumed to obey a circular profile, despite the rectangular shape of the channel. A similar approach was recently employed by Byun et al. (2013), because differences between the calculated values of the apparent cellular mechanical properties of cells that were deformed through a rectangular channel opening or a circular opening of equivalent cross- sectional area were assumed to be small using the shear thinning model (160). The error introduced by this assumption is explored later in this section.

In the power-law model developed by Tsai et al. (1993) the cell is modeled as a sphere composed of an isotropic and homogeneous power-law fluid (cytoplasm) that is surrounded by a cortical (plasma) membrane with a constant surface tension (153). The entry of the cell into the microfluidic channel was modeled as axisymmetric cytoplasmic plug flow and herein this assumption is supported by experimental observations (Figure 37). Furthermore, to simplify the analysis and computation of cell entry into the microfluidic channel the viscosity of the cytoplasm was approximated as a function of the instantaneous spatial average shear rate rather than the local average shear rate which depends both on time and space (153). Thus terms of viscosity were only dependent on changes in time. The assumption that viscosity is governed by temporal but not spatial factors was also employed in the original power-law model (153). Herant et al.

(2003) and Lam et al. (2009) have modeled cell entry into a micropipette by calculating cellular mechanical properties as a function of both space and time, and these models are discussed later in brief (156, 163, 180).

113

Figure 37. Axis symmetric entry of an Hs578T shcontrol cell into the microfluidic channel measuring 10μm in width and 20μm in height. Scale bar = 100μm.

The governing equations of cytoplasmic flow under the assumptions of the power-law model were derived by Tsai et al. (1993) and formulated around a spherical coordinate system. A similar solution strategy for modeling the stress components of single cell mechanics was published by Schmid-Schönbein et al. (1981) (182). The momentum balance for cytoplasmic flow is given by the Naiver-Stokes equations (3).

  p   u j  1   u            i    (3) u j    fuu jij     rji ,,, t xi  xx ij   i  3 xx j  xi 

To simplify the Naiver-Stokes equations apply the continuity equation, which states that mass is conserved. When density is held constant with respect to time the auxiliary expression for the continuity equation (equation 4) yields the conservation of momentum (equation 5).

   ui  (4)  xt i  ui   0 (5) xi

Since flow in the microfluidic device is confined to the microscopic dimensions of the microfluidic channel, the formation and disassociation of eddies that are characteristic of turbulent flow are unlikely to occur. Thus turbulent flow terms   uux iji  are eliminated 114 from the in the Navier-Stokes equation (equation 1). Additionally, Reynolds numbers for cytoplasmic flow were calculated to be on the order of 10-6 and well below the turbulent threshold

(< 4500) further supporting the elimination of turbulent terms.

Gravitational body forces are assumed to be negligible within the microfluidic channel, thus the Naiver-Stokes equation is reduced to an expression for cytoplasmic velocity as a function of pressure (equation 6).

2 p   u j     (6)    2  x j  xi 

Assuming that cell into the microfluidic channel is axis symmetric the stream function r  ),(

implemented by Tsai et al. (1993) is applied such that velocity in the radial direction ur  is

given by equation 7 and the velocity in the  direction u  is given by equation 8.

1  u  (7) r r 2 sin 

1  u  (8) sin rr

From the continuity equation the stream function is given by equation 9.

4 E   0 (9)

Where E2 is defined by the expression given in equation 10.

 2 1  2  2 E 2   ,  cos  (10)  2 rr 2  2

The general solution of equation 9 has been established as equation 11 (316, 317).

 n n2 (11)   n  n IrCrA n   n2 115

In equation 11 the coefficients An and Cn are determined using the boundary conditions. Where

I n  are Gegenbauer functions of the first kind (equation 12), which are composed of Legendre

polynomials Pn   .

  PP   I    n2 n n  ...3,2,1 indices of summation. (12) n n 12

Under the assumptions of the power-law model Tsai et al. (1993) defined the stress boundary conditions governing cytoplasmic deformation for shear stress  r (equation 13) and

normal stress  rr (equation 14) as a function of the constant cortical tension T , the

instantaneous radius of the cell R , the radius of the cell in the microfluidic channel Rf , the reaction stress resultant from contact between the cell and the channel k , the atmospheric pressure at the entry port ( pa ), and the pressure resultant from flow in the microfluidic channel

( p p ) (Figure 38). 116

Figure 38. Diagram depicting the entry of a cell into the microfluidic channel using modeled geometries and a defined constant pressure. "Reprinted from Biophysical Journal, Vol 65, Mientao A. Tsai, Robert S. Frank, and Richard E. Waugh, Passive Mechanical Behavior of Human Neutrophils: Power-Law Fluid, 2078-2088., Copyright (1993), with permission from Elsevier and Biophysical Journal.

 r  0 (13)

  2T    p  , ,1 ,cos cos(  cos()  )     a R  p p p p p p p p p  (14)  rr   k, p p      2T         p p , 1 p   R f 

With the stress boundary conditions defined, Tsai et al. (1993) developed equations for stress in terms of cytoplasmic velocity to define the flow field using equations 15 and 16. 117

u  p  2 r (15) rr r

1 ur   u  r     r   (16) r  r  r 

Thus application of the boundary equations yields equations 17 and 18 that define the cytoplasmic flow field.

n n2 pR    r  n2 1 r   r uu 0  n      n , Pf np 1   (17) 4 n3   R  n  2  R  

  n 2 n2  pR  r  n 1 r  I n    uu 0 sin   nn  2       n , Pf np 1 (18) 4 n3   R  n  2  R   sin

Where u0 is defined as the center velocity of the cell entering the microfluidic channel under the condition that the cell remains in constant contact with the microfluidic channel entrance as it enters into the channel (equation 19).

pR   n 12 3n      u0  pn , nf  P 1 pnp  I pn , (19) 4 n3  n  2 n  2 

The term f pn ,  is given by equation 20. Additionally, ph is the pressure difference between the applied pressure in the microfluidic device and the atmospheric pressure (equation 21),  is a ratio of the cortical tension to the pressure difference (equation 22), p is the pressure drop

acting on the cell (equation 23), Rp is the hydraulic radius of the microfluidic channel of height h and width w (equation 24).

 22 22    nn  112   1 1 p     1 1 p    f ,      I     I   , pn 2     pn    pn  n 12  2 4 p   2 4 p   (20) 118

 ppp pah (21)

2 RT p    (22) ph

  R R    p  p  pp h 1   (23)   R f R 

hw R  (24) p  wh

In order to calculate the instantaneous spatial average shear rate Tsai et al. (1993) introduced equations for viscous energy dissipation that are proportional to and constituted by components

of the cellular deformation tensor ij (equation 25).

1   rji ,,,  (25) 2 ijij

With this definition of viscous energy dissipation the shear rate was defined by Tsai et al., as 

(equation 26) which was approximated in the numerical simulation (Appendix B) by the instantaneous average shear rate a (equation 27) which is an average of for the spherical region of the cell with uniform flow field deformation of the cytoplasm.

   (26)

1  3 tR )(  r 2  2    sin d dr a    3  (27)  2 0 0 R 

Using the aforementioned equations Tsai et al. (1993) defined the (instantaneous spatial average) viscosity,  as a function of the instantaneous average shear rate using equation 28, in which0 is 119

a reference shear rate (equation 29), b is a material parameter, and  0 is a reference viscosity

when a = 0 .

b       a  (28) 0      0 

ph 0  (29) 40

The instantaneous average shear rate ratio is calculated by numerically solving for the deformation rate tensorij . The deformation rate tensor was defined by Tsai et al. (1993) as equation 30 and the constituent parts of the rate tensor were defined by equations 31-36.

  p  Rp Rp   h 1    ˆ (30) ij    ij 4   R f R 

ˆ r  0 (31)

ˆ   0 (32)

  n  n3 2  r  2  r  ˆ rr  n   n  1   n , Pf np 1   (33) n3   R   R 

 2   n3  r  22  r  I n   ˆ r 1    nn 1   f n p ,  (34)   R   n3  R  sin

2 2 n3     r  n 1   r  ˆ    1 nn      , Pf      n np 1  n3   R  n 2  R  (35) 2 2 n3     r  n 1   r    nn  2      , If     2   n np 1 n3   R  n 2  R   120

2 2 n3     r  n 1   r  ˆ    n     , Pf      n np 1 n3   R  n 2  R  (36) 2 2 n3     r  n 1   r    nn  2      , If     2   n np 1 n3   R  n 2  R  

Using this derivation of the deformation tensor the instantaneous average shear rate (equation 37) is calculated by substituting equation 28 and equation 29 into equation 37.

1 3 R r 2   1   2     sin d dr a  3  ijij  (37) 2 0R 0  2  

Numerically the instantaneous average shear rate is calculated using equations 38 and 39.

11 b      a  Rp R p  1    (38)    0   R f R  

1 3 R r 2   2   ˆˆˆˆ 2222 sin d dr  3   rr r     (39) 4 0R 0 

Thus the flow rate in the microfluidic channel is given by equation 40.

 3  b    pR hp  a  R p R p Q    1   qˆ (40)       4  00    R f R 

Tsai et al. (1993) utilized a polynomial extrapolation on qˆ defined by equation 41 such that computations converged fast enough to control the accumulated error at the point of contact between the cell and the microfluidic channel, which is as Rr 1.

n2   n 12  r  qˆ     n p 2, IRf   P 1  pnppn  n3 n  2  R  (41) n2  n  r   r  3   n  If  pnp   .1,, n3 n  2  R   R 121

Where R is defined as / RR p . The cell entry rate p dtdL into the microfluidic channel is calculated using the conservation of mass, which at constant density becomes the conservation of volume and is given by equation 41.

dLp G  (41) 2 Q dt R p

Where G is given by equation 42 and accounts for changes in the geometry of the cell as the cell enters the channel.

 2 1 L  1   2 p G  1 L p (42)    1 L p 1

In equation 42 Lp is a non-dimensional ratio of the length of the cell projection in the channel to the radius of the microfluidic channel. Nondimensionalizing the terms in equation 41 yields equation 43.

b    Ld p G  a  11    1   qˆ (43)  2      td R p  0    f RR 

The nondimensional variables R f , R ,t , and Lp are defined in equation 44.

40 R f R Lp  tt R f R ,,, Lp  (44) ph Rp Rp Rp

The reference viscosity was calculated using the numerical simulation and the empirical cell entry time, te (Figure 39).

122

Figure 39. Cell entry into the microfluidic channel. Cell entry time was calculated as the difference between A) the time at which the cell first makes contact with the channel wall and B) the time at which the cell was fully aspirated into the microfluidic channel. Scale bar = 100μm.

For the damped power-law model the damping factor  was defined as equation 45.

21   Lb p (45)

Where  is defined as a material parameter that ensures that  remains non-negative. Thus the damped power-law model is given by equation 46.

    Ld p G  a  11    1   qˆ (46)  2      td R p  0    f RR 

Note that as the length of the cell becomes large tends toward zero such that the power-law fluid resembles a Newtonian fluid. The radius and position of the cell outside the microfluidic channel was calculated using a volume balance (equation 47) and trigonometric expressions

(equation 48-49).

31    4 3  2 (47)   0    LRRR pp   3  

 R p    arctan  (48)  R 

p   (49)

In an effort to evaluate the error that resulted from modeling the rectangular cross section of the microfluidic device as a circular cross section, a derivation for flow of a non-Newtonian power- law fluid through a rectangular channel was sought. Bird et al. (2000) provide a solution for 123 calculating the mass flow rate of a power-law fluid through a narrow (rectangular) slit (Figure

40), such as the microfluidic channel (318).

Figure 40. Flow through a narrow slit modeling the channel of the microfluidic device utilized in this study.

To calculate the volumetric flow rate through the slit, consider a momentum balance (equation 3) over a narrow slit in which velocity travels in direction z along x . Apply the continuity equation

(equation 4) and the conservation of momentum (equation 5). The density of fluid is assumed to be constant, gravitational body forces are considered negligible, and the fluid travels in the direction thus the expression for the velocity of a power-law fluid as a function of position along x is given by equation 50.

n u p    2u  z    z  uz  2  (50) x xxx   xxz 

Integration of equation 50 (equation 51) and application of the no slip boundary condition at x=h to arrive at equation 52.

n n dp  2u  p  du    z   L    z  (51)   2  x   dxx  xxz  L  dxx 

1 n  n 11   hp  h   x     L    uz   1    0 hx (52)  L  n 11   h  

Since in the microfluidic channel there is symmetry about x=0 equation 52 becomes equation 53. 124

w h w h   (53) zdxdyuQ 2 zdxdyu 0 h 0 0

Integration of equation 53 yields the flow rate through the microfluidic device (equation 54).

  2 1 n 2wh   Lhp  Q    (54) n  2)/1(  L 

By calculating the apparent viscosity of the cell using the flow rate calculated from the narrow slit

(equation 54) and the damped power-law model the geometric error was calculated to be a factor of four, such that geometric error corrections increase the value of the apparent cellular viscosity.

Results

The Mesenchymal-to-Epithelial Transition Generates Breast Cancer Cells that are Resistant to

Deformation

Herein the cellular mechanical properties of breast cancer cells were calculated using a modified (damped) power-law model that was adapted from the work of Tsai et al. (1993). First, a numerical simulation based on the original power-law model was used to calculate the whole cell entry time, which is the time it took the entire cell to enter into the channel. As time progressed the length of the cell projection in the microfluidic channel was calculated as a ratio of

̅̅̅̅ the cell length in the microfluidic channel to the hydraulic radius of the channel 퐿푝 until the

̅̅̅̅ entire cell entered the channel, at which point the nondimensional length of the cell projection 퐿푝

̅̅푒̅ was equivalent to the nondimensional whole cell entry length, 퐿푝. Analysis of the power-law model simulation reveals that as time approached the total cell entry time te, (푡 → 푡푒), breast

̅̅̅̅ cancer cells rapidly deformed, e.g., rapidly increased 퐿푝 , due to the shear-thinning behavior of the power-law fluid (Figure 42).

Although simulations of cell entry using the power-law model appeared similar to results of the cell entry simulations published by Tsai et al. (153), experimental observations of the time- 125 course entry of breast cancer cells into the microfluidic channel deviated from the power-law simulation results, especially as time approached the total cell entry time (Figure 41). Instead, experimental observations revealed that initially breast cancer cells rapidly entered the channel, yet as 푡 → 푡푒the length of the cell did not rapidly increase, instead 퐿̅푝⁄푑푡 appeared to remain constant. Experimental observations of cell deformation indicate that the shear-thinning behavior of the cytoplasm diminished as the breast cancer cell entered into the channel (Figure 41). Thus,

푒 the power-law model was modified with a damping term so that as 퐿̅푝 → 퐿̅푝 the cytoplasm more closely resembled a Newtonian fluid rather than a power-law fluid, so that the effects of shear thinning were diminished as 푡 → 푡푒.

By damping the power-law model the simulation produced results that were more similar to the experimental observations than the results of the (undamped) power-law model simulation

(Figure 41). To assess how well the damped power-law model fit the experimental data, cell entry times were calculated as a function of cell radius using the damped power-law model and compared to the experimental data (Figure 42). The damped power-law model appears to best model cells with a radius of 10.5 – 15.5 μm, and 44.5% of the cells examined herein (average radius of 10.33 ± 3.89 μm) fit within this range (Figure 42).

To determine whether the mesenchymal-to-epithelial transition modified the apparent mechanical properties of breast cancer cells the damped power-law model was used to calculate the reference viscosity(휇0) as a function of the cell radius and entry time (Figure 43). A power law regression for calculating the reference viscosity as a function of the cell radius was developed by calculating 푡 ̅ (equation 44) using the numerical simulation (Appendix B) and then multiplying by the empirical cell entry time (Figure 43). Apparent cellular mechanical properties were calculated for each cell line, and cell lines were segmented into two populations including, those that entered the microfluidic channel in less than five seconds and those that took over five 126 seconds to enter the channel. Hs578T shcontrol cells and Hs578T shTwist cells with an entry times less than five seconds did not demonstrate significantly different mechanical properties, yet the range of viscosities for Hs578T shTwist cells was defined by a maximum value that was greater than the maximum viscosity of the Hs578T shcontrol cells (Figure 44). However, Hs578T shTwist cells with entry times greater than five seconds had significantly greater viscosities than

Hs578T shcontrol cells with entry times greater than five seconds (Figure 45). Thus, from the calculated apparent cellular mechanical properties, it appears that the Hs578T shTwist cells had a greater resistance to cytoplasmic flow (and by extension greater resistance to deformation) compared to the Hs578T shcontrol cell line.

3 Power-law 2 Damped power-law 1 Hs578TExperimental shTwist observation cell 0 0 20 40 60 80 100

Figure 41. Experimental observations of the time-course entry of a breast cancer cell into the microfluidic device and the time-course cell entry calculated by numerical simulations. Aspiration pressure was calculated as 4 kPa using a refill volume of 85μl. Cell radius = 8.7μm. Calculations were performed using an initial reference viscosity of 20 kPa-s.

127

140 Hs578T shcontrol Hs578T shTwist 120 Simulated entry time

100 Poly. (Simulated entry time) 80

60 2 Entry Entry (s)time y = 0.3116x - 4.8231x + 18.716 40

0 0 5 10 15 20 25 Cell radius (μm)

Figure 42. Experimental observations of cell entry time into the channel of the microfluidic device were compared to entry times calculated using the numerical simulation for Hs578T shTwist cells and Hs578T shcontrol cells. Aspiration pressure was calculated as 4 kPa.

100000 s) - y = 13128x-7.3 R² = 0.9687

0 Pa 10000 μ

1000

100

log (reference viscosity, log (reference viscosity, 1 1 1.2 1.4 1.6 1.8 2 2.2 cell radius / hydraulic radius,

Figure 43. The apparent cellular reference viscosities of breast cancer cells were calculated as a function of the cell radius using the damped power-law numerical simulation.

128

Figure 44. Apparent cellular viscosities of breast cancer cells were calculated as a function of empirical observations of cell radius and entry time using a damped-power law model. The apparent cellular viscosities (Pa-s) of A) Hs578T shcontrol cells and B) Hs578T shTwist cells were calculated as a function of the cell radius and empirical entry times, te (s) using a modified power-law model for cells that entered the microfluidic channel in less than five seconds at a pressure of 4kPa. The mean viscosity of the Hs578T shTwist cells were not significantly different from the Hs578T shcontrol cells, yet spanned a range that was defined by a greater maximum value. Each data point was calculated from a different cell that was aspirated into the microfluidic channel.

Figure 45. Apparent cellular viscosities of Hs578T shTwist cells were significantly greater than apparent cellular viscosities of Hs578T shcontrol cells for cells that entered the microfluidic device with 5 < te < 100. Apparent viscosities were calculated for cells modeled as liquid drops enclosed in cortical shells of constant tension using a damped power-law model. ANOVA and post-hoc Tukey’s test, #P<0.05. Mean values are represented with Ꚛ symbol. Median lines separate quartiles and vertical bars indicate the range of the data. Outliers are indicated with a star. N = 8 Hs578T shTwist cells and n = 23 Hs578T shcontrol cells.

129

Discussion

Through the EMT and MET, breast cancer cells can undergo changes in the expression of sialofucosylated glycoconjugates, stem-like traits (53), motility (43), and cellular mechanical properties (47, 49, 144). The EMT has been incorporated into mechanisms of breast cancer metastasis, because through the EMT breast cancer cells can garner enhanced motility (43, 144,

303) and become softer such that they might be better suited to enter the blood stream via transendothelial migration (146). In fact, studies have shown that cells with elevated metastatic potentials are more deformable than non-metastatic cells (192).

Herein the apparent cellular mechanical properties of Hs578T shTwist cells that underwent the MET and Hs578T shcontrol cells were calculated using a damped power-law model. Comparison of the cellular mechanical properties of each cell line revealed that when the entry of cells into the microfluidic channel was characterized with an entry time (te) of greater than five seconds, the Hs578T shTwist cells were more viscous than the Hs578T shcontrol cells and therefore had a greater resistance to cytoplasmic flow into the microfluidic channel. Thus, via the MET the Hs578T shTwist cells became less deformable than the Hs578T shcontrol cells

(Figure 45). There was no significant difference in the apparent cellular mechanical properties of

Hs578T shTwist cells and Hs578T shcontrol cells that had an entry time of less than five seconds, yet this is not surprising as the Newtonian core liquid drop model is not well suited to model cells that are deformed for less than five seconds (155). Collectively, these results are consistent with published reports that show cells of an epithelial phenotype are typically less deformable than cells with a mesenchymal phenotype (142, 145, 190, 319).

Although the damped power-law model described herein better models the time course entry of breast cancer cells into the microfluidic channel than the original power-law model, because it captures the diminishing effects of shear-thinning during cell entry (Figure 41), more advanced models for calculation of biomechanical properties of cells that consider both temporal 130 and spatial variations of the cell and the cortex have been developed. For example, Drury et al.

(1999), utilized finite element method to model the entry of a slippery droplet (an incompressible

Newtonian liquid drop) into the micropipette considering both spatial and temporal variations in cell mechanics. Using this finite element method they explored how varying different parameters in micropipette aspiration, e.g., cortex tension, affected the entry of the droplet into the micropipette (141). After developing the finite element model for entry of the slippery droplet into the micropipette, Drury et al. (2001) used the slippery droplet model to evaluate the effects of cortical tension and shear thinning on the mechanics of cell entry into the micropipette (320).

Specifically, it is noted that Drury et al. (2001) modified the power-law model proposed by Tsai et al. (1993) by introducing a general definition for the viscosity of a power-law fluid (equation

55).

b  s      0 1  (55)  sc 

Where sc was defined as the characteristic shear rate. By introducing the additional term into the definition of viscosity Drury et al. (2001) simulated the entry of neutrophils into pipettes using

Newtonian and non-Newtonian slippery droplets. In this publication Drury et al. (2001) noted that as the exponent or shear ratio tends toward zero the slippery drop adopts a Newtonian profile

(320).

Subsequently, Herant et al. (2003) introduced a model for the aspiration of neutrophils that solves for parameters governing the behavior of the cytosol, cytoskeleton, and membrane cortex in order to more accurately model neutrophil aspiration (184). By defining three parameters, i.e., baseline tension, slack, and dilation viscosity Herant et al. (2003) used the properties of membrane dilation, slack (in the cortical membrane), and polymer expansion to model the three stages of neutrophil aspiration (1. initial rapid entry, 2. relatively constant rate of 131 entry, and 3. rapid exit) that could not be modeled using the simple viscous drop model (184). To- date the model proposed by Herant et al. (2003) is arguably one of the most complete models for simulating the aspiration of neutrophils into a micropipette. Even so, neither this model nor more recent models that depict adherent cell locomotion (180) attempt to simultaneously solve for the interaction between the nucleus and the cytosol during micropipette aspiration, as these cell dynamics are complex and computationally demanding.

Using isolated nuclei Dahl et al. (2004) found that the nuclear envelope maintains a greater cortical tension than the plasma membrane (174). Additionally in subsequent works,

Booth-Gauthier et al. (2012) discovered that the nucleus is the stiffest component of mammalian cells (321). Combining these findings with the knowledge that the nuclei of most cancer cells are large in comparison to healthy cells (322) and experimental observations that show the diameter of the nucleus of Hs578T breast cancer cells to be about 75% as large as the cell diameter (Figure

46), the results reported herein are revisited in an effort to rationalize why the shear-thinning behavior of the cytoplasm appears to diminish as the cell enters the microfluidic channel.

As shown in Figure 41 breast cancer cells rapidly enter the channel. Note that this initial rapid entry has also been documented for neutrophils and is mathematically modeled by the work of Herant et al. (2003) (184). However, unlike the white blood cells that were characterized by

Tsai et al. (1993), the breast cancer cells examined herein do not rapidly deform as the length of the cell in the microfluidic-channel approaches the maximum deformation length of the cell.

Thus, it appears as though shear thinning occurs to a lesser extent as Lp increases. One explanation for the apparent loss of shear thinning behavior is that initially the cytoplasm is shear thinned as the cell enters the channel, yet as the relatively stiff nucleus challenges deformation into the channel the collective shear thinning behavior is no longer observed (Figure 41).

132

Figure 46. The nuclei of Hs578T shTwist cells were approximately 75% as large as the entire breast cancer cell. Green arrows indicate the bounds of the cell membrane, which was identified using anti-CD44 mAb and anti-mouse IgG AlexaFluor 488. Nuclei were stained using DAPI (blue) and measured using the blue arrows. Scale bar = 20μm. Data are representative of n = 3 independent experiments.

Herein it was demonstrated that the mesenchymal-to-epithelial transition decreases the deformability of breast cancer cells as they transition toward an epithelial phenotype (Figure 45).

These results are supported by prior investigations which demonstrated that breast cancer cells with an epithelial phenotype, i.e., MDA-MB-468 cells, are more viscous than Hs578T cells, which have a mesenchymal phenotype (323). Because the mechanical properties of cancer cells are modified by the EMT (47, 49, 144, 193) and MET, they may influence the metastatic potential of CTCs and the CTCs’ path of dissemination during metastasis. For example, highly deformable CTCs may navigate narrow capillaries while less deformable CTCs may form vascular occlusions (85). Additionally, highly deformable CTCs generated by the EMT may enter a distal site that is surrounded by narrow capillaries, e.g., bone marrow, and become trapped as the CTCs undergo the MET, which increases the CTCs resistance to deformation such that the cancer cells can no longer navigate the narrow vasculature. Thus the mechanical properties of cancer cells may determine the final destination of CTCs during metastasis. 133

CHAPTER 6: CONCLUDING REMARKS AND RECOMMENDED FUTURE WORK

The epithelial-to-mesenchymal transition (EMT) has been included in mechanisms of breast cancer metastasis, because through the EMT breast cancer cells can become increasingly invasive, motile, and stem-like to escape the primary tumor into the blood stream as CTCs (44,

53). Prior work has shown that trafficking of CTCs to endothelial cells that line blood vessel walls at the secondary site may occur via E-selectin/ligand interactions and that breast cancer cell lines of mesenchymal and epithelial phenotypes have different E-selectin ligand activities, yet the effects of the EMT on the E-selectin ligand activities of breast cancer cells were unclear (85, 89).

Thus specific aim 1 of the present work was to determine whether the EMT affects the E-selectin ligand activities of breast cancer cells.

In chapter 2, a mechanism for EMT regulation of the E-selectin ligand activities of breast cancer cells was demonstrated as MDA-MB-468 cells with ectopic expression of Snail or Twist transcription factors underwent the EMT and decreased their E-selectin ligand activities (Figure

9). Furthermore, shRNA knockdown of Snail or Twist in Hs578T cells resulted in the MET and increased the E-selectin ligand activities of breast cancer cells (Figure 12). Further studies are needed to elucidate whether specific E-selectin ligands, e.g., Mac-2BP or CD44v, are downregulated during the EMT to decrease the E-selectin ligand activities of breast cancer cells.

However, the E-selectin ligands that are regulated by the EMT or MET may very well be decorated with sialofucosylated carbohydrates other than sLeX, sLeA, or HECA-452 antigens, since these carbohydrates were not detected in flow cytometric analyses of Hs578T shTwist or

Hs578T shSnail cells that increased their E-selectin ligand activities via the MET.

Recent investigations have shown that trafficking of CTCs to the bone marrow is regulated by E-selectin during breast cancer metastasis (275). However, it has not yet been shown whether E-selectin regulates the behaviors of breast cancer cells that metastasize to the bone marrow in a similar manner to which E-selectin regulates the behaviors of hematopoietic stem 134 cells or colon cancer cells (109, 234, 270). Consequently, the second aim of this investigation was to determine if E-selectin modifies the growth behaviors of breast cancer cells.

To model breast cancer metastases in the presence of the constitutive expression of E- selectin presented by bone marrow endothelial cells, breast cancer cells were grown on tissue culture plates coated with E-selectin or on activated HUVEC. While breast cancer cells of an epithelial phenotype maintained a typical morphologies on E-selectin, breast cancer cells with a mesenchymal phenotype and the CD44highCD24low stem-like biomarker profile exhibited atypical growth behaviors (Figure 23). Most notably, the Hs578T cells formed mammosphere-like structures on E-selectin and pursued cell death pathways that were linked to the expression of

BCL-2 and TNF-superfamily genes (Table A3). Additionally, preliminary analyses of gene expression indicated that Wnt (Table A4), Hedgehog (Table A5), and/or Notch (Table A6) signaling pathways may contribute to the formation of these mammosphere-like structures.

Further work is needed to determine which signaling pathways drive the formation of mammosphere-like structures and whether cells that grow as mammosphere-like structures may avoid cell death with the appropriate supplements, e.g., epidermal growth factor.

In chapter 5 it was shown that through the MET breast cancer cells become resistant to deformation. Additionally, the mechanical properties of cancer cells were posited to be influential to the final destination of CTCs, because CTCs that have low resistance to deformation may navigate narrow capillaries that comparably stiff CTCs may occlude. Furthermore, MacKay and

Hammer (2016) found that vascular stiffening enhances E-selectin-mediated adhesion of monocytes to blood vessel walls, thus it is plausible that stiffening of cancer cells via the MET may enhance the adhesion of CTCs to E-selectin (324). Further study is needed to determine whether cellular stiffness modifies the ability of breast cancer cells to adhere to E-selectin and if increased stiffness promotes E-selectin-mediated recruitment of CTCs to the bone marrow during breast cancer metastasis. Moreover, recent works have found that CTC clusters, in addition to 135 individual CTCs, may drive the formation of distal metastasis (80, 166, 325). Consequently, further investigation is needed to elucidate if mechanical properties of CTC clusters affect their metastatic potential and how the EMT or MET affects the mechanical properties of CTC clusters.

To conclude this work a mechanism for breast cancer metastasis is proposed in which the

E-selectin ligand activities of breast cancer cells are regulated by the EMT and MET (Figure 47).

In the primary tumor, breast cancer cells that are stimulated by immunogenic cytokines undergo the EMT and decrease their E-selectin ligand activity (Figure 9). Enhanced motility conferred by the EMT (44, 303, 326, 327) enables the breast cancer cells to more readily escape the primary tumor into the blood stream and reduced levels of E-selectin ligand activity allow the cancer cell to achieve distal metastasis, rather than adhering to nearby endothelium. As the CTC is trafficked, the absence of immunogenic cytokines in the blood results in the MET, which increases presentation of E-selectin ligands on the CTC (Figure 12). Metastasis to the bone marrow is achieved as CTCs engage E-selectin in the vascular niche (275), and immunogenic cytokines that are prevalent in the bone marrow (TGF-β) (200) promote the EMT (32, 51, 328).

Breast cancer cells with reduced E-selectin ligand activity and stem-like traits become dormant

(329) and may pursue programmed cell death via exposure to vascular niche E-selectin (Figure

47). Notably this hypothesized model is most applicable to CTCs that 1) are in a dormant state, 2) achieve a dormant or slow cycling state via the EMT and/or MET, or 3) CTCs that rapidly undergo phenotypic transitions within the CTCs life span (the half-life of CTCs released from primary tumors is approximately two hours (223)).

136

Figure 47. Postulated mechanism of breast cancer metastasis demonstrating the impact of EMT- regulated E-selectin ligand activity. Breast cancer cells proliferate and grow in the primary tumor. The growing tumor and/or the tumor microenvironment, e.g., stromal cells, white blood cells (WBC), and platelets, produce TGF-β, which induces the EMT causing cells to reduce the expression of E-selectin ligands, become invasive, and garner stem-like characteristics. Breast cancer cells with enhanced motility and invasiveness escape the primary tumor and intravasate into the blood stream as circulating tumor cells (CTCs). Low levels of E-selectin ligand activity allow the cell to migrate to a distal site without interacting with activated endothelium. Relative to the primary tumor TGF-β levels in the blood are low and allow the breast cancer cell to return to an epithelial phenotype via the MET with greater levels of E-selectin ligand activity. At the secondary site CTCs tether and roll on the endothelium via E-selectin/ligand interactions and eventually extravasate into the underlying tissue. Breast cancer cells often metastasize to the bone marrow, in which TGF-β is produced by stromal cells. Stimulated by TGF-β, breast cancer cells undergo the EMT. Stem-like breast cancer cells in the vascular niche of the bone marrow adhere to E-selectin, which is constitutively expressed by endothelial cells that compose the sinusoid walls, and adhesion to E-selectin causes breast cancer cells to enter a dormant state.

137

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157

APPENDIX A: PRIMERS AND RT2-PCR PROFILER ARRAYS

Table A1

Forward primers used for qRT-PCR. Symbol Full name Forward primer 5' → 3' SNAI1 Snail family transcriptional repressor 1 CCTCCCTGTCAGATGAGGAC TWIST1 Twist family bHLH transcription factor 1 CGGGTCATGGCTAACGTG CDH1 E-Cadherin TGCCCAGAAAATGAAAAAGG CDH2 N-Cadherin ACAGTGGCCACCTACAAAGG VIM Vimentin GAGAACTTTGCCGTTGAAGC B3GALT4 Beta-1,3-galactosyltransferase 4 ACCGCAACCTCACCCTAAAG B3GALT5 Beta-1,3-galactosyltransferase 5 ATCAGGCAGCCATTCAGCAA B4GALT1 Beta-1,4-galactosyltransferase 1 ATGTTTGCCTGGTCCGTGAG B4GALT3 Beta-1,4-galactosyltransferase 3 GCGGATCCGCCACCATGTTGCGGAGGCTGCTG B4GALNT1 Beta-1,4-N-Acetyl-Galactosaminyltransferase 1 CAACACAGCAGACACAGTCCGGTT ST3GAL1 ST3 beta-galactoside alpha-2,3-sialyltransferase 1 ATGAGGTGGACTTGTACGGC ST3GAL2 ST3 beta-galactoside alpha-2,3-sialyltransferase 2 CCCTGCTTTCACCTACTCG ST3GAL2 ALTERNATE ST3 beta-galactoside alpha-2,3-sialyltransferase 2 CTAGTGACGGAGAGACCCGA ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase ST3GAL5 5 TGCTTGGTGTGACCCACGGA ST8 Alpha-N-Acetyl-Neuraminide Alpha-2,8- ST8SIA1 Sialyltransferase 1 CCCCATTCCAGCTGCCATTG ST8 Alpha-N-Acetyl-Neuraminide Alpha-2,8- ST8SIA5 Sialyltransferase 5 TCTGACTGCTTCACCCTCTTA ST8SIA5 ST8 Alpha-N-Acetyl-Neuraminide Alpha-2,8- ALTERNATE Sialyltransferase 5 ATTAAGAGGGTGAAGCAGTCAGA FUT3 Fucosyltransferase 3 GCCGACCGCAAGGTGTAC FUT4 Fucosyltransferase 4 AAGCCGTTGAGGCGGTTT FUT5 Fucosyltransferase 5 TATGGCAGTGGAACCTGTCA FUT6 Fucosyltransferase 6 CAAAGCCACATCGCATTGAA FUT7 Fucosyltransferase 7 TCCGCGTGCGACTGTTC UGCG UDP-glucose ceramide glucosyltransferase GCCTCTTTGAAGCCCACATTG UGCG ALT UDP-glucose ceramide glucosyltransferase TGGATTATCCCAAATATGAAGTGCT CD44S CD44 Standard CCTCCAGTGAAAGGAGCAGCA CD44v3 CD44 variant 3 GTACGTCTTCAAATACCATCTCAGC CD44v4 CD44 variant 4 TTTCAACCACACCACGGGC CD44v5 CD44 variant 5 GTAGACAGAAATGGCACCACTGC CD44v6 CD44 variant 6 TCCAGGCAACTCCTAGTAGTAC CD44v7 CD44 variant 7 CAGCCTCAGCTCATACCAG CD44v8 CD44 variant 8 TGGACTCCAGTCATAGTATAACGC CD44v9 CD44 variant 9 AGCAGAGTAATTCTCAGAGC CD44v10 CD44 variant 10 ATAGGAATGATGTCACAGGTGG RPL13A Ribosomal Protein L13a GAGGCCCCTACCACTTCC ACTB Actin beta CCAACCGCGAGAAGATGA GAPDH Glyceraldehyde-3-phosphate dehydrogenase AGCCACATCGCTCAGACAC PUM1 Pumilio RNA binding family member 1 AGTGGGGGACTAGGCGTTAG 158

Table A2

Reverse primers used for qRT-PCR. Symbol Full name Reverse primer 5' → 3' Snail family transcriptional SNAI1 repressor 1 CCAGGCTGAGGTATTCCTTG Twist family bHLH transcription TWIST1 factor 1 CAGCTTGCCATCTTGGAGTC CDH1 E-Cadherin GTGTATGTGGCAATGCGTTC CDH2 N-Cadherin CCGAGATGGGGTTGATAAATG VIM Vimentin GCTTCCTGTAGGTGGCAATC B3GALT4 Beta-1,3-galactosyltransferase 4 CGTCTTGAGGACGTATCGGG B3GALT5 Beta-1,3-galactosyltransferase 5 ACGTCGCCAGAAAACACGTA B4GALT1 Beta-1,4-galactosyltransferase 1 GCCAGCACAGCATCTCCTTTA B4GALT3 Beta-1,4-galactosyltransferase 3 CTAGTCTAGAGCGTGTGAACCTCGGAGGGCTG Beta-1,4-N-Acetyl- B4GALNT1 Galactosaminyltransferase 1 GGGCGGGTGTCTTATGCGGATA ST3 beta-galactoside alpha-2,3- ST3GAL1 sialyltransferase 1 AACGGCTCCAGCAAGATG ST3 beta-galactoside alpha-2,3- ST3GAL2 sialyltransferase 2 GCATCATCCACCACCTCT ST3GAL2 ST3 beta-galactoside alpha-2,3- ALTERNATE sialyltransferase 2 TAGGGAGAACACACGTTGGC ST3 Beta-Galactoside Alpha-2,3- ST3GAL5 Sialyltransferase 5 ATCGCAGACCCAGTATCAGCAGC ST8 Alpha-N-Acetyl-Neuraminide ST8SIA1 Alpha-2,8-Sialyltransferase 1 CGGCCACAGCCACTCTTCTTCA ST8 Alpha-N-Acetyl-Neuraminide ST8SIA5 Alpha-2,8-Sialyltransferase 5 TTTGCCTTGGTGACCTTGCT ST8SIA5 ST8 Alpha-N-Acetyl-Neuraminide ALTERNATE Alpha-2,8-Sialyltransferase 5 ACTTGAACTGGTTGGCCTCA FUT3 Fucosyltransferase 3 TGACTTAGGGTTGGACATGATATCC FUT4 Fucosyltransferase 4 ACAGTTGTGTATGAGATTTGGAAGCT FUT5 Fucosyltransferase 5 CGTCCACAGCAGGATCAGTA FUT6 Fucosyltransferase 6 ATCCCCGTTGCAGAACCA FUT7 Fucosyltransferase 7 GTGTGGGTAGCGGTCACAGA UDP-glucose ceramide UGCG glucosyltransferase AAACCCAGATGGAAGACTGGC UDP-glucose ceramide UGCG ALT glucosyltransferase GCTTCTTACATACATCAATGGCTGG CD44S CD44 Standard GTGTCTTGGTCTCTGGTAGCAGGGAT CD44v3 CD44 variant 3 GGTGCTGGAGATAAAATCTTCATC CD44v4 CD44 variant 4 CAGTCATCCTTGTGGTTGTCTG CD44v5 CD44 variant 5 TTGTGCTTGTGAATGTGGGGTCTC CD44v6 CD44 variant 6 CAGCTGTCCCTGTTGTCGAATGGG CD44v7 CD44 variant 7 CCATCCTTCTTCTGCTTG CD44v8 CD44 variant 8 GCGTTGTCATTGAAAGAGGTCCTG CD44v9 CD44 variant 9 TGCTTGATGTCAGAGTAGAAGTTG CD44v10 CD44 variant 10 CGATTGACATTAGAGTTGGAATCTCC RPL13A Ribosomal Protein L13a AACACCTTGAGACGGTCCAG ACTB Actin beta CCAGAGGCGTACAGGGATAG Glyceraldehyde-3-phosphate GAPDH dehydrogenase GCCCAATACGACCAAATCC Pumilio RNA binding family PUM1 member 1 GTTTTCATCACTGTCTGCATCC 159

Table A3

RT2 Profiler PCR array for apoptosis signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are representative of n = 2 independent experiments. Unigene Symbol Fold Unigene Symbol Fold Unigene Symbol Fold Change Change Change Hs.431048 ABL1 1.35 Hs.249129 CIDEA 14.07 Hs.599762 CASP8 0.49

Hs.424932 AIFM1 0.69 Hs.642693 CIDEB 0.42 Hs.329502 CASP9 0.14

Hs.525622 AKT1 0.4 Hs.38533 CRADD 22.15 Hs.355307 CD27 12.34

Hs.552567 APAF1 0.32 Hs.437060 CYCS 0.03 Hs.472860 CD40 22.1

Hs.370254 BAD 0.21 Hs.380277 DAPK1 47.13 Hs.592244 CD40LG 58.99

Hs.377484 BAG1 19.3 Hs.484782 DFFA 0.2 Hs.501497 CD70 2.93

Hs.523309 BAG3 1.46 Hs.169611 DIABLO 0.17 Hs.713833 TNFRSF1A 0.05

Hs.485139 BAK1 1.71 Hs.86131 FADD 0.66 Hs.256278 TNFRSF1B 0.55

Hs.624291 BAX 1.95 Hs.667309 FAS 0.29 Hs.443577 TNFRSF21 0.03

Hs.193516 BCL10 0.83 Hs.2007 FASLG 2.92 Hs.462529 TNFRSF25 0.17

Hs.150749 BCL2 1.86 Hs.80409 GADD45A 5.9 Hs.738942 TNFRSF9 22.95

Hs.227817 BCL2A1 0.28 Hs.87247 HRK 4.53 Hs.478275 TNFSF10 0.53

Hs.516966 BCL2L1 0.14 Hs.643120 IGF1R 2.67 Hs.654445 TNFSF8 71.21

Hs.283672 BCL2L10 0.16 Hs.193717 IL10 213.53 Hs.437460 TP53 8.23

Hs.469658 BCL2L11 5.47 Hs.36 LTA 0.42 Hs.523968 TP53BP2 0.89

Hs.410026 BCL2L2 5.5 Hs.1116 LTBR 0.59 Hs.192132 TP73 0.05

Hs.435556 BFAR 0.01 Hs.632486 MCL1 0.79 Hs.460996 TRADD 0.07

Hs.517145 BID 0.15 Hs.646951 NAIP 0.41 Hs.522506 TRAF2 0.17

Hs.475055 BIK 24.93 Hs.618430 NFKB1 0.54 Hs.510528 TRAF3 0.02

Hs.696238 BIRC2 0.28 Hs.2490 CASP1 0.1 Hs.356076 XIAP 0.12

Hs.127799 BIRC3 1.12 Hs.5353 CASP10 0.14 Hs.738731 NOD1 1.5

Hs.744872 BIRC5 5.32 Hs.466057 CASP14 6.41 Hs.513667 NOL3 0.73

Hs.150107 BIRC6 1.02 Hs.368982 CASP2 0.42 Hs.499094 PYCARD 27.2

Hs.646490 BNIP2 0.21 Hs.141125 CASP3 0.04 Hs.103755 RIPK2 0.01

Hs.144873 BNIP3 0 Hs.138378 CASP4 0.13 Hs.241570 TNF 1.03

Hs.131226 BNIP3L 1.24 Hs.213327 CASP5 0.81 Hs.591834 TNFRSF10A 5.59

Hs.550061 BRAF 0.49 Hs.654616 CASP6 1.82 Hs.661668 TNFRSF10B 0.06

Hs.390736 CFLAR 1.2 Hs.9216 CASP7 4.54 Hs.81791 TNFRSF11B 0.01

160

Table A4

RT2 Profiler PCR array for Wnt signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment. Unigene Symbol Fold Unigene Symbol Fold Unigene Symbol Fold Change Change Change Hs.515053 AES 3.3268 Hs.126057 FRAT1 12.1855 Hs.121540 WNT10A 34.8059

Hs.158932 APC 1.7428 Hs.128453 FRZB 3.8824 Hs.108219 WNT11 5.0266

Hs.592082 AXIN1 1.6359 Hs.94234 FZD1 1.3114 Hs.272375 WNT16 3.1693

Hs.156527 AXIN2 1.9222 Hs.142912 FZD2 3.1909 Hs.567356 WNT2 15.7615

Hs.415209 BCL9 2.3546 Hs.40735 FZD3 0.5021 Hs.258575 WNT2B 18.196

Hs.643802 BTRC 2.6032 Hs.591968 FZD4 2.5928 Hs.445884 WNT3 1.9091

Hs.523852 CCND1 2.3086 Hs.17631 FZD5 2.4301 Hs.336930 WNT3A 3.2951

Hs.376071 CCND2 20.1129 Hs.591863 FZD6 2.1123 Hs.25766 WNT4 2.7402

Hs.529862 CSNK1A1 2.4809 Hs.173859 FZD7 2.1679 Hs.643085 WNT5A 1.4703

Hs.644056 CSNK2A1 4.1043 Hs.302634 FZD8 1.5849 Hs.306051 WNT5B 1.6237

Hs.208597 CTBP1 1.9969 Hs.647029 FZD9 10.1941 Hs.29764 WNT6 2.1098

Hs.712929 CTNNB1 2.2599 Hs.466828 GSK3A 1.231 Hs.72290 WNT7A 0.1182

Hs.685322 CTNNBIP1 5.6379 Hs.445733 GSK3B 1.8022 Hs.512714 WNT7B 0.351

Hs.624057 CXXC4 0.1112 Hs.696684 JUN 2.7775 Hs.591274 WNT8A 5.1247

Hs.654934 DAAM1 2.3767 Hs.229335 KREMEN1 2.8501 Hs.149504 WNT9A 1.0869

Hs.696631 DAB2 2.1961 Hs.743478 LEF1 1.4012 Hs.524348 PRICKLE1 1.5211

Hs.655626 DIXDC1 1.6307 Hs.6347 LRP5 4.5149 Hs.256587 PYGO1 2.1832

Hs.40499 DKK1 0.7094 Hs.584775 LRP6 2.7163 Hs.247077 RHOA 3.0423

Hs.292156 DKK3 7.6784 Hs.138211 MAPK8 2.3293 Hs.647774 RHOU 1.8697

Hs.731450 DVL1 2.1535 Hs.2256 MMP7 8.7359 Hs.710868 RUVBL1 2.5575

Hs.118640 DVL2 1.9066 Hs.202453 MYC 5.0209 Hs.213424 SFRP1 2.5723

Hs.517517 EP300 2.621 Hs.534074 NFATC1 8.1489 Hs.658169 SFRP4 4.6065

Hs.484138 FBXW11 3.9105 Hs.187578 NKD1 4.1607 Hs.98367 SOX17 --

Hs.500822 FBXW4 3.7056 Hs.208759 NLK 1.6492 Hs.573153 TCF7 1.0168

Hs.1755 FGF4 0.3414 Hs.643588 PITX2 2.279 Hs.516297 TCF7L1 2.995

Hs.283565 FOSL1 1.6294 Hs.386453 PORCN 2.2573 Hs.197320 TLE1 1.4663

Hs.58611 FOXN1 0.1125 Hs.696032 PPARD 3.1613 Hs.99477 VANGL2 53.3296

Hs.284122 WIF1 -- Hs.492974 WISP1 2.2002 Hs.248164 WNT1 23.95

161

Table A5

RT2 Profiler PCR array for Hedgehog signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment. Fold Fold Unigene Symbol Unigene Symbol Unigene Symbol Fold Change Change Change Hs.150749 BCL2 62.4459 Hs.111867 GLI2 7.8572 Hs.555016 RAB23 4.1056

Hs.73853 BMP2 -- Hs.21509 GLI3 22.191 Hs.535845 RUNX2 0.8309

Hs.68879 BMP4 -- Hs.40098 GREM1 -- Hs.213424 SFRP1 8.7605

Hs.296648 BMP5 -- Hs.445733 GSK3B 31.309 Hs.606487 SHH --

Hs.285671 BMP6 33.9769 Hs.58650 HHAT 1.2735 Hs.437846 SMO 47.1415

Hs.473163 BMP7 -- Hs.507991 HHIP 1.2201 Hs.738869 STK3 18.8442

Hs.734367 BMP8B -- Hs.444332 IFT52 3.4881 Hs.471404 STK36 13.5481

Hs.591318 BOC 0.7287 Hs.654504 IHH -- Hs.404089 SUFU 11.5595

Hs.643802 BTRC 2.6446 Hs.592112 KCTD11 0.6437 Hs.437460 TP53 7.9485

Hs.38034 CDON 0.8409 Hs.549084 LATS1 0.5165 Hs.73793 VEGFA 67.0844

Hs.529862 CSNK1A1 11.7642 Hs.78960 LATS2 5.681 Hs.284122 WIF1 --

Hs.474833 CSNK1E 8.5351 Hs.657729 LRP2 -- Hs.248164 WNT1 --

Hs.712929 CTNNB1 0.1113 Hs.431850 MAPK1 0.3797 Hs.121540 WNT10A --

Hs.524382 DHH 49.128 Hs.691454 MOB1B 14.051 Hs.91985 WNT10B 6.541

Hs.528817 DISP1 -- Hs.700429 MTSS1 1.7084 Hs.108219 WNT11 --

Hs.355645 DISP2 -- Hs.187898 NF2 0.8797 Hs.272375 WNT16 --

Hs.390729 ERBB4 -- Hs.464779 NPC1 1.2394 Hs.567356 WNT2 --

Hs.563205 FAT4 0.5887 Hs.654609 NUMB 17.642 Hs.258575 WNT2B --

Hs.484138 FBXW11 1.3216 Hs.288655 OTX2 -- Hs.445884 WNT3 0.6823

Hs.111 FGF9 -- Hs.631630 PRKACA 0.0221 Hs.336930 WNT3A --

Hs.1420 FGFR3 6.1269 Hs.487325 PRKACB 11.061 Hs.25766 WNT4 79.6307

Hs.173464 FKBP8 4.0893 Hs.494538 PTCH1 -- Hs.643085 WNT5A 3.5223

Hs.159234 FOXE1 -- Hs.591497 PTCH2 -- Hs.306051 WNT5B 0.998

Hs.434914 FRMD6 1.652 Hs.319503 PTCHD1 -- Hs.29764 WNT6 --

Hs.65029 GAS1 1.3296 Hs.202355 PTCHD2 -- Hs.72290 WNT7A --

Hs.632702 GLI1 19.9546 Hs.631832 PTCHD3 -- Hs.512714 WNT7B 1.9782

Hs.326420 WNT9B -- Hs.149504 WNT9A -- Hs.591274 WNT8A --

162

Table A6

RT2 Profiler PCR array for Notch signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment. Unigene Symbol Fold Unigene Symbol Fold Unigene Symbol Fold Change Change Change Hs.431048 ABL1 28.0858 Hs.250666 HES1 15.6022 Hs.169002 PTCRA 1125.82

Hs.643357 ADAMTS1 1.2321 Hs.57971 HES5 -- Hs.196384 PTGS2 0.55

Hs.502182 BDNF 29.3145 Hs.434828 HES7 7.489 Hs.479396 RBPJ 25.68

Hs.513811 CBFA2T3 10.7239 Hs.234434 HEY1 4.2368 Hs.248217 RBPJL 13.62

Hs.523852 CCND1 32.5771 Hs.144287 HEY2 32.4683 Hs.447901 RHOV --

Hs.376071 CCND2 -- Hs.472566 HEYL 951.9683 Hs.124940 RND1 92.89

Hs.436975 CLOCK 32.0072 Hs.400095 HSPB8 7.4069 Hs.149261 RUNX1 148.35

Hs.459759 CREBBP 0.229 Hs.504609 ID1 0.3534 Hs.535845 RUNX2 6.5

Hs.531668 CX3CL1 303.0183 Hs.180919 ID2 47.7381 Hs.585118 S1PR3 4.85

Hs.522891 CXCL12 12.0026 Hs.76884 ID3 8.9875 Hs.499984 SGPL1 1.25

Hs.75765 CXCL2 15.6989 Hs.519601 ID4 534.6562 Hs.48029 SNAI1 122.15

Hs.40499 DKK1 0.6887 Hs.450230 IGFBP3 12.9166 Hs.445498 SNW1 159.71

Hs.372152 DTX1 22.9535 Hs.731660 IL33 2.9517 Hs.527973 SOCS3 0.28

Hs.511899 EDN1 15.456 Hs.224012 JAG1 2.7623 Hs.647409 SOX9 32.7

Hs.630847 EFNA1 39.7978 Hs.696684 JUN 58.6 Hs.437 TCF15 143.93

Hs.144700 EFNB1 15.6561 Hs.13281 KALRN 0.56 Hs.479670 TEC 2.31

Hs.534313 EGR3 519.2614 Hs.661108 KITLG 4.43 Hs.143250 TNC 18.32

Hs.26770 FABP7 -- Hs.654380 KRT14 75.43 Hs.73793 VEGFA 173.24

Hs.39384 FJX1 23.8087 Hs.159142 LFNG 59.26 Hs.492974 WISP1 1.61

Hs.594454 FLT1 0.7375 Hs.497806 MARK1 1924.53 Hs.25766 WNT4 22.1

Hs.348883 FOXC1 18.8758 Hs.442619 MSC 16.77 Hs.643085 WNT5A 27.34

Hs.546573 FOXD3 7.1961 Hs.178023 MYF5 -- Hs.29764 WNT6 457.11

Hs.690163 FOXF1 5.4899 Hs.489615 NAMPT 26.04 Hs.8546 NOTCH3 391.71

Hs.128453 FRZB 103.1618 Hs.527971 NES 1.67 Hs.535075 NRARP --

Hs.17631 FZD5 5.1785 Hs.370414 NODAL 5.9 Hs.270303 PAX6 3.21

Hs.110571 GADD45B 10.7225 Hs.495473 NOTCH1 11.2 Hs.557097 PBX1 2.82

Hs.584901 GPSM2 11.6873 Hs.74615 PDGFRA 5.73 Hs.19492 PCDH8 20.6

Hs.799 HBEGF 53.3939 Hs.509067 PDGFRB 12.33 Hs.1976 PDGFB --

163

Table A7

RT2 Profiler PCR array for extracellular signaling mechanisms in Hs578T cells cultured on rhE/Fc. Data are n = 1 experiment. Unigene Symbol Fold Unigene Symbol Fold Unigene Symbol Fold Change Change Change Hs.643357 ADAMTS1 0.1116 Hs.632226 ITGB4 -- Hs.2399 MMP14 4.2981

Hs.131433 ADAMTS13 2.8176 Hs.13155 ITGB5 1.9844 Hs.80343 MMP15 --

Hs.271605 ADAMTS8 2.8176 Hs.521869 ANOS1 0.2852 Hs.546267 MMP16 0.967

Hs.502328 CD44 1.675 Hs.270364 LAMA1 0.1738 Hs.513617 MMP2 3.5962

Hs.461086 CDH1 2.8176 Hs.200841 LAMA2 0.0429 Hs.375129 MMP3 0.0101

Hs.476092 CLEC3B 0.5301 Hs.436367 LAMA3 1.7053 Hs.2256 MMP7 66.9334

Hs.143434 CNTN1 2.8176 Hs.650585 LAMB1 0.8661 Hs.161839 MMP8 0.2046

Hs.523446 COL11A1 2.5686 Hs.497636 LAMB3 0.7082 Hs.297413 MMP9 0.0884

Hs.101302 COL12A1 1.6538 Hs.609663 LAMC1 0.8804 Hs.503878 NCAM1 --

Hs.409662 COL14A1 3.5236 Hs.83169 MMP1 0.1862 Hs.514412 PECAM1 --

Hs.409034 COL15A1 25.301 Hs.2258 MMP10 3.5214 Hs.82848 SELE --

Hs.368921 COL16A1 251.2441 Hs.143751 MMP11 0.0392 Hs.728756 SELL 8.7681

Hs.172928 COL1A1 8.4141 Hs.1695 MMP12 0.3601 Hs.73800 SELP --

Hs.508716 COL4A2 3.3269 Hs.2936 MMP13 -- Hs.371199 SGCE 3.5521

Hs.210283 COL5A1 4.1495 Hs.644352 ITGA1 0.7961 Hs.111779 SPARC 19.7901

Hs.474053 COL6A1 1.5519 Hs.482077 ITGA2 0.1508 Hs.185597 SPG7 0.5888

Hs.420269 COL6A2 2.5878 Hs.265829 ITGA3 1.0209 Hs.313 SPP1 0.0524

Hs.476218 COL7A1 0.7359 Hs.440955 ITGA4 0.2454 Hs.369397 TGFBI 2.4203

Hs.654548 COL8A1 3.9934 Hs.505654 ITGA5 2.7167 Hs.732539 THBS1 0.417

Hs.410037 CTGF 0.9007 Hs.133397 ITGA6 1.6203 Hs.371147 THBS2 221.9258

Hs.656653 CTNNA1 1.3976 Hs.524484 ITGA7 5.306 Hs.169875 THBS3 2.9329

Hs.712929 CTNNB1 1.5359 Hs.592472 ITGA8 22.1679 Hs.522632 TIMP1 0.4081

Hs.166011 CTNND1 1.3585 Hs.174103 ITGAL 0.0889 Hs.633514 TIMP2 1.5947

Hs.314543 CTNND2 3.8772 Hs.172631 ITGAM 120.5247 Hs.644633 TIMP3 3.3773

Hs.81071 ECM1 0.3349 Hs.436873 ITGAV 0.4791 Hs.143250 TNC 0.9253

Hs.203717 FN1 0.9313 Hs.643813 ITGB1 1.1351 Hs.109225 VCAM1 --

Hs.57697 HAS1 -- Hs.375957 ITGB2 -- Hs.643801 VCAN 0.4882

Hs.643447 ICAM1 3.1281 Hs.218040 ITGB3 2.7494 Hs.2257 VTN 38.2151

164

APPENDIX B: MATHEMATICAL MODELING

%%% Mathematical modeling of cellular viscosity using the power law model %%% the model was developed by Tsai et al 1993 Biophys J %%% the code below was developed and written by Grady E. Carlson %LegendreP functions are recognized by Matlab 2015a but not 2012a/b %b is 0.5 alpha is 0.15 % Cell radius may not exceed 13.6 microns. Utilize polynomial extrapolation of %simulated data for larger cells. clear all; close all; clc; epsilon = 0.1; inputR = input('enter the cell radius in microns'); inputtime = input('enter the cell entry time in seconds'); inputpressure = input('enter the pressure differential in kPa');

%Initialize vectors for legendre functions p=3; q=202; n = (p:q); nV =200; % number of components in the vector P = zeros(nV,nV); Pp = zeros(nV,nV); P_1 = zeros(nV,nV); P_1p = zeros(nV,nV); P_2 = zeros(nV,nV); P_2p = zeros(nV,nV); GegenbauerP = zeros(nV,nV); Gegenbaur = zeros(nV,nV); Gegenbauer_squiggle_plus_epsilon = zeros(nV,nV); Gegenbauer_squiggle_minus_epsilon = zeros(nV,nV); fn_squigglep_epsilon = zeros(nV,nV); combined_components_of_stress_tensor_rr = zeros(nV,nV); stress_tensor_rr = zeros(nV,nV); total_stress_tensor_squared=zeros(nV,nV);

R0 = (inputR*1e-6); % Initial radius of the cell Rp = 6.66e-6;% Hydralic radius of microfluidic channel Rf = Rp;% Rf is the radius of the cell in the pipette Rf_bar = Rf/Rp; Lp0=0e-6;% initial volume of the projection is zero Lp = Lp0; t0 = 0;% Initialize time to zero t=t0;% Initialize time to zero %delta_t = 0.5; dtbar=0.5; n_2 = n - 2; n_1 = n - 1; Vtotal = (4/3)*pi*(R0^3);%Volume of the cell. Note that this volume does not change R=R0;%Initial radius of the cell R_bar = R/Rp; jjj=1; betal=0.01; ql = 1;%0.08; fgh = 0.01;% prevents sin(0) cos(0)from yielding NaN fgh2 = 0.999*pi;%prevents sin(0) cos(0) from yielding NaN dLpbar=0; %mu0=30;%reference viscosity deltahydrolicpressure=inputpressure; b=0.5; tbar=0; %pltlprp(jjj) = Lp/Rp; tplt(jjj)=tbar; iii=0; while R_bar > 1 165 theta = linspace(fgh,fgh2,nV);% vector for theta in spherical coordinates iii=iii+1 %Lpbar = Lp/Rp; Rf=Rp;% assume that the cell radius in the pipette is the same radius Rp r = linspace(0,R,nV); % any location on the radius r within R Rf_bar = Rf/Rp; ttheta = atan(Rp/R); thetap = pi - (ttheta);% change in cell position relative to channel %Prepare the legendre functions squiggle = cos(theta); squigglep = cos(thetap); squigglep_plus_epsilon = squigglep + epsilon; squigglep_minus_epsilon = squigglep - epsilon; P = legendreP(n,squiggle); P_1 = legendreP(n_1, squiggle); P_2 = legendreP(n_2, squiggle); Pp = legendreP(n,squigglep); P_1p = legendreP(n_1, squigglep); P_2p = legendreP(n_2, squigglep); Pp_plus_epsilon = legendreP(n,squigglep_plus_epsilon); P_1p_plus_epsilon = legendreP(n_1, squigglep_plus_epsilon); P_2p_plus_epsilon = legendreP(n_2, squigglep_plus_epsilon); Pp_minus_epsilon = legendreP(n,squigglep_minus_epsilon); P_1p_minus_epsilon = legendreP(n_1, squigglep_minus_epsilon); P_2p_minus_epsilon = legendreP(n_2, squigglep_minus_epsilon); GegenbauerP=(P_2p-Pp)/((2*n)-1); Gegenbauer=(P_2-P)/((2*n)-1); Gegenbauer_squiggle_plus_epsilon =(P_2p_plus_epsilon-Pp_plus_epsilon)/((2*n)-1); Gegenbauer_squiggle_minus_epsilon = (P_2p_minus_epsilon-Pp_minus_epsilon)/((2*n)-1); fn_squiggle_p1a=(((2*n)-1).*(n-1)); fn_squiggle_p1b=((2*(n.^2))+1); fn_squiggle_p1 = (fn_squiggle_p1a./fn_squiggle_p1b); fn_squiggle_pa = (1-(squigglep^2)-(epsilon^2))/(4*squigglep*epsilon); fn_squiggle_p2 = (0.5-fn_squiggle_pa)*Gegenbauer_squiggle_minus_epsilon; fn_squiggle_p3 = (0.5+fn_squiggle_pa)*Gegenbauer_squiggle_plus_epsilon; fn_squigglep_epsilon= fn_squiggle_p1*(fn_squiggle_p2+fn_squiggle_p3); %components of stress tensor rr rr_1 = (((n.^2).*((r./R).^2))-((n.^2)-1)); rr_2 = (r./R).^(n-3); rr_3 = fn_squigglep_epsilon.*P_1; combined_components_of_stress_tensor_rr = rr_1.*rr_2.*rr_3; stress_tensor_rr = sum(combined_components_of_stress_tensor_rr); %components of stress tensor r_theta r_theta_1 = (1-((r./R).^2)); r_theta_2 = (n.^2).*((n.^2)-1).*((r./R).^(n-3)); r_theta_3 = fn_squigglep_epsilon.*(Gegenbauer./sin(theta)); %combined_components_of_stress_tensor_r_theta(n) = r_theta_2.*r_theta_3 combined_components_of_stress_tensor_r_theta = r_theta_2.*r_theta_3; stress_tensor_r_theta = r_theta_1.*sum(combined_components_of_stress_tensor_r_theta); %% components of stress tensor theta theta theta_theta_1 = (n+1).*((n.*((r./R).^2))-(((n-1).^2)./(n-2))).*((r./R).^(n-3)).*(fn_squigglep_epsilon.*P_1); theta_theta_2 = (squiggle./(1-(squiggle.^2))); theta_theta_3= n.*(((n+2).*((r./R).^2))-(((n-1).^2)./(n-2))).*((r./R).^(n-3)).*(fn_squigglep_epsilon.*Gegenbauer); stress_tensor_theta_theta = (-1.*sum(theta_theta_1))+ (theta_theta_2.* sum(theta_theta_3)); %% components of stress tensor phi phi phi_phi_1 =((n.*((r./R).^2))-(((n-1).^2)/(n-2))).*((r./R).^(n-3)).*(fn_squigglep_epsilon.*P_1); phi_phi_2 = (squiggle./(1-(squiggle.^2))); phi_phi_3 =n.*(((n+2).*((r./R).^2))-(((n-1).^2)./(n-2))).*(((r./R).^(n-3)).*fn_squigglep_epsilon.*Gegenbauer); stress_tensor_phi_phi = sum(phi_phi_1)- (phi_phi_2.* sum(phi_phi_3)); %% calculate the value of function_capital_phi total_stress_tensor_squared = ((stress_tensor_rr.^2)+(stress_tensor_r_theta.^2)+ (stress_tensor_theta_theta.^2)+ (stress_tensor_phi_phi.^2)); helpmetotal_stress_tensor_squared = (((total_stress_tensor_squared)).*sin(theta)); aaaa=trapz(helpmetotal_stress_tensor_squared); trysums= (1/3)*(pi/(nV))*(helpmetotal_stress_tensor_squared(1)+2*sum(helpmetotal_stress_tensor_squared(3:2:end- 2))+4*sum(helpmetotal_stress_tensor_squared(2:2:end))+helpmetotal_stress_tensor_squared(end));%integral approx simpsons rule

166

%integralaroundR = ((r.^2)/(R^3)).*helpmetotal_stress_tensor_squared; integralaroundR = ((r.^2)/(R^3)).*trysums; aaa=trapz(integralaroundR); trysums2 = (1/3)*(R/(nV))*(integralaroundR(1)+2*sum(integralaroundR(3:2:end-2))+4*sum(integralaroundR(2:2:end))+ integralaroundR(end)); abg = trysums2-aaa;% comparison of simpson vs trapezoid integration, trapezoid over estimates gh = (1-(betal*((Rp/Rf)-(Rp/R)))); alpha = 0.15;%damping factor guesss = b - (alpha*((dLpbar)^(1/2))) K = trysums2; %K = aaa; cap_ital_phi = (((3/4)*K)^(1/2)); %guesss=b; shear_ratio = (gh*cap_ital_phi)^(1/(1-guesss)); dampedshear = shear_ratio + (alpha*(sqrt(shear_ratio))); %gh2 = ((pi*(Rp^3)*deltahydrolicpressure)/(4*mu0)); %Qflow = gh2*(shear_ratio^guesss)*gh; if dLpbar <= 1 G = 2/(1+(dLpbar^2)); else G = 1; end gh3 = G/(pi*(Rp^2)); %Lp = Lp+(gh3*Qflow*1*delta_t); dLpbar = dLpbar+((G*(shear_ratio^guesss)*gh)*dtbar) Lp = dLpbar*Rp; Vpipette = pi*(Rp^2)*(dLpbar*Rp); R = ((Vtotal-Vpipette)/((4/3)*pi))^(1/3); R_bar = R/Rp

tbar = tbar + dtbar mu0 = (inputpressure*inputtime)/(4*tbar(end)) muu = mu0*(shear_ratio^(-guesss)); %muew = fprintf('your characteristic viscosity is in kPa-s', muu); jjj = jjj+1; % counter pltlprp(jjj) = dLpbar; tplt(jjj)=tbar; mew(jjj)=muu; collectedshearrate(jjj)=shear_ratio; end plot (tplt,pltlprp,'--') xlabel('t(s)') % x-axis label ylabel('Lp/Rp') % y-axis label Mshearrate=mean(collectedshearrate) Mmew = mean(mew) time = tplt(end); L_over_Rp = pltlprp(end); mew2=Mmew; timet=tplt' instmoo=mew'; LP_RP=pltlprp'; cellr = R0/Rp; instshratio=collectedshearrate'; munotnew = (t*deltahydrolicpressure) sshear=Mshearrate; T2 = table(timet,instmoo,LP_RP, instshratio) T = table(time,L_over_Rp,mew2,sshear,cellr,mu0) tt=tplt(end) writetable(T2); writetable(T); instshratio=collectedshearrate'; munotnew = (t*deltahydrolicpressure) sshear=Mshearrate; T2 = table(timet,instmoo,LP_RP, instshratio) T = table(time,L_over_Rp,mew2,sshear,cellr,mu0) tt=tplt(end) 167

Figure A1. Numerical simulation of the time-course entry of a cell into a microfluidic channel. Simulations were carried out using a constant pressure of 4kPa for A) the shear-thinning power- law model of Tsai et al. (1993) and B) the damped power-law model proposed herein. 푅̅̅0̅ = 1.8.

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