Researchers and clinicians don’t fully understand why some cancers spread, while others don’t. What they know is that when cancer spreads, the survival rate will drop dramatically. If the doctor can predict the possibility of primary tumor metastasis, they can choose the best treatment for the patient. However, current tests focus only on tumor genetics because it can mutate and change.
Chris Yankaskas, a Ph.D. student in the Department of Chemistry and Biomolecular Engineering at Johns Hopkins University, wondered if he could predict cancer cell metastasis from another perspective by observing the phenotype of cancer cells or observable cell characteristics and behavior. Under the direction of the core member of the Nanobiology Institute and professor Konstantinos Konstantopoulos, Yankaskas and a team of researchers created microfluidic analysis to quantify cell invasion (MAqCI) – a diagnostic tool and method for predicting breast cancer metastasis by observing the behavior required for two key cell metastases (rather than tumor genetics).
Yankaskas said: “The complexity of cancer progression and the differences between cancer cells in each patient make it difficult to predict metastases on a case-by-case basis. Our goal is to continue to use the cells in the biopsy of patients to study breast cancer and hope to extend this technology to other cancer types. ”
Cancer treatment is laborious for the body and can be expensive. Some patients require chemotherapy, radiation therapy, surgery, targeted therapy, or a combination of the above. MAqCI can help clinicians and patients determine the most appropriate treatment for aggressive cancers and avoid over-treatment of less aggressive cancers.
In order to develop their equipment, Yankaskas must first train MAqCI to identify the characteristic behavior of normal mammary epithelial cells (control), non-invasive breast cancer cells, and invasive/metastatic breast cancer cells.
Once these parameters were determined, the team used independent cell populations, including specimens from breast cancer patients, to verify that MAqCI correctly measured and characterized cells.
The test measures two key cellular behaviors necessary for metastasis: cell viability – the ability to measure distant metastasis of cells into the body, and the ability to proliferate – the extent of cell proliferation.
The study, published in the journal Nature Biomedical Engineering, shows that the accuracy, sensitivity, and specificity of MAqCI are sufficient to predict whether a breast cancer population will metastasize. This technique has potential clinical value because it uses a small sample size to provide results in one to two days and can separate these cells for further characterization.
Another advantage of MAqCI testing is that it can observe the observable characteristics of cells and is relatively simple and easy to interpret, unlike genetic screening, which is difficult to predict whether a cancer population can metastasize, and this behavioral approach provides a simpler and more effective prediction method.
Konstantopoulos said: “MAqCI has the potential to diagnose tumor metastasis trends and screen treatments for metastatic priming cells for individualized treatment based on patient specific conditions. We are currently testing methods to predict life expectancy in patients with brain cancer. We believe MAqCI will be an important tool for diagnosis, prognosis and accurate treatment of patients with solid tumors.”