Application of Immunomagnetic Cell Separation in Cancer Cell Detection: Development and Optimization
APPLICATION OF IMMUNOMAGNETIC CELL SEPARATION IN CANCER CELL DETECTION: DEVELOPMENT AND OPTIMIZATION
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Liying Yang, B.S.
*****
The Ohio State University 2008
Dissertation Committee: Approved by Prof. Jeffrey J. Chalmers, Adviser Prof. James C. Lang Prof. Jim F. Rathman Adviser Graduate Program in Chemical and Biomolecular Engineering
ABSTRACT
Detection of rare, circulating tumor cells (CTCs) in peripheral blood is a
potential prognostic/diagnostic tool in oncology. The use of immunomagnetic cell
separation has been shown to improve the target cell purity and thus detection sensitivity. In this dissertation, a repeatable enrichment process including a flow
through immunomagnetic cell separation system, the quadrupole magnetic cell sorter
(QMS), was continuously developed and optimized. Molecular analysis technologies
such as immunocytochemical assay and the Reverse Transcriptase Polymerase Chain
Reaction (RT-PCR) were combined with the enrichment process to reliably and
accurately detect the presence of CTCs in peripheral blood. The novel technique was applied involving samples from head and neck patients undergoing surgery.
Immunochemical staining and RT-PCR analysis of the same, enriched sample result in congruent outcome in all but one cases. Furthermore, the data with respect to the quantitative detection of CTCs is generally consistent with the pathological report on these patients. Data suggested that if a sample had 10 or more CTCs per ml of blood, a metastatic disease was present in the corresponding patient.
ii In order to further improve the final purity of cancer cells, to be eligible for
studies including cDNA microarray, continuous development and optimization of the
novel system was desired. A kinetics model was used to describe the process of
ligand binding to cell surface receptors, which demonstrated that the ratio between
the initial free antibody concentration and its dissociation constant (L0/KD) is the
limiting factor for a given magnetic labeling system. Based on the theory, an optimal
labeling scheme is identified, including the use of a tetrameric antibody complex,
resulting in significantly better separation performance with order of magnitude
higher log depletion.
In the second part of the study, a reaction-diffusion model was constructed to
describe the in vitro tissue dissociation process. It is not only useful in recognizing
the rate-limiting factor in the tissue dissociation process, but also provides
quantitative guidelines to establish an optimal tissue dissociation technology. A rapid
tissue dissociation process is established and characterization followed by a positive selection of EGFR targeted cancer cells. Evidence presented in this dissertation proofed the concept that it is feasible to isolate pure cancer cells from solid tumor biopsies within 2 hrs by applying the newly established rapid tissue dissociation method followed by a positive selection of cancer cells by immunomagnetic labeling.
iii
Dedicated to my parents and husband
iv ACKNOWLEDGMENTS
This work would have not been possible without the guidance, support, and encouragement by my advisor Dr. Chalmers, Jeffrey J. His great personality and understanding of cultural differences have made my staying here much smooth. I would like to thank Dr. Lang, James C. for his valuable insights and tremendous knowledge in the molecular biology area, which makes my research much easier. I also wish to thank Dr. Rathman, Jim, Dr. Yang, ST, and Dr. Palmer, Andre F. for having been on my committee and providing criticisms on my work.
I would like to thank Dr. Schuller, David, Dr. Agrawal, Amit, and Dr. Jatana,
Kris R. in the Department of Otolaryngology, Head and Neck Surgery for their effort and enthusiasm in this work, which makes the preclinical investigation possible.
I would like to thank Dr. Zborowski and Mr. Lee R Moore in the Cleveland
Clinic Foundation for their continuous help and comments during the development of work.
I would like to thank Dr. Tong, Xiaodong and Dr. Metha, Bhavya for their guidance and help at the time I started this research. They are both mentors and friends to me.
I wish to express my appreciation to every group member in Chalmers’s research group, especially Priya Balasubramanian for her contribution, Dr. Zhao,
v Yang for his technical support, Ying Xiong, Rustin Shenkman for their suggestions to the completion of this work. I would like to thank Mr. Bryan McElwain at the Ohio
State University Analytical Cytometry Laboratory for his help on flow cytometry analysis.
Special thanks to those blood donors including Dr. Tong, Xiaodong, Dr.
Chalmers, Jeffrey J. and Priya Balasubramanian.
Finally, I would like to thank my parents, who love me and have faith in me, which had make me stronger and be able to come to this far. My husband, He
Yuesheng, has always been standing by my side during these years. His encouragement and love has been the source of my spiritual power.
vi VITA
March 22, 1981……………………………….Born – Suzhou, Jiangsu, China July, 2003……………………………………..B.S. Bioengineering, Zhejiang University, China September, 2003-December, 2007……………Graduate Research Associate, Chemical & Biomolecular Engineering The Ohio State University
PUBLICATIONS
Xiaodong Tong, Liying Yang , James C Lang , Maciej Zborowski , Jeffrey J Chalmers. Application of immunomagnetic cell enrichment in combination with RT- PCR for the detection of rare circulating head and neck tumor cells in human peripheral blood. Cytometry Part B (Clinical Cytometry). 2007. 72B:310–323. Chunhui Zhao, Jianping Lin, Liying Yang, Jianghua Mu, Peilin Cen. Effect of Glycine and Succinate on 5-Aminolevulinic Acid Production by Photobacteria. Chemical reaction engineering and technology. 2004. 20(3): 275-279.
FIELD OF STUDY Major Field: Chemical Engineering Specialization: Biochemical engineering
vii TABLE OF CONTENTS Page
ABSTRACT...... ii DEDICATION...... iv ACKNOWLEDGMENTS ...... v VITA...... vii LIST OF TABLES...... xiii LIST OF FIGURES ...... xv
Chapters:
1. INTRODUCTION ...... 1 1.1 Immunomagnetic cell separation ...... 1 1.2 Magnetophoretic mobility and CTV...... 3 1.3 Commercial magnetic cell sorters...... 8 1.4 Novel magnetic cell separation systems ...... 9 1.4.1 Quadrupole magnetic cell sorters (QMS) ...... 9 1.4.2 Magnetic deposition system...... 12 1.5 Detection of Circulating Tumor Cells (CTCs)...... 14 1.5.1 Biology of CTCs...... 14 1.5.2 Current CTC detection status...... 16 1.6 QMS enrichment of rare cancer cells...... 18 1.7 Molecular analysis technologies in cancer research ...... 19 1.7.1 ImmunoCytochemical Staining (ICCS)...... 19 1.7.2 RT-PCR...... 20 1.7.3 cDNA microarray...... 27 1.8 Technologies to obtain pure cancer cells from solid tumors ...... 28 1.8.1 Importance of obtaining pure cell samples in cancer research ...... 28 1.8.2 In vitro degradation of tissue ...... 29 1.8.3 Histology of the ECM...... 30 1.9 Research objectives...... 32 1.10 Dissertation organization ...... 33
viii 2. APPLICATION OF IMMUNOMAGNETIC CELL SEPARATION IN COMBINATION WITH RT-PCR FOR THE DETECTION OF RARE CIRCULATING HEAD AND NECK TUMOR CELLS IN HUMAN PERIPHERAL BLOOD...... 35 2.1 Motivation...... 35 2.2 Material and Methods ...... 38 2.2.1 Cell sources...... 38 2.2.2 Immunomagnetic labeling ...... 39 2.2.3 Immunomagnetic cell separation ...... 40 2.2.4 Optimization of QMS separation...... 41 2.2.5 Detection/analysis of enriched samples...... 42 2.2.6 RT-PCR assay...... 43 2.2.7 Sensitivity of RT-PCR by EGFR for HNSCC Cells...... 44 2.2.8 Statistical Analysis...... 44 2.3 Results...... 45 2.3.1 Labeling Saturation Studies ...... 45 2.3.2 Assay Sensitivity...... 48 2.3.3 QMS Optimization Studies...... 52 2.3.4 Statistical Analysis for Optimization Studies ...... 56 2.3.5 Detection of CTCs by ICCS ...... 58 2.3.6 Enrichment Model of CTC Suspension ...... 60 2.3.7 Detection of CTCs by RT-PCR ...... 63 2.4 Discussion...... 65
3. IMMUNOCYTOCHEMICAL AND PCR DETECTION AFTER MAGNETIC ENRICHMENT OF CIRCULATING TUMOR CELLS IN HEAD AND NECK CANCER PATIENTS: EARLY RESULTS...... 69 3.1 Motivation...... 69 3.2 Material and Methods ...... 73 3.2.1 Patients and sample collection ...... 73 3.2.2 Overall enrichment process...... 75 3.2.3 Red cell lysis step ...... 75 3.2.4 Immunomagnetic labeling ...... 75 3.2.5 Magnetic cell separation step...... 76 3.2.6 Staining of tumor cells...... 78 3.2.7 RT-PCR assay...... 78 3.2.8 Cell culture...... 79 ix 3.3 Results...... 80 3.4 Discussion...... 87
4. IDENTIFICATION OF RESIDUAL CELLS AFTER QMS ENRICHMENT AND
POTENTIAL IMPROVEMENT STRATEGIES...... 91 4.1 Motivation...... 91 4.2 Material and Methods ...... 94 4.2.1 Blood samples collection ...... 94 4.2.2 Overall enrichment process...... 95 4.2.3 Sample preparation and labeling...... 95 4.2.4 FACS analysis...... 96 4.2.5 Cytospin and Wright’s stain...... 99 4.3 Results...... 100 4.3.1 Identification of residual cells...... 100 4.3.2 Quantification of subpopulations depletions...... 102 4.3.3 Differential CD45 expression among PBLs ...... 106 4.3.4 Study on the expression of myeloid markers...... 110 4.3.5 Normal blood control...... 112 4.4 Discussion ...... 113
5. FURTHER IMPROVEMENTS IN DEPLETION PERFORMANCE USING A NOVEL MAGNETIC LABELING STRATEGY...... 116 5.1 Motivation...... 116 5.2 Theory: Cell surface receptor/ligand binding ...... 118 5.3 Material and Methods ...... 124 5.3.1 Cell sources and blood sample collection...... 124 5.3.2 Overall enrichment process...... 124 5.3.3 Magnetic cell labeling...... 124 5.3.4 Magnetic cell separation ...... 127 5.3.5 Log depletion determination ...... 128 5.3.6 Statistical analysis...... 128 5.3.7 BCA protein assay ...... 129 5.4 Results...... 130 5.4.1 The depletion results of 63 experiments ...... 130 5.4.2 Effect of magnetic labeling method...... 134 5.4.3 Estimation of reagent concentrations...... 135 5.4.4 Economic considerations ...... 137 5.5 Discussion ...... 138
x 6. MAGNETIC ISOLATION OF TUMOR CELLS FROM SOLID TUMOR FOR FURTHER MOLECULAR ANALYSIS...... 140 6.1 Motivation...... 140 6.2 Theory: A simplified diffusion reaction model...... 142 6.3 Material and Methods ...... 145 6.3.1 Tissue procurement...... 145 6.3.2 In vitro tissue degradation: A rapid dissociation protocol ...... 145 6.3.3 Quantitative analysis of collagen content ...... 146 6.3.4 RT-PCR...... 146 6.3.5 Immunomagnetic cell separation using magnetic deposition system147 6.3.6 Flow cytometry ...... 147 6.3.7 CTV analysis...... 149 6.4 Results and Discussion ...... 149 6.4.1 Collagen quantification results ...... 149 6.4.2 Theoretical determination of the significant factors in tissue dissociation ...... 151 6.4.3 Experimental determination of a rapid tissue dissociation method 155 6.4.4 Immunomagnetic separation design ...... 160 6.4.5 Application of established technique on clinical samples ...... 164
7. CONCLUSIONS AND FUTURE WORK ...... 168 7.1 Conclusions...... 168 7.2 Future work...... 171 7.2.1 Further refinements in RT-PCR assay ...... 171 7.2.2 Improvement of the magnetic deposition system ...... 171 7.2.3 Improvement in immunocytochemical assay...... 173 7.2.4 cDNA microarrays...... 174 7.2.5 Application in other areas ...... 175
Appendix A: T-test of nucleated cell depletion by sample source ...... 177
Appendix B: Breakdown and normal range of human leukocytes ...... 179
Appendix C: Statistical analysis of parameter effect by fitting effect model...... 181
xi Appendix D: ANOVA test and student’s t test of nucleated cell depletion by labeling methods...... 183
Appendix E: T test of collagen content on tumor type ...... 188
Appendix F: ANOVA and student’s t test on cel yield by enzyme concentration ....190
LIST OF REFERENCES...... 192
xii LIST OF TABLES
Table Page
1.1 Application of PCR technology in the detection of CTCs in peripheral blood of cancer patients...... 24 2.1 Results of statistical analysis for the influencing factors on the performance of QMS ...... 57 2.2 Experimental data of rare cancer cell enrichment using an indirect immunomagnetic labeling...... 61 2.3 Experimental data of rare cancer cell enrichment using a direct immunomagnetic labeling...... 62 2.4 Sensitivity of RT-PCR detection of EGFR mRNA for HNSCC ...... 67 3.1 Patient data and number of tumor cells detected, total and per ml of blood.....74 3.2 Specific details of enrichment performance per patients sample...... 81 3.3 Numerical values with respect to the samples used for RT-PCR analysis ...... 85 4.1 Detailed breakdowns of PBLs into subpopulations pre and post QMS enrichment...... 104 4.2 Quantitative comparison of CD45 expression level...... 109 5.1 Summary of six different magnetic labeling methods ...... 126 5.2 Log depletion results of 63 blood samples using six different magnetic labeling methods...... 131 5.3 Summary of depletion data using six labeling methods ...... 133 5.4 p-value of three variables in the least square model fitting by JMP...... 134 5.5 Comparison of reagents concentration...... 136 5.6 Calculated cost of labeling reagents for a total of 108 cells...... 137 6.1 Primer sequences for RT-PCR study C ...... 147
xiii 6.2 Comparison of cell yield of five tumor tissues in 0.5 hr quick digestion and 3hr digestion...... 158
xiv LIST OF FIGURES
Figure Page
1.1 Cartoon of immunomagnetical labeling methods...... 3 1.2 Diagram of the Cell Tracking Velocimetry system ...... 7 1.3 Schematic diagram of the MiniMACS® system ...... 9 1.4 (A) Diagram of the QMS channel within the quadrupole magnetic field; (B) Schematic diagram of the QMS system in operation...... 10 1.5 Schematic diagram of the magnetic deposition system ...... 13 1.6 Magnetic field around the interpoler gap...... 14 1.7 Micrometastases of cancer...... 15 2.1 (A-D) (A) Log plot of the magnetophoretic mobility of unlabeled PBL and labeled PBL with anti-CD45-MACS (B) Saturation curve of CD45 surface receptor on PBL in terms of concentration of antibody (µg/ml) versus normalized FI; (C) A saturation curve of the magnetophoretic mobility of the PBL cells, previously labeled with anti-CD45 PE (15µg/ml), versus amount of PE-MACS reagent; (D) A saturation curve of the magnetophoretic mobility of the PBL cells labeled with anti-CD45 MACS...... 46 2.2 Representative examples of RT-PCR targeting the mRNA of EGFR ...... 48 2.3 Representative photographs of agarose gel analysis of RT-PCR amplification of various samples...... 50 2.4 Representative examples of the effect of operational parameters on the separation performance of QMS...... 54 2.5 (A-D)Various wavelength filtered, photographs of the same slide to which PBL and Detroit-562 cells were attached ...... 59 2.6 PCR detection of the enriched sample by EGFR and HPRT...... 64 3.1 Flow diagram of process to enrich for rare cancer cells...... 77 xv 3.2 Photographs of microscopic images of a cytospin of one of the 10 enriched peripheral blood samples from cancer patients...... 52 3.3 Immunocytochemical staining (ICCS) of three other samples of cytospins from three other cancer patients ...... 83 3.4 Detection of EGFR mRNA in enriched samples by RT-PCR technique...... 84 4.1 Statistical analysis of nucleated blood cell depletion by sample sources ...... 92 4.2 Demonstration of two different gatings ...... 98 4.3 PE histogram of QuantiBRITE PE beads ...... 99 4.4 Dot plot of pre and post QMS enrichment samples...... 101 4.5 A representative photograph of Wright’s staining of residual cells...... 102 4.6 Comparison of changes in subpopulation pre and post QMS separation among 8 peripheral blood samples from HNSCC patients...... 105
4.7 Comparison of log depletions for PBLs and subpopulations...... 106 4.8 PE histograms of cell samples before (a) and after (b) QMS enrichment ...... 107 4.9 Differential expression of CD-45 antigen on subpopulations of PBLs before (a) and after (b) QMS enrichment...... 108 4.10 Calibration curve for Quantibrite® PE beads...... 109 4.11 (A-D) A,C: analysis of CD13 and CD33 before QMS separation. B,D: the analysis of CD13 and CD33 after QMS separation. Numbers show the percentage of double positive cells...... 111 4.12 Comparison of log depletions for PBLs and subpopulations from normal controls...... 113
5.1 3-D plot of antigen binding percentage (θeq) as a function of L0 and KD ...... 122
5.2 A-B 2-D plot of θeq versus KD at fixed initial antibody labeling concentrations123 5.3 Schematic diagrams of six different magnetic labeling methods targeting CD45 antigen on PBLs...... 127
xvi 5.4 Variability Chart for Log depletion of PBLs grouped by magnetic labeling method...... 133 5.5 Oneway ANOVA test and student’s t-test results...... 135 6.1 Flow cytometry analysis and gating for D-562 cell line...... 148 6.2 Standard curve for collagen quantification...... 150 6.3 Comparison of collagen content between breast tumor and HNSCC tumor tissues...... 150 6.4 Concentration profiles of free enzyme in a tissue slab of 1mm thick within 20hrs...... 153 6.5 2-D plots of concentration profiles at fixed time (a) and at fixed x distance (b) when diffusivity is 7.8 *10-8cm2/s ...... 154 6.6 Oneway Analysis of Cell yield (*106) By Enzyme conc. (mg/ml) ...... 157 6.7 Dissected cell yield based on initial tissue weight during 3-hour incubation time ...... 159 6.8 Gene expression of three selected genes in 20 randomly selected HNSCC cases ...... 161 6.9 Primary and secondary antibody labeling titration ...... 163 6.10 a, b Summary of the rapid tissue dissociation results ...... 165 6.11 Microscopic photograph of magnetically deposited cancer cells from a HNSCC solid tumor specimen...... 167 6.12 Immunostaining of cells before and after magnetic separation ...... 167 7.1 Expression of Cytokeratin 17 by immunohistochemistry...... 174
xvii CHAPTER 1
INTRODUCTION
1.1 Immunomagnetic cell separation
Immunomagnetic cell separation technique has been widely employed in the
area of biological research and some clinical applications (Zborowski and Chalmers,
2007; Kantor, 1998). The fundamental principle of this technique is based on the separation of specific cell subsets under a magnetic field when they are labeled by
specific antibodies conjugated with magnetic particles. Basically, the magnetic cell
separation is driven by the differences in the magnetic susceptibility between
different cell subsets. Theoretically, the isolation of any cell subpopulation has been
made possible by a distinct antigen marker. Cell surface antigens are most commonly
used markers for immunomagnetic cell separation, although both cell surface antigens
and intracellular antigens can be utilized. The major advantages of this technology
include the high accuracy and specificity of the separation, relatively high cell
throughput, the reliability of magnetophoretic mobility compared to fluorescent
intensity measurement by flow cytometry, its relative low capital and operating cost
and its wide application.
1 Generally speaking, there are two modes of operation: positive selection and
negative depletion. In the positive selection mode, the target cells are magnetically
labeled and subsequently collected at the magnetic fraction from the output of the separator. One the other hand, in negative depletion mode of operation, those unwanted cells are magnetically labeled while target cells are collected at the non- magnetic fraction from the outlet cell separator.
Currently, both micro-sized and nano-sized magnetic particles have been used in the immunomagnetic labeling. They are typically made of a magnetically susceptible material, such as magnetite, coated by biocompatible polymer on the surface. They exhibit superparamagnetic phenomena due to the quantum size effect and high surface areas (Goya et al, 2003). Micro-sized particles, typically in the range of 1μm to 5μm, have high magnetic moment thus can be used in relatively low magnetic field (Zborowski and Chalmers, 2007). Nano-sized particles, also referred to as colloidal magnetic beads, ranging from 50nm to 300nm, can be used in high magnetic gradient field. Much smaller in size than the cells allows the immunomagnetic labeling proportional to the antigen expression level (Zborowski and Chalmers, 2007). Dynabeads® and MACSbeads® are examples of the micro- sized and nano-sized particles respectively and are widely used in a lot of applications.
There are different ways to impart cells magnetic moment (Figure 1). Only cell surface labeling is discussed in the scope of this thesis. The simplest method is direct labeling, or one-step labeling, which is shown in Figure 1.1a. It is achieved by the directly interaction between a cell surface antigen and a specific antibody conjugated with a magnetic particle. Indirect labeling or two-step labeling methods
2 are illustrated in Fig 1.1b and c. Figure 1.1b shows a cell labeled by a primary
antibody specific to a cell surface antigen, and then a secondary antibody recognizing
the molecules on the tail of the primary antibody. The use of bivalent, tetrameric
antibody complexes can be classified in the indirect labeling too (Figure 1.1c).
Cell Cell Cell
a b c
Fig.1.1 Cartoon of immunomagnetical labeling methods. a) one-step labeling, with antibody conjugated with magnetic bead; b) two-step labeling, with primary antibody conjugated with fluorochrome and secondary antibody conjugated with magnetic bead; c) two-step labeling, with tetrameric antibody and magnetic particles.
1.2 Magnetophoretic mobility and CTV
Magnetic susceptibility is a property of a material describing its response to a
magnetic field (Zborowski and Chalmers, 2007; Moore et al, 2004). It is defined by
the amount of force exerted on a defined amount of substance in a well defined
magnetic field. Thus, the magnetic force acting on a subject of volume V can be
expressed in the following equation (Zborowski and Chalmers, 2007):
dB FH= χV 1.1 mag dx
where χ is the dimensionless volumetric magnetic susceptibility, H is the magnetic field strength, B is the magnetic field intensity or magnetic flux intensity. In a free 3 space, the magnetic flux intensity is proportional to the magnetic field strength:
H= μ0B 1.2
-7 where μ0 is the magnetic permeability of free space, a constant being 4π⋅10 T m/A.
1 2 Thus Fmag =∇χV B 1.3 2μ0
∇B2 in which the term is normally designated as magnetic energy gradient Sm. 2μ0 For the typical case of a particle/cell moving in a medium under a magnetic energy gradient, the magnetic force can be derived based on equation 1.3:
1 2 Fmag, c =Δ∇χVC B, 1.4 2μ0 where Δχ is the difference in magnetic susceptibility between particle/cell and the medium (Δχ=χp-χm) and Vc is the particle/cell volume. In most magnetic separation system, the magnetic force is designed to be perpendicular to gravity force, thus at steady state, the magnetic force is balanced by the viscous drag force (Fd), which follows the Stokes formula (assuming cell motion is within the stokes region):
Fdc=6π R ηΔv 1.5
Where Rc is cell diameter, η is the viscosity of the medium, Δv is the cell velocity relative to the medium (Δv=vc, where vc is the velocity of the particle/cell and assuming the medium is stationary). Combining equations 1.4and1.5, one can obtain:
ΔχV ⎛⎞B2 vc =⋅∇⎜⎟ 1.6 62π Rcημ⎝⎠0
Equation 1.6 indicates that the movement of the particle/cell in a medium is a function of two main quantities, independent of each other. One of them is clearly the
4 ⎛⎞B2 ΔχV magnetic field energy gradient Sm =∇⎜⎟, while the other term is a ⎝⎠2μ0 6π Rcη combination of particle/cell material and fluid properties. This leads to the definition
ΔχV of magnetophoretic mobility m = 1.7 6π Rcη
and vSc= m m 1.8
Therefore, for a given particle/cell and medium system, the velocity of the particles is proportional to Sm, the magnetic energy gradient. The higher the magnetic energy gradient, the faster they move.
However, in majority of the cases, such as most cells and biological entities, the magnetic susceptibility is a very small, negative value, thus the magnetically induced motion is too small to be noticed (Zborowski, M and Chalmers, J J, 2007
Zhang et al, 2005). Most of these cells are designated as ‘diamagnetic’ in nature because the major components of most of the cells, for example water, lipids, proteins and DNA, are diamagnetic. However, there are a few exceptions such as deoxygenated erythrocytes, which are ‘paramagnetic’ (Moore et al, 2006; Zborowski et al, 2003 ).
The value of the magnetophoretic mobility can be experimentally measured by an instrument called Cell Tracking Velocimetry (CTV) (McCloskey et al, 2001;
McCloskey et al, 2000; Nakamura et al, 2001; Moore et al, 2000; Chalmers et al,
1999). CTV is a microscope and computer based instrument, which measures the cell motion under a well-defined magnetic field at a cell-by cell basis. A diagram of the
CTV system is shown in Figure 1.2. The permanent magnetic pole pieces produce a
5 constant magnetic energy gradient Sm perpendicular to the gravity force. Different field strength magnetic pole pieces have been designed for different applications.
Strong magnetic energy field is required to measure low magnetophoretic mobility system, while low magnetic energy field is used for highly magnetic particles/cells.
The movement of particles/cells are visualized by an inverted microscope and images captured by a CCD camera and then transferred to a computer. The images are then digitalized into two dimensional matrixes by μTech imaging grabbling board. The number of pixels that a cell travels between frames is direct measurement and is processed by CTV algorithm. On the order of 1000 particles/cells can be simultaneously processed at the same time and their magnetophoretic mobility distributions can be obtained eventually.
Recently, a newer version of CTV processing software allows the analyzing of settling velocities simultaneously with magnetically induced velocities (Zborowski and Chalmers, 2007). Settling velocities are directly associated with particle/cell size, providing us a better way of controlling the system internally. For example, standard particles of known magnetophoretic mobility and significantly different size than the cells of interest can be used as an internal control in every experiment. This instrument is now being continually developed for broader applications.
6
Figure 1.2 Diagram of the Cell Tracking Velocimetry system (Zborowski and
Chalmers, 2007).
7 1.3 Commercial magnetic cell sorters
A number of immunomagnetic separation systems are commercially available which are specifically designed for different types of magnetic particle regents. A list of MACS® family system from Miltenyi Biotec, Magnetic Particle Concentrator
(MPC) from Dynal (Invitrogen), Immunicon Captivate system from Invitrogen,
Easysep® system from Stemcell technology, BDTMImag cell separation systems are representatives of the most popular commercial systems. Recently, half or full automated systems become available. CellSearchTM system from Veradix, a Johnson
& Johnson company, has obtained FDA approval in the application of metastatic breast cancer (Veridix). Robosep® from Stemcell technology is another example of an automated cell separation system. However, most commercially available magnetic cell sorters are batch systems, which normally don’t allow high throughput and are difficult to scale-up (Zborowski and Chalmers, 2007).
A typical example is the Miltenyi biotec’s MACS systems, such as the
MiniMACS®. Basically, the system uses a column packed with small steel spheres and put into a magnetic field (Figure 1.3). Magnetically labeled cells are retained in the column, and then washed out when the column is removed from magnetic field.
Ideally, all unlabeled cells are collected from the “unlabeled” fraction and all labeled cells are collected in the “labeled” fraction. However, this is not always the case. In some cases, the most highly magnetic cells are permanently trapped within the column as shown in Fig.1.3 (Comella et al, 2001). On the contrast, if the target cells don't have sufficiently high magnetophoretic mobility, it will not be retained in the column and directly washed out. It is very difficult to scale-up this type of batch
8 system to separate large number of cells.
Figure 1.3 Schematic diagram of the MiniMACS® system (Chalmers, grant proposal).
A continuous, flow-through magnetic cell sorter has been developed in
Chalmers and Zborowski’s lab, referred to as Quadrapole Magnetic cell Sorter (QMS)
(Sun et al, 1998; Chalmers et al, 1998).
1.4 Novel magnetic cell separation systems
1.4.1 Quadrupole magnetic cell sorters (QMS)
The theory of QMS operation was developed on the basis of the theory of the split-flow thin (SPLITT) fractionation, developed by Giddings and co-workers
(Giddings et al, 1985; Williams et al, 1999; Hoyos, 2000). As depicted in Figure 1.4, the QMS system consists of two concentric cylinders (the inner one is solid) surrounded by four pole pieces generating the magnetic quadrupole field (B0) of 1.37
8 2 T and a mean force field strength, Sm, of 2.382 × 10 (T-A)/m (Sun et al, 1998). The
9 sample mixture is arranged to enter the channel through inlet a’ and after binary sorted to outlet a (negative fraction) and outlet b (positive fraction). Carrier fluid enters through inlet b’. Cells with high enough magnetophoretic mobility can pass through the transport lamina and get out from the outlet b’. Unlabeled cells whose magnetophoretic mobility is relatively low remains in the negative fraction and get out from outlet a’.
A B
Figure 1.4 (A) Diagram of the QMS channel within the quadrupole magnetic field; (B) Schematic diagram of the QMS system in operation. Solid and open circles represent magnetic and non-magnetic cells, respectively.
The magnetic field strength, Sm, inside the cylinder is axially symmetric (Dawson et al, 1976; Zborowski and Chalmers, 2007):
2 B0 S m = ρ μ 0 ro 1.9
10 where ρ=r/r0 is the dimensionless radial distance from the axis, r is the actual distance from the axis. r0 is the outer cylinder inner wall radius. B0 is the maximum magnetic field intensity in the flow region. Hence, the radial velocity of cell induced by magnetic field can be expressed as:
2 ⎛⎞⎛ΔχV B0 ⎞ vcm=⋅mS =⎜⎟⎜ ⋅ ρ ⎟ 1.10 ⎝⎠⎝6πηRrc μ00 ⎠ Williams et al established a model to optimize the flow rate of QMS based on the magnetophoretic mobility distribution of cell populations (Williams et al, 1999).
Cell mobility has to be higher than some critical values to be able to pass through the transport lamina and get out from outlet b’. Average and distribution of target cell mobility could be measured directly from CTV. Then critical values of cell mobility could be determined by specific QMS separation requirement. Therefore, the desired flow rates of the QMS system can be determined.
Different operation modes could be possible for QMS system, positive selection of target cells and negative depletion of unwanted cells. Either target cell or non-target cell is magnetically labeled to increase the magnetophoretic mobility. After a positive selection process, labeled target cells are obtained at enriched fraction
(outlet b); after a negative depletion process, labeled non-target cells are depleted at outlet b and target cells are retained in the enriched fraction (outlet a). There are several limitations of application of positive mode of operation on rare cancer cell detection. Under the positive operation mode, the target cells (cancer cells) are magnetically labeled and the labeling process is irreversible. So the major limitation of the positive mode is the impossibility to get purified and unlabeled cancer cells for
11 further molecular analysis, since immunomagnetic labeling has negative impact on molecular analysis of cells. This is also an outstanding advantage of QMS system over the MACS system.
Recently, a revised version of QMS has been design and tested, which have shown to address several of the limitations of the original version (Patent submitted).
The channel of the new separator has been made simpler and eliminated the possibility of bubbles formation and trapping. The new system consists of only one pump at the outlet of the separator, which prevents the removal of magnetic tagging from cell surface due to high local shear stress. It is designed such that it can be operated at exactly controlled flow rate which can prevent any non-specific attaching of cells to any surface inside the channel. Compared to previous designs, this new version of separator not only makes the manufacture cost cheaper but also specifies strict dimensions for all parts that come in contact with cells, such as tubings, tubing fittings.
1.4.2 Magnetic deposition system
A prototype immunomagnetic cell separation apparatus, called magnetic deposition system, has been developed in Cleveland Clinic Foundation (CCF). (See
Fig1.5) Using this system, magnetically labeled cells are directly accessible to cytological and cytochemical examination on microscopic slides. This system is specifically designed for positive selection of cells. Detailed designing aspects of this system, as well as a mathematical model have been described before. (Fang et al,
1999; Zborowski et al, 1995) Briefly, the high gradient magnetic field is generated by a magnetic interpolar gap of a permanent magnet (Figure 1.6). The flow chamber is
12 composed of a top and a bottom cover, and a silicon rubber spacer, which is tightly sandwiched between the top and bottom covers. The fluid flow is contained in a parallel piped channel of known width w (6mm), height h (16mm), and thickness d
(0.5mm). There are holes on the top plate to connect to inlet and outlet flow tubings.
A150μm-thick microscopic glass coverslip (Fisher scientific) is used as bottom plate for deposition.
Syringe Pump
3. Top cover
2. Rubber spacer Feed
Connecting Plate
1. Deposition plate
Eluate Pole pieces Permanent magnets
Figure 1.5 Schematic diagram of the magnetic deposition system.
13
Figure 1.6 The magnetic field around the interpoler gap (Zborowski et al, 1995)
1.5 Detection of Circulating Tumor Cells (CTCs)
1.5.1 Biology of CTCs
Nowadays, metastases are the major causes of cancer related death in patients with solid epithelial malignancies (Mocellin et al, 2006a; Pantel and Woelfle, 2005;
Braun et al, 2000). Cancer arises as a result of accumulation of genetic alterations
(Tannock IF, 2004). Once a primary tumor has formed, some of the tumor cells may leave the site and invade the circulatory system and then migrate to a new site where they adhere to the walls of the capillary and form a metastatic tumor (Pantel et al,
2004; Stathopoulou et al, 2003; Pantel et al, 1999). (Figure 1.7) These cells have undergone specific genetic or chromosomal alterations, which allow them to infiltrate and metastasize. Currently, solid tumor metastasis can only be detected at late stages, when the metastatic tumor reaches a certain size limit. For patients with such a large
14 metastatic tumor mass, there are often few curative treatment options available. So, the failure in treatment is largely associated with incapability in early detection.
Therefore, the early detection of circulating cancer cells in peripheral blood is particularly important in cancer diagnosis and prognosis (Mocellin et al, 2006b,
Zieglschmid et al, 2005; Vlems et al, 2003; Jiang et al, 2002; Braun S, 2000&2004;
Ghossein and Bhattacharya, 2000). A reliable and accurate technique to detect CTCs could serve at least three purposes that are clinically important: 1) As an unambiguous evidence for an early micrometastasis of cancer cells; 2) As an additional relative factor for the metastasis of cancer, i.e. poor prognosis; 3) As a marker for monitoring treatment susceptibility and outcome. Furthermore, genotyping and phenotyping of isolated pure CTCs should provide detailed insights to the fundamental causes of metastasis and allow identification of potential treatment entities.
Primary tumor Infiltration and invasion
Dormancy
Metastases
Micrometastases
Figure 1.7 Micrometastases of cancer. Draw from Pantel, 1999.
15 1.5.2 Current CTC detection status
Although many different types of assays have been developed over the past 10 years to detect circulating cancer cells in body fluids, the two main approaches that are commonly used involve ImmunoCytoChemical Staining (ICCS) and Reverse
Transcriptase-PCR (RT-PCR) (Mataki et al, 2004; Kinjo et al, 2004; Pantel et al, 2003;
Stathopoulou et al, 2002; Kostler et al, 2000; Berios et al, 2000; Battaglia et al, 1998;
Izbicki et al, 1997). Each technique makes use of different cell features to detect cancer cells. ICCS involves the use of specific antibodies that bind to cell markers, proteins that are expressed by cancer cells, but are absent in the other cells. RT-PCR approach is used to amplify and detect genetic changes that are specifically associated with cancer cells. For detailed fundamental description of these methods, see section
1.7.
A common complaint with both of these two methods is the sensitivity
(Mocellin et al, 2006a; Schittek et al, 2001; Ghossein et al, 2000). It is rather difficult to detect cancer cells at a frequency lower than 1 tumor cell/105 total cells. The detection result is often times unreliable, non-reproducible, and the process is time- consuming and labor-intensive. Besides this sensitivity limitation, the specificity of a
RT-PCR assay is largely affected by several factors including illegitimate transcription (Park et al, 2001; Zippelius et al, 2000; Battaglia et al, 1998; Chelly et al,
1989). This phenomenon is caused by the leakiness of promoters, which leads to an estimated expression level of one tumor marker gene transcript in 500-1000 non- tumor cells. Thus, the specificity of RT-PCR assay can be enhanced when the number of unwanted cells harboring illegitimate transcripts is potentially reduced beyond the
16 detection level. In a summary, despite of the detection method to be used, it is highly desirable to perform one or more pre-enrichment steps before the actual detection process.
Although red cell lysis and density-gradient cell separation are commonly used cell enrichment methods, these technique mainly removes red blood cells leaving CTCs greatly outnumbered by peripheral blood leukocytes, or white blood cells. Immunomagnetic cell separation technique can be employed for further enrichment by either positively targeting CTCs or negatively depleting CD45- expressing peripheral blood leukocytes. The FDA approved CellSearch system manufactured by Veradix (Johnson & Johnson company) is an example of the positive selection system. An example of negative depletion is Lara’s work (Lara et al, 2004).
While the question whether positive or negative immunomagbetic separation results in greater performance still remains controversial, negative depletion mode is preferable because in this way, target cells are kept intact after the procedure without any cell surface modifications and potential gene expression changes.
Melanoma and breast cancer are two most studied solid malignancies in terms of the detection of CTCs. However, the prognostic relevance of the presence of disseminated tumor cells (DTCs) in the bone marrow of breast cancer patients remained ambiguous until a recent pooled study (Braun et al, 2005). A large body of literature has suggested that the detection of CTCs and or DTCs do not provide congruent results. In contrast to breast and melanoma cancer, far fewer studies on the presence, and relationship, of CTC and head and neck cancer have been conducted.
Three of the more recent studies (Guney et al, 2007; Partridge et al 1999; Wirtschafter
17 et al, 2002) all suggest a relationship between the presence of CTC and the stage of cancer as well as patient outcome; however, all three studies state that further studies are needed.
1.6 QMS enrichment of rare cancer cells
Lara et al (Lara et all, 2004) has developed a process to enrich cancer cells by negative depletion of normal blood leukocytes using QMS system, which is a continuous, flow-through cell separator. In this study, a breast cancer cell line, MCF-7, was spiked into human leukocytes at a ratio of 1:105 to test the system performance.
Two sample preparation techniques to remove majority of the RBCs, red cell lysis and the Ficoll-Hypaque density gradient separation, have been tested and compared.
It turns out that the Ficoll-Hypaque density gradient separation methods results in higher nonspecific cell loss and thus low cancer cell recovery than the red cell lysis method. The effects of magnetic labeling, flow rate and other operating conditions are investigated. Two major parameters are used to describe the separation efficiency of the negative depletion mode process: Log enrichment and the recovery of cancer cells.
They are defined as follows:
Purity of cancer cells after separation Log enrichment= Log ( ) 10 Purity of cancer cells before separation
Number of cancer cells recovered after separation Recovery = Number of cancer cells input into the system
They are basic indicators of how well the separation goes. For an ideal separation system, we want high level of log enrichment (7-8log) and high cancer cell recovery
(>90%). However, reality is not this case with a lot of factors complicating the 18 separation performance. Thus, the effects of magnetic labeling, flow rate and other operating conditions are investigated.
In this study, deposition mode of operation for the QMS has been demonstrated to be superior to completely flow-through mode in terms of separation performance. The deposition mode of operation is conducted when the carrier fluid flow through inlet b’is turned off (Qb’=0). Under this condition, significantly higher log enrichment (5.17) was achieved while maintaining a reasonable cancer cell recovery (47%). The final, optimized process consisted of: a red cell lysis step, immunomagnetically staining leukocytes with a two-step labeling method, immunomagnetic sorting using a deposition mode of QMS system, and a final cell analysis step using an automated cell counter, filtration, and visual counting or a cytospin analysis.
1.7 Molecular analysis technologies in cancer research
1.7.1 ImmunoCytoChemical Staining (ICCS)
Cancer detection and diagnosis by antibody-based methods is dependent on the ability of antibodies to distinguish between cells of different tissue origins, i.e. epithelial-originated tumor cells versus hematopoietic cells. This technique is applicable to both dissected cells and tissue slice, and is currently used in most pathological diagnosis (Chaiwun et al, 1992).
The most widely used epithelial-specific antigen markers are a group of intermediate filament proteins called cytokeratins, which are expressed in all epithelial-originated cells and are shown to be overexpressed in some carcinoma
19 cases (Zieglshmid et al, 2005). Once antibodies have bound to cytokeratins, they are analyzed by fluorescent microscopy, flow cytometry analysis, or immunocytochemistry using peroxidase or alkaline phosphatase staining techniques.
The advantages of the ICCS technique include the potential of cell/tissue characterization by using multiple staining methods, for example, the combination of cell nuclei stain and the cytoplasmic staining, together with morphological cell analysis. However, in most of the cases, screening for tumor cells by ICCS with antibodies against cytokeratins requires the fixing and permeabilization of target cells to stain the intracellular compartment. Once a cell is fixed and permeabilized, viability is lost, limiting a number of further analyses: 1) difficult to discriminate between dead and viable carcinoma cells. 2) Adds difficulties in obtaining cellular materials, such as RNA, for genetic analysis. Moreover, both false-positive and false- negative results are observed by nonspecific binding to Fc receptor–baring cells or low sensitivity (van Houten et al, 2000; Borgen et al, 1998). According to the antibody used, a false-positive detection rate of 1% to 3% can be expected
(Hinterberger et al, 2002). On the other hand, it is prone to false-negative results when dealing with cell samples with low purity of target cells in a mixture of contaminating cells (ratio of target to total cell number lower than 1:106 is difficult for visual detection). Furthermore, ICCS screening is a labor-intensive time- consuming process (van Houten et al, 2000).
1.7.2 RT-PCR
PCR-based techniques target nucleic acids instead of proteins as discrimination markers between tumor and normal samples. A major problem in the
20 use of PCR with DNA as a starting material is that only a few tumor types show characteristic genomic alterations so that specific detection of mutations is difficult
(Zieglshmid et al, 2005). RT-PCR is an alternative technique which utilizes the detection of mRNA. This is based on the fact that tumor cells often express markers that are specific for the tissue from which the tumor originates. However, because of the instability of mRNA, viable cells are normally required for the extraction and isolation of mRNA (Chai et al, 2005). Basically, a RT-PCR detection method consists of the isolation of RNA from the specimen, reverse transcription of the RNA, amplification of specific target cDNA, and final detection of the PCR product.
Although isolation of mRNA has been made possible by several novel techniques, the majority of protocols published so far used total RNA as starting material by applying the commercially available RNA isolation kits. After isolation, target transcripts are reverse transcribed into cDNA by the enzyme called ‘reverse transcriptase’. The reverse transcription is initiated by oligo-dT-primers which bind specifically with the poly-A tail of eukaryotic mRNA and random primers (normally random hexamer nucleotides). The cDNA is then subjected to PCR amplification using primers, which are designed specific to the genes of interest. The primer sets should be designed so that the two of them bind on different exons of the target genes to permit size differentiation of the amplicon derived from a contaminating genomic
DNA. After amplification of the first strand of cDNA sequence with normally 25 to
40 cycles, the amplicon is generally visualized by gel electrophoresis after staining with DNA dyes such as ethidium bromide.
21 Sensitivity and specificity are two most important factors in RT-PCR assay
(Davids et al, 2003). In the event of rare cell detection, the assay must have enough sensitivity to enable the detection of small number of target cells in a sample mixture.
The presence of circulating cancer cells is anticipated to be very low and in the range of 10-5 to 10-7, dependent upon the clinical stage and types of the disease (van Houten et al 2000, Kantor et al 1998). On the other hand, a serious problem for PCR-based assay is the specificity, which can be largely affected by several factors such as illegitimate transcription, carryover contamination, etc. An approach to improve the specificity of the RT-PCR assay is the quantitative RT-PCR, which detects the change in the number of transcripts by assessing a definite cut-off value of the specific marker transcript numbers. All numbers below that value is considered as illegitimate and transcript numbers above that value is considered as a real event.
Another advantage of the real time quantitative PCR is that false-positive results are easily identified and removed.
A considerable number of gene transcripts can be utilized as PCR markers to detect CTCs in the blood or bone marrow. However, an ideal molecular marker for
CTCs should meet the following criteria: it should be specifically expressed in CTCs but not expressed in normal blood cells; it should also be easy to detect by molecular technologies like RT-PCR. Many investigators have identified reliable tumor markers for major types of cancer, such as breast, melanoma, colon, and head and neck cancer.
A list of PCR markers and their applications are summarized and listed in Table 1.1.
Commonly used molecular markers include CEA, EGFR, Cytokeratins (CK),
Her-2, MUC, and tyrosinases. The CEA represents the CarcinoEmbryonic Antigen,
22 which is expressed in 95% of colorectal, gastric, and pancreatic cancers as well as majority of non-small cell lung cancer, squamous cell carcinoma of the head and neck and in approximately 50% of the breast cancers (Goldenberg et al, 1976). However, its specificity is problematic since several studies have reported the detection of CEA mRNA in healthy donors (Guadagni et al, 2001; Ko et al, 1998; Hampton et al, 2002).
Cytokeratins belong to the intermediate filament protein, which are widely utilized in the histopathological diagnosis of tumors. They are supposed to be exclusively expressed in cells with epithelial origin instead of hematopoitic cells. Thus, theoretically, they are good candidates for detecting CTCs in peripheral blood. CK-19 is one of the frequently used molecular markers in detecting CTCs in peripheral blood of patients with solid malignancies, such as breast, prostate, and head and neck cancers. CK-20 is a marker for gastricintestinal cancer and colorectal cancer. Both of the two markers have been reported in some studies about their presence in blood samples from either healthy donors or from non-malignant patients (Ko et al, 2000;
Champelovier et al, 1999; Silva et al, 2001; Jung et al, 1999). The false-positive detection of these markers in non-tumor cases may be attributed to several reasons:
First, pseudogenes are shown to interfere with the RT-PCR process (Ruud et al, 1999;
Savtchenko et al, 1988). Second, illegitimate transcription in hematopoietic cells, which is caused by the leakiness of promoters, results in detection of genes in normal blood. Thus, special precautions, such as careful primer design and pre-enrichment step, should be taken into consideration.
23
Table 1.1 Application of PCR technology in the detection of CTCs in peripheral
blood of cancer patients
24 Pre- Molecular Molecular Type of tumor enrichment detection Sensitivity Reference marker method approach Pancreatic, 100fg Clarke LE et N/A nested RT-PCR EGFR Lung, Colon total Mrna al, 2003 1 per 105 Conventional Mitsuhashi et Cervical N/A EGFR NBCs RT-PCR al, 2003 cells Conventional 5 per 5ml Gazzaniga et Bladder N/A EGFR RT-PCR blood al, 2001a,b IMS (Epcam Multigene RT- EGFR, Epcam, Not O'Hara et al, Prostate positive PCR etc (30 genes) specified 2004 selection) IMS 1 per 5 (Epcam Quantitative Fellowes et Breast EGFR, CK-19 million positive RT-PCR al, 2004 NBCs selection) 1:105 Colon, lung, De Luca et N/A nested RT-PCR EGFR RNA breast al, 2000 dilution Quantitative EGFR, c- Not Pawlowski et Breast N/A RT-PCR erbB2,3,4 specified al, 2000 Not Guadagni et Colon N/A nested RT-PCR CEA specified al, 2001 Quantitative Hampton et Colon N/A CEA N/A RT-PCR al. 2002 10 per 1 Datta et al, Breast N/A nested RT-PCR CK-19 million 1994 NBCs 1 in 5 Quantitative Aerts et al, Breast N/A CK-19 million RT-PCR 2001 NBCs 1 per 1 Kahn et al, Breast N/A nested RT-PCR CK-19 million 2000 NBCs 1 per 1 Competitive Trummer et Breast N/A CK-19 million RT-PCR al, 2000 NBCs 1 per 10 Zhong et al, Breast N/A nested RT-PCR CK-8, 18, 19 million 1999 NBCs 1 per 1 Stathopoulou Breast N/A nested RT-PCR CK-19 million et al, NBCs 2001&2002 semiquantitative Not Wong et al, Breast N/A CEA, CK-19 RT-PCR specified 2001
25 Table 1.1 : Continued. Conventional Not Chen et al, Gastric N/A CK-20 RT-PCR specified 2004 CEA, CK-19, Not Huang et al, Gastrointestinal N/A nested RT-PCR CK-20 specified 2003 IMS (Epcam Quantitative 1 per 1ml Ady et al, Prostate Her-2 positive RT-PCR blood 2004 selection) 1:106 Semi-nested Wasserman Breast N/A Her-2 RNA RT-PCR et al, 1999 dilution 1 per 1 Fonseca et al, Breast N/A nested RT-PCR Her-2 million 2002 NBCs Quantitative Mammaglobin, Leone et al, Breast N/A Vary RT-PCR Maspin, Her-2 2001 Colon, Head RT-PCR CK-19, 20, Not Gradilone et and neck, N/A followed by EGFR specified al, 2003 Breast southern blot Conventional 10 per ml Pajonk et al, Head and neck N/A CK-19 RT-PCR blood 2001 Head and neck Conventional 10 per ml Zen et al, N/A EGFR, SCCA cell line RT-PCR blood 2003 IMS (Epcam Conventional 10 per 8 Makarovskiy Prostate PSA positive RT-PCR ml blood et al, 1997 selection) IMS 1 per 1 (CD45 Iinuma et al, Colon nested RT-PCR K-ras and p53 million negative 2000 NBCs depletion) 10 per 5 Kinjo et al, Bladder N/A nested RT-PCR MUC-7 ml blood 2004 IMS Colon 1 per 105 (Epcam PCR-RFLP k-ras codon 12 Hardingham carcunoma cell NBCs positive Analysis mutation et al, 1993 line cells selection) 1 per 105 Ko et al, Lung N/A nested RT-PCR CK-19 NBCs 2000 cells Not Smith et al, Melanoma N/A nested RT-PCR tyrosinase specified 1991 1 per 10 Osella-Abate Melanoma N/A nested RT-PCR tyrosinase ml blood et al, 2003 tyrosinase, Conventional Not Wascher et Melanoma N/A MART-1, RT-PCR specified al, 2003 uMAGE-A
26 EGFR is another widely used molecular marker for CTCs detection.
Compared to the markers mentioned above, it has been describe as a highly specific marker in a variety of studies (Gradilone et al, 2003; Raynor et al, 2002, Hildebrandt et al, 1997; Clarke et al, 2003; Mitsuhashi et al, 2003). In most of the papers reviewed,
EGFR mRNA has been detected in blood samples from patients with malignant tumors, but not in blood from healthy donors or other diseases. We can conclude that it is a reliable marker for the detection of CTCs in a variety of solid malignancies.
Other markers like Prostate Specific Antigen (PSA) and tyrosinase are tissue specific markers which are specific for prostate tumor and melanoma. All carcinomas are derived from epithelial-origin, while melanoma is a type of tumor originated from melanocytes or nevus cells (Geilen et al, 2006). Hence, the above-mentioned epithelial markers are not applicable in the case of melanoma.
1.7.3 cDNA microarray
While PCR-based method may be used to examine the pattern of expression of a limited group of genes of interest, which are associated with tumor invasion and metastases, cDNA based technologies can provide a complete understanding of the global gene expression pattern (NIH website). Using cDNA microrray, a group of
10,000 to 20,000 genes can be analyzed simultaneously, allowing a more comprehensive characterization of the cells. This technology could lead to the identification of robust signature genes of cancer disease, or signature genes for metastatic cancer.
27 1.8 Technologies to obtain pure cancer cells from solid tumors
1.8.1 Importance of obtaining pure cell samples in cancer research
With the realization that cancer arises as a result of accumulation of genetic alterations instead of a single mutation, the interest in characterizing this complicated disease has increased. A lot of molecular analysis technology has been developed to help understanding the genetic complexity and biological mechanism of cancer, including cDNA microarray, FISH (Fluorescence In Situ Hybridization), SNPs
(Single Nucleotide Polymorphisms), etc (Tannock et al, 2004). However, tumors are characteristic for their heterogeneity (Foulds, 1975). A typical solid tumor is composed of malignant cells and many different kinds of stromal and infiltrating host cells (Pretlow II, 1983). The ability to get uncontaminated cancer cells is one of the major bottlenecks in the study of tumor development and cancer biology (Sieben et al,
2000; Tomlinson et al, 2002).
Currently, the majority of samples for molecular analyses in cancer research are prepared by pulverizing frozen whole tumors, without regard to its components, for extraction of nucleic acids. Laser Capture Microdissection, LCM, was invented to selectively microdissect single cell or a small group of cells (Emmert-Buck et al,
1996). It utilizes an infrared laser integrated into a standard microscope (NCI website). A transparent cap is attached to a thermoplastic transparent membrane which lies directly on the surface of a routinely prepared tissue section on a glass slide. A pathologist examines the tissue section microscopically and activates the laser when the desired cells underlie the target. It then activates the membrane with subsequent binding and procurement of target cells. However, there are some
28 disadvantages of this technique. First, this technique depends largely on direct human vision, which can sometimes be affected by different factors. Microscopic visualization is troublesome when there is no mounting medium and a coverslip.
Second, it is not compatible with live-cell analysis. It is not possible to separate intact, viable cell populations from tissue. Third, it is expensive, time-consuming, and labor-intensive. Therefore, a better technology, which is not entirely dependent on direct human vision, to sort cancer cells from surrounding non-malignant cells is required.
1.8.2 In vitro degradation of tissue
Tissue dissociation technique has been widely used to obtain single cell suspension from solid tumor for various diagnostic or research purposes, such as primary cell culture, flow cytometry analysis (Visscher et al, 1994; Ensley et al,
1993&1987a,b; Boyd et al, 1990; Emerman et al, 1990; Sacks et al, 1988; Kedar et al,
1982; Cassiman et al, 1981; Noel et al, 1977). Generally, it can be classified into two major categories: mechanical dissociation and enzymatic digestion. Mechanical dissociation basically uses mechanical means, such as mincing, scraping, and homogenization,etc, to disrupt solid tumor tissue into suspension. None of those methods has been accepted as the best way (Visscher et al, 1994; Pretlow II, 1983).
Enzymatic digestion uses specific enzymes, matrix metalloproteinases (MMPs), to target the extracellular matrix (ECM) protein in the tissue. Commonly, collagenase is used to digest collagen, which is the major type of protein in the ECM (Hefley et al,
1983). Each approach has its own advantages and limitations, and it is clear that each type of neoplasm should be considered as a separate entity in choosing dissociation
29 techniques. Even within a certain types of malignancy, it is difficult to generalize a routine protocol to take tumor tissue apart due to the tumor heterogeneity. The ‘best’ protocol also varies according to the following analysis. For example, the best protocol for primary tumor cell culture does not necessarily produce a best cell population for flow cytometry analysis (Ensley et al, 1993; Emerman et al, 1990;
Ljung et al, 1989).
1.8.3 Histology of the ECM
Human ECM is commonly referred to as connective tissue; it is a complex structural entity surrounding and supporting cells within human body (Hay, 1991). In simple terms, a connective tissue can be considered as a porous matrix (Truskey et al,
2004), which is composed of three major classed of biomolecules:
1. Structural proteins: collagens, elastins.
2. Specialized proteins: e.g. fibrillin, fibronectin, and laminin.
3. Proteoglycans.
The structure of this porous matrix is mainly make up of collagen and elastin fibers (figure 1.8), where as the intricacies are filled with gel materials such as proteoglycans and glycoproteins (Hay, 1991; Ronnov-Jessen and Bissell, 1996).
Histological studies have shown that collagen is the most abundant type of protein in the ECM (Personal communication with Mehta, B). In vivo, they are predominantly synthesized by fibroblasts, but some epithelial cells also produce them.
The basic structural unit of collagen fiber is collagen molecules (Beckman, et al, 2004). The main body of this molecule is a triple helix structure with an approximate diameter of 1.5nm, length of 300nm (molecular weight of 285,000). It
30 contains three alpha chains (MW ~ 95,000) coiled in a rope-like fashion to form the triple helix. Depending upon the amino acid composition of the alpha chains, the collagen molecules can be classified into at least 12 types (Biochemistry website from
Indiana State University). These proteins provide the tensile strength to a tissue. The collagen content of a tissue is usually measured by the amount of hydroxyproline, since it is assumed that this specific amino acid is only present in collagen.
Under normal situations, the collagen in the connective tissues turns over at a very slow and controlled rate (Beckman et al, 2004). However, during rapid growth and in some disease conditions such as cancer, the extent of collagen degradation can be quite extensive. Normally, only specialized enzymes, such as collagenases, can attack the collagen molecules.
Numerous proteases have been investigated for their ability to hydrolyze human tissue. Collagenases, from the group of enzymes called Matrix
Metalloproteinases (MMPs), are the specialized enzyme which attacks collagen molecules. They catalyze the degradation reaction by binding tightly to the surface of the triple-helix structure, and remain bound to the substrate throughout ongoing collagen degradation process. Collagenases are commonly produced from bacteria called Clostridium Histolyticum. They may be used as a crude enzyme or may be purified as pure types of enzymes. There are at least six distinct types of collagenases from the crude enzyme (Bond, 1984). However, purified collagenases are not only much more expensive than crude enzyme but much less efficient in dissociating tissues due to incomplete hydrolysis activities.
31 1.9 Research objectives
This dissertation comprises two major parts of investigation. One part of the study is to develop and optimize a process to isolate circulating cancer cells from peripheral blood of patients with solid malignancies. Second part is on the investigation of isolating pure tumor cells from solid tumor biopsies for further molecular analysis. Both of the two parts employ the principle of immunomagnetic cell separation and many other techniques in a broad field. The specific objectives of this study are:
1) Develop and optimize a process including immunomagnetic cell
separation, immunocytochemical technique and RT-PCR assay to reliably
detect CTCs from peripheral blood of patients with malignancies;
2) Apply this technique on clinical samples from HNSCC patients prior to
surgery to detect CTCs and identify any clinical correlations;
3) Establish a method to isolate pure cancer cells from solid tumor biopsies
in a rapid manner.
32 1.10 Dissertation organization
The outline of chapter 2 through chapter 7 is as follows:
Chapter 2 discussed optimization of an enrichment process for circulating cancer cells in human peripheral blood to a sufficient extent that RT-PCR analysis can be performed on the final sample to generate reliable detection results. Two molecular analysis methods, RT-PCR and immunocytochemical staining, were validated and incorporated after the cell separation process. The sensitivity of those molecular analysis methods, purity of target cells needed, the level of enrichment required, and the total numbers of cells required for RT-PCR detection are quantitatively determined.
Chapter 3 summarized some early results on the detection of CTCs in peripheral blood of patients with head and neck cancer. Techniques developed in
Chapter 2, including a immunocytochemical staining and a RT-PCR assay in combination with the imunomagnetic cell separation, has been applied on blood samples from HNSCC patients undergoing surgical removal of squamous cell carcinoma of the head and neck. We attempted to quantify the number of CTCs enriched from the peripheral blood of the blood samples and identify any clinical correlations that may be significant.
In Chapter 4, the presence of large number of contaminating cells in the enriched fraction after magnetic cell separation has been identified. Extensive investigations were performed to identify the residual cells and elucidate the reason why they are not depleted. Potential solutions are then proposed to solve this problem.
Chapter 5 firstly discussed the use of a theoretical model in describing the
33 magnetic labeling performance, which allows better understanding of the question.
Afterwards, the study of 6 types of magnetic labeling methods leads to an optimal solution with a significant improvement in separation performance.
Chapter 6 presented a preliminary study on the isolation of pure cancer cell population from solid tumor biopsies for further molecular analysis. A reaction- diffusion model has been developed to quantitatively describe the tissue degradation process. Based on this model, a rapid in vitro tissue degradation technique, including a mechanical dissociation and an enzymatic digestion, has been established to generate a single cell suspension from solid tumors. Tumor specific markers have been screened for the positive selection of cancer cells. Application of this process on clinical samples shows promising results.
Chapter 7 summarizes the conclusions and presents future work suggestions.
34
CHAPTER 2
APPLICATION OF IMMUNOMAGNETIC CELL SEPARATION IN
COMBINATION WITH RT-PCR FOR THE DETECTION OF RARE
CIRCULATING HEAD AND NECK TUMOR CELLS IN HUMAN
PERIPHERAL BLOOD
The content of this chapter has been published in Cytometry Part B: Tong X,
Yang L, Lang J C, Zborowski M, Chalmers J J. Cytometry Part B (Clinical
Cytometry). 2007. 72B:310–323.
2.1 Motivation
Detection of CTCs in peripheral blood is a potential indicator of diagnosis and prognosis in oncology. The presence of cancer cells in body fluid has been correlated with clinical stages, patient survival after therapy, and tumor metastasis (Braun and
Pantel, 2001; Naume et al, 2004; Fehm et al, 2006). Current detection technologies, such as the IMCS and RT-PCR, all have a common problem, detection sensitivity. It is very difficult to detect very rare cells in a large cell mixture. It is quite clear that a pre-analytical enrichment step is highly desired in order to get reliable, accurate, and reproducible detection or even quantification.
35 In this study, an enrichment process including a red cell lysis step as well as a flow through immunomagnetic cell separation system was developed and optimized.
The QMS system was operated at deposition mode of operation by negative depleting normal human peripheral blood leukocytes, which express a pan marker CD45. To mimic the potential concentration of CTCs in blood from cancer patients and to validate our system performance, cancer cell lines were spiked into normal PBLs at an initial concentration of 1:105. Both one-step and two-step magnetic labeling methods are used to target CD45 expressing PBLs. Performance of the QMS separation was characterized by three major parameters: Recovery of cancer cells, recovery of PBLs, and enrichment rates of cancer cells. Three system operating variables, flow rate, feed cell concentration, and magnetophoretic mobility of labeled cells, are experimentally studied to elucidate their effects on the final separation outcome. Although an ideal separation results in close to 100% recovery of cancer cells, close to 0% recovery of PBLs, and an extremely high enrichment rate, it is normally not feasible in reality. The criteria for optimizing the system were maximizing cancer cell recovery, minimizing PBLs recovery, and maximizing enrichment rate. A statistical approach has been carried out in the optimization process.
Although the ultimate goal of this research is to develop a technology for the detection of CTCs for patients with any types of solid malignancies, HNSCC was chosen in this current study as a model. First, CTCs detection has been mostly carried out in breast and melanoma patients, the information about CTCs in head and neck patients is very limited. Second, molecular characterization of this disease has been
36 made possible with the help of Dr. Lang from the Department of Otolaryngology.
Head and neck cancer comprises about 6% of call cancers which results in
550,000 cases a year globally. According to statistics of American Cancer Society,
39,250 new cases of cancer of oral cavity, oropharynx, pharynx, and larynx are estimated to occur in the US in 2005 and 10,090 deaths were estimated. More than
90% of these cases of head and neck cancer are squamous cell carcinomas, HNSCC.
It is a heterogeneous disease with distinct patterns of presentation and behavior.
Despite the improvements in the treatment of this disease due to the clear progress in surgery and radio therapy, the 5-year survival rate of all stages (approximately 58.7%) has not increased substantially. About 40- 50% of the patients will later develop local or distant recurrences later on. Therefore, there is a further need to identify prognostic biomarkers that will allow early detection of aggressive primary tumors capable of metastatic spread or later recurrence.
Epidermal growth factor receptor (EGFR) is an oncogene that is highly overexpressed in most cases of HNSCC, thus it is selected as a marker for RT-PCR assay. It has been described as a highly specific marker in a variety of studies
(Gradilone et al, 2003; Raynor et al, 2002; Hildebrandt et al, 1997). Meanwhile, the sensitivity of RT-PCR assay for detecting the (EGFR) was determined experimentally.
Since cancer is notorious for its heterogeneity, study of a single cultured cell line could be inaccurate and inconclusive. Thus, three HNSCC cell lines were selected and studied. Minimum required cancer cell purity for RT-PCR detection is determined for the three cancer cell lines, respectively.
37 2.2 Material and methods
2.2.1 Cell sources
Three HNSCC cell lines, Detroit-562 (Referred to as D-562 from now on), SCC-
4, and CAL-27, were purchased from ATCC (Manassas, VA). D-562 cells were grown in Earle’s Minimum essential medium (ATCC, Manassas, VA) supplemented with
10% fetal bovine serum (FBS; JRH Biosciences, USA). SCC-4 cells were cultured in a 1:1 mixture of Dulbecco's modified Eagle's medium and Ham's F12 medium
(ATCC, Manassas, VA) plus 10% FBS. CAL-27 cells were cultivated in Dulbecco's modified Eagle's medium (ATCC, Manassas, VA) containing 10% FBS. All the cell lines were maintained in a 75 cm2 tissue culture flask (Corning) and incubated at 37
˚C in 5% CO2. The cells were harvested using Accutase™ (Innovative Cell
Technologies) as the manufacturer’s instruction. Cell number was determined using hemocytometer, and cell viability was measured using a dye exclusion method
(Invitrogen Corporation).
Buffy coats from healthy donors were obtained from American Red Cross,
Central Ohio Region. Red blood cells (RBC) were lysed by fresh lysing buffer (154 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA), and incubated for 5 min at room temperature with occasional shaking. After 5 min of centrifugation at 300g, the cell pellets were washed twice using the labeling buffer (PBS supplemented with 2 mM
EDTA and 0.5 % Bovine serum Albumin), and resuspended in the buffer to obtain the peripheral blood leukocytes (PBL) suspension. If necessary, the lysis protocol was repeated to attempt to remove more RBCs. The concentration of leukocytes was determined by hemocytometer using Unopette® Microcollection system (BD
38 Biosciences).
2.2.2 Immunomagnetic labeling
For the depletion of leukocytes, one-step (direct, labeling method A) and two- step (indirect, labeling method B) immunomagnetic staining protocols were used to target the peripheral blood leukocytes, respectively. For the one-step magnetic labeling protocol, CD45-MACS (Cat# 130-045-801, Miltenyi Biotec, Auburn, CA) was used and FcR blocking reagent (Cat# 130-059-901, Miltenyi Biotec, Auburn, CA) was also added to block potential nonspecific binding. Typically, the pellet of
80.0×106 cells was resuspended into 50µl labeling buffer, 50µl FcR blocking reagent and 200µl of CD45-MACS placed in a 15-ml Falcon tube. The cell suspension was incubated for 15 minutes in the dark at 4 oC with occasional shakings. The cells were washed by adding labeling buffer and then centrifuged at 300g for 5 min. After centrifugation, the cell pellet was then resuspended carefully in proper volume of labeling buffer to make the desired input cell concentration.
In the two-step labeling protocol, the primary antibody used was a mouse- anti-human CD45-PE (Cat# IM 2078, Beckman Coulter, France) and the secondary antibody was anti-PE MACSbeads (Cat# 130-048-801, Miltenyi Biotec, Auburn, CA).
Similar to the one-step labeling protocol, 80.0×106 cells were suspended in 50µl PBS labeling buffer, 50µl FcR blocking reagent and 200µl of primary antibody in a 15-ml falcon tube. The cell suspension was incubated for 15 minutes in the dark at 4 oC.
The cells were washed once in labeling buffer as mentioned. Upon resuspension in
50µl labeling buffer, 50µl FcR blocking reagent and 200µl of anti-PE MACSbeads were added. After incubation at 4 oC for 15 min in the dark with occasional shakings, 39 the cells were washed a second time in labeling buffer and resuspended in labeling buffer for further QMS separation.
The fluorescence analyses of samples were performed on a FACS Calibur®
Flow Cytometer (Becton Dickinson), and magnetophoretic mobility measurements were made on our previously developed and reported CTV instrument (Chalmers et al,
1999).
2.2.3 Immunomagnetic cell separation
The design and construction of the QMS system has been described in Section
1.4. Before starting an actual separation, the QMS column was filled with degassed labeling buffer to reduce the potential of bubble formation and entrapment with the device and tubing. To load the samples, magnetically labeled cells were diluted in labeling buffer to a final concentration in the range of 1 to 5×106 cells/ml.
Separations in this study were operated in a deposition mode, where the sheath fluid flow rate is zero. This mode of operation has been shown to result in higher depletion of normal blood cells while maintain reasonable high cancer cell recovery. In this mode, the total flow rates at the inlet and outlet remained constant using three syringe pumps. Two pumps were used at the inlet: the first pump injected the sample at the beginning of the operation until the sample has been inputted into the column (a’ inlet). Upon completion of sample injection, flow entering the a’ inlet was switched to the second pump using a three-way valve which start pumping sheath fluid to the a’ inlet to flush remaining sample from the system and to maintain a constant flow rate at the inlet. A third pump was connected to the outlet a to collect sorted fractions. 40 2.2.4 Optimization of QMS separation
Cell separation performance in the QMS is affected by several parameters, including flow rate, magnetophoretic mobility of labeled cells, cell feed concentration, properties of the sheath fluid, and the percentage of initial magnetic cells in the total cell suspension. In this study, three adjustable operational parameters were studied: (1) the flow rate, Qt, (2) the magnetophoretic mobility of the labeled cells, and (3) the cell feed concentration. The flow rate is responsible for the residence time of the cell inside the column; therefore, Qt, is directly related to the radial displacement of an immunomagnetically labeled cell within the QMS channel. The magnetophoretic mobility of cells, in combination with the magnetic energy gradient, is responsible for the force which creates the radial movement of an immunomagnetically labeled cell.
Since the magnetic energy gradient is fixed for the given system, the higher the magnetophoretic mobility of cells, the faster the cell travels in the radial direction, correspondingly, the less time the cells needs to travel a given radial displacement.
In this optimization study, three response parameters describing the performance of separation process were used: recoveries of cancer cells, recoveries of leukocytes, enrichment rate of cancer cells. They are defined as follows, respectively:
NPi Recovery of cancer cells= [ final final,C ]i 100 2.1 NPinitiali initial,C
NPi Recovery of PBLs=[ final final,P ]i 100 2.2 NPinitiali initial,P
P Enrichment rate = final,C 2.3 Pinitial,C where Ninitial is the total number of cells in the initial sample before separation, while
41 Nfinal is the total number of cells in the final, enriched cell sample. Pinitial and Pfinal are cell purities before and after separation, respectively, and the subscript P and C referred to PBLs or cancer cells. Enrichment rate of cancer cells is the fold changes of cancer cell purities post and pre separation. The criteria for the optimization are to achieve high enrichment rate, high cancer cell recovery as well as low PBL recovery.
2.2.5 Detection/analysis of enriched samples
The enriched fraction from the non-magnetic outlet, stream a (Figure 1.4), was evaluated in two ways: a) ICCS, b) RT-PCR assay. For all the experiments, two parallel runs of magnetic cell separation were conducted under exactly the same conditions, from which, samples are collected for the two detection methods mentioned above, respectively. Specifically, to perform the analysis of ICCS, the nonmagnetic flow stream from the QMS sorter (stream a) was split into two samples, one of which was centrifuged and collected using a typical cytospin protocol, and then stained by anti-cytokeratin (CK3-6H5)-FITC (Cat No. 130-080-101, Miltenyi
Biotec) and Hoechst 33342 (Invitrogen Corporation). The other fraction was used to measure the number of remaining leukocytes by hemocytometer using the Unopette® microcollection system.
The cytospin staining protocol was as follows: the enriched fraction from the
QMS a outlet was centrifuged for 5 min at 300g, the pellet was resuspended in 100 μl
PBS buffer, and fixed upon addition of 100μl 3.7% formaldehyde for 15 min at room temperature. The cells were washed once using PBS buffer, and then resuspended in
200 μl PBS buffer. Appropriate cell suspension was added to the cytospin funnel for
4 min at 1000 rpm. After spin, 50 μl of Hoechst 33342 dye (working solution 2 μg/ml)
42 was applied on the coated slide, and incubated in dark for 10 min at room temperature.
The slide was washed three times (at least 5 minutes each) with PBS. Next, 10 μl of anti-cytokeratin-FITC antibody was diluted by 90 μl of 0.5% Triton X-100 to 1:10.
100 μl of diluted cytokeratin-FITC solution was dropped on the slides and incubated for 15 minutes at room temperature. The slide was washed three times (at least 5 minutes each) with PBS. After being air-dried, the slide was covered with mounting medium (Tissue-Tek® Sakura Finetek) and coverslips for observation and preservation. Cells that are double positive for Hoechst 33342 and cytokeratin-FITC were considered as cancer cells.
2.2.6 RT-PCR assay
The mRNA expression of EGFR in the HNSCC cells, D-562, SCC-4, and
CAL-27, was analyzed by RT-PCR. Total RNA was extracted from cell suspension of interest using Trizol reagent kit (Invitrogen Corporation, Carlsbad, CA). The RNA concentration was determined by a spectrophotometry (Amershan Biosciences,
Sweden), and its integrity checked electrophoretically. The RT-PCR assay was performed using the Stratascript® first-strand synthesis system (Stratagene, La Jolla,
CA). Briefly, 1 μg of RNA was incubated with 1.5 μl of oligo-dT and 1.5 μl of random primers for 5 min at 65°C in a total volume of 20.5 μl; subsequently the reaction samples were cooled down to room temperature to allow the primers to anneal to the RNA. To synthesize first-strand cDNA, 4.5 μl of master mix containing
2.5 μl RT buffer, 1.0 μl dNTPs, 0.5 μl RNase block reagent, and 0.5 μl
Reverse Transcriptase (Stratagene, La Jolla, CA) was added to the reaction samples, and the samples incubated in a PCR instrument (Gen Amp ® PCR System
43 9700, Applied Biosystems, Foster City, CA) at 42°C for 60 min. To amplify the target cDNA, 2 μl of RT-reaction sample was added to a final volume of 25 μl including 5 μl of 5× buffer, 5 μl of GC melt, 0.5 μl of dNTPs, 1 μl of EGFR primers,
0.5 μl of GC polymerase, and 11 μl of DEPC-treated water (Advantage™ GC cDNA
PCR Kit, BD Biosciences, San Jose, CA). The PCR reaction was performed as follows: 1 cycle of denaturing at 94°C for 1 min; 32 cycles of 94 °C for 30 s, 60 °C for 1 min and 72 °C for 2 min; and extension incubation at 72 °C for 5 min.
2.2.7 Sensitivity of RT-PCR by EGFR for HNSCC Cells
To evaluate the sensitivity of the RT-PCR assay, we suspended 101, 102, 103,
104, and 105 HNSCC cells into 107 PBL from a healthy donor. Then, total RNA was extracted and RT-PCR performed. In addition, the detection sensitivity (i.e. how many micrograms of RNA was needed for detection) of EGFR in a HNSCC cell line by RT-PCR was further determined in a serial dilution study from 10-8 to 10-2 mg of
RNA derived from only the HNSCC cells.
2.2.8 Statistical Analysis
Unless specially stated, the data shown are medians in the text. Statistical analyses were performed using JMP software (SAS Institute, Cary, NC), which has an option that allows multiple least squares analysis. With this multiple least squares analysis function, a screening analysis for the effects of the influencing factors on
QMS performance was performed with a significance level set at 0.05. The variables chosen in this multiple parameter analysis followed the effect hypothesis tests, which is part of the Fit Model command routine in the software.
44 2.3 Results
2.3.1 Labeling Saturation Studies
Figure 2.1A is a representative histogram, in log format, of the magnetophoretic mobility of unlabeled PBL and PBL labeled with anti-CD45 MACS
(single step protocol) obtained by CTV analysis. Figure 2.1B corresponds to the normalized fluorescence intensity as a function of the concentration of the primary, anti-CD45 PE antibody (mg of antibody per ml of cell suspension with a total of
1.0*106 cells), while Figure 2.1C is a saturation curve of the mean magnetophoretic mobility of the PBL, previously labeled with anti-CD45 PE (15 mg/ml), as a function of the secondary antibody concentration, in units of microlitres (ml) of MACS reagent per total microlitre (ml) of suspension. Figure 2.1D presents a saturation curve of the mean magnetophoretic mobility of the PBL as a function of the concentration of CD45–MACS reagent (ml/ml total solution). Vertical bars represent the standard deviation of the mean magnetophoretic mobility at the given labeling condition (n=3). (Note that Miltenyi Biotec does not provide concentration of their reagents in units of micrograms.)
The potential of nonspecific binding of the anti-CD45 antibody to HNSCC cell lines was tested by flow cytometric analysis. No nonspecific binding was detected for Detroit- 562 and SCC-4 cells, while 10% of CAL-27 cells were found to be bound with anti-CD45 antibody. Thus, Detroit-562 cells were chosen for spiking studies in the QMS.
45
Figure 2.1 (A-D) (A) Log plot of the magnetophoretic mobility of unlabeled PBL and
labeled PBL with anti-CD45-MACS (B) Saturation curve of CD45 surface receptor
on PBL in terms of concentration of antibody (µg/ml) versus normalized FI; (C) A saturation curve of the magnetophoretic mobility of the PBL cells, previously labeled with anti-CD45 PE (15µg/ml), versus amount of PE-MACS reagent; (D) A saturation
curve of the magnetophoretic mobility of the PBL cells labeled with anti-CD45
MACS.
46 A 0.14 Unlabeled PBL 0.12 Labeled PBL by CD45 MACS
0.10
0.08
0.06 Fraction (-) Fraction
0.04
0.02
0.00 1e-6 1e-5 1e-4 1e-3 1e-2 Magnetophoretic mobility (mm3/A.T.s) B 1.2 C 3.5E-04
1 3.0E-04 /T.A.s) 3 0.8 2.5E-04
0.6 2.0E-04
0.4 1.5E-04 Normalized FI (-) FI Normalized 1.0E-04 0.2
5.0E-05 0 (mm mobility magntophoretic Mean 05100.0E+00 0 0.2 0.4 0.6 0.8 1 Anti-CD45 PE concentration ( g/ml) μ MACS concentration (μl/μl in total)
3.5E-04 D 3.0E-04
/T.A.s) 2.5E-04 3
2.0E-04
1.5E-04
1.0E-04 Magnetophoretic mobility mobility (mm Magnetophoretic 5.0E-05
0.0E+00 00.20.40.60.81 CD45-MACS Concentration (μl/μl in total)
47 2.3.2 Assay Sensitivity
In the preliminary experiments, RT-PCR using EGFR was performed on the
RNA of PBL from healthy donors using 30, 32, 35, and 38 cycles (Data not shown).
A cycle number of 32 for RT-PCR were chosen as the highest level which presented no false positives for any of the samples tested. Figure 2.2 presents representative examples of photographs of gel analysis of RT-PCR assays targeting the mRNA of
EGFR from several different cell sources. Specifically, RT-PCR of mRNA from
Detroit-562, from PBL of healthy donors, and primary tumor cells from head and neck cancer patients are presented in lanes 2–5, respectively. As can be observed, no
EGFR gene expression was detected in samples from the PBL of healthy donors; in contrast, significant bands can be observed in the samples from the Detroit-562 cells and the cells from head and neck cancer patients. This band, at 301 bp, is the expected size of RT-PCR amplification product for EGFR.
301bp
Figure 2.2 Representative examples of RT-PCR targeting the mRNA of EGFR. Each lane corresponds to samples from the following: 1, DNA ladder 100 bp difference per band (Invitrogen, Carlsbad, CA); 2, Detroit-562 cell line; 3, PBLs; and lanes 4 and 5 are from frozen tumor specimens from head and neck cancer patients.
48 Figure 2.3(A–E) present representative photographs of agarose gel analysis of
RT-PCR amplification of various samples. Specifically, Figure 2.3A illustrates the sensitivity of detection of EGFR mRNA from Detroit-562 cells diluted in PBL. A visual band can begin to be observed at a dilution of one Detroit-562 cell per 104 PBL
(lane 5). In contrast, the concentration of SCC-4 needs to be higher, one cancer cell in
103 PBL (Fig. 2.3B, lane 5), while the detection concentration is the lowest for the
CAL-27 cells, i.e. one cancer cell in 105 PBL (Fig. 2.3C, lane 3). Figures 2.3D and
2.3E demonstrate the amount of RNA needed, in micrograms, to detect the EGFR mRNA in Detroit-562 and SCC-4 cells, respectively.
49
Figure 2.3 Representative photographs of agarose gel analysis of RT-PCR
amplification of various samples. (A) The sensitivity of detection of EGFR mRNA
from Detroit-562 cells diluted in PBL. Lane 1 corresponds to a DNA ladder, lane 2 pure PBL, lanes 3 through 8 to dilutions of cancer cell to PBL of 10-6, 10-5, 10-4, 10-3,
and 10-2, and lane 8 corresponds to pure Detroit-562 cells. (B) and (C) equivalent to
(A) except for the use of SCC-4 and CAL-27 cell lines respectively and the omission of the DNA ladder in lane 1. (D) and (E) present the sensitivity of detection of EGFR mRNA of Detroit-562 and SCC-4 cells, respectively, as a function of total amounts of
RNA, ranging from 10-7 to 10-2 µg.
50 A
301bp
B
301bp
C 301bp
D
301bp
E
301bp
51 2.3.3 QMS Optimization Studies
To determine the optimal operational parameters, a series of experiments using four bags of buffy coats from healthy donors was conducted, and was subsequently analyzed using JMP software. A significantly higher level of spiked cancer cell concentration (3%, Detroit-562 cells per leukocytes) than would normally appear in patient blood was used to better quantify the enrichment performance in the optimization studies. At a feed concentration of cancer cells of 3%, an ideal separation process which resulted in the complete removal of all PBL would yield an enrichment rate of 33. Consequently, in the figures that follow, the enrichment rate, presented on the right y-axis, ranges from 0 to 33.
To illustrate the changing performance with the operational parameters, some results of these studies are presented in three figures where one of the three variables studied was varied holding the other variables constant. Figure 2.4a presents an example of the effect that the magnetophoretic mobility, ranging from 0.8 *10-4 to 3.5
* 10-4 mm3/(T.A.s), had on the separation performance, while holding the flow rate fixed at 10 ml/min and the feed concentration at 1 * 106 cells/ml. A number of salient features can be observed. First, a significant increase in recovery of the cancer cells, up to 80%, was obtained in the outlet a as the magnetophoretic mobility of the PBL decreases. Second, the enrichment rate of the cancer cells increased as the mobility of
PBL increased.
Figure 2.4b presents a representative of the effect that changing the flow rate has on separation performance, while holding the magnetophoretic mobility constant at 2.14*10-4 mm3/(T.A.s) and the feed concentration constant at 1*106 cells/ml. In
52 contrast to varying the magnetophoretic mobility, overall, varying the flow rate had less effect on the system performance. It is not known why there is a peak and then drop in the recovery of the Detroit-562 cells with increasing flow rate.
Figure 2.4c presents an example of the effect that changing the cell concentration has on separation performance, while holding the magnetophoretic mobility constant at 1.2*10-4 mm3/(T.A.s) and the feed rate at 10 ml/min. Again, relatively minor changes in performance were observed with changes in concentration.
The initial increase in performance with increase in concentration could reflect an increase in accuracy of analysis as the total sample size and concentration increases.
53
Figure 2.4 Representative examples of the effect of operational parameters on the separation performance of QMS. (a) Effect of magnetophoretic mobility on the enrichment performance; (b) Effect of flow rate on the enrichment performance; (c) Effect of cell feed concentration on the enrichment performance. In all of the above experiments, a ratio between cancer cell and PBL is 0.03.
54
55 Figure 2.4 : Continued.
2.3.4 Statistical Analysis for Optimization Studies
In the process of fitting the statistical model, three variables (the magnetophoretic mobility of the labeled cells, flow rate, and cell feed concentration) and one numerical variable (buffy coat) were chosen. These three variables were evaluated relative to the three response variables of QMS performance (recovery of
PBL, recovery of cancer cells, and enrichment of cancer cells) at a 95% confidence limit. The statistical package used in this analysis provides P values of the fit of the data. Table 2.1 presents the value of the P factor for the recovery of PBL, cancer cells, and the enrichment rate of cancer cells, as a function of the five variables: magnetophoretic mobility, flow rate, flow rate^2, cell feed concentration, and specific buffy coat.
56 P value Influencing Enrichment Recovery of Recovery of factor rate of PBL cancer cells cancer cells Magnetophoretic 0.0207 0.062 <0.0001 mobility Flow rate 0.0088 0.0506 0.0138 Flow rate ^ 2 0.0141 0.0006 0.1905 Cell feed 0.655 0.2688 0.9032 concentration Buffy coat 0.7222 0.1246 0.6076
Table 2.1 Results of statistical analysis for the influencing factors on the performance of QMS.
The magnetophoretic mobility of the labeled cells is the most significant factor on the performance of the QMS separation (P = 0.0207 for the recovery of PBL,
P = 0.062 for the recovery of cancer cells, and P < 0.0001 for the enrichment of cancer cells). The flow rate is another significant factor for the performance (P =
0.0088 for the recovery of PBL, P = 0.0506 for the recovery of cancer cells, and P =
0.0138 for the enrichment of cancer cells).
Additionally, the second power of flow rate is also significant for the recoveries of PBL (P = 0.0141) and cancer cells (P = 0.0006), which demonstrates that the relationship to the flow rate is nonlinear. This phenomenon is also observed in Figure 2.4b. Note that no significant influences on the separation performance of
QMS were observed for the cell feed concentration and buffy coat in the studied range.
The above discussion indicates that three measures of performance: recovery of PBL, cancer cells, and the enrichment rate of cancer cells, are functions of three primary operational parameters of the QMS. Due to the contradictory impact of these 57 operational parameters on QMS performance, the weight of each measure for the performance must be considered in search of the optimized values of the operational parameters. Generally, the enrichment of cancer cells and the recovery of cancer cells are most desirable in our studies. To simplify the prediction, the cell feed concentration was set to be 5*106 cells/ml. Therefore, the ranges of optimized values for the other operation parameters were determined as follows: flow rate in the range from 3 to 5 ml/min and the magnetophoretic mobility in the range of 1.5*10-4 –
2.5*10-4 mm3/(T.A.s).
To validate the feasibility of the above assumption, a preliminary experiment was performed using the above optimized parameters, i.e. a flow rate of 3 ml/min, a magnetophoretic mobility of 1.8*10-4 mm3/(T.A.s) obtained using the one-step labeling protocol, and a cell feed concentration of 5*106 cells/ml. With these conditions, a 61.6% recovery of initial spiked cancer cells and an enrichment rate of
98.7. (Note, for this run, the initial cancer cell concentration was one cancer cell in
103 total PBL).
2.3.5 Detection of CTCs by ICCS
Figure 2.5 (A–D) illustrate a set of representative photographs showing the
ICCS techniques used for visual, microscopic observations to identify the presence and number of cancer cells before and after the enrichment experiments.
All four photos are of the same slide, which was stained with an anticytokeratin–FITC conjugate and Hochest 33342 (nuclei stain) stain. The larger,
Detroit-562 cells are clearly visible in Figures 2.5A, 2.5C, and 2.5D and are the only cells stained “green" in Figure 2.5A. Figure 2.5D is a filtered photo for Hochest
58 33342 where cell nuclei appear blue, while Figure 2.5C is a combination of Figures
2.5A and 2.5B. As a point of reference, Figure 2.5D is a brightfield image. In subsequent analysis, to be considered a cancer cell, the cell must be doubled stained, i.e. green and blue, while to be a PBL it must be stained only blue, and any other object is considered a red blood cell or cell debris.
Figure 2.5 (A-D)Various wavelength filtered, photographs of the same slide to which PBL and Detroit-562 cells were attached. The slide was stained with anti-cytokeratin- FITC (green) and Hochest 33342 (blue) labels. (A) Filtered photograph of FITC stained cells. (B) Filtered photograph of Hochest 33342 stained cells. (B) Filtered image combining (a) and (b), and (D) Bright-field image of the same slides.
59 2.3.6 Enrichment Model of CTC Suspension
To mimic the potential concentration of CTCs in clinical samples, Detroit-562 cells were spiked at a ratio of one cell to 105 total PBL. Based on the results of the preliminary optimization studies, two sets of experiments were conducted to enrich spiked cancer cells in human PBL: one set using the two-step and the other set using the one-step immunomagnetic labeling, respectively. The results of those two sets of enrichment experiments are listed in Tables 2.2 and 2.3.
In those spiked cell studies, the final, optimized process produced a final enrichment of the rare cancer cells of 14.2 ± 6.2 with a final recovery of (47.4 ±
18.4)% using the two-step immunomagnetic labeling, as shown in Table 3.
Summarizing the data obtained in the enrichment experiments using the one-step immunomagnetic labeling (Table 2.3), the rare cancer cells could be enriched to an average enrichment rate of 57.6 ± 30.3 with a final recovery of (77.8 ± 6.6)%.
60 Run Average 1 2 3 4 5
Flow rate (ml/min) 10 5 5 4 5
Cell throughput rate (cells/s) 8.3×105 4.2×105 4.2×105 3.3×105 4.2×105 3 Magnetophoretic mobility (mm /A.s.T) 2.4×10-4 1.96×10-4 1.3×10-4 1.89×10-4 1.46×10-4
Feedstock Number of PBL used 8.0×107 7.0×107 8.0×107 8.0×107 8.0×107 Number of total Detroit-562 used 800 700 800 800 800 61 Detroit-562 concentration (cells/PBL) 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5
Cell numbers after QMS Number of PBL recovered in the outlet a 3.4×106 5.35×106 1.3×106 1.35×106 4.4×106 Number of Detroit-562 recovered in the 560 450 278 242 300 outlet a Final Detroit-562 Purity (cells/Total cells) 1.65×10-4 8.41×10-5 2.14×10-4 1.79×10-4 6.82×10-5 1.42±0.63×10-4 Detroit-562 recovery in the outlet a (%) 70.0 64.4 34.8 30.3 37.5 47.4±18.4 Enrichment (-) 16.50 8.50 21.20 17.93 6.82 14.2±6.2
Table 2.2 Experimental data of rare cancer cell enrichment using an indirect immunomagnetic labeling.
61 Run Average 1 2 3 4 5 6 Flow rate (ml/min) 5 5 3 5 3 3 Cell throughput rate (cells/s) 4.2×105 4.2×105 2.5×105 4.2×105 2.5×105 2.5×105 Magnetophoretic mobility (mm3/A.s.T) 1.77×10-4 1.58×10-4 1.58×10-4 2.51×10-4 2.51×10-4 2.26×10-4
Feedstock Number of PBL used 8.0×107 8.0×107 8.0×107 8.0×107 8.0×107 9.0×107 Number of total Detroit-562 used 800 800 800 800 800 900 Detroit-562 concentration (cells/PBL) 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 1.0×10-5 62
Cell numbers after QMS Number of PBL recovered in the outlet a 3.6×106 2.04×106 6.5×105 1.09×106 7.8×105 1.17×106 Number of Detroit-562 recovered in the outlet 540 640 640 700 600 696 a Final Detroit-562 Purity (cells/Total cells) 1.50×10-4 3.14×10-4 9.85×10-4 6.45×10-4 7.69×10-4 5.95×10-4 5.76±3.03×10-4 Detroit-562 recovery in the outlet a (%) 67.5 80.0 80.0 87.5 75.0 76.9 77.8±6.6 Enrichment (-) 15.0 31.4 98.5 64.5 76.9 59.5 57.6±30.3
Table 2.3 Experimental data of rare cancer cell enrichment using a direct immunomagnetic labeling.
62 2.3.7 Detection of CTCs by RT-PCR
Detection of EGFR positive cancer cells in the enriched fraction using RT-
PCR in addition to ICCS was also performed, and representative examples are presented in Figure2.6. Figure 2.6A indicates that the EGFR-specific band was detected in all of the enriched samples, yet could not be detected in the unspiked PBL, and the spiked but not enriched PBL (lanes 1 and 2 of Fig. 2.6A). Furthermore, significantly brighter bands were obtained in the samples from the single-step immunomagnetic labeling protocol (lanes 5 and 6), which is consistent with the four fold higher purity that results from the single step labeling protocol. It should be noted that there was no significant difference in the expression level of ubiquitously expressed control gene HPRT among the tested samples (Fig. 2.6B).
63
A 1 2 3 4 5 6 7
301 bp
B
177 bp
Figure 2.6 PCR detection of the enriched sample by EGFR and HPRT, respectively. A: 1, PBL; 2, initial cell suspension; 3, sample 3 in Table 2.2; 4, sample 5 in Table 2.2; 5, sample 5 in Table 2.3; 6, sample 6 in Table 2.3; 7, Detroit-562 cells. B: The same samples were analyzed with RT-PCR for HPRT primers. DNA ladder was purchase from Sigma, 50 bp per band.
64 2.4 Discussion
The primary goal of this study was to optimize an enrichment process for rare cancer cells in human blood to a sufficient extent that RT-PCR analysis can be performed on the final sample in a reliable manner. Previously reported work in our laboratory has indicated that a negative depletion mode of enrichment using our QMS system produced reproducible, significantly enriched samples (Lara et al, 2004). To properly determine the level of enrichment needed, the sensitivity of RT-PCR for
EGFR in three HNSCC lines was also investigated. Semiquantitative observations of the gel electrophoresis of RT-PCR product indicated significant difference in both the final purity of cancer cells in the total cell suspension needed for detection, as well as significant differences in the total amount of RNA needed from the three different
HNSCC lines. Table 2.4 attempts to quantify these observations. Specifically, for
PBL and the three HNSCC lines, the amount of RNA, the amount of RNA per cell, and the calculated number of cells needed for RT-PCR detection is listed. In addition, the purity needed (number of cancer cells to number of total cells) for detection is also presented. While care should be taken in the use of these numbers, on an order of magnitude basis, one can observe a significant range in the number of cancer cells needed for detection with RT-PCR as well as a significant range in the purity needed.
Also note the consistency in that, the purity needs to be higher in the low EGFR expressing cells relative to the high expressing cells. Such ranges in both the total amount of RNA needed as well as the purity for the same mRNA marker helps to explain variability reported in the literature for cancer cell detection in human peripheral blood.
65 Sensitivity Cells limit of RNA RNA mass per needed for Cell line mass for cell (µg) RT-PCR EGFR (µg) detection PBL -- 2.4±0.6x10-6 -- Detroit- 1.0x10-4 14.2±2.0x10-6 7.1±1.0 562 CAL-27 1.0x10-5 12.8±2.9x10-6 0.8±0.2 SCC-4 1.0x10-3 35.3±5.8x10-6 28.3±4.7
Table 2.4 Sensitivity of RT-PCR detection of EGFR mRNA for HNSCC
While a 100% recovery of tumor cells without any unwanted blood cells in the final enrichment suspension is ideal, it is well known in the chemical process community that the overall recovery of a target product in a multistep process is the product of the recovery of each step. For example, in the enrichment for a rare cancer cell in a blood sample, one of the steps involves removal of the RBCs. According to our previous publication, a densitygradient centrifuge will lead to a 30% loss of spiked tumor cells while a red cell lysis procedure will result in a 10% loss in tumor cells (Lara et al, 2004). In the current studies, since the focus of the work was to optimize the magnetic depletion step for RT-PCR analysis, our starting blood sample was a buffy coat in which we had also performed a red cell lysis procedure, and to which we subsequently spiked the tumor cells.
A further example of the loss of cells in processing steps is observed in comparing the final recovery of spiked cancer cells using a single step versus a two step labeling protocol, which results in a 78% versus 47% recovery, respectively
(Tables 2.2 and 2.3). We have previously reported that an average recovery of ~93% per centrifuge step was obtained for washing the free antibody from the cell 66 suspension. Consequently, it is highly likely that the increased recovery, and corresponding purity, is at least partially the result of less processing steps.
Our previous studies have demonstrated that the magnetophoretic mobility of an immunomagnetically labeled cell significantly affects the magnetic separation performance of the QMS system as well as other magnetic separation systems (Lara et al, 2004; Comella et al, 2001; McCloskey et al, 2003; Lara et al, 2006). Our previous studies have also demonstrated that a “drafting” phenomena exists in which magnetically labeled cells can “pull” unlabeled cells in the direction that the magnetically labeled cells move as a result of the imposed magnetic energy gradient
(Zhang et al, 2005).
In addition to increasing the recovery, to have a 100% enrichment one needs to remove all the unwanted cells. Comparison of this current study to Lara et al (2004) indicates that despite targeting the same surface marker, CD45, having similar magnetophoretic mobilities, and separating similar number of total cells, the enrichment of cancer cells 171.9 ± 151.0 was three times better in the Lara et al.’s study than the current one of 57.6 ± 30.3. The most obvious reason for this difference is the age of the blood sample. In the Lara et al. study, the blood sample used was fresh (under 2 h after being drawn from the person) whereas the blood used in this study was at least 24 h old. Tanner et al. (2002) indicated the significant changes in the relative expression of several genes for cytokine, chemokines, and CD surface markers in blood because of storage at room temperature. Although Schmidtke et al.
(1999) reported there is no obvious change of CD45 expression for monocytes and lymphocytes at 4 and 25ºC during 3-day storage, the upregulation or downregulation
67 of several gene expressions would result in the activation of phagocytes, such as neutropils, macrophages, and natural killer cells. Also, while on average, the expression level of a particular cell might not change significantly if the distribution was to “broaden”, with a significant increase in the low expressing, yet positive,
CD45+ cells, the probability of separating these lower expressing cells would decrease.
All these speculations indicate the importance of using fresh blood. Other studies of 4-day-old blood (data not shown) further confirm the need for the rapid whole blood analysis. In conclusion, despite incomplete enrichment of the rare cells, the enrichment is sufficient to allow RT-PCR detection of only a few spiked cancer cells into a human. Ongoing studies are currently focused on improving the final purity (improve the enrichment) as well as identification of other relevant mRNA targets, and testing of the system on the peripheral blood of cancer patients.
68
CHAPTER 3
IMMUNOCYTOCHEMICAL AND PCR DETECTION AFTER
MAGNETIC ENRICHMENT OF CIRCULATING TUMOR CELLS IN HEAD
AND NECK CANCER PATIENTS:
EARLY RESULTS.
The content of this chapter is submitted to Clinical Cancer Research for publication. List of authors: Liying Yang, James C. Lang, Priya Balasubramanian,
Kris R. Jatana, David Schuller, Amit Agrawal, Maciej Zborowski, Jeffrey J. Chalmers.
3.1 Motivation
HNSCC has a propensity to spread to regional lymph nodes (Forastiere et al,
2001). The frequency of metastatic spread is dependent on both the site of the primary tumor and the extent of the disease. Thus regional metastatic spread for floor of mouth tumors is 30%, 47% and 53% for (pathologically staged) pT2, pT3 and pT4
(TNM staging groups for HNSCC are based on American Joint Committee on Cancer
Staging, AJCC criteria) cancers respectively (Suen, 1996) and regional metastatic spread for pT4 base of tongue tumors approaches 85% (Yamamoto et al, 1984).
Currently, the most accurate prognostic indicator for HNSCC is use of TNM staging
69 criteria in combination with co-morbidity factors. A study of 137 patients with pT4 oral cancers demonstrated a 5-year survival rate of 42.8% for the pathologically- determined pT4N0 group and 17.5% for the pT4N+ group (Tankere et al, 2000).
However, classification of the TNM stage of a tumor may not always predict outcome.
This may often be the case for clinically staged, early-stage cT1 and cT2 node- negative tumors, where the presence of occult metastatic disease may later result in the development of recurrent metastatic neck disease. The incidence of occult regional lymph node metastases in clinically node negative (cN0) tumors is approximately 20-30%, and for this reason, elective neck dissection is standard surgical procedure as a preventive measure for regional disease recurrence in the neck.
This practice leads to prophylactic neck dissection also in patients who later show no evidence of occult micrometastatic disease after resection and pathological examination.
A better understanding of the genetic alterations responsible for development of metastatic disease could allow the use of molecular biological markers to aid therapeutic decision-making. In this way the surgeon may be able to make an informed decision concerning the aggressiveness of the therapeutic approach or the necessity to perform a selective/radical neck dissection. This may be especially important in the patient population who initially present with clinically cT1N0 or cT2N0 disease, where regional metastatic disease may be present but may not yet be sufficiently developed to be evident as frank malignancy. Consequently, there exists a need to utilize cellular and molecular prognostic biomarkers to specifically identify those patients with primary carcinoma and associated occult micrometastatic disease
70 who may develop regional recurrent disease in the neck and who would ultimately benefit from more aggressive therapy, including neck dissection.
Approximately 50-60% of patients who present with primary HNSCC will later develop recurrent disease locally at the original site of treatment or regionally as metastatic disease within the neck (Gath and Brakenhoff, 1999; Shin and Lippman,
1999). Median survival time for patients with recurrent HNSCC is only 6 months, and the 1-year survival rate is only 20% (Schantz et al, 2001). While locoregional recurrence, including lymph node metastases, and the subsequent therapeutic control of this form of the disease, represents one of the major challenges in present therapy for head and neck cancer, it is inevitable that a number of patients will later present with advanced, recurrent disease including distant metastases.
The genesis of overt metastases in a number of different types of cancer is based on the idea that tumor cells that dissociate from the primary cancer get access to circulation either directly into blood vessels or after transit in lymphatic channels.
Thus, detection of such cells in patients with newly diagnosed solid tumors has been an appealing strategy to provide evidence of future metastases (Pantel and Brakenhoff,
2004). Overall, the existence of CTCs and the settlement of these cells in secondary organs, such as liver, bone and lungs, as metastatic disseminated tumor cells (DTC) is generally accepted. These cells are believed to be rare members among the cellular population of primary tumor cells (Fidler and Kripke, 1977). Genome and transcriptome analyses of single disseminated tumor cells demonstrated that the majority of DTCs are cells with genetic aberrations compatible with malignancy and therefore most likely direct descendants of the primary tumor, although the genetic
71 changes generally were incongruent with the dominant genotype of the corresponding primary tumor (Klein et al, 1999, 2002a and 2002b; Schmidt-Kittler, 2003).
In contrast to breast and melanoma cancer, far fewer studies on the presence, and relationship, of CTCs and head and neck cancer have been conducted. Three of the more recent studies (Guney et al, 2007; Partridge et al, 2003; Wirtschafter et al,
2002) all suggest a relationship between the presence of CTCs and the stage of cancer as well as patient outcome; however, all three studies state that further studies are needed. Furthermore, while these studies provide reasonably detailed information on the patients, none of the studies provide complete quantitative information on the selection and/or enrichment of the CTCs from the blood samples.
In the study presented in this chapter, techniques developed and described in chapter 2 were applied on blood samples from HNSCC patients undergoing surgical removal of squamous cell carcinoma of the head and neck. Once the sample is enriched for the CTCs using a red cell lysis and magnetic depletion of CD45 cells, the resulting cell suspension is split, one sample centrifuged and stained for immunocytochemical analysis while the second sample is lysed and subjected to a
RT-PCR analysis targeting the Epidermal Growth Factor Receptor, EGFR. Chapter 2 discussed both relative presence of EGFR in head and neck tumors as well as the level of peripheral blood enrichment needed before RT-PCR detection could be reliably achieved. In addition, using this approach, approximately 77% of the spiked cancer cells were recovered after the enrichment process.
In this chapter, we also attempted to quantify the number of CTCs enriched from peripheral blood of HNSCC patients and identify any potential clinical
72 correlations that may be of clinical significance.
3.2 Material and methods
3.2.1 Patients and sample collection
Blood samples were collected from ten patients who presented with SCCHN.
Age, gender, tumor location, tumor stage, as well as other pathologically significant parameters are listed in Table 3.1. Operators were blinded to clinical correlative information during the cell suspension processing and analysis. From 10 to 18.5 ml peripheral blood was taken from each SCCHN patient, who was undergoing surgical resection for squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx and that have not been previously treated for this disease. Fresh blood was collected into green-top BD Vacutainer® blood collection tubes containing sodium heparin (Cat# 367874, BD).
73
Lymph Extra- Lymph/ # of Tumor Age/ Pathologic EGFR Patient Tumor site Node capsular Vascular tumor cells/ml Sex stage mRNA + spread Invasion cells blood Supraglottic Diagnostic 1 58/F laryngeal SCCa, N/A N/A N/A + 532 29.7 biopsy only* 2nd primary** Supraglottic 2 66/M T3 N2b Mx 3 of 33 Present Absent + 108 10.8 laryngeal SCCa Floor of mouth, 3 55/M T4 N2 Mx 5 of 46 Present Absent + 185 10.0 SCCa Flour of mouth 4 55/M T1 N0 Mx 0 of 32 N/A Absent + 130 7.7 SCCa 5 64/M T1 N0 Mx Laryngeal SCCa 0 of 1 N/A Absent + 128 7.1
74 6 40/M T2 N0 Mx Floor of mouth 0 of 24 N/A Absent + 57 4.32 Tongue/anterior 7 52/M T4 N0 Mx 0 of 20 N/A Absent + 20 1.11 floor of mouth 8 33/M T3 N2c Mx Right tonsil SCCa 6 of 72 Present Present + 0 0 Left mandibular 9 72/F T2 N0 Mx 0 of 6 Absent Absent - 0 0 gingival SCCa 10 79/F T2 Nx Mx Gingival SCCa N/A N/A Absent - 0 0
Table 3.1 Patient data and number of tumor cells detected, total and per ml of blood. SCCa=squamous cell carcinoma; N/A=not applicable/available; *pre-incision; **neuroendocrine carcinoma of lung with nodal metastases.
74 3.2.2 Overall enrichment process
Blood samples were either processed immediately or stored at 4 oC overnight and processed early the next day. If more than one Vacutainer tube was used for collection, the blood samples were pooled, subjected to a red cell lysis step, immunomagnetically labeled, the immunomagnetically labeled cells were removed in the magnetic separation system and the enriched cell suspension containing the untouched tumor cells were collected. This cell suspension was then split into two aliquots, one for a cytopsin and immunocytochemical analysis, and the second sample for RT-PCR analysis. Figure 3.1 presents a flowchart of this overall process.
3.2.3 Red cell lysis step
Red blood cells were removed by applying a lysis buffer (154 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA), at a ratio of 25ml lysis buffer to 1 ml of blood and incubating at room temperature for 5min. After 5 min centrifugation at 300xg, the cell pellet was washed and then resuspended in labeling buffer (PBS supplemented with 2 mM EDTA and 0.5 % Bovine serum Albumin). Concentration of nucleated cells was determined by hemacytometer using the Unopette® Microcollection system (Cat#
365856, BD Biosciences). This cell suspension consisting of predominately nucleated cells was then ready for magnetic labeling.
3.2.4 Immunomagnetic labeling
As described in chapter 2, either a one-step (Method A in this study) or two- step immunomagnetic labeling technique (Method B) targeting CD45+ cells was used.
A new protocol C has been developed recently which is an extension of protocol B.
FcR blocking reagent (Cat# 130-045-801, Miltenyi Biotec) was employed in all
75 methods prior to primary antibody labeling to block unwanted non-specific bindings.
Details of protocol A and B were discussed in section 2.2.2. For the new two- step labeling protocol C, CD45-PE (Cat# IM 2078, Beckman Coulter, France) was used as a primary antibody while anti-PE DM particle (Cat# 557899, BD Biosciences) and anti-PE MACS microbeads (Cat# 130-048-801, Miltenyi Biotec) were added sequentially as secondary antibodies. The anti-PE DM particles were concentrated
(2:1) prior to magnetic labeling by placing a tube on a magnet for 15min and removing half volume of the solution. (e.g. 200µl of original solution was concentrated to 100µl.) For cell labeling, 1*108 cells were resuspended in 100ul of labeling buffer, 50ul of FcR and 350µl of CD45-PE antibody in a 15ml falcon tube.
The mixture was incubated at 4 oC in dark for 15 min followed by a wash step as described above. The cell pellet was then resuspended in 100µl of labeling buffer,
200µl of pre-enriched anti-PE DM particles and incubated at 4oC in dark for 15 min.
Finally, 200ul of anti-PE MACS beads were added and another15 min incubation at
4oC was conducted. After washing and centrifugation, cells were resuspended in the appropriate volume of labeling buffer and were ready for separation.
3.2.5 Magnetic cell separation step
The design and specific details of the operating of the magnetic cell separation technique used in the study has been described previously (Chapter 1).
76 Magnetic Magnetic Blood sample Red cell Final detection lysis labeling Separation
PE-Magnetic CD45-MACSbeads particle Immuno- RT-PCR cytochemistry CD45-PE
WBC WBC 77
Figure 3.1 Flow diagram of process to enrich for rare cancer cells.
77 3.2.6 Staining of tumor cells
As presented above, the enriched cell suspension collected from the magnetic separation step was split into two aliquots: one for immunocytochemistry, the second for RT-PCR. The sample for ICC was first subjected to a cytospin procedure using a
Shannon cytospin instrument (Thermo Scientific, Pittsburg, PA) and fixed in 1% formaldehyde for 10 min. For the immunostaining, a 1:10 dilution of the anti- cytokeratin FITC antibody CK3-6H5 (Cat# 130-080-101, Miltenyi Biotec, Auburn,
CA), which recognizes cytokeratin 8,18, and 19, was applied to the cytospin and incubated at 37oC for 30 min in a humidity chamber. Next, the slide was washed in
PBS buffer 3 times for 5 min each time and air-dried. The air-dried slides were mounted in Vectashield® mounting medium with DAPI (Cat# H-1200, Vector
Laboratory) to simultaneously preserve the fluorescence and counterstain the cell nuclei. Following a similar criteria as that described by Partridge et al and Reithdorf et al to identify a cancer cell, the cell must be: 1) double positive for FITC and DAPI staining; 2) the cell must have an intact membrane, and 3) the cell must have a high nuclear:cytoplasmic ratio and be as large, or larger when compared to surround PBL’s;
4) the cell must be PE negative in those cases that CD45-PE are used initially to label the WBCs (immunomagnetic labeling protocols B and C).
3.2.7 RT-PCR assay
For EGFR mRNA detection, the RNA was extracted from samples using either
Trizol reagent (Cat# 15596-018, Invitrogen) or Picopure RNA isolation kit
(Cat#KIT0204, Molecular Devices). Picopure kit was chosen when the total number of cells was less than 106. RNA concentration was determined by a
78 spectrophotometer (Amershan Biosciences, Sweden). Aliquots of 1µg RNA were reverse transcribed in a final volume of 20µl using Affinityscript multiple temperature reverse transcriptase following the manufacture’s guidance (Cat# 200436, Stratagene).
Then, 2µl of the cDNA product was subject to a PCR amplification using the Hotstar
DNA Polymerase kit (Cat# 203203, Qiagen). The sequences of the EGFR primers were as follows: GGGAGCAGCGATGCGA and CTCCACTGTGTTGAGGGCAAT, generating a band of 301bp. The PCR reaction was performed for 35 cycles under the following conditions: 94 oC for 1 min, 60 oC for 1 min and 72 oC for 1.5 min. For every set of RT-PCR experiment, a positive control (a sample with RNA extracted from a head and neck cancer cell line D-562) and a negative control (a sample without RNA) were included.
Additionally, PCR of a house-keeping gene, HPRT, was performed as a control to confirm the integrity of RNA and the efficiency of RT-PCR. The primers used were:
GTAATGACCAGTCAACAGGGGAC and TGGTCAAGGTCGCAAGCTTGCTTG generating a 177bp product. PCR products were analyzed by 1.5% agarose gel electrophoresis.
3.2.8 Cell culture
A head and neck cancer cell line, Detroit-562 was purchased from ATCC
(Manassas, VA) and was maintained in Earle’s Minimum essential medium (ATCC,
Manassas, VA) supplemented with 10% fetal bovine serum (FBS; JRH Biosciences,
Lenexa, KS). Cells were harvested using Accutase™ (Innovative Cell Technologies,
Carlsbad, CA) as per manufacturer’s instruction.
79 3.3 Results
Table 3.2 presents the results of the enrichment of the peripheral blood of the ten patients in this study. In addition to the total blood volume collected, the total blood cell count (RBC and PBL), the immunomagnetic labeling protocol used, the overall log10 enrichment, the number of cytokeratin and nuclei positive cells, whether the sample was positive or negative for EGFR, and the final purity (number of cancer cells divided by the total number of cells) is reported. The overall log10 depletion, which is a measure of the level of normal cell removal, was determined by taking the log10 of the initial number of total cells divided by the final number of total cells. In an attempt to put some order to the 10 samples and to assist in comparing the data on the presence, and number, of tumor cells per ml of blood, the samples are ordered from 1 to 10 with 1 having the highest concentration of tumor cells per milliliter of blood to samples 8 through 10 with no detectable tumor cells by ICC or RT-PCR.
Figure 3.2 presents photographs of a brightfield, 2A, a filtered image for FITC,
2B, a filtered image for DAPI, 2C, and an electronically combined image, 2D, of images in 2B and 2C of a cytospin of one of the enriched peripheral blood samples.
To demonstrate the consistency of this visual identification technique, photographs of cytospins from three other enriched PBL samples from the cancer patients are presented in Figure 3.3 (with the brightfield image omitted).
80
Final Total, Final Blood number Overall Total No. No. of Labeling Initial RT- concentration Patient volume of log of cancer cancer cells protocol Blood Cell 10 PCR (cancer (mls) nucleated enrichment cells /ml blood Count cell/total cells cells 1 18.0 C 8.01×1010 4.7 × 106 4.20 532 + 1.1 × 10-4 29.6 2 10.0 A 6.27×1010 7.5 × 105 4.92 108 + 1.4 × 10-4 10.8 3 18.5 B 6.8×1010 4.63×107 3.17 185 + 4.0 × 10-6 10 4 17.0 C 6.1×1010 1.2 × 106 4.76 130 + 1.12 × 10-4 7.5 5 18.0 C 1.14×1011 2.79 × 106 4.60 128 + 4.59 × 10-5 7.1 6 18.0 A 5.89×1010 4.17 × 106 4.15 57 + 1.4 × 10-5 3.2 7 18.0 A 8.17×1010 4.9 × 106 4.22 20 + 4.1 × 10-6 1.1
81 8 18.0 A 5.6×1010 1.55×107 3.56 0 + ? 0.0 9 18.0 B 9.1×1010 1.39 × 107 3.82 0 - 0 0.0 10 18.5 C 8.44 ×1010 2.07 × 106 4.60 0 - 0 0.0 Average 7.6 × 1010 9.6 × 106 4.2
Table 3.2 Specific details of enrichment performance per patients sample.
81
Figure 3.2 Photographs of microscopic images of a cytospin of one of the 10 enriched peripheral blood samples from cancer patients. Figure 2A is a brightfield image, 2B is an image filtered for FTIC staining, 2C is a image filtered for DAPI staining, and 2D is an electronic superposition of images 2B and 2C. Original magnification ×200.
82
Figure 3.3. Immunocytochemical staining (ICCS) of three other samples of cytospins from three other cancer patients. In all three cases, “A” corresponds to images filtered for FITC, “B” corresponds to images filtered for DAPI, and “C” is an electronic superposition of “A” and “B”. Original magnification ×200.
83 Figure 3.4 presents photographs of the EGFR bands in the gels derived from samples 1, 4, 5, and 9. In addition, a positive control from the Detriot-562 cell culture and a negative control without RNA, is also included. Lanes 1, 4 and 5 were considered positive, while lane 9 was considered negative. Finally, the
HPRT control for each of these four samples is also included in this Figure. Table
3.3 presents the approximate number of enriched cells from which RNA was extracted, the ratio of cancer cells to total cells (from Table 3.2), the concentration of RNA in the cell pellet extract, and the total amount of RNA extracted.
Figure 3.4. Detection of EGFR mRNA in enriched samples by RT-PCR technique. Number above each column corresponds to the sample number in table 1; ‘+’ refers to a positive control and ‘-’ means a negative control without RNA. Upper panel shows EGFR mRNA expression and lower panel shows HPRT expression for the same samples.
84 Number of RNA Final Approximate nucleated cells concentratio Total RNA concentration number of Sample from which n in extracted (cancer cancer cells RNA was extractant cell/total cells) in sample (ug) extracted (ug/ml) 1 3.3 x 106 1.1 x 10-4 360 238 3.6 2 3.8 x 105 1.4 x 10-4 53 376 4.1 3 1.0 x 107 4.0 x 10-6 40 41.5 2.1 4 5.1 x 105 1.1 x 10-4 57 107 1.2 6 -5 85 5 1.3 x 10 4.6 x 10 60 417 6.3 6 1.4 x 106 1.4 x 10-5 20 92.8 1.9 7 2.0 x 106 4.1 x 10-6 8 177 5.3 8 1.2 x 107 ? ? 158 7.9 9 1.1 x 107 0 0 35 1.8 10 1.6 x 106 0 0 464 7.0
Table 3.3 Numerical values with respect to the samples used for RT-PCR analysis
85 A number of initial observations can be made from the data in Table 3.2. First, with the exception of sample 8, the positive presence of tumor cells in the blood sample correlates with the detection of EGFR expression in the blood (based on RT-
PCR analysis), and a lack of tumor cells in the blood correlates with a lack of EGFR expression. The one exception exhibited a lack of ICC detection of tumor cells, yet
EGFR expression was detectable by RT-PCR. (It should be noted that for that specific sample, the log10 enrichment was below normal which makes visual detection of tumor cells more difficult.
Second, in Chapter 2, we reported that the ability to positively detect SCCHN cell lines spiked into peripheral blood using a RT-PCR analysis for the presence of
EGFR was a function of two factors: the final purity of the spiked cancer cells in the blood and the specific cancer cell line spiked. Specifically, it was demonstrated that a final purity of between 1 tumor cell in 103 total cells to 1 tumor cell in 105 total cells was necessary for detection, depending on which cell line was used. It should be noted that the final purities obtained in this study (second to last column of data in
Table 3.2) were in the mid to upper limit of purity that this previous study reported is necessary to detect a CTC using RT-PCR targeting EGFR mRNA.
Third, the data on the number of tumor cells per ml of blood is, in general, consistent with the pathological reports on the patient (Table 3.1). As stated previously, the data on the blood samples are organized in descending order from the sample in which we detected the most CTC per ml of blood (sample 1), to the two samples in which we did not detect any CTCs by ICC or RT-PCR (samples 9 and 10).
Correlation of the CTC count with pathological information demonstrates that if a
86 sample had 10, or more, CTC per ml of blood, the pathological report on the patient indicates that metastatic disease, either regional (lymph node), or at a distant site was present. Interestingly, blood sample 1, which contained the highest concentration of tumor cells (29.7 cells/ml of blood) was obtained from a patient who had a second primary tumor (small cell neuroendocrine carcinoma of the lung with mediastinal metastases) in addition to her locoregionally advanced laryngeal cancer. For the cases demonstrating between 1 and 10 CTC per ml of blood, no detectable metastatic disease was evident from the pathology reports. The one exception was patient specimen 8, in which ICC of CTC was negative, but EGFR mRNA was positive by
RT-PCR. This patient was also positive for lymph node involvement. However, as stated previously, this enrichment was below normal and lack of detection of CTC by
ICC could have been due to the difficulty in visually finding the CTC’s.
3.4 Discussion
This study is encouraging with respect to the use of CTCs in SCCHN patients as a diagnostic tool. Specifically, the suggestion that >10 CTC per ml of blood correlates with metastatic disease, while not surprising, is potentially significant as an independent diagnostic or prognostic marker. The importance of this correlation may be further underscored by the potential utility of CTC detection as an additional significant technique to reveal the presence of occult micrometastases, which may present with 20 to 30% incidence in SCCHN patients staged with clinically node- negative disease. Inspection of Table 3.1 indicates that for patient samples 4 through
7, despite being lymph node negative, a significant concentration of CTC’s were
87 detected. An argument can be made that the high number of CTC detected is the result of the surgical procedure; however, for patient number 1, with 29.7 CTC per ml of blood, the sample was taken before an incision was made (Note, as discussed above, this patient had both laryngeal squamous cell carcinoma and small cell carcinoma of the lung). Also, all blood samples were drawn from a line from which blood was already taken; therefore, epithelial cells from skin would not be a contaminant.
This is the first report, for which the authors are aware, of such a high concentration of CTC (number of CTCs per ml of blood) in patients that have not been previously treated for SCCHN. Of the most recent studies on CTCs and head and neck cancer patients, only Wirtschafter et al (2002), reports the actual number of
CTCs obtained, and in all cases they detected less than one CTC per ml of blood. In contrast, in one patient, who is not listed in this report and apparently had another type of metastatic cancer, we obtained over 200 CTC per ml of blood. The only other reports that list the concentration of CTC we have detected in this study is a recent one by Riethdorf et al (2007) using the CellSearch system (positive CTC selection) on metastatic breast cancer patients. In one case, they report 198 CTCs per ml of blood; however a more typical numbers ranged from 0.5 to 60 CTCs per ml of blood.
The suggestion that the detection of CTCs, or DTCs, has a negative prognostic indication, despite the patient having a negative lymph node diagnosis, has precedence in the literature for other types of cancer. Specifically, a study by
Cristofanilli et al (2004) of 177 patients with measurable metastatic beast cancer
88 indicates that patients with greater than or equal to 5 CTC per 7.5 mls of blood had a shorter median progression-free survival and shorter overall survival than patients with less than 5 CTCs per 7.5 ml of blood. Of more relevance to the data in this current study, however, is the numerous reports (Coombes et al, 1986; Diel et al, 1998;
Gerber et al, 2001), including a pooled analysis of over 4,000 breast cancer patients
(Braun et al, 2005), that indicates that the presence of DTCs, in the bone marrow of breast cancer patients is associated with a poor prognosis of the disease. This is especially relevant in that it has been reported that in approximately 33% of the cases no other clinical indication of metastatic disease is present (Fehm et al, 2006).
The positive correlation between the presence of CTCs and the presence of
EGFR mRNA is also encouraging. To our knowledge, this is the first study in which the presence of CTC in the blood of cancer patients has been detected by both ICC and RT-PCR analysis. In Chapter 2, we reported on the importance of pre enriching a blood sample before conducting a RT-PCR analysis (in the range of 1 cancer in 103 to
1 cancer cell in 105 total cells). Of equal, but not surprising, significance, was the observation in the study by Tong et al. that not only is the purity of the sample important with respect to the ability to detect a spiked cancer cell into human blood with RT-PCR, but the absolute number of cancer cells in the blood sample is also important. Depending on the cell lines used to spike, it has been demonstrated in
Chapter 2 that from 1 x 10-5 to 1 x 10-3 µg of total RNA from a SCCHN cell line was needed before an EGFR band could be detected on a gel. It was further suggested that this range in the quantity of EGFR mRNA needed was due to variation in the expression level of EGFR mRNA in the three cell lines tested.
89 With respect to this current study, the amount of RNA extracted from the cells and used in the RT step was 1 µg. The fact that detectable EGFR bands were observable using this amount of RNA, and final purities obtained in this study were relatively low compared to that in Chapter 2, indicates that the CTCs we detected in these patient samples express a significant amount of mRNA for EGFR.
Finally, it should be noted that this study is only the first report of this on going study. In addition to continuing to follow the clinical outcome of patients in this study, we are continuing to improve/developing other visual, pathological techniques to confirm the presence of CTC in the enriched cytospins and other markers which can be targeted with RT-PCR. Also, further on going work in our laboratory is focused on improvements of the overall enrichment process. Ideally, we wish to obtain a pure population of CTCs after the enrichment process, which would require at least, on average, a three log10 improvement in the enrichment process.
Such an improvement will involve an improved separator design, improved labeling reagents and protocols, and further refinement of the overall process to improve the speed and make the process simpler. It is estimated that if final purities on the order of one CTC in 100 total cells, or better, is obtained, microarray analysis of the CTCs will be possible.
90
CHAPTER 4
IDENTIFICATION OF RESIDUAL CELLS AFTER QMS ENRICHMENT
AND POTENTIAL IMPROVEMENT STRATEGIES
4.1 Motivation
Previous two chapters have discussed the application of novel techniques to enrich and detect CTCs on both model system (cultured cancer cell lines spiked into nucleated blood cells from healthy donors) and real clinical settings (peripheral blood from cancer patients). While the results are promising, we believe that there is still room for improvement in terms of the final cancer cell purity. Currently, the final cancer cell purity is in the range of 10-5 to 10-3, with a significant number of contaminating cells or residual cells present. The presence of these contaminating cells in the final enriched sample is a major obstacle for ongoing research and future molecular analysis. For example, to be able to conduct a microarray study on CTCs and allowing for the accurate analysis and illustration of global gene expression pattern, the purity of the CTC samples has to be at least 1/500, which means 1-3 higher log depletion is desired.
Furthermore, it is also noticed that the number or percentage of contaminating cells have been greater from samples of cancer patients from the Operating Room
91 than that of modeled systems under exactly the same experimental conditions. Since the initial number of CTC’s in the clinical samples from cancer patients are unknown,
Log10 depletion of total nucleated cells is the primary measure of system performance
(although the final number of cancer cells in the enriched sample is containing a positive performance outcome).
T- test has been carried out using JMP statistical software (SAS) to compare between different sample sources and results are shown in Figure 4.1. The quantitative results presented in Figure 4.1 confirm the observation that the samples from cancer patients gave rise to significantly lower depletion of nucleated blood cells, thus higher number of normal contaminating cells (p-value = 0.0015 See appendix A for detailed analyses).
2.25
2
1.75
1.5 Log depletion Nucleated cell Nucleated 1.25
1
buffy coat cancer
Sample
Figure 4.1 Statistical analysis of nucleated blood cell depletion by sample sources.
These quantitative observations (Figure 4.1) are the motivation for the work presented in this chapter. Specifically, studies were conducted to attempt to identify
92 the residual cells in the enriched fractions after QMS separation and understand why they are not completely depleted. Specific potential questions include: Are the cells normal cells still expressing CD45 antigens or do they lost their antigen during the processes? Why do samples from cancer patients result in worse depletion performance than the normal blood samples?
Before specific details of this specific study and subsequent analysis, a brief description about human blood and its compositions is in order. Human blood is a heterogeneous mixture of a number of cell types, which are classified according to both morphological features and functional aspects. All the cells are derived from hematopoietic stem cells in the bone marrow. Since the Red Blood Cells (RBCs) are normally depleted by lysis using a hypotonic solution before the immunomagnetic separation, white blood cells (or peripheral blood leukocytes, PBLs) are to be the focus of this study. Under normal physiological conditions, PBLs are present in a range of 4.5-11 million per ml of peripheral blood (Abbas and Lichtman, 2003). They can be divided into three major subpopulations, granulocytes, lymphocytes, and monocytes. The normal ranges, and relative percentages, of these three subtypes of
PBLs are listed in Appendix B.
Among those subpopulations, granulocytes constitute about 50-70% of the total PBLs, while lymphocytes contribute approximately 20-30%. Granulocytes are further grouped into neutrophils, eosinophils, and basophils, with the latter two types having very low frequency. The neutrophils, also named ‘PMN’ (polymorphonuclear leukocyte), are the most abundant PBLs subset and are produced at a rate on the order of 1011 cells a day by a normal adult. Under normal physiological conditions, they
93 circulate in the blood for about 6 hours and undergo apoptosis if there is no infection.
They have short life times relative to other types of cells in the blood (Abbas and
Lichtman, 2003).
Cell surface antigens are commonly described in a binary manner, either the markers are present or absent from a specific cell type. In the case of human hematopoietic-origin cells, however, the antigen expression is a more quantitative feature than a qualitative parameter. For example, studies have shown that the expression of CD45 antigen changes quantitatively during the differentiation and maturation of cells in the bone marrow (Shah et al, 1988). CD45 is expressed in different amount on different lineages of hematopoitic cells, or even on the same lineage but during distinct differentiation stages. In this study, the expression of
CD45 antigen was quantified on different subpopulations of leukocytes. The expression pattern of myeloid markers including CD13 and CD33 was also studied in order to decide if using multi-marker targeting strategy can increase their depletion performance.
4.2 Materials and methods
4.2.1 Blood samples collection
Blood samples were collected from patients who presented with HNSCC
(General details of the type of patients can be found in Chapter 3). From 10 to 18.5 ml peripheral blood was taken from each HNSCC patient, who was undergoing surgical resection for squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx. Fresh blood samples were also collected from healthy donors
94 in the same manner. Fresh blood was collected into green-top BD Vacutainer ® blood collection tubes containing sodium heparin (Cat# 367874, BD).
4.2.2 Overall enrichment process:
The same enrichment process is performed as described in Section 3.2.
4.2.3 Sample preparation and labeling:
Cells before and after enrichment processes were collected and labeled with fluorescent tags for FACS analysis. Red blood cells (RBC) were lysed by fresh lysing buffer (154 mM NH4Cl, 10 mM KHCO3, 0.1 mM EDTA), and incubated for 5 min at room temperature with occasional shaking. Right after red cell lysis and before magnetic labeling step, aliquots of 1 million cells was collected in 5 ml round bottom polypropylene tubes (BD, US) as the pre-enrichment samples. Remaining cell samples proceeded to magnetic labeling and QMS separation. After QMS separation, the non-magnetic fraction from outlet a of QMS was collected as the post-separation samples. The nucleated cell numbers were numerated by the Unnopette system, and aliquots of 0.5 or 1 million cells were placed in 5 ml round bottom polypropylene tubes.
Anti-CD45-PE antibody (Cat# IM 2078, Beckman Coulter, France) was used to label both before and after separation samples. Anti-CD13-FITC (Cat# IM0778U,
Beckman Coulter, France) and Anti-CD33-FITC(Cat#IM1135U, Beckman Coulter,
France) were also utilized to explore these marker expressions. Negative controls containing unlabeled cells and IgG isotype controls were included in each experiment.
The antibody labeling step was performed following manufacture’ instructions. The sample tubes containing cells and antibodies were incubated for 15mins at 4 oC. Cells
95 were washed twice using labeling buffer after labeling. Specifically, 2ml labeling buffer was added to sample tubes, which were subsequently centrifuged at 300g, 4 oC for 5 min. The supernatant was discarded carefully and was followed by another wash step. Cells were fixed in 3.7% formaldehyde solutions for 15 mins and stored at 4 oC in dark. All samples were normally analyzed by FACS on the second day of sample preparation.
4.2.4 FACS analysis
Cells pre and post enrichment process from the same patient were analyzed using a FACS Calibur Flow Cytometer (Becton Dickinson, Florida, US). The
FACSCalibur system is a dual-laser and four-color bench-top system. The dyes such as FITC and PE are excited by the 488 nm blue laser and APC can be excited by the
633 nm red laser.
Unstained and appropriate single fluorescence stained samples were run first to adjust the voltage setting and compensation of the flow cytometer. Then the isotype control samples and tested samples were processed by CellQuest, typically collecting
10,000 events for most samples. Listmode data files were analyzed by WinMDI 2.9 software.
Two gating techniques were employed for different purposes (Figure 4.2a and b). Figure 4.2a shows the first gating methods (A) for peripheral blood leukocytes
(R1), which then can be broken down into three major populations: granulocytes (R2), monocytes (the R3), and lymphocytes (R4). This method is based on the fact that all
PBLs should be CD45-PE positive and the subpopulations are gated according to their size differentiation. This method is more advantageous than the conventional
96 forward scatter-side scatter gating technique mainly because it is more accurate, eliminating the inclusion of cell debris, RBCs, and platelets (Nicholson et al, 1996).
Figure 4.2b shows a gating method (B) based on the CD45 expression level. M1, M2, and M3 represent low, medium, and high CD45 expressing cells.
Quantitative measurement of CD45 expression level on the cell surface was made possible by quantifying the Fluorescent Intensity (FI) signals when the cells were saturated with CD45-PE antibody. To be more specific, the actual numbers of
PE molecules per cell were obtained through the use of QuantiBRITE® PE beads
(BD Biosciences, Catalog # 340495) following the manufactures protocol. Each package of QuantiBRITE® PE beads contains a lyophilized pellet of four subpopulations of beads, each subpopulation has different amount of PE molecules conjugated to the bead surface. A typical PE histogram of the beads is shown in figure
4.3.
97 (a)
PE (b)
Figure 4.2 Demonstration of two different gatings. In figure 4.2a, x-axis represents the fluorescent intensity of cells labeled with CD45-PE antibody while y-axis corresponds to the side-scatter measurements; in figure 4.2b, x-axis is the fluorescent intensity of PE while y-axis indicates the number of events.
98 PE
Figure 4.3 PE histogram of QuantiBRITE PE beads. x-axis is the fluorescent intensity of PE while y-axis indicates the number of events.
4.2.5 Cytospin and Wright’s stain
Cytospins were made from cell samples after QMS the enrichment process.
About 5000 nucleated cells were place on each slide for a better view of individual cells. Specific cytospin protocols are described in section 2.2.5. Fresh slide were sent to Clinical Laboratory Services at OSU Medical Center for a Wright’s stain (Thanks for the help from Dr. Gewirtz). Slides were read and confirmed by Dr. Gewirtz.
Wright’s stain is a technique in histology use to differentiate cells from peripheral blood smears. It is the combination of an acid dye called ‘eosin’ (red color) and a basic dye called ‘methylene blue’ (blue color) to produce differences in intracellular structures which can be visible under light microscope.
99 4.3 Results and discussion
4.3.1 Identification of residual cells for cancer patients
As presented in Figure 4.1A and B, PBLs can be differentiated into three major subpopulations: granulocytes, monocytes, and lymphocytes based on size differentiation. Performing the same technique on both before and after separation cell samples leads us to the identification of the residual cells (Figure 4.4). Figure
4.4(a) and (c) are side-scatter versus forward-scatter dotplot of before and after separation samples, respectively. Figure 4.4 (b) and (d) are side-scatter versus PE dotplot of before and after separation samples, respectively. Red color indicated PE positive cells based on gating R1. Significant amount of the PE positive cells are still remaining in the non-magnetic fraction after QMS enrichment as shown in the figures.
More interestingly, a majority of the residual cells fall into R2 which is the gating set for granulocytes. Very few of the residual cells are lymphocytes (R4) or monocytes
(R3).
In order to further confirm the fact that a majority of the residual cells are granulocytes, cytosopins were made and Wright’s stain was performed. Figure 4.5 shows a representative picture of the stained cells. Almost all the cells show polymorphonuclear structures, which are characteristic for neutrophils. Furthermore, most of them are mature neutrophils, which have a large nucleus with about 3-5 lobes.
100 (a) (b)
R1
(c) (d)
R1
Figure 4.4 Dot plot of pre and post QMS enrichment samples. Upper two figures (a and b) are pre QMS separation samples, Lower two figures (c and d) are post separation samples.
101
Figure 4.5 A representative photograph of Wright’s staining of residual cells.
A small portion of these cells are immature neutrophils (band neutrophils), eosinophils, and basophils. Finally, we conclude that most of the contaminating cells after QMS enrichment are granulocytes, majority of which are neutrophils.
4.3.2 Quantification of subpopulations depletions:
Based on the gating technique A, the percentage of different subpopulations among PBLs can be obtained using the WinMDI software. Based on hemacytometer counting of nucleated blood cell number, it is easy to calculate the number of different subpopulation of PBLs before and after QMS enrichment process. Data from
8 blood samples from patients are summarized in Table 4.1. As shown in the table, granulocytes constitute the highest percentage of the total PBLs. To be specific, 80.12
± 8.62 % of the total PBLs are granulocytes, which is higher than normal literature value for granulocytes (40%-70%). This phenomenon can be explained by several factors as cancer, surgery, and panic situations, which have been shown to correlate with over production and increased release of granulocytes (Wikipedia: Neutrophilia).
102 After QMS separation, the percentage of granulocytes relative to total PBLs increases to 90.64 ± 12.15 %, which also confirms the fact that majority of the residual cells are granulocytes. Granulocytes are not depleted as well as other cell subpopulations such as monocytes. We can clearly see selectivity between the cell subpopulations in terms of the CD45 depletion.
A more visual comparison is shown in figure 4.6. Changes in the number of subsets of cells are plotted as bars, with the top of bars showing the pre-separation cell number and the bottom of bars representing the post-separation cell number. The bar chart is shown in logarithmic scale, so the length of the bars corresponds to the log depletion of each subpopulation in each experiment. Surprisingly, although there might be sample-to-sample variances among different patients, this chart shows very consistent patterns in terms of the depletion of subpopulations. The log depletion of granulocytes is much lower than that of the monocytes and lymphocytes.
Figure 4.7 illustrates the average log depletion of the total PBLs, granulocytes, monocytes, and lymphocytes, respectively. Although the log depletion of the overall total PBLs is a result of the log depletions of all three subpopulations, it is predominantly determined by the depletion of granulocytes because they are the most abundant subpopulation.
103 Samples 1 2 3 4 5 6 7 8 Before Total PBLs 1.31E+08 1.17E+08 1.31E+08 9.66E+07 4.30E+08 1.20E+08 1.24E+08 1.05E+08 QMS Granulocytes 84.24% 78.40% 76.40% 90.72% 86.69% 86.67% 64.63% 73.23% % Lymphocytes 8.00% 9.76% 18.88% 7.57% 6.88% 7.35% 28.88% 17.74% % Monocytes % 6.69% 10.65% 3.55% 1.19% 5.33% 5.05% 5.88% 7.07% No. of 1.10E+08 9.15E+07 1.00E+08 8.76E+07 3.73E+08 1.04E+08 8.01E+07 7.69E+07 granulocytes No. of 1.05E+07 1.14E+07 2.48E+07 7.31E+06 2.96E+07 8.82E+06 3.58E+07 1.86E+07 lymphocytes 104 No. of 8.76E+06 1.24E+07 4.66E+06 1.15E+06 2.29E+07 6.06E+06 7.29E+06 7.42E+06 monocytes Total PBLs 4.90E+06 1.55E+07 1.50E+07 1.37E+07 4.63E+07 1.39E+07 4.77E+06 2.79E+06 Granulocytes 97.76% 97.02% 90.71% 96.73% 96.08% 96.23% 61.65% 88.95% % Lymphocytes 0.97% 0.12% 7.87% 2.81% 2.55% 2.71% 35.84% 7.21% % After Monocytes % 0.10% 0.06% 0.04% 0.23% 0.21% 0.20% 0.33% 0.21% QMS No. of 4.79E+06 1.50E+07 1.36E+07 1.33E+07 4.45E+07 1.34E+07 2.94E+06 2.48E+06 granulocytes No. of 4.75E+04 1.86E+04 1.18E+06 3.85E+05 1.18E+06 3.77E+05 1.71E+06 2.01E+05 lymphocytes No. of 4.90E+03 9.29E+03 6.02E+03 3.15E+04 9.72E+04 2.78E+04 1.57E+04 5.86E+03 monocytes
Table 4.1 Detailed breakdowns of PBLs into subpopulations pre and post QMS enrichment.
104
Figure 4.6 Comparison of changes in subpopulation pre and post QMS separation among 8 peripheral blood samples from HNSCC patients. Top of each bar represents the initial cell number while bottom of bar represents the final cell number. Length of the bars corresponds to the log depletions.
105 Log10 depletion of PBLs and subpopulations from cancer samples 3.50
3.00
2.50
2.00
1.50 2.66 1.00 1.72
0.50 1.12 1.07
0.00 Total PBLs Granulocytes Monocytes Lymphocytes
Figure 4.7 Comparison of log depletions for PBLs and subpopulations from cancer
samples.
4.3.3 Differential CD45 expression among PBLs:
It has been speculated that a differential CD45 expression on different PBLs is the reasons that cause the non-uniform performance of the CD45 depletion. A typical
CD45 PE histogram of labeled peripheral blood sample is shown in figure 4.1b. It is quite clear that there are two peaks within the PE positive fraction, which means cells have various levels of CD45 antigen expression on their surface. According to the second gating method based on CD45 expression level, three subpopulations are identified, being CD45 low (M1), medium (M2), and high (M3) expressing cells, respectively. An example is shown in Figure 4.8 and 4.9 using data from sample 2 in
Table 4.1.
106 Figure 4.8(a) and (b) show PE histograms of cell samples before and after
QMS enrichment. Figure 4.9a shows the dot plot of a pre-enrichment sample with color representing their CD45 expression levels. It is obvious that a majority of the granulocytes fall into the category with medium level of CD45 expression (blue), while most of the monocytes and lymphocytes present a high level of CD45 antigen.
Figure 4.9b shows the dot plot of a post-enrichment analysis of the same sample, which indicates that the CD45 high expressing cells are successfully depleted during the enrichment process while some CD45 medium expressing cells still remain in the non-magnetic fraction. Both sets of figures indicate that the CD45 high expressing cells are depleted much better than the cells with medium or low levels of CD45 expression. Hence, the suboptimal depletion of the granulocytes can be due to their relative lower level of CD45 antigen expression on their cell surface.
(a) (b)
Figure 4.8 PE histograms of cell samples before (a) and after (b) QMS enrichment.
Events in gating M1, M2, and M3 are designated as CD45 low, medium, and high expressing cells, respectively.
107 (a) (b)
Figure 4.9 Differential expression of CD-45 antigen on subpopulations of PBLs before (a) and after (b) QMS enrichment. Green color represents events with low
CD45 expression level (M1); Blue shows events with medium CD45 expression level
(M2); margenta represents events with high CD45 expression level (M3).
The CD45 antigen expression levels on the cell surface are quantified with the help of QuantiBRITE® PE beads. Average number of PE molecules per bead is provided by the manufacture on a lot basis. The number of PE molecule per cell/bead is linearly associated with the geometric mean readings from flow cytometry at a logarithmic scale. A typical calibration curve is shown in Figure 4.10. Subsequently, average PE molecules per cell can be calculated from the geometric mean readings for each gated cell population (Table 4.2). The CD45 high expressing cells have
120.7±12.7 ×103 PE molecules / cell, while the CD45 medium expressing cells have
34.1±6.9 ×103 PE molecules / cell, which is less than one third that of the CD45 high expressing cells.
108 Knowing the PE to antibody ratio for the antibody to label the cells (0.5-1.5 in this case provided by the manufacture), we can calculate the antibody binding capacity (ABC). If we assume the average PE to antibody ratio to be 1, the ABC is equivalent to the number of PE molecules per cell.
Quantibrite PE Calibration curve 4
3.5 y = 0.992x - 1.4081 R2 = 1 3
2.5
Log (Geometric mean) (Geometric Log 2
1.5
1 2.5 3 3.5 4 4.5 5 Log (PE/bead)
Figure 4.10 Calibration curve for Quantibrite® PE beads. y=Log(G mean), x=Log(PE/particle)
CD45 expression PE/cell (×103) level Low (M1) 0.07±0.0 Medium (M2) 34.1±6.9 High (M3) 120.7±12.7
Table 4.2 Quantitative comparison of CD45 expression level.
109 4.3.4 Study on the expression of myeloid markers
The expression of myeloid markers, CD13 and CD33 are studied initially in order to explore the possibility of using multiple markers. CD45 has been shown to have lower expression on granulocytes which may result in the worse depletion of this cell subset. Even though the usage of fewer markers is favored due to the cancer heterogeneity, it might be advisable to add other granulocyte specific marker to increase their labeling efficiency.
Examples of CD45-PE versus CD13-FITC dot plots were shown in Figure
4.11A and 4.11B for cells before and after QMS separation. Analysis of the same sample for the CD33 antigen before and after separation was shown in Figure 4.11C and 4.11D. CD13 antigen is shown to have a moderate expression on a small portion of the cells (9.79%). After QMS separation, majority of CD13 positive cells are depleted resulting in a post-separation percentage of 1.57%. CD33 antigen also has a moderate expression level but the positive percentage is generally higher than the
CD13 antigen. 56.18% of these CD33 positive cells before separation result in
60.55% of them after separation. However, due to its expression pattern, the quantitative analysis of this marker is difficult and inaccurate because the gating set is ambiguous and objective. A slight change in the quadrant setting may result in significant difference. Overall, although these two markers have moderate expressions on some portions of the cells, their expression level is far below that of
CD45 antigen expression. Thus, the addition of such markers will be questionable in their ability to increase the labeling efficiency.
110 A B
9.79% 1.57%
CD
56.18% 60.55%
Figure 4.11 (A-D) A,C: analysis of CD13 and CD33 before QMS separation.
B,D: the analysis of CD13 and CD33 after QMS separation. Numbers show the percentage of double positive cells.
111 4.3.5 Normal blood control
It is interesting to compare the depletion performance from fresh normal blood sample with the cancer patients’ samples which has been demonstrated in section 4.3.1. Depletion of 3 normal blood samples was performed and the same flow cytometry analysis was performed. Similar observations were made with respect to the results from cancer patients’ samples: 1) The majority of the contaminating cells remaining after the QMS depletion are granulocytes; 2) These cells present a lower
CD45 antigen expression. However, the number of cells after separation of normal blood is generally lower than that from cancer patients’ samples.
Figure 4.12 was plotted in order to compare the log depletion of normal blood samples to that from cancer patients sample shown in Figure 4.7. Generally speaking, the level of log depletion from normal blood samples is comparable that from cancer samples. Although a slightly higher log depletion of each cell subpopulation was observed, it is not statistically significant due to the small sample size. Hence, the difference in the cell number after QMS separation was obvious mainly due to the high percentage of granulocytes present in the sample from cancer patients
(80.12±8.62%, n=8). Normal blood samples used in this study showed a granulocytes percentage of 66.91±2.98% (n=3), which is consistent with literature range (40-70%).
We can conclude that the difference in their performance is a direct result of their granulocyte percentage.
112 Log10 depletion of PBLs and subpopulations from normal samples
3.50
3.00
2.50
2.00
1.50 3.01
1.00 1.95 1.26 0.50 1.12
0.00 total PBL Granulocytes Monocytes Lymphocytes
Figure 4.12 Comparison of log depletions for PBLs and subpopulations from normal controls.
4.4 Discussion
The evidence presented in this chapter show that granulocytes are the major contaminating cells in the final enriched fraction for non-magnetic cells. Significantly lower CD45 antigen expression level is also observed on these cells relative to the other subpopulations in the peripheral blood leukocytes. Numerous reports in the literatures have indicated that moderate to low CD45 expression level on the surface of granulocytes (Terstappen et al, 1990; Shah et al, 1988). Results confirm that the
CD45 antigen is differentially expressed on subsets of human leukocytes. Monocytes and lymphocytes have higher CD45 expression, while granulocytes, including neutrophils, basophils, and eosinophils, have relatively lower CD45 expression. From
113 a statistical point of view, these cells have a lower probability to be accessible to the antibodies in the solution, which partially explains why they are difficult to be depleted than other cell types.
Consequently, the increase in the number and/or percentage of granulocytes in the starting samples could lead to lower overall depletion. Samples from cancer patients do have an increased number and/or percentage of granulocytes than buffy coat samples from healthy donors for two reasons. First of all, buffy coats from healthy donors tend to have a lower number of granulocytes than fresh blood samples since the age of buffy coat are at least 24 hrs and granulocytes normally have relatively shorter life time (approximately 6 hrs). This means that significant amount of granulocytes are already dead by the time they are delivered, even though they are kept at a lower temperature. Second, fresh peripheral blood samples from cancer patients have higher than normal number and/or percentage of granulocytes. This can be a result of multiple conditions, such as acute infection, stress, and malignancy
(Wikipidia: Neutrophilia). Certain hormones, such as cortisol, can increase the number of neutrophils by causing marginated neutrophils enter the blood stream.
Fresh blood samples were taken when cancer patients were undergoing surgeries, so nervousness played an important role in causing higher number of granulocytes.
Furthermore, Almond et al, 2001 have shown the increased accumulation of immature blood cells in peripheral blood of cancer patients. They characterized the phenotype of these cells and explored their possible functions in cancer patients. They have demonstrated that the population of immature cells is composed of a small percentage (<2%) of hematopoietic progenitor cells, with all other cells presented by
114 the MHC class one positive myeloid cells. About two thirds of the immature cells are immature myeloid cells at earlier stage of differentiation, while about one thirds of the cells are immature macrophages and dendritic cells. It turned out that these cells can differentiate into mature white blood cells in the presence of certain growth factors. They believe that this cell population, especially in advanced cancer patients, is an important factor in the immunosuppression in cancer. Although not shown in our study, it is highly possible that there are small amount of immature cells in peripheral blood of cancer patients, which may have contributed to lower CD45 antigen expression and worse depletion performance.
Although the above-mentioned factors that played significant role in the CD45 depletion performance are uncontrollable and highly dependent on patients, there are still potential ways to solve the problem. Increased antibody concentration in the magnetic labeling solution and higher antibody antigen binding affinity might compensate the fact that these cells have lower CD45 antigen expression. Using supermagnetic particles, such as Dynabeads®, instead of nanoparticles may solve this problem since less number of particles is required to bind to a cell for it to enter the magnetic fraction. Multiple markers could be used to target those CD45 low expressing cells to achieve high enough magnetophoretic mobility.
115
CHAPTER 5
FURTHER IMPROVEMENTS IN DEPLETION PERFORMANCE USING A
NOVEL MAGNETIC LABELING STRATEGY
5.1 Motivation
In the previous chapter, we demonstrated that the CD45 depletion of large amounts of granulocytes is the biggest obstacles in the current research progress.
Chapter 4 also showed that the residual cells in the enriched fraction are CD45 antigen expressing cells, although at a lower expression level, relative to a majority of the CD45 positive cells prior to separation. However, it is believed that this level of expression should be more than enough to make these “low” expressing cells
“magnetic” enough to be depleted. It is hypothesized that once the cells are successfully labeled with enough magnetic particles, they can be depleted using the system. The question becomes how we can label a large amount of cells efficiently so that a all (most) of these “low” expressing cells can be magnetically targeted, and subsequently depleted.
To assist in the goals of this chapter, the mechanism of binding between a cell surface receptors and a suspended, free ligand or antibody was used from previous publications. From these reports, a simplified mathematical model was developed to
116 characterize and quantify the binding performance in a given situation. Cell surface receptors are usually transmembrane molecules that can bind specifically to specific ligands. It is presumed that all receptor-ligand interaction have a specific cell function(s) (Lauffenburger and Linderman, 1993); however many of these function are still unknown. In addition to specific ligands, antibodies can be raised to ligands.
The receptor/antibody binding phenomena are subject to the influence of a variety of extrinsic variables, such as the concentration of receptors, concentration of free ligand in the solution, the binding affinity, and temperature. The properties of the labeling medium can be a significant factor as well. The labeling reaction is often performed at a lowered temperature in order to eliminate the potential for non-specific binding
(Lauffenburger and Linderman, 1993).
Fundamentally governing this binding is a chemical equilibrium that exits between the receptors and ligands, which is a function of the temperature. It is clear in our current situation that higher concentrations of both receptors (thus cells) and free antibodies in the solution are desired to achieve a high depletion. For any given ligand – antibody pair, a specific binding affinity exist. As might also be imagined, this binding affinity can vary from antibody clone to antibody clone targeting the same ligand. Following from basic binding kinetics, higher binding affinity requires less amount of antibody in suspension to achieve the same fraction of ligands bound than antibodies of lower binding affinity. When one wishes to achieve a high level of performance, such as a level of depletion, as in this study, or large cell sample, these binding affinity questions can be very significant. Equations are derived to quantitatively describe this phenomenon and limiting factors are identified.
117 Later in this chapter, multiple magnetic labeling methods are studied in terms of their binding and separation performance. Observations and comparisons are made and an optimal solution is proposed.
5.2 Theory: Cell surface receptor/Antibody binding
A theoretical model characterizing the antibody binding to cell surface receptor has been addressed in a recent publication Zhang et al (2006) and in
Lauffenburger and Linderman (1993). The following theory and derivations are based upon these two publications.
Consider the simplest case where the binding between antibody in the solution and receptor on cell surface is monovalent and specific, which means one ligand binds to one receptor without any other non-specific binding phenomenon. This equilibrium reaction can be expressed in the following equation:
k R + Ab f RAb 5.1 kd where R, Ab, and RAb represent receptors on the cell surface, free antibody in the
-1 -1 -1 solution, and receptor/antibody complex, respectively. kf (M time ) and kr (time ) are association and dissociation rate constants. Let [R] be the receptor concentration in the unit of [# of receptors/Liter], and [Ab] be the antibody concentration in the solution in M. [RAb] is defined as the complex concentration in the solution [# of complexes/Liter]. Assume R0 is the initial concentration of free receptors on cell surface [# of receptors/Liter], it can be expressed in the following equation:
R0 = nri 5.2
118 where n represents the cell concentration (cells/Liter)while r is the mean number of receptors per cell (# receptors/Liter). Let L0 (M) be the initial antiobdy concentration in the solution.
Thermodynamic principles indicate that the chemical equilibrium of this reaction is regulated by the dissociation constant KD, which is defined as:
kr KD = 5.3 k f
A small value of KD indicates a high affinity binding while a larger KD indicates a low affinity binding. Typical antigen-antibody binding affinities are in the range of 10-12-
10-7M.
By simple kinetics, we have an equation describing the time rate of change of the complex: d[] RAb =−k[][] R Ab k [ RAb ] 5.4 dt fr
Overall mass balance gives:
R0=[R]+[RAb] 5.5
L0=[Ab]+[RAb]/Nav 5.6
23 where Nav is the Avogadro’s constant being 6.02*10 number/mole.
Substituting eq5.5 and 5.6 into eq5.4, one can obtain: d[] RAb =−k([])([]/)[ R RAb L − RAb N − k RAb] 5.7 dt f00 avr
Equation 5.7 is a single ordinary differential equation which can be solved given rate constant and initial experimental parameters. However, in our case, the equilibrium concentrations of all species are of significance other than the concentrations change
119 with time. Thus, a steady-state assumption is made so that d[] RAb = 0 5.8 dt
In order to quantify the efficiency of magnetic labeling, it is more convenient to define a dimensionless value, ‘fraction of receptors occupied’, or θ:
θ = []/RAb R0 , where θ ∈[0,1] 5.9
This parameter can be considered as the effectiveness of how well the cells are labeled. Combining Equation 5.3, 5.7-5.9, one obtains:
θeq iR0 (1−−θeq )(LK0 ) −Dθ eq = 0 5.10 Nav where the fraction of receptors occupied at equilibrium, θeq can be calculated analytically as
()()LK++γ − LKL ++γγ2 −4 θ = 00DD0 5.11 eq 2γ
R0 nri where γ == for simplicity. For given L0, KD, n, and r, θeq can be obtained NNav av analytically.
Parameter estimations and simulation:
The labeling cell concentration ‘n’ is a well controlled parameter in the experiments. Typically, concentration as high as 2*108 cell/ml are used. The receptor expression level is an uncontrollable factor which can be estimated by flow cytometry analysis. In chapter 4, the number of PE molecules per cell is characterized based on their fluorescent intensity. Assuming that the valence of antibody binding is 1, and the ratio of PE molecule to anti-CD45 antibody is 1, receptor expression level is equal to 120 the level of PE molecules per cell. We have identified that there are two CD45 expression levels among the CD45 positive cells, whose mean expression levels are
4 5 3.41*10 and 1.21*10 molecules per cell. Now, θeq is simply a function of L0 and KD and can be simulated using Matlab. To generate the simulation profile, L0 is chosen to span the range from 10-7M to 10-5M mainly based on some protein assay results
-9 -6 (Refer to table 5.5), while KD is shown in the range from 10 M to 10 M. A 3-D plot of θeq, L0 and KD is generated by Matlab and shown in Figure 5.1.
The 3-D surface plot shows quantitative relationship between θeq and antibody labeling parameters, including the initial free antibody concentration (L0) and the dissociation constant (KD). Model simulation results fit very well with speculations.
Generally, θeq decreases with decreasing initial antibody concentration and increasing
KD, which means lower binding affinity. It further confirms the hypothesis that in order to achieve better labeling performances or percentage, high antibody labeling concentration as well as small KD are desired. The contour plot on the X-Y surface of this figure shows the contour lines where the θeq is equal to 0.9, 0.8,…,0.1. They are roughly straight lines, which indicates that certain L0/KD ratios are corresponding to these θeq values. For example, L0/KD ratio has to be at least 10 so that θeq achieves 0.9 or larger.
The same trend can be observed in Figure 5.2 A and B. Figure 5.2A shows a
2-D plot of θeq versus KD by setting the initial antibody concentration at 4 levels, 0.5
µM, 1µM, 2.5µM, and 5µM, respectively. A closer look of the plot at the red framed box is shown in figure 5.2B. In this figure, the red dotted line represents the theoretical prediction where 99% of the receptors are bounded. It is clear that an
121 antibody labeling concentration of 1µM is required to achieve 99% binding of the cell surface antigens if the antibody dissociation constant is about 10-8 M.
The receptor/ligand binding theory indicates the effect of the ratio of initial antibody labeling concentration to the dissociation constant, L0/KD. In order to achieve better magnetic labeling performance/higher percentage of magnetically labeled cells, a high L0/KD is indispensable.
Binding fraction Binding fraction
L0, M
KD, M
Figure 5.1 3-D plot of antigen binding percentage (θeq) as a function of L0 and KD.
122 A Fraction of antigenFraction of binding
KD,M
B
Figure 5.2 A-B 2-D plot of θeq versus KD at fixed initial antibody labeling concentrations. Figure 5.2 B shows a zoomed in look of the red box area in figure 5.2A.
123 5.3 Material and methods
5.3.1 Cell sources and blood sample collection
Both buffy coat and fresh peripheral blood samples are involved in this study.
Methods for sample acquisition were described in Section 2.2.1 and 3.2.1, respectively.
5.3.2 Overall enrichment process
The overall enrichment process was described in section 3.2.2. Nucleated blood cell numbers before and after separation was determined by hemocytometer using Unopette® Microcollection system (BD Biosciences).
5.3.3 Magnetic cell labeling
Six magnetic labeling methods targeting the leukocyte common antigen,
CD45, were performed on a total of 63 samples, including fresh peripheral blood from cancer patients and buffy coat from healthy donors. Table 5.1 summarizes the information for the six types of magnetic labeling methods, including their particle size and order of magnitude of their magnetophoretic mobility. Schematic diagrams of these methods are illustrated in figure 5.3. Labeling method A and B, the direct and indirect labeling methods using MACSbeads®, were described in section 2.2.2.
Labeling method C was described in section 3.2.4, and consists of a two-step labeling method using DM particles (BD) in addition to the MACS particles.
Anti-CD45 Dynalbeads was utilized in labeling method D, realizing that using bigger beads requires less number of beads attached to cell surface to be able to get depleted. Dynabeads were provided in a concentration of 4*108 beads /ml. Before labeling, the amount of Dynabeads required was calculated based on the labeling ratio
124 of number of bead to number of cells being 5:1. (The manufacture recommended bead/cell ratio 4:1). Appropriate amount of the well mixed Dynabeads were transferred to a clean eppendorf tube while the same volume of labeling buffer was added. If the volume was less than 1ml, 1ml of buffer was added. The tube was placed on a magnet (For example, the QMS or the MiniMACS® separation magnet) for 1min and supernatant was discarded. The washed dynabeads were resuspended in labeling buffer at the original volume. Red cell lysed cell samples were washed in labeling buffer and pelleted in eppendorf centrifuge tubes while FcR blocking reagent and the dynabeads solution was added to the cell pellet. The mixture was pippetted up and down at least five times to make sure uniform mixing. Samples were incubated at 4 oC for 30min with occasional shakings. Without a washing step, cells were resuspended in the appropriate volume of labeling buffer and were ready for QMS separation.
Both method E and F involve a special type of antibody called ‘Tetrameric
Antibody Complex (TAC)’, which are bivalent antibodies recognizing both cell specific antigens (e.g. CD45) and dextran molecules on the surface of their magnetic particles. Methods E used a 200-nm magnetic particle which came with the kit provided by the manufacture (Human CD45 depletion kit, Cat # 18259, Stemcell technology, Canada). Method F is a modified version of E by replacing the nanoparticles by a micron-sized magnetic particle (Magnetic microparticle, Cat #
19250, Stemcell technology, Canada). The detailed labeling protocols are as follows:
Cell samples were placed in eppendorf tubes and supernantant was removed after centrifugation. The cell pellet was resupended in proper volume of FcR blocking
125 reagent (Typically 1µl per million cells) and appropriate volume of CD45-TAC antibody (Typically 0.5 µl per million cells) without any addition of buffer. The solution was gently mixed by pippeting up and down at least five times without any vortex. The mixture was incubated at 4 oC for 15mins with continuous shakings.
Magnetic particles were gently mixed by pippetting up and down several times before adding to the samples. 1µl magnetic nano- and micro-particles per million cells were added to the labeling solution per manufacture’s instruction. The mixture is then incubated at 4 oC for another 15mins with continuous shakings. Cells were resuspended in an appropriate volume of labeling buffer and were ready for QMS separation.
Mobilit Particle Labeling 1st Company 2nd Company y size method antibody Catalog # Reagent Catalog # (mm3/ (nm) A.T.s) Miltenyi CD45- A Biotec 130- 100 ~10-4 MACS 045-801 Miltenyi CD45- Immunotech B PE-MACS Biotec 130- 100 ~10-4 PE IM2078 048-801 PE- CD45- Immunotech C DM+PE- BD Imag 230 ~10-4 PE IM2078 MACS CD45- Invitrogen/D D 4500 ~10-1 Dynal ynal 111.53 Stemcell CD45 Stemcell Stemcell E Nanopartic 200 ~10-4 TAC tech 18259 tech18259 le Stemcell CD45 Stemcell Stemcell F Micropartic 1000 ~10-3 TAC tech 18259 tech19250 le
Table 5.1 Summary of six different magnetic labeling methods.
126
Cell Cell
(A) (B)
Cell Cell
(C) (D)
Cell Cell
(E) (F)
Figure 5.3 Schematic diagrams of six different magnetic labeling methods targeting CD45 antigen on PBLs.
5.3.4 Magnetic cell separation
The QMS operational procedure has been discussed in section 2.2.3. With the effort in continuously improving the system, a modified version of the QMS has been developed and described in the introduction 1.4. This new system has been utilized to sort 15 of the experiments presented in this chapter. Operational procedures are similar with the original QMS system. Before an actual experiment, the channel was filled with degassed labeling buffer to reduce the potential of bubble formation and entrapment. Cell samples are suspended in labeling buffer at a concentration of 1-5
×106 cells/ml. After loading the sample, the syringe pump at the outlet of the channel started to run in a refill mode at the desired flow rate. Upon completion of the sample
127 input, the 3-way valve at the inlet was switched to sheath fluid. About 20ml of sheath fluid was flushed through the channel before turning off the pump. In addition, higher flow rates (~10ml/min) were selected for labeling method D and F, which include magnetic micro-particles. Lower flow rate range (3-5ml/min) was selected for labeling method A,B,C, and E, which involve magnetic nanoparticles.
5.3.5 Log depletion determination
Final, enriched fraction from the separators was collected and centrifuged.
Cell pellet was resuspended in the appropriate volume of buffer for hemacytometer counting after applying the Unopette® Microcollection system (BD Biosciences). In most of the experiments, it is difficult to get quantitative estimation of the initial cancer cell number, so cancer cell recovery is not a good indicator of the separation performance. On the other hand, log depletion of normal blood cells is chosen to be the only indicator of the separation performance in terms of CD45 depletion. Again,
Log depletion of nucleated blood cells was determined as follows:
N Log depletion of PBLs=Log [initial,P ], 5.12 Nfinal,P
Where Ninitial,P represents PBLs number before separation, while Nfinal,P is the PBLs number after QMS separations.
5.3.6 Statistical analysis
Statistical analyses were performed using JMP software (SAS Institute, Cary,
NC). The “Fit model function” was used set to a “single response” with respect to the log10 depletion of PBLs. The indpedent variables included: the magnetic labeling method, the type of blood sample (Either buffy coat or fresh peripheral blood), and
128 the scale of separation indicated by the number of initial PBLs to be depleted. The type of blood sample was classified as an “uncontrollable nuisance variable” in this model. Such a designation allows this variable to potentially have some effect on the log depletion. With a built-in least square analysis function, an effect model has been constructed to describe the effect of each variable on the response: the log10 depletion of the PBLs.
5.3.7 BCA protein assay
BCA protein assay was performed to quantitatively determine the concentration of antibody in the solution. BCA protein assay kit was purchased from
Pierce Chemical Company (Rockford, Illinois). Assay was carried out per manufactures instructions. Specifically, reagent A and reagent B were pre-mixed 24- hrs prior to experiments at a ratio of 50:1. Standard solutions of BSA at various concentrations were prepared to generate a standard curve. 0.5ml controls, sample solutions, or standard solutions, were placed in clear 4-sided cuvet (Fisher Scientific).
1.5ml pre-mixed solution A and B were added to the cuvet followed by incubation at
37 oC for 30mins. Absorbance at 562nm was measured using a UV spectrophotometer
(SHIMADZU, UV-1601, Japan).
129 5.4 Results
5.4.1 The depletion results of 63 experiments
A summary of the 63 separation results using six different magnetic labeling methods was listed in table 5.2. Sample origin, labeling method used, type of the sample (either fresh blood or buffy coat), scale of the separation, and log depletion of
PBLs were listed as columns in the table. 33 buffy coat samples and 30 fresh blood samples were included in this study. Three variables, ‘labeling method’, ‘type of blood’, and ‘scale of separation’, were studied for their effect on the log depletion of
PBLs. Labeling method is a nominal variable with six levels, while type of blood is a nominal variable with two levels. ‘Scale of the separation’ is a continuous numeric variable, ranging from 2*107 to 4*108 nucleated blood cells. Mean log depletion of these 63 separations are 1.68 with a standard deviation of 0.87. Variations are quite large with respect to the log10 depletion: ranging from 0.55 to 4.35. Almost a 4 log depletion difference is observed between the highest and lowest depletion performance.
A visual description of the log depletion data grouped into six magnetic labeling methods is shown in figure 5.4. Original data points, mean, and range of the log depletion within each group are shown in the plot. It indicates that labeling methods E and F give rise to higher log depletion while method D seems to have the lowest log depletion. These hypotheses were verified using statistics means and are presented below.
130 Scale of Sample Labeling Type of Log depletion separation origin method blood of PBLs (cells) BRCP1 A Fresh blood 1.01E+08 1.63 BRCP2 A Fresh blood 6.12E+07 1.34 BRCP3 A Fresh blood 4.68E+07 1.50 BRCP5 A Fresh blood 1.31E+08 1.37 BRCP7 A Fresh blood 7.80E+07 1.05 BRCP8 A Fresh blood 1.67E+08 1.60 BRCP9 A Fresh blood 1.31E+08 1.43 BRCP10 A Fresh blood 1.17E+08 0.88 BRCP11 A Fresh blood 2.33E+07 1.49 BRCP12 A Fresh blood 1.31E+08 0.88 BRCP13 A Fresh blood 9.66E+07 0.85 BRCP14 B Fresh blood 4.30E+08 0.97 BRCP15 B Fresh blood 1.20E+08 0.94 BRCP16 C Fresh blood 4.65E+07 1.60 BRCP17 C Fresh blood 1.24E+08 1.41 BRCP18 C Fresh blood 9.45E+07 1.66 BRCP19 C Fresh blood 1.05E+08 1.57 BRCP20 C Fresh blood 7.40E+07 2.71 BRCP21 C Fresh blood 2.10E+07 0.91 BRCP20 E Fresh blood 7.40E+07 2.70 BRCP21 E Fresh blood 2.10E+07 2.78 BRCP22 E Fresh blood 1.18E+08 4.35 BRCP23 E Fresh blood 6.26E+07 2.28 BRCP24 E Fresh blood 4.81E+07 3.37 BRCP25 E Fresh blood 5.72E+07 2.94 Buffy coat1 A Buffy coat 8.00E+07 1.35 Buffy coat2 A Buffy coat 8.00E+07 1.59 Buffy coat3 A Buffy coat 8.00E+07 2.09 Buffy coat4 A Buffy coat 8.00E+07 1.87 Buffy coat5 A Buffy coat 8.00E+07 2.01 Buffy coat6 A Buffy coat 8.00E+07 1.89 Buffy coat7 B Buffy coat 8.00E+07 1.37
Table 5.2 Log depletion results of 63 blood samples using six different
magnetic labelin g methods. (Continued. )
131 Table 5.2 Continued.
Buffy coat8 B Buffy coat 7.00E+07 1.17 Buffy coat9 B Buffy coat 8.00E+07 1.79 Buffy coat10 B Buffy coat 8.00E+07 1.77 Buffy coat11 B Buffy coat 8.00E+07 1.26 Buffy coat12 D Buffy coat 2.00E+07 0.55 Buffy coat12 D Buffy coat 2.00E+07 0.57 Buffy coat13 D Buffy coat 1.00E+07 1.11 Buffy coat13 D Buffy coat 1.00E+07 1.06 Buffy coat14 D Buffy coat 2.00E+07 0.77 Buffy coat15 D Buffy coat 2.00E+07 0.51 Buffy coat16 D Buffy coat 1.50E+07 0.52 Buffy coat17 D Buffy coat 1.50E+07 0.59 Buffy coat18 D Buffy coat 2.00E+07 0.69 Buffy coat18 D Buffy coat 2.00E+07 0.79 Buffy coat19 D Buffy coat 2.00E+07 1.29 Buffy coat19 D Buffy coat 2.00E+07 1.27 Buffy coat 20 F Buffy coat 4.00E+07 3.30 Buffy coat 20 F Buffy coat 4.00E+07 2.95 Fresh blood 1 F Fresh blood 7.50E+07 3.36 Buffy coat 21 F Buffy coat 7.00E+07 2.51 Buffy coat 22 A Buffy coat 2.00E+07 1.52 Buffy coat 22 A Buffy coat 2.00E+07 1.94 Buffy coat 23 A Buffy coat 2.00E+07 1.22 Buffy coat 23 A Buffy coat 2.00E+07 1.26 Buffy coat 24 E Buffy coat 1.00E+08 2.86 Buffy coat 25 E Buffy coat 1.00E+08 2.92 Buffy coat 25 E Buffy coat 1.00E+08 3.14 Fresh blood 2 A Fresh blood 3.00E+07 1.13 Fresh blood 3 A Fresh blood 2.14E+07 1.53 Fresh blood 4 A Fresh blood 2.75E+07 1.11 Melanoma1 E Fresh blood 1.05E+08 3.02
132
Figure 5.4Variability ChartforLogdepl method. Data points,andmethod. rangeareshown. Data Table 5.3Summaryofdepletion Labeling
method Log depletion of PBLs 4.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 D 12 A 24 C 6 B 7 E 10 F 4 Labeling method A N B Log depletion etion ofPBLsgroupedbymagnetic labeling 133 C data usingsixlabelingmethods Mean 3.030 3.036 1.643 1.324 0.810 1.438 Centerlinesshowmean ofeachgroup. D EF Standard 0.166 0.153 0.083 0.204 0.129 0.117 Error
5.4.2 Effect of magnetic labeling method
From the JMP software analysis, the P-values of the three primary, independent variables are listed in table 5.4. Detailed model fit parameters are listed in Appendix C. The low p-value confirms the observation that the magnetic labeling method is the most significant factor in affecting the depletion performance. The type of blood may have some minor effect, but the scale of separation is not a significant factor to consider in this situation.
Factors p-value
Labeling method <0.0001
Type of blood 0.0547
Scale of separation 0.9050
Table 5.4 p-value of three variables in the least square model fitting by JMP.
Furthermore, the ANOVA and each pair student’s t-test were performed on the labeling method to further study the effect of magnetic labeling method. It shows in figure 5.5 that method E and F produced significantly higher log depletion than any other labeling method, while method D results in the lowest log depletion. Summary of the ANOVA test and the student’s t-test is shown in Appendix D.
134 4.5 4
3.5 3
2.5
of PBLs 2 Log depletion 1.5 1
0.5 A B C D E F Each Pair Student's t Labeling method 0.05
Figure 5.5 Oneway ANOVA test and student’s t-test results
5.4.3 Estimation of reagent concentrations
Concentrations of MACS reagent (Miltenyi Biotec) and the CD45-TAC
(Stemcell technology) have been measured using the BCA protein assay. The molar concentration is obtained by assuming that the molecular weight of IgG is 150,000 dalton. The concentration of TAC has been measured directly using the BCA protein assay kit since there is no protein additives in the reagent solution (confirmed by protein chromatography). The concentrations of MACS reagents have been estimated by performing a size-exclusion chromatography followed by a BCA protein assay.
The detailed procedures for the chromatography are described in Zhang et al, 2006.
The molar concentration of Dynabeads is determined from its particle concentration of 4*108 particles/ml. The results are summarized in table 5.5.
135 Type of Reagents Molar Concentration (M)
PE-MACS 1.53*10-6
CD45-Dynabeads 6.64 * 10-13
CD45-TAC (Stemcell tech.) 1.20*10-6
Table 5.5 Comparison of reagents concentration
It is shown that the concentration of MACS and TAC reagents are on the same order of magnitude, while the concentration of Dynabeads is significantly lower.
However, Dynabeads do not require a high level of labeling to the cell surface receptors (θeq) since a single particle is sufficiently magnetic to remove the cell. In this manner, the micro-sized particle might be incomparable to those nano-sized particles. Having same order of magnitude concentrations, the performance of
MACS and TAC reagents is dependant upon their antibody binding affinities according to the kinetics. Although no binding affinity data of these antibodies is shown here, it is generally accepted that the tetrameric antibody complexes preserved the original binding affinity of the antibody while the covalent coupling between the antibody and the magnetic particles results in certain degree loss of the antibody binding affinity (Zhang et al, 2006; Lansdorp, 1986)
136 5.4.4 Economic considerations
Reagent cost for labeling a total of 108 cells was calculated for the six types of labeling methods. Table 5.6 shows that the cost of using the TAC kit from Stemcell technology is significantly less than any other magnetic labeling reagents. TAC reagents are highly recommended for magnetic labeling due to their demonstrated performance and low cost.
Labeling Cost for 108 cells methods ($) A 100 B 188 C 172 D 173 E 27 F 27
Table 5.6 Calculated cost of labeling reagents for a total of 108 cells.
137 5.5 Discussion
Covalent coupling methods are normally used to produce monoclonal antibody conjugates (antibody with fluorescent tag, enzyme, or magnetic beads), although these procedures usually result in some loss of the antibody activity / binding affinity (Lansdorp, 1986). As an alternative method, tetrameric antibody complex (TAC) structure was invented as a simple and efficient way to construct bifunctional molecules (Lansdorp, 1986). This technique is based on the observation that two monoclonal antibodies of a given isotype can be crosslinked together in a stable tetrameric structure by means of two isotype specific antibody molecules.
Specifically, two mouse IgG1 monoclonal antibodies (one recognizing a specific cell surface antigen on target cells and the other binds to a molecule coupled to a magnetic particle) can be bound together by two F(ab)2 fragments of rat anti mouse IgG1 monoclonal antibody. The schematic diagram of a TAC has been shown figure 5.3.
One can speculate that the most important advantage of this type of structure is that the original binding affinity of the TAC antibodies is preserved since they have not been covalently modified to bind to other molecules or particles. Consequently, the TAC would have a higher binding affinity to a cell than a conventional system such as Miltenyi Biotec or BD IMAG system. (Note, Zhang et al. 2006, clearly demonstrated experimentally that the binding of molecules and magnetic particles lowered the antibody binding affinity).
The implication of a higher binding affinity is quantitatively demonstrated next. Suppose the antibodies are supplied at the same concentration, L0 (M), L0/KD ratio is reversely associated with the dissociation constant KD. This means that the
138 lower the dissociation constant KD, the higher the L0/KD ratio, and the higher the percentage of antigen bound at equilibrium. It is wise to choose reagents with a high
L0/KD ratio, such as the TAC reagents.
Thus far, we can conclude that the above-mentioned kinetics model is efficient in determining the significant factors in the magnetic labeling process. It gives quantitative guidelines to help us understand the process of receptor/ligand binding in terms of the criteria to select the antibody reagents, the amount of antibody to use, the optimal labeling protocol, etc. Furthermore, the tetrameric antibody complex labeling system gives the highest magnetic labeling performance because of its high antibody binding affinity. The separation performance using TAC has been orders of magnitudes higher than that of the other reagents in this specific situation. Further work is needed to determine whether the speculation that the improved performance is due to a higher binding affinity, or other mechanism, remains to be determined.
139
CHAPTER 6
MAGNETIC ISOLATION OF TUMOR CELLS FROM SOLID TUMOR FOR
FURTHER MOLECULAR ANALYSIS
The content of this chapter is a continuation of Dr. Mehta, Bhavya’s work. It is anticipated to be joint published with Dr. Mehta, Bhavya, Dr. Chalmers, Jeffrey J.
6.1 Motivation
Cancer is well-known for its heterogeneity (Foulds, 1975). The ability to quickly obtain pure cancer populations from solid tumor biopsies is one of the major obstacles in cancer research (Sieben et al, 2000; Tomlinson et al, 2002; Ko et al,
2000). Tumor biopsies from cancer patients often contain a heterogeneous population of cells so that the results of the molecular analyses, for example the cDNA microarray assay, do not represent the expression profile of tumor cells alone, but a
‘mean’ of the entire cell mixture. This has made the diagnosis and valid experimental conclusions difficult to obtain and interpret. The understanding of cancer would be greatly enhanced if there were a method to rapidly obtain pure cancer cell population from solid tumor.
140 Having discussed the disadvantages of current techniques such as the LCM, the need to develop a better technology to sort cancer cells from non-malignant cells is highly enhanced. In this chapter, preliminary studies on developing a technique based on magnetic cell separation have been carried out. First of all, a reaction- diffusion model is developed to describe the tissue degradation process and mass transfer or diffusion is identified to be the rate-limiting step in the process. An in vitro tissue degradation technique is then established and characterized to obtain a single cell suspension in a very quick manner. Both mechanical dissociation and enzymatic digestion methods are considered and employed. Theoretical calculations and experimental means are utilized in determining parameters for the tissue degradation process.
Several tumor-specific markers are screened for the magnetic labeling and isolation of head and neck cancer cells. The specific marker is selected for the positive selection of cancer cells from the non-cancer cells in the entire cell mixture.
Magnetic labeling experiments are performed to characterize the labeling parameter and validate the labeling performance.
6.2 Theory: a simplified diffusion reaction model
The in vitro degradation of a tissue is a complex process involving a molecule in the liquid phase (enzyme) and a solid, or semi-solid mass (the structural components of tissue). The enzymatic reaction acting on the tissue is essentially non- steady state in nature as the solid is consumed during the process. Such a system can potentially be quantitatively evaluated using different models such as the
141 heterogeneous shrinking core model, general model, truly homogeneous model or grain model, depending on different assumptions. Due to the many unknown features of the biological system (tumor tissues), we will derive a reaction diffusion model for the degradation of a tissue matrix by a specific enzyme by making a number of simplifying assumptions.
The first assumption is that the tissue is modeled as a simple semi-infinite slab with the enzyme diffusing in positive x-direction. We assume that for a small change in time, the concentration of collagen in the tissue does not change significantly, thus the diffusivity of the tissue does not change substantially over a period of time.
Applying the overall mass balance for the enzyme in a tissue piece, we have:
(Truskey et al, 2004)
∂C N ( ) +Φ−Φ+⋅−∇= R , ∂t LB rxn 6.1 where C denotes the concentration of free, unbound enzyme in the tissue, N is the enzyme flux in the tissue due to convection and diffusion, ΦB is the rate of enzyme transport across a blood vessel per unit volume of tissue, ΦL is the rate of enzyme transport across a lymph vessel per unit volume of tissue, and Rrxn is the term describing rate of reaction. Obviously, the tissue digestion is an in vitro process where
ΦB and ΦL and the convection term in N are zero.
While, technically, the enzyme is not consumed as it cuts the collagen, in practice once the enzyme binds the collagen, and makes the first cut, it remains bound to the collagen fiber and moves down the fiber to another location and cuts it again.
In this way, in practice, once an enzyme molecule binds to a fiber, it is not available
142 to diffuse to another fiber. In this way, the enzyme is “consumed” as if it had participated in the reaction as a reactant.
Further, assuming the tissue is an isotropic and uniform material, we have a simpler equation:
∂C 2 +∇= RCD , 6.2 ∂t eff rxn The reaction between the substrate- collagen and the enzyme- collagenase in solution has been shown to follow the common Michaelis-Menten kinetics (Welgus, et al,
1981a,b, 1982; Van wart and Steinbrink, 1985; Mallya et al, 1992; Tzafriri, et al,
2002),
cat ⋅⋅ CCk S Rrxn = , 6.3 + CK SM where kcat and KM are rate constant and Michaelis – Menten constants respectively and CS is concentration of collagen in the tissue. Again, this term is a measurement of the binding speed of enzyme to the binding sites on the solid substrate. At the beginning of the process, it can be assumed that CS is constant for a small amount of time, thus equation (6.3) can be simplified to:,
rxn '⋅= CkR 6.4 ⋅Ck where, k'= Scat = constant 6.5 + CK SM
Substituting equation (6.4) into equation (6.2);
∂C 2 '⋅−∇= CkCD 6.6 ∂t eff
The negative sign is due to consumption of enzyme as it penetrates through the
tissue matrix. The boundary conditions for this reaction diffusion model are 143 I.C. t = 0, C = 0 for all x = 0 6.7
B.C. t > 0, C = C0 for all x = 0 6.8
Equation (6.6) can be solved analytically with initial conditions and boundary conditions given in equation (6.7) and (6.8). The analytical solution was derived by
Crank (1975)
t ⎛ x ⎞ ⎛ x ⎞ ()= ', erfcCktxC ⎜ ⎟ − tk '' '+⋅ erfcCdte ⎜ ⎟⋅e− tk '' 6.9 ∫ 1 ⎜ tD '2 ⎟ 1 Dt'2 0 ⎝ eff ⎠ ⎝ ⎠
Where C1 is analytical solution (6.11) of simple diffusion equation (6.10) for the same initial and boundary conditions given by equation (6.7) and (6.8)
∂C ∂2C = D 6.10 eff ∂t ∂x2
⎛ x ⎞ 1 (), = 0erfcCtxC ⎜ ⎟ 6.11 ⎝ 2 Dt ⎠
The solution of equation (6.6) can be obtained by substituting equation (6.11) in equation (6.9). This equation can be plotted analytically to obtain the spatial distribution of free enzyme concentration in tissue slab with respect to time.
144 6.3 Material and methods
6.3.1 Tissue procurement
Fresh human Carcinoma tissues, including HNSCC, breast, and colon tumors, were procured from patients at the James Cancer Hospital who signed the informed consent statement approved by the Ohio State University Institutional Review Board
(IRB). The fresh tissue specimens are stored at 4°C in DMEM/Ham’s F-12 media supplemented with 10%FBS. A small portion of the tissue is stored in RNAlater
(Ambion, Austin, TX) for later analysis.
6.3.2 In vitro tissue degradation: A rapid dissociation protocol
Tissue specimens are washed in sterile Phosphate Buffered Saline (PBS)
(Gibco) before tissue digestion, while non-cellular portions (eg. fat) as well as bloody part are removed. Weight of thus obtained tissue pieces is measured. 2mg/ml collagenase (C9263, Sigma) and 50U/ml DNase (Worthington Biochemical Corp.) are dissolved in DMEM supplemented with 10% FBS and sterilized by passing through a
0.22um filter (Fisher scientific, Cat# 09-719A). Tissue pieces are then placed in a
60*15mm cell culture petri dish (Fisher scientific, Cat# 8-757-13A) containing 8ml enzymatic solution mentioned above. The tissue is then mechanically dissociated by mincing into small pieces using a sterile surgical scalpel. Then, the petri dish is incubated at 37°C for 30mins with continuous shakings in a tissue culture incubator.
The digested tumor was then filtered through a 40µm nylon mesh (Millipore, Bedford,
MA) to remove the remaining tissue matrix. The cell suspension thus obtained was washed and cell number determined by hemacytometer and trypan blue exclusion.
6.3.3 Quantitative analysis of collagen content
145 The presence of collagen in the tumor tissues is commonly quantified by measuring the amount of hydroxyproline in a colorimetric assay (Woessner 1961).
Specifically, small samples of tissue specimens (1-3mg) are weighed and digested for
16hrs with continuous shaking at 37°C in 2mg/ml collagenase solution in reaction buffer (0.05 M Tris-HCl, 0.15 M NaCl, 5 mM CaCl2 and 0.2 mM sodium azide, pH
7.6). The digested tissue is hydrolyzed by adding HCl to a final concentration of 6N.
The samples are sealed in small glass test tubes with caps and incubated at 130°C for
3hrs. Then, the test tubes are allowed to cool down to room temperature followed by neutralization using 2.5N NaOH and two drops of 0.02% methyl-red indicator. The solution is then diluted to 4ml by adding distilled water after the neutralization reaction. 1ml Chlramine T is added into the test tube and incubated for 20min at room temperature to oxidize the hydroxyproline. The reaction is stopped by adding 1 ml of
70 % perchloric acid and incubates for 5 minutes at room temperature. The mixture is then further treated with freshly prepared 20% p-diaminobenzaldehyde at 60 ºC in a water bath for 20mins. The test tubes are then cooled down under tap water. The absorbances at 557nm of the samples are measured using Shimadzu UV – visible spectrophotometer (Shimadzu UV – 1601, Columbia, MD). A standard curve describing the absorbance at 557nm for different amount of collagen was made for every set of experiments.
6.3.4 RT-PCR
Detailed RT-PCR procedure is described in section Chapter 2. Sequences and product information of the genes studied are listed in table 6.1.
146 Product Tm Gene Primer sequences size (bp) (˚C ) GGGAGCAGCGATGCGA EGFR 301 60 CTCCACTGTGTTGAGGGCAAT GGCTCTTTAAGGCCAAGCAGTG Epcam 626 55 TTCCCTATGCATCTCACCCATCTC GCAGGGCTTCTTCTGTCCAGAC Her2/neu 601 62 TGGGTCCTGGTCCCAGTAATAGAG GTAATGACCAGTCAACAGGGGAC HPRT 177 54 TGGTCAAGGTCGCAAGCTTGCTTG
Table 6.1 Primer sequences for RT-PCR study
6.3.5 Immunomagnetic cell separation using magnetic deposition system
The detailed specifications of the magnetic deposition system have been described in Section 1.4. Separation protocols have been described in previous publications (Fang, 1999). Briefly, before each separation experiment, the flow channel and all connecting tubings are primed with carrier fluid in order to make sure the cells enter the magnetic field in a fully developed flow. When the air–liquid interface of the test samples reached the Tygon tubings, the pump is stopped and the outlets were blocked. The upper syringes and pipette tips are replaced by new sets containing 100µl washing buffer partly filled with air to wash the residual suspension.
6.3.6 Flow cytometry
In order to quantify and titrate the primary antibody labeling, flow cytometry technique is employed. Flow cytometry studies are performed on a FACS Calibur system (BD Biosciences). Analyses of forward scatter and side scatter data were used to gate viable D-562 cells. The circled region in Figure 6.1a shows the gating for D-
562 cells excluding dead cells and cell debri from the PE antibody analysis. Cells 147 selected by the gate were then further analyzed using PE single parameter analysis in the logarithmic mode. Figure 6.1b and c show the PE histogram of unlabeled and labeled D-562 cells, respectively.
a
c b
Figure 6.1 Flow cytometry analysis and gating for D-562 cell line. a, forward and side scatter dotplot; b, PE histogram of unlabeled D-562 cells; c, EGFR-PE labeled D-562 cells.
148 6.3.7 CTV analysis
A detailed description of CTV has been discussed in Chapter 1 and the protocol of cell tracking has been discussed elsewhere (Chalmers et al. 1999). Briefly, cell suspension is adjusted to a concentration of 0.5-1 million cell/ml and injected into the flat glass channel using a syringe. Images are taken when the fluid flow is stable.
20 sets of images are taken for one sample. Normally, over 1000 cells are tracked.
Original image files are extracted in a imaging processing software developed in our group.
6.4 Results and discussion
6.4.1 Collagen quantification results
A typical standard curve is shown in Figure 6.2. 28 breast tumor tissues and
13 HNSCC tissues were procured for collagen content quantification. These 41 data points are analyzed in JMP (SAS, NC) to test if there are significant variations of collagen content with respect to the type of malignancies (Figure 6.3). It is surprising that no significant difference has been shown in the collagen content between these two types of tumor in the t-test (p-value=0.65. See Appendix E for details). One may also notice higher variability in different breast tumor tissues, most probably due to the high variations in the fat disposition in breast tissue. The average collagen content based on tissue weight is 56.34 ± 29.21 ug collagen/mg wet tissue. Assuming a molecular weight of 300kDa for a single collagen monomer and the density of the tissue approximately 1g/ml, one found Cs = 1.881*10-4 μM, which will be used in the diffusion-reaction model to predict tissue digestion time.
149 0.8
0.7 y = 0.0067x + 0.021 R2 = 0.99 0.6
0.5
0.4
0.3 Absorbance @557nm 0.2
0.1
0 0 20 40 60 80 100 120 Mass of Collagen (ug)
Figure 6.2 Standard curve for collagen quantification
150
125
100
75
50 collagen/ mg tissue) collagen/ Collagen content (ug content Collagen 25
0 Breast (n=28) HNSCC (n=13)
Type of tumor tissue
Figure 6.3 Comparison of collagen content between breast tumor and HNSCC tumor tissues
150 6.4.2 Theoretical determination of the significant factors in tissue dissociation
Having experimentally determined collagen concentration in tumor tissue, the simplified reaction diffusion model depends on only four model parameters: the initial enzyme concentration (C0), two kinetic parameters kcat and KM, and the diffusivity D. Since the enzyme concentration is a controllable factor, only the remaining three parameters have to be estimated to solve the model equation.
The kinetic parameters of collagen fiber degradation by collagenases have been thoroughly studied. For example, experiments have been carried out on fibrillar collagen gels with skin fibroblast colalgenase, where the kcat value has been
-1 determined to be 25hr . (Welgus et al, 1980) KM has been estimated to be approximately 0.45μM under a quansi-steady-state approximation (Tzafriri et al,
2002). Thus, the constant k’ from equation 5 can be calculated to be 1.05*10-2 hr-1.
The diffusivity of collagenases in tumor tissues is difficult to measure experimentally. However, its dependence upon the molecular weight allows us to estimate the diffusivity of the enzymes, which can be described in the power law expression (Jain, 1987):
DaMW=⋅()−b 6.12
The crude collagenases from Clostridium Histolyticum chosen to be used here contain at least six types, whose molecular weight range from 68kDa to 125kDa.
Therefore, the effective diffusivity can be estimated to be in the range of 3.9*10-8 cm2/s and 7.8*10-8 cm2/s using empirical a and b values from carcinomas (Jain, 1987,
Nugent and Jian 1984). The concentration profiles of the free enzyme under these two extreme conditions are plotted in MATlab shown in figure 6.4a, and figure 6.5 a,b.
151 Figure 6.4 indicates the significant effect of diffusional limitations on the penetration of free enzyme into the tissue piece. In this proposed model, the enzyme is assumed to be ‘consumed’ in that once it is bind to a collagen fiber; it will not come off to the solution. It stays on that specific collagen fiber or act on adjacent ones.
Given these assumptions, and the results of the plots in Figure 6.4, it is quite apparent that despite the high level of activity of the collagenase at sufficient concentrations, diffusional limitations will prevent the enzyme from penetrating very far into the tissue, even at high external enzyme concentrations and at extended time period. Thus, the mass transfer/diffusion of free enzyme into the tissue pieces is the rate-limiting step in this entire process. Both figure 6.4 a and b show very similar profile pattern with a change in the diffusivity from 3.9*10-8 to 7.8*10-8 cm2/s. Comparing a and b, it is true that the lower diffusivity produced a slightly steeper profile than the higher diffusivity, which indicates that even a slight drop in the diffusivity can make the enzyme more difficult to penetrate into the tissue piece.
Furthermore, 2D plots of the concentration profile are generated in figure 6.5 a and b with fixed time (t, hr) and x distance (x, micrometer) at the higher diffusivity situation (D=7.8*10-8 cm2/s). Figure 6.5a indicates that the diffusion of free enzyme into a thick tissue piece is greatly hindered even after an extended period of time.
Figure 6.5b further shows the effect of size of tissue piece, in that smaller tissues result in significantly better diffusion performance even during a short period of time.
152 a
b
Figure 6.4 Concentration profiles of free enzyme in a tissue slab of 1mm thick within
20hrs. a, Diffusivity= 3.9*10-8 cm2/s; b, Diffusivity= 7.8*10-8cm2/s.
153 a b 154
Figure 6.5 2-D plots of concentration profiles at fixed time (a) and at fixed x distance (b) when diffusivity is 7.8 *10-8cm2/s.
154 6.4.3 Experimental determination of a rapid tissue dissociation method
It is quite obvious that there is no best way to digest any type of tissue for any research purpose. The goal here is to generalize an optimal protocol for the subsequent immunomagnetic separation of cancer cells. Thus, our criteria are: 1)
High cell yield; 2) High cell viability; 3) Rapid. We want to recovery as many viable cells as possible during a relatively short period of time in order to magnetically label them with high specificity and sensitivity. In fact, a shorter dissociation time is not only very important for better cell viability and overall cell yield, it is also required for faster analysis of samples.
To establish a tissue dissociation process, we need to determine: 1) Either using mechanical dissociation or enzymatic digestion or combination of both; 2)
Media for enzymatic digestion; 3) Type of enzymes and their concentration; 4)
Digestion time.
From the previous section, we found that mass transfer/diffusion of the enzyme collagenase inside the tissue played a significant role in enzymatic tissue digestion. It is the rate-limiting step in the whole tissue digestion procedure. Simply mincing tissue samples into tiny pieces (<1mm in each dimension) could greatly facilitate enzyme diffusion inside the tissue. So, a mechanical dissociation step before the enzymatic step is desired to decrease the time required for enzymatic digestion.
Obviously, a combination of both mechanical and enzymatic method is desired.
Using the richest medium supplemented with 10% FBS to dissolve the enzyme instead of any other reaction buffer is advisable to keep more cells viable.
Thus, DMEM supplemented with 10% FBS is selected as a medium for enzymatic
155 digestion.
Crude collagenase from bacterial source typically contains a number of components with different activities toward collagen, which are necessary for the complete enzymatic digestion of human tissue. Compared to those collagenases that are highly purified, these crude enzymes are not only much less expensive, but are generally much more effective in rapid tissue degradation. Purified collagenases alone are ineffective in releasing cells from tissue or it takes a long time. A crude collagenase, C9263 from Sigma, has been tested for its ability to digest tissue in previous study. It has been selected as the enzyme for the rapid digestion protocol.
DNase is also added to the enzymatic solution to remove the DNA released by dead cells. When a significant amount of cells are killed or damaged, this nuclear material forms a gel within which the separated cells are trapped. The use of DNase reduces the amount of DNA fibers which favor aggregation and clumping of cells lowering the viable cell yield.
Significantly low cell viability can result from excessive enzyme proteolytic activity (or high enzyme concentration) or prolonged digestion time. Although it is impossible to generalize a routine protocol for tissue digestion for any purpose due to heterogeneity of sample, variations in the proteolytic activity of collagenase, etc, there are basically two trends: lower enzyme concentration/low proteolytic activity for longer period of time or higher enzyme concentration/high proteolytic activity for short period of time (Noel et al, 1977; Kedar et al, 1982; Engelholm SA, 1985). In order to determine the appropriate digestion time and enzyme concentration for this rapid digestion protocol, following experiments are carried out.
156 Three enzyme concentrations (1mg/ml, 2mg/ml, and 5mg/ml) have been studied for their ability to digest tissue and effect on viable cell recovery. (Figure 6.6)
Statistical analyses are performed using JMP software (Details in Appendix F). Anova test and each pair student’s t-test are carried out for n=15 total samples. Results show that using 1mg/ml enzyme produced a significantly lower cell yield while 2mg/ml and 5mg/ml don’t have significant difference on cell yield. 2mg/ml is selected as final enzyme concentration.
35
30
25
20
15 Cell yield (*106)
10
5 1 25Each Pair Student's t 0.05 Enzyme conc. (mg/ml)
Figure 6.6 Oneway Analysis of Cell yield (*106) By Enzyme conc. (mg/ml)
Tissue digestion time is determined in the following set of experiments. Tissue
specimens are minced into tiny pieces in a petri dish with enzyme solution. They
are then incubated in a tissue culture incubator. After 0.5hr incubation, cell
suspensions are collected into a falcon tube, while fresh enzyme solution is added
to remaining tissue matrixes for further digestion. After 3hr digestion, when almost
157 all tissue matrixes are digested, cell suspensions are collected again and cell number and viability determined. Results in table 6.2 show that using 0.5-hr quick digestion, we are able to recovery about 81% of the viable cells that can be recovered with a 3-hr incubation. Furthermore, Figure 6.7 shows the bar chart of dissected cell yield at different time duration for tissue #3 from table 6.2. It is clear that more than 80% of the overall dissociated cells can be recovered during the first half hour of digestion. We need also note that, after 0.5-hr digestion, the dissociated cells are removed from the enzyme solution, which protected them from being killed or significantly stressed. In other words, overall viable cell yield during the 3-hr incubation could be lower than what we calculated in the table.
Thus, the percentage of viable cells obtained in the first half hour incubation with respect to the overall 3-hr incubation should be higher than calculated value.
Subsequently, 0.5 hour incubation time is selected for the rapid digestion protocol.
Tissue12345Average Cell yield between t=0~0.5hr 1.45E+07 1.41E+07 1.80E+07 2.37E+07 9.10E+06 (cells/gm tissue) Cell yield between t=0.5~3hr 5.24E+06 2.77E+06 2.28E+06 7.58E+06 1.81E+06 (cells/gm tissue) Accumulative cell yield 1.97E+07 1.69E+07 2.03E+07 3.13E+07 1.09E+07 (cells/gm tissue) % cell recovery in the first 73.45% 83.58% 88.77% 75.77% 83.41% 81.0±6.3% 0.5hr
Table 6.2 Comparison of cell yield of five tumor tissues in 0.5 hr quick digestion
and 3hr digestion.
158 Cell yield during enzymatic digestion
Cell yield/gm tissue (*106) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
0-0.5 18.00
0.5-1 1.14
1-2 0.67 Incubation time (hr)
2-3 0.47
Figure 6.7 Dissected cell yield based on initial tissue weight during 3-hour incubation time
As a summary, a rapid tissue digestion protocol has been established for
immunomagnetic isolation of cancer cells. Mechanical dissociation and enzymatic
digestion techniques are combined. Minced tissue pieces are incubated in 2mg/ml
collagenases (C9263, Sigma) and 50U/ml DNase (Worthington Biochemical Corp.)
for half hour. Cell suspension is then passed through a 40um nylon mesh to obtain
a single cell suspension.
159 6.4.4 Immunomagnetic separation design
6.4.4.1 Marker selection
One or a combination of several cell surface markers has to be identified for
HNSCC in order to immunomagnetically isolate cancer cells. The criteria are straightforward: markers have to be expressed on cancer cell surface. After literature review (Chin et al, 2005; Choi et al, 2005; Plzak et al, 2005; Sok et al, 2003; Al
Moustafa et al, 2002), 3 transmembrane markers are selected for study. Epcam is reported to be greatly overexpressed in most of carcinomas. (Munz et al, 2005; Went et al, 2004) Her2/neu is overexpressed in about 30-50% of the cases. EGFR is overexpressed in over 90% of the HNSCC cases, and plays a critical role in HNSCC growth, invasion, metastasis, and angiogenesis. (Chin et al, 2005; Kalyankrishna and
Grandis, 2006) mRNA expressions of these three genes are examined on 20 samples of HNSCC using RT-PCR technique.
Results are shown in Figure 6.8. It is shown that Epcam mRNA is overexpressed in 4 out of 20 cases. Her2/neu is expressed in about 10 out of 20 cases, but its expression level is not very high. EGFR is highly overexpressed in all 20 cases studied. Obviously, EGFR is a strong marker to magnetically label head and neck cancer cells.
160 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 (-)
EpCam
Her2/Neu
EGFR
Figure 6.8 Gene expression of three selected genes in 20 randomly selected HNSCC
cases
However, EGFR expression is common in normal epithelial cells.
Consequently, non-cancer epithelial cells immunomagnetic labeling is possible as well. By exploiting the fact that the number of normal epithelial cells present in a tumor is often quite low, and normal cells expression of EGFR is also low, it is proposed that operating the magnetic separation system is such a manner that only highly magnetic cells will be deposited on the slide will prevent the capture of normal epithelial cells. A maple program has been developed by Lee Moore at the CCF to quantify the movement of cells in this system. Cell capture rate could be calculated under specific flow rate when the magnetophoretic mobility distribution of cells is known. Using this program, an optimal flow rate can be easily calculated when the mobility distribution of cancer cells and normal epithelial cells are known.
161 6.4.4.2 Magnetic labeling study
As EGFR is selected as a strong marker to isolate cancer cells from the cell mixture, the following two-step labeling method is proposed: Anti EGFR-PE (BD
Bioscience) is used as a primary antibody while anti PE-MACSbeads (Miltenyi
Biotec) is used as a secondary antibody. The head and neck cancer cell line, D-562, is used to determine the amount of antibody required to label certain number of cells.
Figure 6.9 shows the primary and secondary antibody titration curves measured by flow cytometry and CTV. Figure 6.9.a&b show both normalized fluorescent intensity
(FI) and percentage of PE-positive cells versus the changing primary antibody amount. Figure 6.9c represents the relationship of magnetophoretic mobility versus changing secondary antibody amount. X-axis represents antibody labeling concentration in ul / ul total volume, since the antibodies are not supplied with mass concentration in mg/ul by the manufacture. All labeling experiments are carried out based on 1*106 cells with a total labeling volume of 100ul. Figure 6.9a implies that normalized FI does not even saturate when 100ul primary antibody is applied on one million cells. On the other hand, figure 6.9b shows that percentage of PE-positive cells saturate at 0.4 ul / ul total volume. This is to say, more than 90% of the cancer cells can be made magnetic at this concentration, without respect to how magnetic they are. Thus, 40 ul anti EGFR-PE/100 ul total volume for one million cells is considered as an optimal labeling condition. Similarly, 40 ul anti PE-
MACSbeads/100ul total volume is also determined as optimal labeling condition.
Consequently, the optimal labeling method produces a mean magnetophoretic mobility of 5*10-5 mm3/A.s.T on cultured D-562 cell line.
162 Primary ab. titration curve (a) Primary ab. titration curve (b) 1.2 120
1.0 100
.8 80
.6 60 .4 40 Positive cell % cell Positive Normalized FI .2 20
0.0 0
0.0.2.4.6.81.01.2 0.0.2.4.6.81.01.2 EGFR-PE conc. (ul/ul total) EGFR-PE conc. (ul/ul total)
Secondary ab. titration curve (c) 6e-5
5e-5
4e-5
3e-5
Mobility 2e-5
1e-5
0
-1e-5 0.0.2.4.6.81.01.2 PE-MACS conc. (ul/ul total)
Figure 6.9 Primary and secondary antibody labeling titration. a, Normalized FI versus antibody labeling concentration; b, Percentage of positive cells versus antibody labeling concentration; c, magnetophoretic mobility versus antibody concentration.
163 6.4.5 Application of established technique on clinical samples
6.4.5.1 Rapid tissue dissociation results
The rapid tissue dissociation protocol previously established has been applied on various tumor types to test its efficacy and reproducibility. Viable cell yield from unit weight of wet tissue has been studied as an indicator of efficacy as well as cell viability. (Figure 6.10a, b) Malignant HNSCC, breast, and colon tissue specimens have been obtained from tissue procurement and digested using the same protocol.
Viable cell yields are calculated as (1.61±1.14)*107, (1.20±1.02)*107, and (1.62±0.11)
*107cells per gram of tissue for HNSCC, breast, and colon tumor tissue respectively.
Large error bars strongly indicate the sample heterogeneity within the same tumor type. For example, in malignant breast tumor specimens, the amount of non-cellular components, such as the amount of fat, greatly affect the cell yield per tissue weight.
Cell viability is normally very high (~90%) with small error bars except the breast tumor tissue. The lowest cell viability of all samples we’ve got is from a breast tissue specimen, which is lower than 50%. We suspect that there are some cells in the breast tumor tissue, which are sensitive to environmental stress.
164 Cell yield after rapid tissue dissociation (a) 3.00
2.50
2.00
cells/g) 1.61 1.62 7 1.50 1.20
1.00 weight (*10 0.50 Viable cell yield per gram tissue
0.00 HNSCC (n=6) Breast (n=6) Colon (n=2) Tumor tissue type
Cell viability after dissociation (b) 100 89.87 94.36 90 79.35 80 70 60 50 40
Cell viability (%) 30 20 10 0 HNSCC (n=6) Breast (n=6) Colon (n=2) Tumor tissue type
Figure 6.10 a, b Summary of the rapid tissue dissociation results.
165 6.4.5.2 Magnetic deposition results
Cell mixture after tissue dissociation is subject to magnetic labeling using the two-step labeling method by anti-EGFR PE, and anti-PE MACSbeads. Then, cells are run through the magnetic deposition system. By visual observations, a band of cells is deposited onto the slides. Figure 6.11 shows an example of magnetically deposited cancer cells on a microscopic slide. Although more elaborate methods are to be developed to characterize these cells, their size and morphology fit those of cancer cells’. Using the two-step immuno-fluorescent staining method, cytokeratin expression of these cells could be illustrated. Figure 6.12 compares the cells before separation and after separation. Less non-CK expressing cells are present in the ‘after separation’ fraction, indicating cancer cells have been enriched. One interesting observation here is that we are not able to get consistent CK-FITC staining in all of the samples. That is to say, those magnetically deposited cells are CK-negative in some cases. However, this does not necessarily indicate that they are not cancer cells.
Furthermore, more elaborate methods need to be developed to quantify and optimize this magnetic separation process. Non-specific binding of non-cancer cells needs to be eliminated. Proposed future work will be discussed in the next chapter.
166 20um
Figure 6.11 Microscopic photograph of magnetically deposited cancer cells from a HNSCC solid tumor specimen
(a) (b)
(b)
Figure 6.12 Immunostaining of cells before and after magnetic separation. (a) Cells before magnetic separation; (b) Cells after magnetic separation. Blue: Nuclei staining; Green: Cytokeratin-FITC staining.
167
CHAPTER 7
CONCLUSIONS AND FUTURE WORK
7.1 Conclusions
The present work has performed an experimental investigation and optimization on the immunomagnetic cell separation technique in combination with novel molecular analysis technologies to enrich and detect CTCs in the blood stream of patients with solid malignancies. The development and optimization of the separation process has been a continuation of the study of Lara et al (2004). The effect of operating parameters was investigated and optimal values were determined.
Two molecular analysis methods, RT-PCR and immunocytochemical staining, were validated and incorporated after the cell separation process. The sensitivity of those molecular analysis methods, purity of target cells needed, the level of enrichment required, and the total number of cells required for RT-PCR detection is quantitatively determined. .
Studies on the peripheral blood from patients undergoing surgical resection for squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx have suggested a potential correlation between the number of CTCs per volume blood and
168 the pathological reports of the patients. It demonstrates that if a sample had 10, or more, CTCs per ml of blood, the pathological report on the patient indicates that metastatic disease, either regional (lymph node), or at a distant site was present. On the other hand, if the number of CTCs per ml of blood is lower than 10, there is no evidence of metastasis present. This finding also agrees with the theory that the presence of CTCs is necessary but not sufficient for the metastasis to occur (Mocellin,
2006). It has been suggested that a great percentage of the CTCs are apoptotic and thus unable to settle in secondary organs. Furthermore, the understanding of this phenomenon, called ‘metastatic inefficiency’, has also raised the question that if it is appropriate to use number of CTCs per volume of blood as an indicator for prognosis.
Sophisticated molecular analysis and genetic profiling are also anticipated in order to generate more accurate patient data.
The above-mentioned data leads to the investigation on the magnetic labeling process, which indicates that the effect of the magnetic labeling reagents on the labeling efficiency and thus separation performance is significant. The kinetics model presented is appropriate to describe the ligand binding to cell surface receptors. The ratio between the initial free antibody concentration and its dissociation constant
(L0/KD) is the limiting factor for a given magnetic labeling system. Based on the theory, an optimal labeling scheme is identified, including the use of a tetrameric antibody complex, which works significantly better than traditional methods. The optimized magnetic labeling method and operating conditions result in an average log depletion of 3 during the single step of QMS separation and an overall 5-7 log depletion for the entire process, starting from the red cell lysis to the final enriched
169 sample. The final cancer cell purity has improved order of magnitudes higher, but it is difficult to give a specific number since the initial CTC number is a variable.
In the second part of the study, a reaction-diffusion model was constructed to describe the in vitro tissue dissociation process. It is not only useful in recognizing the rate-limiting factor in the tissue dissociation process, but also provides quantitative guidelines to establish an optimal tissue dissociation technology. EGFR is selected as a cell surface marker to target head and neck cancer cells in this specific case.
Magnetic labeling is shown to be efficient and generate a mean magnetophoretic mobility of about 5*10-5 mm3/A.s.T on cultured cell lines, which is sufficient to be isolated. Although further refinements of the magnetic deposition system are needed, this study proved the concept that it is feasible to isolate pure cancer cells from solid tumor biopsies within 2 hrs by applying the newly established rapid tissue dissociation method followed by a positive selection of EGFR expressing cells by immunomagnetic labeling.
Future work is organized in three major areas: further improvement of the molecular analysis techniques, including PCR and immunocytochemical assay; improvement in the design and operation of the magnetic deposition system; and application of this technique in a broader area, such as the isolation of cancer stem cells.
170 7.2 Future work
7.2.1 Further refinements in RT-PCR assay
The detection of rare CTCs in peripheral blood has come to the phase about ready to initiate a clinical trial on patients with solid head and neck tumor. Although
EGFR has been selected and validated to be a good molecular marker to detect the presence of CTCs from peripheral blood, the use of multiple markers is desired since tumor cells are characterized by their heterogeneity. Both sensitivity and specificity will be improved by the use of multiple markers and real time PCR as well.
The quantitative real time PCR method detects the presence of CTCs by defining a cut-off value of the marker transcript numbers. Any number below this cut- off value is considered illegitimate while numbers above this value can be considered as tumor cell derived. Real time, continuous quantification of specific gene transcript number is made possible by this technique. Another advantage of this method over the conventional PCR technology is that false-positive results, which produced a non- linear amplification curve, could be identified easily. It is favored in future study although the cost it higher than the conventional RT-PCR method since it provides much reliable results and more information.
7.2.2 Improvement of the magnetic deposition system
To achieve higher accuracy and better separation performance, the magnetic deposition system needs to be improved in terms of design and operation. The major problems right now are the non-specific deposition of non-target cells on the glass slide and the lack of sophisticated analysis methods after the deposition.
Nonspecific deposition of non-target cells is a result of multiple factors,
171 including the non-specific binding of the marker to normal cells. Currently, EGFR is a sufficient strong marker to isolate cancer cells from a mixture of cells. However, normal epithelial cell contamination is inevitable even though we can control this contamination at minimal level by controlling experimental parameters. Novel tumor- specific surface markers are to be identified and validated.
Furthermore, systematic modeling and calculations need to be carried out in addition to searching for more specific tumor markers. The separation performance is a function of several factors, including the flow rate, magnetophoretic mobility, and distance from the flow chamber to the magnet pole piece. A better system can be developed so that the shear stress inside the flow chamber is high enough to eliminate any non-specific bindings and retain all magnetically targeted cells.
In order to facilitate the downstream molecular analysis process, a novel ‘in- place’ RNA extraction method is to be developed for quick isolating total RNA from those cells that are magnetically deposited onto the deposition plate. The magnetic deposition system needs to be re-designed so that a simple ‘in-place’ RNA extraction procedure can be carried out. Since the cell number on the slides is quite small
(normally less than 1*105 cells), the RNA extraction method has to be sensitive enough to work on very small number of cells. PicoPure RNA isolation kit (Arcturus,
CA) enables the recovery of total cell RNA from pico-scale samples, even a single cell. To minimize the processing steps, RNA extraction buffer (PicoPure RNA isolation kit) is injected into the flow chamber using the syringe pump of the magnetic deposition system. The chamber needs to be sealed by blocking inlet and outlet, and incubated at 42˚C for half hour. After that, the extaction buffer is pumped
172 into a microcentrifuge tube and centrifuge at 3000xg for 2min. Supernatant containing the extracted RNA will be transferred into a clean tube for further RNA isolation. Remaining steps will follow the PicoPure RNA isolation kit manual provided by the Arcturus Company.
7.2.3 Improvement in immunocytochemical assay
After cells are deposited onto a microscopic coverslip, a rigorous and strict assay is highly required to verify the success of this procedure. A more reliable, specific molecular marker is needed to confirm the presence of cancer cells. As mentioned in Chapter 6, an anti-Cytokeratin (CK3-6H5)-FITC antibody (Miltenyi
Biotec) is used to specifically stain cancer cells. This antibody is a pan cytokeratin- marker for most carcinoma cells, which recognizes human cytokeratin 8,18,19
(CK8,18,19). This marker may not be specific enough for the case of HNSCC. The major concern is that some of the normal epithelial cells may also express CK 8,18,19.
Furthermore, referring to the cDNA microarray results on HNSCC samples on a total of 13,000 genes studied in Dr. Lang’s lab, these three genes are inconsistently expressed in the case of HNSCC with respect to normal matched epithelia. For example, CK19 is observed to be both up-regulated and down-regulated in different cases. This is also the potential reason why we could not get consistent CK-FITC staining of the magnetically deposited cells. In some cases, these genes could be shut down instead of up-regulated. Going through the results of the cDNA microarray study, these three genes are identified, CK6, CK16, and CK17. They are found to be consistently highly overexpressed in all 7 HNSCC cases studied, thus are chosen as the ‘tumor-specific marker’ for this study. The expression of CK17 has been
173 confirmed by immunohistochemical staining on a HNSCC tissue section. (Fig. 7.1)
A new immunofluorescent staining method is being developed using the three
HNSCC tumor-specific markers, CK6, CK16, and CK17. There is no commercially available staining kit for them. A monoclonal antibody, mouse anti human CK17
(clone E3, Isotype: IgG1), is purchased from Chemicon. Alexa Fluor® 488 anti mouse IgG1 (Invitrogen), is selected to be the secondary antibody to impart the fluorescent signal. A two-step labeling method is under development where dilution factors of the primary and secondary antibody are to be determined. DAPI nucleus staining is still employed to help identify cell nuclei.
(a) (b) Figure 7.1 Expression of Cytokeratin 17 by immunohistochemistry (Personal communication with Dr. Lang): (a) Normal matched epithelium; (b) HNSCC
7.2.4 cDNA microarrays
While PCR-based method may be used to examine the pattern of expression of a limited group of genes of interest, microarray based technologies can provide a complete understanding of the global gene expression pattern. Using cDNA microrray,
174 a group of 10,000 to 20,000 genes can be analyzed simultaneously, allowing a more comprehensive characterization of the cell function. Genetic profiles of CTCs from blood stream and tumor cells from solid tumor specimens can be generated independently and compared. This could lead to the identification of robust signature genes of cancer disease, or signature genes for metastatic cancer, which could allow the identification of novel treatment agents.
7.2.5 Application in other areas
With the development of this technology, other rare cell populations of strong interested can be isolated, such as cancer stem cell. A number of recent studies have reported data suggesting the presence of cancer stem cells (Hill RP, 2006, Wicha MS,
2006; Al-Hajj M, 2003). The basic concept is that only a fraction of cancer cells might have the ability to restore the tumor, i.e., are cancer stem cells. Studies also show that, the proportion of cells in a tumor with the ability to renew themselves after treatment may be very low. If this is true, only those cancer stem cells need to be killed in order to cure cancer disease. Currently, much more effect is required to identify/isolate those cancer stem cells. If cancer stem cells can be successfully identified and isolated, it not only could help understand the biological mechanism of cancer disease, but help developing novel treatment agents, which target directly those cancer stem cells.
From technical point of view, isolation of cancer stem cells is practical if those cancer stem cells have a ‘signature’ surface marker. Our system has been evaluated for its ability to isolate rare cell populations from a large cell mixture. In the work on breast cancers reported by Al-Hajj et al, surface markers (CD44+, CD24-, and ESA+)
175 are identified for a group of tumorigenic cancer cells, which are believed to be cancer stem cells. These markers are to be studied and tested using our system. If cells with such surface antigen expression can be magnetically isolated, they are then subject to molecular characterizations such as cDNA microarray.
176
APPENDIX A
T-TEST OF NUCLEATED CELL DEPLETION BY SAMPLE SOURCE
177 Oneway Analysis of Nucleated cell Log depletion By Sample
2.25
2
1.75
1.5 Log depletion Nucleated cell Nucleated 1.25
1
buffy coat cancer
Sample
t Test cancer-buffy coat
Assuming unequal variances
Difference -0.52545 t Ratio -3.60961 Std Err Dif 0.14557 DF 11.1568 Upper CL Dif -0.20560 Prob > |t| 0.0040 Lower CL Dif -0.84531 Prob > t 0.9980 Confidence 0.95 Prob < t 0.0020
-0.6 -0.4 -0.2 .0 .1 .2 .3 .4 .5 .6
178
APPENDIX B
BREAKDOWN AND NORMAL RANGE OF HUMAN LEUKOCYTES
179
Normal range Percentage (106cells/ml) (%) WBC 4.5-11.0 100 (leukocytes)
Granulocytes 1.8-8.9 50-70 Lymphocytes 1.0-4.8 20-30 Monocytes 0.2-0.8 1.7-9 Neutrophils 1.8-7.7 40-70 Eosinophils 0-0.45 0-7 Basophils 0-0.2 <1
180
APPENDIX C
STATISTICAL ANALYSIS OF PARAMETER EFFECT BY FITTING EFFECT
MODEL
181 Response Log depletion of PBLs
Summary of Fit
RSquare 0.810869 RSquare Adj 0.786798 Root Mean Square Error 0.39989 Mean of Response 1.680317 Observations (or Sum Wgts) 63
Analysis of Variance
Source DF Sum of Squares Mean Square F Ratio Model 7 37.707827 5.38683 33.6862 Error 55 8.795167 0.15991 Prob > F C. Total 62 46.502994 <.0001
Lack Of Fit
Source DF Sum of Squares Mean Square F Ratio Lack Of Fit 30 6.8919084 0.229730 3.0176 Pure Error 25 1.9032583 0.076130 Prob > F Total Error 55 8.7951667 0.0031 Max RSq 0.9591
Parameter Estimates
Term Estimate Std Error t Ratio Prob>|t| Intercept 1.8635546 0.096676 19.28 <.0001 Scale of separation 1.242e-10 1.036e-9 0.12 0.9050 Type of blood[Buffy coat] 0.1296473 0.066032 1.96 0.0547 Labeling method[A] -0.412109 0.090336 -4.56 <.0001 Labeling method[B] -0.611505 0.155979 -3.92 0.0002 Labeling method[C] -0.100196 0.161138 -0.62 0.5366 Labeling method[D] -1.185375 0.131594 -9.01 <.0001 Labeling method[E] 1.2145468 0.122447 9.92 <.0001
Effect Tests
Source Nparm DF Sum of Squares F Ratio Prob > F Scale of separation 1 1 0.002298 0.0144 0.9050 Type of blood 1 1 0.616444 3.8549 0.0547 Labeling method 5 5 36.572296 45.7405 <.0001
182
APPENDIX D
ANOVA TEST AND STUDENT’S T TEST OF NUCLEATED CELL
DEPLETION BY LABELING METHODS
183 Oneway Analysis of Log depletion of PBLs By Labeling method
4.5
4
3.5
3
2.5
of PBLs of 2 Log depletion 1.5
1
0.5 A B C D E F Each Pair Student's t Labeling method 0.05
Oneway Anova Summary of Fit
Rsquare 0.79674 Adj Rsquare 0.77891 Root Mean Square Error 0.40722 Mean of Response 1.680317 Observations (or Sum Wgts) 63
Analysis of Variance
Source DF Sum of Squares Mean Square F Ratio Prob > F Labeling method 5 37.050786 7.41016 44.6857 <.0001 Error 57 9.452207 0.16583 C. Total 62 46.502994
Means for Oneway Anova
Level Number Mean Std Error Lower 95% Upper 95% A 24 1.43875 0.08312 1.2723 1.6052 B 7 1.32429 0.15391 1.0161 1.6325 C 6 1.64333 0.16625 1.3104 1.9762 D 12 0.81000 0.11755 0.5746 1.0454 E 10 3.03600 0.12877 2.7781 3.2939 F 4 3.03000 0.20361 2.6223 3.4377 Std Error uses a pooled estimate of error variance
Means Comparisons Comparisons for each pair using Student's t t Alpha 184 t Alpha 2.00247 0.05
185 Abs(Dif)-LSD E F C A B D E -0.3647 -0.4764 0.9716 1.2903 1.3099 1.8768 F -0.4764 -0.5766 0.8603 1.1509 1.1946 1.7492 C 0.9716 0.8603 -0.4708 -0.1676 -0.1346 0.4256 A 1.2903 1.1509 -0.1676 -0.2354 -0.2358 0.3404 B 1.3099 1.1946 -0.1346 -0.2358 -0.4359 0.1265 D 1.8768 1.7492 0.4256 0.3404 0.1265 -0.3329
Positive values show pairs of means that are significantly different.
Level Mean E A 3.0360000 F A 3.0300000 C B 1.6433333
186 A B 1.4387500 B B 1.3242857 D C 0.8100000
186
Levels not connected by same letter are significantly different.
Level - Level Difference Lower CL Upper CL p-Value Difference E D 2.226000 1.87685 2.575152 <.0001 F D 2.220000 1.74920 2.690797 <.0001 E B 1.711714 1.30986 2.113570 <.0001 F B 1.705714 1.19461 2.216821 <.0001 E A 1.597250 1.29033 1.904172 <.0001 F A 1.591250 1.15086 2.031640 <.0001 E C 1.392667 0.97157 1.813760 <.0001 F C 1.386667 0.86030 1.913034 <.0001 C D 0.833333 0.42561 1.241055 0.0001 A D 0.628750 0.34045 0.917053 <.0001 B D 0.514286 0.12646 0.902107 0.0103 C B 0.319048 -0.13462 0.772719 0.1645 187 C A 0.204583 -0.16761 0.576781 0.2757 A B 0.114464 -0.23582 0.464748 0.5155 E F 0.006000 -0.47642 0.488423 0.9802
187
APPENDIX E
T TEST OF COLLAGEN CONTENT ON TUMOR TYPE
188 Oneway Analysis of Collagen content (ug collagen/ mg tissue) By Type of tumor tissue
150
125
100
75
50 collagen/ mg tissue) Collagen content (ug 25
0 Breast (n=28) HNSCC (n=13)
Type of tumor tissue
Oneway Anova Summary of Fit Rsquare 0.005274 Adj Rsquare -0.02023 Root Mean Square Error 29.21054 Mean of Response 56.34293 Observations (or Sum Wgts) 41 t Test HNSCC (n=13)-Breast (n=28) Assuming equal variances
Difference -4.458 t Ratio -0.45473 Std Err Dif 9.803 DF 39 Upper CL Dif 15.371 Prob > |t| 0.6518 Lower CL Dif -24.287 Prob > t 0.6741 Confidence 0.95 Prob < t 0.3259
-30 -20 -10 0 10 20 30
Means for Oneway Anova Level Number Mean Std Error Lower 95% Upper 95% Breast (n=28) 28 57.7564 5.5203 46.591 68.922 HNSCC (n=13) 13 53.2985 8.1015 36.912 69.685
Std Error uses a pooled estimate of error variance
189
APPENDIX F
ANOVA AND STUDENT’S T TEST ON CEL YIELD BY ENZYME
CONCENTRATION
190 Oneway Analysis of Cell yield (*106) By Enzyme conc. (mg/ml)
35
30
25
20
15 Cell yield (*106)
10
5 1 25Each Pair Student's t 0.05 Enzyme conc. (mg/ml)
Means Comparisons
Comparisons for each pair using Student's t
T Alpha 2.20099 0.05
Abs(Dif)-LSD 5 2 1 5 -9.0909 -6.8155 1.5966 2 -6.8155 -7.4227 0.9053 1 1.5966 0.9053 -9.0909
Positive values show pairs of means that are significantly different.
191
LIST OF REFERENCES
Abbas A K., Lichtman A H. Cellular and molecular immunology. 5th edition. Saunders. 2003.
Ady N, Morat L, Fizazi K, Soria JC, Mathieu MC, Prapotnich D, Sabatier L, Chauveinc L. Detection of HER-2/neu-positive circulating epithelial cells in prostate cancer patients. Br J Cancer 2004; 90: 443–448.
Aerts J, Wynendaele W, Paridaens R, Christiaens MR, van den Bogaert W, van Oosterom AT, Vandekerckhove F. A real-time quantitative reverse transcriptase polymerase chain reaction (RTPCR) to detect breast carcinoma cells in peripheral blood. Ann Oncol 2001; 12: 39–46.
Al Moustafa AE; Alaoui-Jamali MA; Batist G; Hernandez-Perez M; Serruya C; Alpert L; Black MJ; Sladek R; Foulkes WD Identification of genes associated with head and neck carcinogenesis by cDNA microarray comparison between matched primary normal epithelial and squamous carcinoma cells. Oncogene. 2002, 21(17): 2634-40.
Almond B, Clark JI, Nikitina E, Beynen J, English NR, Kight SC, Carbone DP, Gabrilovich DI. Increased production of immature myeloid cells in cancer patients: A mechanism of immunosuppresion in cancer. American association of immunologist. 2001.
Answer.com: http://www.answers.com/topic/connective-tissue?cat=health 192
Battaglia M; Pedrazzoli P; Palermo B; Lanza A; Bertolini F; Gibelli N; Da Prada G A; Zambelli A; Perotti C; Robustelli della Cuna G Epithelial tumour cell detection and the unsolved problems of nested RT - PCR : a new sensitive one step method without false positive results. Bone marrow transplantation . 1998, 22(7): 693-8. Beckman MJ, Shields KJ, Diegelmann RF: Collagen. In Encyclopedia of Biomaterials and Biomedical Engineering, eds Gary Wnek and Gary Bowlin, Marcel Dekker Inc., New York, NY, 2004.
Berois, N.; Varangot, M.; Aizen, B.; Estrugo, R.; Zarantonelli, L.; Fernandez, P.; Krygier, G.; Simonet, F.; Barrios, E.; Muse, I.; Osinaga, E. Molecular detection of cancer cells in bone marrow and peripheral blood of patients with operable breast cancer. Comparison of CK19 , MUC1 and CEA using RT-PCR. European Journal of Cancer. 2000, 36(6): 717-723.
Berois N, Varagot M, Sonra C, Zarantonelli L, Pressa C, Lavina R, Rodriguez JL, Delgado F, Porchet N, Aubert JP, Osinaga E. Detection of bone marrow-disseminated breast cancer cells using an RT-PCR assay of MUC5B mRNA. Int J Cancer 2003;103:550–555.
Biochemistry website from Indiana State University: http://web.indstate.edu/thcme/mwking/
Bond MD VWH. 1984. Characterization of the individual collagenases from Clostridium histolyticum. Biochemistry 23(13):3085-91.
Borgen E, Beiske K, Trachsel S, Nesland JM, Kvalheim G, Herstad TK, Schlichting E, Qvist H, Naume B. Immunocytochemical detection of isolated epithelial cells in
193 bone marrow: non-specific staining and contribution by plasma cells directly reactive to alkaline phosphatase. J Pathol 1998; 185: 427–434.
Boyd JA; Rinehart CA Jr; Walton LA; Siegal GP; Kaufman DG Ultrastructural characterization of two new human endometrial carcinoma cell lines and normal human endometrial epithelial cells cultured on extracellular matrix. In vitro cellular & developmental biology : journal of the Tissue Culture Association (1990), 26(7), 701-8.
Braun S, Pantel K, Muller P, Janni W, Hepp F, Kentenich CR, Gastroph S, Wischnik A, Dimpfl T, Kindermann G, Riethmuller G, Schlimok G. Cytokeratin-positive cells in the bone marrow and survival of patients with stage I, II, or III breast cancer. N Engl J Med 2000;342:525–533.
Braun S, Pantel K. Clinical significance of occult metastatic cells in bone marrow of breast cancer patients. Oncologist 2001;6:125–132.
Braun S, Marth C. Circulating tumor cells in metastatic breast cancer - Toward individualized treatment? N Engl J Med 2004;351:824–826.
Braun S, Vogl FD, Naume B, Janni W, Osborne MP, Coombes RC, Schlimok G, Diel IJ, Gerber B, Gebauer G, Pierga JY, Marth C, Oruzio D, Wiedswang G, Solomayer EF, Kundt G, Strobl B, Fehm T, Wong GY, Bliss J, Vincent-Salomon A, Pantel K. A pooled analysis of bone marrow micrometastasis in breast cancer. N Engl J Med 2005;353:793–802.
Cassiman JJ; Brugmans M; Van den Berghe H Growth and surface properties of dispase dissociated human fibroblasts. Cell biology international reports (1981), 5(2), 125-32.
194 Champelovier P, Mongelard F, Seigneurin D. CK20 gene expression: technical limits for the detection of circulating tumor cells. Anticancer Res 1999; 19: 2073–2078.
Chai V, Vassilakos A, Lee Y, Wright JA, Young AH. Optimization of the PAXgene blood RNA extraction system for gene expression analysis of clinical samples. J Clin Lab Anal 2005;19:182-188.
Chaiwun B, Saad AD, Groshen S, Chen SC, Mazumder A, Imam A, Tayloe CR, Cote RJ. Immunohistochemical detection of occult carcinoma in bone marrow and blood. Diagn Oncol 1992;2:267–276.
Chalmers J, Zborowski M, et al. Flow Through, Immunomagnetic Cell Separation. Biotechnology Progress 1998; 14:141-148
Chalmers JJ, Zhao Y, Nakamura M, Melnik K, Lasky L, Moore L, Zborowski M. An instrument to determine the magnetophoretic mobility of paramagnetic particles and labeled, biological cells. J Magn Magn Mater 1999;194:231–241.
Chelly J; Concordet J P; Kaplan J C; Kahn A. Illegitimate transcription : transcription of any gene in any cell type. Proceedings of the National Academy of Sciences of the United States of America (1989), 86(8), 2617-21.
Chen XM, Chen GY,Wang ZR, Zhu FS,Wang XL, Zhang X. Detection of micrometastasis of gastric carcinoma in peripheral blood circulation. World J Gastroenterol 2004; 10: 804–808.
Chin D; Boyle GM; Williams RM; Ferguson K; Pandeya N; Pedley J; Campbell CM; Theile DR; Parson PG; Coman, WB. Novel markers for poor prognosis in head and neck cancer. Int. J. Cancer (2005), 113: 789-797.
195 Choi P; Chen C. Genetic expression profiles and biologic pathway alterations in head and neck squamous cell carcinoma. Cancer (2005), 104(6), 1113-28.
Clarke LE, Leitzel K, Smith J, Ali SM, Lipton A. Epidermal growth factor receptor mRNA in peripheral blood of patients with pancreatic, lung, and colon carcinomas detected by RT-PCR. Int J Oncol 2003; 22: 425–430.
Comella K, Nakamura M, Melnik K, Chosy J, Zborowski M, Cooper MA, Fehniger TA, Caligiuri MA, Chalmers JJ. The effect of antibody concentration on the separation of human natural killer cells in a commercial immunomagnetic separation system. Cytometry 2001;45:285–293.
Coombes RC, Berger U, Mansi J, et al. Prognostic significance of micrometastases in bone marrow in patients with primary breast cancer. J Natl Cancer Inst. 1986; 1:51- 53.
Cristofanilli M, Budd GT, Ellis MJ, et al: Circulating tumor cells, disease progression and survival in metastatic breast cancer. N. Engl. J. Med. 351(8):781-791, 2004.
Datta YH, Adams PT, Drobyski WR, Ethier SP, Terry VH, Roth MS. Sensitive detection of occult breast cancer by the reverse-transcriptase polymerase chain reaction. J Clin Oncol 1994; 12: 475– 482.
Davids V, Kidson SH, Hanekom GS. Accurate molecular detection of melanoma nodal metastases: An assessment of multimarker assay specificity, sensitivity, and detection rate. Mol Pathol 2003;56:43–51.
Dawson, P.H. Quadrupole mass spectrometry and its applications, Elsevier Scientific Publishing Co.:New York, 1976
196 De Luca A, Pignata S, Casamassimi A, D’Antonio A, Gridelli C, Rossi A, Cremona F, Parisi V, De Matteis A, Normanno N. Detection of circulating tumor cells in carcinoma patients by a novel epidermal growth factor receptor reverse transcription- PCR assay. Clin Cancer Res 2000; 6: 1439– 1444.
Diel IJ, Kaufmann M, Costa SD et al. Micrometastatic breast cancer cells in bone marrow at primary surgery: prognostic value in comparison with nodal status. J Natl Cancer Inst. 1998, 90:1099-1101.
Emerman JT; Wilkinson DA. Routine culturing of normal, dysplastic and malignant human mammary epithelial cells from small tissue samples. In vitro cellular & developmental biology: journal of the Tissue Culture Association (1990), 26(12), 1186-94.
Emmert-Buck MR BR; Smith PD; Chuaqui RF; Zhuang Z; Goldstein SR; Weiss RA; Liotta LA. Laser capture microdissection. Science 1996; 274(5289): 998-1001.
Engelholm SA S-TM, Brunner N, Nohr I, Vindelov LL. 1985. Disaggregation of human solid tumours by combined mechanical and enzymatic methods. Br. J. Cancer 51(1):93-8.
Ensley JF; Maciorowski Z; Pietraszkiewicz H; Klemic G; KuKuruga M; Sapareto S; Corbett T; Crissman J. Solid tumor preparation for flow cytometry using a standard murine model. Cytometry (1987a), 8(5), 479-87.
Ensley JF; Maciorowski Z; Pietraszkiewicz H; Hassan M; Kish J; Al-Sarraf M; Jacobs J; Weaver A; Atkinson D; Crissman J Solid tumor preparation for clinical application of flow cytometry. Cytometry (1987b), 8(5), 488-93.
Ensley JF, Maciorowski Z, Hassan M, Pietraszkiewicz H, Sakr W, Heilbrun LK.
197 Variations in DNA aneuploid cell content during tumor dissociation in human colon and head and neck cancers analyzed by flow cytometry. Cytometry 1993;14:550-558.
Fang B, Zborowski M, Moore LR: Detection of rare MCF-7 breast carcinoma cells from mixtures of human peripheral leukocytes by magnetic deposition analysis. Cytometry. 1999, 36:294-302.
Fehm T, Braun S, Muller V, Janni W, Gebauer G, Marth C, Schindlbeck C, Wallwiener D, Borgen E, Naume B, Pantel K, Solomayer E, A concept for the standardized detection of disseminated tumor cells in bone marrow from patients with primary breast cancer and its clinical implementation. Cancer 2006; 107(5):885-92.
Fellowes VS, Husebekk A, Gress RE, Vance BA. Minimal residual disease detection in breast cancer: improved sensitivity using cytokeratin 19 and epidermal growth factor receptor RT-PCR. Int J Oncol 2004;24:861-867.
Fidler IJ, Kripke ML. Metastasis results from preexisting variant cells within a malignant tumor. Science. 1977; 197:337-342.
Fonseca FL, Soares HP, Manhani AR, Bendit I, Novaes M, Zatta SM, Arias V, Pinhal MA, Weinschenker P, del Giglio A. Peripheral blood c-erbB-2 expression by reverse transcriptase polymerase chain reaction in breast cancer patients receiving chemotherapy. Clin Breast Cancer 2002; 3: 201–205.
Forastiere A, Koch W, Trotti A, Sidransky D. Head and neck cancer. N. Engl. J. Med., 2001, 345: 1890–1900.
Foulds L : Neoplastic development II. Academic press, New York, 1975.
Gath HJ, Brakenhoff RH. Minimal residual disease in head and neck cancer. Cancer
198 Metastasis Rev. 1999, 18, 109-126.
Gazzaniga P, Gandini O, Giuliani L, Magnanti M, Gradilone A, Silvestri I, Gianni W, Gallucci M, Frati L, Agliano AM. Detection of epidermal growth factor receptor mRNA in peripheral blood: a new marker of circulating neoplastic cells in bladder cancer patients. Clin Cancer Res 2001a; 7: 577–583.
Gazzaniga P, Gradilone A, Frati L, Aqliano AM. Epidermal growth factor receptor mRNA expression in peripheral blood of bladder cancer patients: a potential marker to detect treatment failure. Clin Cancer Res 2001b;7:4288-4289.
Geilen C, Georgieva J, Milling A, Farthmann B. Malignant Melanoma – Markers for Progression and Prognosis in Malignant Melanoma. In:Sternemann M, Wiegel T, Geilen CC, Orfanos CE, Hinkelbein W (eds): Controversies in the Treatment of Skin Neoplasias. Front Radiat Ther Oncol. Basel, Karger, 2006, vol 39, pp 120-126.
Gerber B, Krause A, Muller H, et al. Simultaneous immunohistochemical detection of tumor cells in lymph nodes and bone marrow aspirates in breast cancer and its correlation with other prognostic factors. J Clin Oncol. 2001, 19:960-971.
Ghossein RA, Bhattacharya S. Molecular detection and characterization of circulating tumour cells and micrometastases in solid tumours. European Journal of Cancer (2000), 36(13), 1681-1694.
Giddings JC: A system based on split-flow lateral-transport thin (SPLITT) separation cells for rapid and continuous particle fractionation. Sep Sci Technol 20:749-768, 1985.
199 Goldenberg DM, Sharkey RM, Primus FJ. Carcinoembryonic antigen in histopathology: immunoperoxidase staining of conventional tissue sections. J Natl Cancer Inst 1976; 57: 11–22.
Goya GF, Berquo TS, Fonseca FC. 2003. Static and dynamic magnetic properties of spherical magnetite nanoparticles. J Appl Phys 94(5):3520-3528.
Gradilone A, Gazzaniga P, Silvestri I, Gandini O, Trasatti L, Lauro S, Frati L, Agliano AM. Detection of CK19, CK20 and EGFR mRNAs in peripheral blood of carcinoma patients: Correlation with clinical stage of disease. Oncol Rep 2003;10:217–222.
Guadagni F, Kantor J, Aloe S, Carone MD, Spila A, D’Alessandro R, Abbolito MR, Cosimelli M, Graziano F, Carboni F, Carlini S, Perri P, Sciarretta F, Greiner JW, Kashmiri SV, Steinberg SM, Roselli M, Schlom J. Detection of blood-borne cells in colorectal cancer patients by nested reverse transcription-polymerase chain reaction for carcinoembryonic antigen messenger RNA: longitudinal analyses and demonstration of its potential importance as an adjunct to multiple serum markers. Cancer Res 2001; 61: 2523–2532.
Guney K, Yoldas B, Ozbilim G,Derin A T, Sarihan S,Balkan E. Detection of micrometastatic tumor cells in head and neck squamous cell carcinoma. A possible predictor of recurrences? Saudi Medical Journal .2007, 28(2): 16-20.
Hampton R, Walker M, Marshall J, Juhl H. Differential expression of carcinoembryonic antigen (CEA) splice variants in whole blood of colon cancer patients and healthy volunteers: implication for the detection of circulating colon cancer cells. Oncogene 2002; 21: 7817–7823.
200 Hardingham JE, Kotasek D, Farmer B, Butler RN, Mi JX, Sage RE, Dobrovic A. Immunobead-PCR: A technique for the detection of circulating tumor cells using immunomagnetic beads and the polymerase chain reaction. Cancer Res 1993;53:3455–3458.
Hardingham JE, Hewett PJ, Sage RE, Finch JL, Nuttall JD, Kotasek D, Dobrovic A. Molecular detection of blood-borne epithelial cells in colorectal cancer patients and in patients with benign bowel disease. Int J Cancer 2000;89:8–13.
Hay E. 1991. Cell biology of extracellular matrix. New York: Plenum Press.
Hefley TJ; Stern PH; Brand JS. Enzymic isolation of cells from neonatal calvaria using two purified enzymes from Clostridium histolyticum. Experimental Cell Research (1983),149(1), 227-36.
Hematopoiesis. http://commons.wikimedia.org/wiki/Image:Hematopoiesis_(human)_diagram.png
Hildebrandt M, Mapara MY, Korner IJ, Bargou RC, Moldenhauer G, Dorken B. Reverse transcriptase-polymerase chain reaction (RT-PCR)-controlled immunomagnetic purging of breast cancer cells using the magnetic cell separation (MACS) system: a sensitive method for monitoring purging efficiency. Exp Hematol 1997; 25: 57–65.
Hinterberger W, Buxhofer V, Ogris E, Zelenka P, Kier P, Ruckser R, Dorner S, Habertheuer KH, Vedovelli H, Schindler S, Hinterberger-Fischer M. [Significance of minimal residual disease for the estimation of the prognosis and for therapeutic decisions in solid tumors]. Acta Med Austriaca 2002; 29: 2–8.
201 Hoyos M, Moore LR, McCloskey KE, Margel S, Zuberi M, Chalmers JJ, Zborowski M. Study of magnetic particles pulsed-injected into an annular SPLITT-like channel inside a quadrupole magnetic field. J Chromatogr A 2000;903:99–116.
Huang P,Wang J, Guo Y, XieW. Molecular detection of disseminated tumor cells in the peripheral blood in patients with gastrointestinal cancer. J Cancer Res Clin Oncol 2003; 129: 192–198.
Iinuma H, Okinaga K, Adachi M, Suda K, Sekine T, Sakagawa K, Baba Y, Tamura J, Kumagai H, Ida A. Detection of tumor cells in blood using CD45 magnetic cell separation followed by nested mutant allele-specific amplification of p53 and K-ras genes in patients with colorectal cancer. Int J Cancer 2000;89:337–344.
Izbicki J R; Hosch SB; Pichlmeier U; Rehders A; Busch C; Niendorf A; Passlick B; Broelsch CE; Pantel K. Prognostic value of immunohistochemically identifiable tumor cells in lymph nodes of patients with completely resected esophageal cancer. The New England journal of medicine. 1997, 337(17): 1188-94.
Jiang WG; Martin TA; Mansel RE. Molecular detection of micro - metastasis in breast cancer. Critical reviews in oncology/hematology (2002), 43(1), 13-31.
Jung R, Petersen K, Kruger W, Wolf M, Wagener C, Zander A, Neumaier M. Detection of micrometastasis by cytokeratin 20 RT-PCR is limited due to stable background transcription in granulocytes. Br J Cancer 1999; 81: 870–873.
Kahn HJ, Yang LY, Blondal J, Lickley L, Holloway C, Hanna W, Narod S, McCready DR, Seth A, Marks A. RT-PCR amplification of CK19 mRNA in the blood of breast cancer patients: correlation with established prognostic parameters. Breast Cancer Res Treat 2000; 60: 143–151.
202 Kalyankrishna S; Grandis JR. 2006. Epidermal growth factor receptor biology in head and neck cancer. J. Clin. Oncol. 24: 2666-2672
Kantor AB, Gibbons I, Miltenyi S, Schmitz J. Magnetic cell sorting with colloidal superparamagnetic particles. In: Recktenwald D, & Radbruch A, ed. Cell Separation Methods and Applications 1998; 153–173. Marcel Dekker, Inc., 1998.
Kedar E; Ikejiri B L; Bonnard G D; Herberman R B A rapid technique for isolation of viable tumor cells from solid tumors : use of the tumor cells for induction and measurement of cell-mediated cytotoxic responses. European journal of cancer & clinical oncology (1982),18(10), 991-1000.
Kinjo M, Okegawa T, Horie S, Nutahara K, Higashihara E. Detection of circulating MUC7-positive cells by reverse transcription-polymerase chain reaction in baldder cancer patients. Int J Urol 2004;11:38-43.
Klein CA, Schmidt-Kittler, O., Schardt, JA, et al.: Comparative Genomic Hybridization, Loss of Heterozygosity, and DNA sequence analysis of Single Cells. PNAS. 96:4494-4499, 1999.
Klein CA, Seidl S, Petat-Dutter K, et al: Combined transcriptome and genome analysis of singel micrometastatic cells. Nat. Biotech. 20:387-392, 2002a.
Klein CA, Blankenstein TJF, Schmidt-Kittler O, et al: Genetic heterogeneity of single disseminated tumour cells in minimal residual cancer. The Lancet 360:683-689, 2002b.
Ko Y, Klinz M, Totzke G, Gouni-Berthold I, Sachinidis A, Vetter H. Limitations of the reverse transcription-polymerase chain reaction method for the detection of
203 carcinoembryonic antigenpositive tumor cells in peripheral blood. Clin Cancer Res 1998; 4: 2141–2146.
Ko Y, Grunewald E, Totzke G, Klinz M, Fronhoffs S, Gouni-Berthold I, Sachinidis A, Vetter H. High percentage of false-positive results of cytokeratin 19 RT-PCR in blood: A model for the analysis of illegitimate gene expression. Oncology 2000;59:81–88.
Lansdorp P M; Aalberse R C; Bos R; Schutter W G; Van Bruggen E F Cyclic tetramolecular complexes of monoclonal antibodies : a new type of cross-linking reagent. European journal of immunology. 1986, 16(6): 679-83.
Lara O, Tong X, Zborowski M, Chalmers J. Enrichment of rare cancer cells through depletion of normal cells using density and flow-through, immunomagnetic cell separation. Exp Hematol 2004;32:891–904.
Lara O, Tong X, Zborowski M, Farag SS, Chalmers JJ. Comparison of two immunomagnetic separation technologies to deplete T cells from human blood samples. Biotechnol Bioeng 2006;94:66–80.
Leone F, Perissinotto E, Viale A, Cavalloni G, Taraglio S, Capaldi A, Piacibello W, Torchio B, Aglietta M. Detection of breast cancer cell contamination in leukapheresis product by real-time quantitative polymerase chain reaction. Bone Marrow Transplant 2001; 27: 517– 523.
Ljung BM; Mayall B; Lottich C; Boyer C; Sylvester SS; Leight GS; Siegler HF; Smith HS Cell dissociation techniques in human breast cancer -variations in tumor cell viability and DNA ploidy. Breast cancer research and treatment (1989),13(2), 153-9.
204 Makarovskiy AN, Ackerley W 3rd, Wojcik L, Halpert GK, Stein BS, Carreiro MP, Hixson DC. Application of immunomagnetic beads in combination with RT-PCR for the detection of circulating prostate cancer cells. J Clin Lab Anal 1997;11:346-350.
Mataki Y; Takao S; Maemura K; Mori S; Shinchi H; Natsugoe S; Aikou T. Carcinoembryonic antigen messenger RNA expression using nested reverse transcription- PCR in the peripheral blood during follow-up period of patients who underwent curative surgery for biliary - pancreatic cancer : longitudinal analyses. Clinical Cancer Research. 2004, 10(11): 3807-3814.
McCloskey KE, Moore LR, Hoyos M, Rodriguez A, Chalmers JJ, Zborowski M. Magnetophoretic cell sorting is a function of antibody binding capacity. Biotechnol Prog 2003;19:899–907.
Mitsuhashi A, Tanaka N, Suzuka K, Matsui H, Seki K, Sekiya S. Detection of epidermal growth factor receptor mRNA in peripheral blood of cervial cancer patients. Gynecol Oncol 2003;89:480-485.
Mocelline S, Keilholz, Rossi C R, Nitti D. Circulating tumor cells:the ‘leukimic phase’ of solid cancers. Trends in molecular medicine. 12(3):130-139. 2006a
Mocellin S, Hoon D, Ambrosi A, Nitti D, Rossi C R. The Prognostic Value of Circulating Tumor Cells in Patients with Melanoma: A Systematic Review and Meta- analysis. Clin. Cancer Res. 12:4605-4613. 2006b.
Moore LR, Milliron S, Williams PS, Chalmers JJ, Margel S, Zborowski M, Control of magnetophoretic mobility by susceptibility-modified solutions as evaluated by cell tracking velocimetry and continuous magnetic sorting, Analytical Chemistry 2004, 76:3899-3907.
205 Munz Markus; Zeidler Reinhard; Gires Olivier The tumour-associated antigen EpCAM upregulates the fatty acid binding protein E-FABP. Cancer letters (2005), 225(1), 151-7.
Nakamura M, Decker K, Chosy J, Comella K, Melnik K, Moore L, Lasky LC, Zborowski M, Chalmers JJ. The separation of a breast cancer cell line from human blood using a quadrupole magnetic flow sorter. Biotechnol Prog 2001;17:1145–1155.
Naume N, Wiedswang G, Borgen E, et al. Clinical significance of isolated tumor cells inperipheral blood in breast cancer patients three years after diagnosis: comparison between analysis of peripheral blood and bone marrow. Proc. ASCO 23:844 (abstr #9554), 2004.
NCI website: http://cgap-mf.nih.gov/Protocols/Microdissection/LCM.html
NIH website: http://www.niehs.nih.gov/research/atniehs/core/microarrays/index.cfm
Nicholson J KA, Hubbard M, and Jones B M. Use of CD45 Fluorescence and Side- Scatter Characteristics for Gating Lymphocytes When Using the Whole Blood Lysis Procedure and Flow Cytornetry. Cytornetry (Communications in Clinical Cytornetry) 1996, 26:16-21.
Noel JS; Zucker RM; Wu NC; Demaray SY. The dissociation of transplantable tumors. Journal of Histochemistry and Cytochemistry (1977), 25(7), 544-53.
O’Hara SM, Moreno JG, Zweitzic DR, Gross S, Gomella LG, Terstappen LWMM. Multigene reverse transcription-PCR profiling of circulating tumor cells in hormone- refractory prostate cancer. Clin Chem 2004;50: 826–835.
206 Osella-Abate, S. Osella-Abate, S.; Savoia, P.; Quaglino, P.; Fierro, M. T.; Leporati, C.; Ortoncelli, M.; Bernengo, M. G. Tyrosinase expression in the peripheral blood of stage III melanoma patients is associated with a poor prognosis: a clinical follow-up study of 110 patients. Br. J. Cancer. 2003, 89, 1457–1462.
Pajonk F, Schlessmann s, Guttenberger R, Henke, M. Epithelial cells in the peripheral blood of patients with cancer of the head and neck: incidence, detection and possible clinical significance.
Pantel K; Cote R.J; Fodstat Ø. 1999. Detection and clinical importance of micrometastatic disease.
Pantel K; Mueller V; Auer M; Nusser N; Harbeck N; Braun S. Detection and Clinical Implications of Early Systemic Tumor Cell Dissemination in Breast Cancer. Clinical Cancer Research . 2003, 9(17): 6326-6334.
Pantel K; Brakenhoff RH. Dissecting the metastatic cascade. Nature Reviews Cancer (2004), 4(6), 448-456.
Pantel K, Woelfle U. Detection and molecular characterisation of disseminated tumour cells: Implications for anti-cancer therapy. 2005, 53-76.
Park S, Lee B, Kim I, Choi I, Hong K, Ryu Y, Rhim J, Shin J, Park SC, Chung H, Chung J. Immunobead RT-PCR versus regular RT-PCR amplification of CEA mRNA in peripheral blood. J Cancer Res Clin Oncol 2001;127:489–494.
Partridge M, Phillips E, Francis R, Li SR. Immunomagnetic separation for enrichment and sensitive detection of disseminated tumor cells in patients with head and neck SCC. J Pathol 1999;189:368–377.
207 Pawlowski V, Revillion F, Hebbar M, Hornez L, Peyrat JP. Prognostic value of the type I growth factor receptors in a large series of human primary breast cancers quantified with a real-time reverse transcription-polymerase chain reaction assay. Clin Cancer Res 2000; 6: 4217– 4225.
Plzak J; Smetana K Jr; Chovanec M; Betka J. Glycobiology of head and neck squamous epithelia and carcinomas. ORL; journal for oto-rhino-laryngology and its related specialties (2005), 67(2), 61-9.
Pretlow II TG; Pretlow TP. Cell separation methods and selected applications, volumes 1. Academic Press, Orlando, 1983.
Raynor M, Stephenson SA,Walsh D, Pittman K, Dobrovic A. Optimisation of the RT- PCR detection of immunomagnetically enriched carcinoma cells. BMC Cancer 2002; 2: 14.
Riethdorf S, Fritsche H, Muller V, et al. Detection of Circulating Tumor Cells in Peripheral Blood of Patients with Metastatic Breast Cancer: A Validation Study of the CellSearch System. Clin. Cancer Res. 2007; 13(3):920-928.
Ronnov-Jessen L PO, Bissell MJ. 1996. Cellular changes involved in conversion of normal to malignant breast: importance of the stromal reaction. Physiol Rev. 76(1):69-125.
Ruud P, Fodstad O, Hovig E. Identification of a novel cytokeratin 19 pseudogene that may interfere with reverse transcriptase-polymerase chain reaction assays used to detect micrometastatic tumor cells. Int J Cancer 1999; 80: 119–125.
208 Sacks PG; Parnes SM; Gallick GE; Mansouri Z; Lichtner R; Satya-Prakash KL; Pathak S; Parsons DF. Establishment and characterization of two new squamous cell carcinoma cell lines derived from tumors of the head and neck. Cancer research (1988), 48(10), 2858-66.
Savtchenko ES, Schiff TA, Jiang CK, Freedberg IM, Blumenberg M. Embryonic expression of the human 40-kD keratin: evidence from a processed pseudogene sequence. Am J Hum Genet 1988; 43: 630–637.
Schantz SP, Harrison LB, Forastiere AA. Tumors of the nasal cavity and paranasal sinuses, nasopharynx, oral cavity and oropharynx. In: Cancer: Principles and Practice of Oncology 6th edition, editors: Vincent T. Devita, Jr., Samuel Hellman, and Steven A Rosenberg. Lippincott Williams and Wilkins. 2001.p.797-860.
Schittek B; Blaheta HJ; Ellwanger U; Garbe C. Polymerase chain reaction in the detection of circulating tumor cells in peripheral blood of melanoma patients. Recent Results in Cancer Research. 2001, 158(Minimal Residual Disease in Melanoma): 93-104.
Schmidtke G, Schmucker U, Brittinger G, Hoffkes HG. Comparative flow cytometric study of clonal excess in leukaemic peripheral blood from patients suffering from chronic lymphocytic leukaemia (B-CLL) by different antibodies, staining techniques and the effects of blood storage. Clin Lab Haematol 1999;21:103–112.
Schmidt-Kittler O, Ragg T, Daskalakis A, et al. From latent disseminated cells to overt metastasis: Genetic analysis of systemic breast cancer progression. PNAS :1331931100, 2003
Shah VO, Civin Cl., Loken MR. Flow cytometric analysis of human bone marrow: IV. Differential quantitative expression of T-200 common leukocyte antigen during
209 normal hematopoiesis. J. Immunol. 140,1861, 1988.
Shin DM, Lippman SM. Paclitaxel-based chemotherapy for recurrent and/or metastatic head and neck squamous cell carcinoma: current and future directions. Semin. Oncol. 1999, 26: 100-105.
Sieben NLG; Cornelisse CJ; Fleuren GJ; Cleton-Jansen AM. PCR artifacts in LOH and MSI analysis of microdissected tumor cells. Hum. Pathol. 2000, 31(11):1414-19.
Silva AL, Diamond J, Silva MR, Passos-Coelho JL. Cytokeratin 20 is not a reliable molecular marker for occult breast cancer cell detection in hematological tissues. Breast Cancer Res Treat. 2001, 66: 59–66.
Smith B, Selby P, Southgate J, Pittman K, Bradley C, Blair G E. Detection of melanoma cells in peripheral blood by means of reverse transcriptase and polymerase chain reaction. Lancet. 1991, 338: 1227–1229.
Sok JC; Kuriakose M Abraham; Mahajan Vinit B; Pearlman Aaron N; DeLacure Mark D; Chen Fang-An Tissue-specific gene expression of head and neck squamous cell carcinoma in vivo by complementary DNA microarray analysis. Archives of otolaryngology--head & neck surgery (2003), 129(7), 760-70.
Stathopoulou A, Angelopoulou K, Perraki M, Georgoulias V, Malamos N, Lianidou E. Quantitative RT-PCR luminometric hybridization assay with an RNA internal standard for cytokeratin-19 mRNA in peripheral blood of patients with breast cancer. Clin Biochem 2001; 34: 651–659.
Stathopoulou A, Vlachonikolis I, Mavroudis D, Perraki M, Kouroussis C, Apostolaki S, Malamos N, Kakolyris S, Kotsakis A, Xenidis N, Reppa D, Georgoulias V. Molecular detection of cytokeratin-19- positive cells in the peripheral blood of
210 patients with operable breast cancer: evaluation of their prognostic significance. J Clin Oncol 2002; 20: 3404–3412.
Stathopoulou A, Mavroudis D, Perraki M, Apostolaki S, Vlachonikolis I, Lianidou E, Georgoulias V. Molecular detection of cancer cells in the peripheral blood of patients with breast cancer: comparison of CK-19, CEA and maspin as detection markers. Anticancer Res 2003; 23: 1883–1890.
Suen JY. Management of the N0 neck. Proceedings of the Fourth International Conference of Head and Neck Cancer. The Society of Head and Neck Surgeons and American Society of Head and Neck Surgeons, Toronto, Madison WI: Omnipres, 1996, 557.
Sun, L., Zborowski, M., Moore, L., Chalmers, J.J., Continuous, Flow-Through Immunomagnetic Cell Separation in a Quadrupole Field. Cytometry. 1998,33:469- 475.
Tankere, F., Camproux, A., Barry, B., et al. Prognostic value of lymph node involvement in oral cancers: a study of 137 cancers. Laryngoscope. 2000, 110, 2061- 2065.
Tanner MA, Berk LS, Felten DL, Blidy AD, Bit SL, Ruff DW. Substantial changes in gene expression level due to the storage temperature and storage duration of human whole blood. Clin Lab Haematol 2002;24: 337–341.
Tannock IF; Hill RP; Bristow RG; Harrington L. The basic science of oncology. 4th edition.McGrawHill. 2004.
211 Terstappen L.W.M.M., Hollander Z, Meiners, H., Loken M.R. Quantitative comparison of myeloid antigens on five lineages of mature peripheral blood cells. Journal of leukocyte biology 48: 138-148. 1990.
Tomlinson IP LM; Roylance RR. Loss of heterozygosity analysis: practically and conceptually flawed? Genes Chromosomes Cancer 2002, 34(4):349-53.
Tong X, Yang L, Lang J C. Zborowski M, Chalmers J J. Application of immunomagnetic cell enrichment in combination with RT-PCR for the detection of rare circulating head and neck tumor cells in human peripheral blood. Cytometry B Clin cytom. 2007. 72B:310–323.
Trummer A, Kadar J, Arseniev L, Petersen D, Ganser A, Lichtinghagen R. Competitive cytokeratin 19 RT-PCR for quantification of breast cancer cells in blood cell suspensions. J Hematother Stem Cell Res 2000; 9: 275–284.
Truskey GA YF, Katz DF. 2004. Transport phenomena in biological systems. Upper Saddle River, N.J.: Pearson Prentice Hall.
Tzafriri Abraham R; Bercovier Michel; Parnas Hanna Reaction diffusion model of the enzymatic erosion of insoluble fibrillar matrices. Biophysical journal (2002), 83(2), 776-93. van Houten VM, Tabor MP, van den Brekel MW, Denkers F, Wishaupt RG, Kummer JA, Snow GB, Brakenhoff RH. Molecular assays for the diagnosis of minimal residual head-and-neck cancer: methods, reliability, pitfalls, and solutions. Clin Cancer Res 2000; 6: 3803–3816.
Veridix www.veridix.com
212 Vlems FA, Ruers TJM, Punt CJA, Wobbes Th, van Muijen GNP. Relevance of disseminated tumour cells in blood and bone marrow of patients with solid epithelial tumours in perspective. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2003, 29(4), 289-302.
Visscher DW; Crissman JD. Dissociation of intact cells from tumors and normal tissues. Methods in cell biology (1994), 41 1-13.
Wascher, R.A. et al. Molecular tumor markers in the blood: early prediction of disease outcome in melanoma patients treated with a melanoma vaccine. J. Clin. Oncol. 2003, 21: 2558–2563.
Wasserman L, Dreilinger A, Easter D,Wallace A. A seminested RT-PCR assay for HER2/neu: initial validation of a new method for the detection of disseminated breast cancer cells. Mol Diagn 1999; 4: 21–28.
Went PTH; Lugli A; Meier S; Bundi M; Mirlacher M; Sauter G; Dirnhofer S. Frequent EpCam protein expression in human carcinomas. Human Pathology (2004), 35(1), 122-128.
Wikipedia: Neutrophilia http://en.wikipedia.org/wiki/Neutrophilia
Williams PS, Zborowski M, Chalmers JJ: Flow rate optimization for the quadrupole magnetic cell sorter. Analytical Chemistry 71:3799-3807, 1999.
Wirtschafter A, Benninger M S, Moss T J, Umiel T,Blazoff K, Worsham M J. Micrometastatic tumor detection in patients with head and neck cancer: a preliminary report. Archives of otolaryngology--head & neck surgery. 2002, 128(1), 40-3.
213
Woessner JF, Jr. 1961. The determination of hydroxyproline in tissue and protein samples containing small proportions of this imino acid. Arch Biochem Biophys 93:440-7.
Wong IH, Yeo W, Chan AT, Johnson PJ. Quantitative correlation of cytokeratin 19 mRNA level in peripheral blood with disease stage and metastasis in breast cancer patients: potential prognostic implications. Int J Oncol 2001; 18: 633–638.
Yamamoto, E., Miyakawa, A., Kohama, G. Mode of invasion and lymph node metastasis in squamous cell carcinoma of the oral cavity. Head Neck Surg. 1984, 6, 938-947.
Zborowski M , Fuh C B, Green R, Sun L P, Chalmers J J. Analytical magnetapheresis of ferritin-labeled lymphocytes. Anal. Chem. 1995, 67:3702-3712.
Zborowski, M and Chalmers, J J. Magnetic Cell Separation, Vol. 32 of Series: Laboratory Techniques in Biochemistry and Molecular Biology, Elsevier, Amsterdam, 2007.
Zen H, Nakashiro KI, Shintani S, Sumida T, Aramoto T, Hamakawa H. Detection of circulating cancer cells in human oral squamous cell carcinoma. Int J Oncol 2003;23:605–610.
Zhang H, Zborowski M, Williams PS, Chalmers JJ. Establishment and implications of a characterization method for magnetic nanoparticle using cell tracking velocimetry and magnetic susceptibility modified solutions, The Analyst 2005, 130:514-527.
Zhang H, Williams P S, Zborowski M, Chalmers J J. Binding Affinities/Avidities of Antibody–Antigen Interactions: Quantification and Scale-Up Implications.
214 Biotechnology and Bioengineering. Biotechnology and Bioengineering. 2006. 95(5): 812-829.
Zhong XY, Kaul S, Diel I, Eichler A, Bastert G. Analysis of sensitivity and specificity of cytokeratin 19 reverse transcriptase/polymerase chain reaction for detection of occult breast cancer in bone marrow and leukapheresis products. J Cancer Res Clin Oncol 1999; 125: 286–291.
Zieglshmid V, Hollmann C, Bo¨cher O. Detection of disseminated tumor cells in peripheral blood. Crit Rev Clin Lab Sci 2005, 42:155–196.
Zippelius A; Pantel K. RT - PCR - based detection of occult disseminated tumor cells in peripheral blood and bone marrow of patients with solid tumors. An overview. Annals of the New York Academy of Sciences. 2000, 906(Circulating Nucleic Acids in Plasma or Serum): 110-123.
215