Cell Surface Marker: Some protein or receptor sticking out of the cell membrane. I explain this more in depth in my immunocapture post.
CTC: Circulating tumor cell. Read about what they are and why they’re important here.
Microemboli: A small mass of cells or tissue inside the bloodstream.
Platelets: aka thrombocytes, important in the formation of blood clots.
Phenotype: Observable characteristics of a cell.
RBCs: Red blood cells, aka erythrocytes.
Senescence: When a cell hits the Hayflick limit and can no longer divide naturally.
WBCs: White blood cells, aka leukocytes.
Benchmarking is critical for assessing CTC isolation tech performance
In my “How to Sort CTCs” series, I covered a variety of sorting methodologies used for patient prognosis. However, before clinical implementation, it is important characterize device performance with a series of standards. This is impossible to do with a patient blood sample, because there is an unknown number of CTCs floating around with other blood cells, which can be effected by the cancer treatment process (e.g. radiation patients often have anemia)1. Furthermore, this is all changing dynamically as a function of both time and treatment.
For this reason, engineers need an alternative system that can serve as a patient blood model, but it is repeatable and controllable. Immortalized cancer cell lines—derived from cancers of various organs—are commonly used for this purpose. A normal human cell can only divide a set number of times before it undergoes senescence; this is called the Hayflick limit. Immortalized cell lines have been genetically altered to surpass the Hayflick limit and continue dividing indefinitely.
This enables researchers to create standardized systems to benchmark their technology with. A known number of cancer cells can be spiked in varying ratios with different blood components, allowing for measurement of sensitivity and specificity of capture, along with other metrics. For this reason…
Cancer cell lines are excellent test platforms
- Were originally derived from a tumor source.
- Are much easier to culture outside the body.
- Have predictable cell surface marker expression.
- Are relatively homogenous in size.
- Can be uniformly distributed in solution.
- Have much better characterized genetic profiles.
Cell lines can be selected to match the organ type of the CTC they are modeling, and spiked at known concentrations in controllable scenarios to explore a device’s operational space. However…
CTCs are a mosaic of phenotypes
- Span a broad size distribution.
- Have variable cell surface marker expression. One CTC population may overexpress the target marker, while another population might not express it at all, and everything in between2,3,4.
- Have extraordinarily heterogeneous genetic profiles. Recent studies analyzing CTCs have shown there is little consistency in genetic mutation in CTCs from patients with the same cancer, or between CTCs in the same cancer patient sample5,6. Additionally, there is debate on cancer cell line applicability, as they’ve spent decades evolving in plastic culture flasks while CTCs are evolving organ tissue7.
- Don’t always travel alone. Several CTC isolation systems have observed what are called “CTC clusters” or “CTC microemboli”, where CTCs form a mass with other blood cell types (e.g. WBCs and platelets)8-12. The implications of these clusters are unknown, but are under increasing study. This makes capture not just a function of CTC phenotype, but also dependent on cells the CTC is attached to. This phenomenon can be mimicked using cancer cell lines, but does not occur when simply placing cancer cells in blood13.
What’s the takeaway?
From comparing the two, it becomes clear that cancer cell lines are significantly different than the in vivo target they are used to model. While cancer cell lines are sometimes referred to interchangeably with CTCs, it is important to remember that cancer cell lines are an approximation used for benchmarking, while CTCs are the prognostic indicator used to inform clinical outcomes.
My last image shows examples of the things I discussed, but using actual patient CTC data that has been immunofluorescently labeled to distinguish between the different cell types (click to enlarge).
1. Harrison L., Shasha D., Shiaova L., White C., Ramdeen B. & Portenoy R. Prevalence of anemia in cancer patients undergoing radiation therapy, Seminars in Oncology, 28 (2F) 54-59. PMID: 10.1053/sonc.2001.25399
2. Nagrath S., Sequist L.V., Maheswaran S., Bell D.W., Irimia D., Ulkus L., Smith M.R., Kwak E.L., Digumarthy S. & Muzikansky A. & Isolation of rare circulating tumour cells in cancer patients by microchip technology., Nature, PMID: 18097410
3. Kirby B.J., Jodari M., Loftus M.S., Gakhar G., Pratt E.D., Chanel-Vos C., Gleghorn J.P., S Santana, Liu H. & Smith J.P. & (2012). Functional characterization of circulating tumor cells with a prostate-cancer-specific microfluidic device., PloS one, PMID: 22558290
4. Lazar D.C., Cho E.H., Luttgen M.S., Metzner T.J., Uson M.L., Torrey M., Gross M.E. & Kuhn P. (2012). Cytometric comparisons between circulating tumor cells from prostate cancer patients and the prostate-tumor-derived LNCaP cell line., Physical biology, PMID: 22306736
5. Powell A.A., Talasaz A.H., Zhang H., Coram M.A., Reddy A., Deng G., Telli M.L., Advani R.H., Carlson R.W. & Mollick J.A. & (2012). Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines., PloS one, PMID: 22586443
6. Magbanua M.J.M., Sosa E.V., Roy R., Eisenbud L.E., Scott J.H., Olshen A., Pinkel D., Rugo H. & Park J.W. (2012). Genomic profiling of isolated circulating tumor cells from metastatic breast cancer patients., Cancer research, PMID: 23135909
7. Borrell B. (2010). How accurate are cancer cell lines?, Nature, 463 (7283) 858-858. DOI: 10.1038/463858a
8. Hofman V.J., Ilie M.I., Bonnetaud C., Selva E., Long E., Molina T., Vignaud J.M., Flejou J.F., Lantuejoul S. & Piaton E. & (2010). Cytopathologic Detection of Circulating Tumor Cells Using the Isolation by Size of Epithelial Tumor Cell Method: Promises and Pitfalls, American Journal of Clinical Pathology, 135 (1) 146-156. DOI: 10.1309/AJCP9X8OZBEIQVVI
9. Molnar B., Ladanyi A., Tanko L., Sréter L. & Tulassay Z. Circulating tumor cell clusters in the peripheral blood of colorectal cancer patients., Clinical cancer research : an official journal of the American Association for Cancer Research, PMID: 11751505
10. Stott S.L., Hsu C.H., Tsukrov D.I., Yu M., Miyamoto D.T., Waltman B.A., Rothenberg S.M., Shah A.M., Smas M.E. & Korir G.K. & (2010). Isolation of circulating tumor cells using a microvortex-generating herringbone-chip., Proceedings of the National Academy of Sciences of the United States of America, PMID: 20930119
11. Marrinucci D., Bethel K., Luttgen M., Bruce R.H., Nieva J. & Kuhn P. Circulating tumor cells from well-differentiated lung adenocarcinoma retain cytomorphologic features of primary tumor type., Archives of pathology & laboratory medicine, PMID: 19722757
12. Cho E.H., Wendel M., Luttgen M., Yoshioka C., Marrinucci D., Lazar D., Schram E., Nieva J., Bazhenova L. & Morgan A. & (2012). Characterization of circulating tumor cell aggregates identified in patients with epithelial tumors., Physical biology, PMID: 22306705
13. Tormoen G.W., Cianchetti F.A., Bock P.E. & McCarty O.J.T. (2012). Development of coagulation factor probes for the identification of procoagulant circulating tumor cells., Frontiers in oncology, PMID: 22973554