1. Towards a fast, efficient assay for isolating circulating tumor cells
July 30, 2009
PI: Professor David Eddington
Grad Student: Cari Launiere
Me: Joey Labuz

2. Introduction
Breast, colon, prostate, and lung cancers accounted for nearly half of cancer deaths (American Cancer Society, 2008)
All 4 can be metastatic diseases
Circulating tumor cells (CTCs)
Rare in blood (as low as 1 in 1,000,000,000)
Alternative to biopsy screenings
High expression of epithelial cell adhesion molecule (EpCAM) (Went et al., 2004)

3. CTC-chip assay Posts fabricated from Si wafer
100 µm diameter
100 µm tall
Posts coated with anti-EpCAM
Whole blood flowed through device by pressure source
mL-scale volumes
SEM of Si posts with captured cancer cell (colored red for visibility)
(S Nagrath, et al. 2007)

4. CTC-chip assay (cont.) Cons Pros Complex fabrication process (DRIE)
Simpler than other methods (immunomagnetic beads)
No pre-processing of blood necessary
High sensitivity (99.1%)
Improved purity (over two times better)
Cons
Complex fabrication process (DRIE)
Max flow of ~1 mL/hr
1-2 hours to run sample
We can do ~6x faster
High cost
Low efficiency (~60%)
Low purity (~50%)

5. Photo/Soft Lithography
Rapid prototyping of polydimethylsiloxane channels
Benefits of PDMS
Good optical clarity
Good scalability
PDMS channel placed on glass slide with proteins
Rapid prototyping of PDMS channels (JS Mohammed, et al. 2008)

6. Computer generated images of various Cav-1 conformations (Cai, et al)
Caveolin-1 Capture
Cav-1 expression generally inversely proportional to EpCAM expression
Explore as way to isolate CTCs with low EpCAM expression (i.e. MDA-MB-231)
(Sieuwerts, et al, 2009)
Computer generated images of various Cav-1 conformations (Cai, et al)

7. E-Selectin Binding
Present in physiological flow situations (e.g. blood vessels)
Binds to cancer as well as blood cells (e.g. leukocytes)
Catch bond mechanism pulls cells out of flow
Chinese finger trap of proteins
Catch bonds’ strength increases as tensile force, until a maximum, where the force begins to overcome the bond strength (Thomas W, 2009).

8. Mixer Optimization Force cells down to proteins on slide
Channel height: 100 µm
Groove height: 160 µm
Grooves lead to transverse flow
Channel
Groove
Transverse Flow
Flow
Slide with protein coat
(NS Lynn and DS Dandy, 2007)

9. Imaging Problem – Clumped Cells
Clumped cells are often counted as one, instead of several
Watersheding methods inadequate for separating cells and maintaining image quality

10. Imaging Solution – Clumped Cells
Use ImageJ
Macro executes series of commands
Output text file to MatLab
Use MatLab
Find clumped cells based on average area and standard deviation
Using average, separate clumps into individual cells
Cell area histogram: All cells with areas greater than the mean + standard deviation are considered clumps

11. Imaging Solution – Clumped Cells
Validate method by using hand counts
Image 1
By hand: 97
Using program: 98
Error: 1 %
Image 2
By hand: 841
Using program: 831
Error: 1.2 %
Image 1
Image 2

12. Imaging Problem – Mixer
Mixer pattern diffracts light
Creates problems during image processing

13. Imaging Solution – Mixer
Use subtract function in ImageJ
Subtracts grayscale values pixel by pixel
Subtract image from control _
Control image
Image with cells

14. Imaging Solution – Mixer

15. Preliminary results
Run trials with HL-60 and MDA-MB-231 cells, respectively
Cells roll on E-selectin as expected
Observed under the microscope at 0.1 mL/min
Anti-EpCAM helped maintain new capture
Anti-CAV1 helped facilitate stationary capture
Cells detach upon entering mixer
Could be due to overly turbulent flow
Or due to poor protein coating – adjust method for future experiments

16. Summary CTCs attractive option for cancer screening
Less invasive than biopsy
Broader, earlier detection
Channel optimized to increase cell contact with protein-functionalized surface
Use protein cocktail to optimize capture
E-selectin to pull cells out of flow
Anti-EpCAM and anti-CAV1 to bind CTCs
Wrote programs for rapid image analysis

17. Acknowledgements Financial support Cari Launiere Prof. David Eddington
NSF
DoD
Cari Launiere
Prof. David Eddington
REU advisors
The BML lab
My roommate

18. References
Cai, Q. C. et al. Putative caveolin-binding sites in SARS-CoV proteins. Acta Pharmacologica Sinica 24, (2003).
Cancer Facts & Figures American Cancer Society (2008).
Lynn NS and DS Dandy. “Geometrical optimization of helical flow in grooved micromixers” Lab on a Chip. 7:
Mohammed, JS, HH Caicedo, et al. “Microfluidic add-on for standard electrophysiology chambers.” Lab on a Chip. 8:
Monahan, J., Gewirth, A. A. & Nuzzo, R. G. A method for filling complex polymeric microfluidic devices and arrays. Analytical Chemistry 73, (2001).
Nagrath, S, LV Sequist, et. al. “Isolation of rare circulating tumour cells in cancer patients by microchip technology.” Nature. 450:
Sieuwerts, A. M. et al. Anti-Epithelial Cell Adhesion Molecule Antibodies and the Detection of Circulating Normal-Like Breast Tumor Cells. Journal of the National Cancer Institute 101, (2009).
Thomas, W. Research Projects: Catch Bonds.<
Went, P. T. et al. Frequent EpCam protein expression in human carcinomas. Human Pathology 35, (2004).
Zen K, Liu D-Q, Guo Y-L, Wang C, Shan J, et al. (2008) CD44v4 Is a Major E-Selectin Ligand that Mediates Breast Cancer Cell Transendothelial Migration. PLoS ONE 3(3): e1826.