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    Home > Biochemistry News > Natural Products News > Nature journal: new technology uses cancer cell behavior rather than genetic information to predict its metastasis

    Nature journal: new technology uses cancer cell behavior rather than genetic information to predict its metastasis

    • Last Update: 2019-05-28
    • Source: Internet
    • Author: User
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    May 2019 / BIOON / - researchers and clinicians don't fully understand why some cancers spread and some don't What they know is that when cancer spreads, the survival rate drops dramatically If doctors can predict the possibility of primary tumor metastasis, they can choose the best treatment for patients However, the current testing only focuses on tumor genetics, because it can mutate and change Photo source: Chris yankaskas, Ph.D candidate, Department of chemical and Biomolecular Engineering, Johns Hopkins University, wants to know whether he can predict cancer cell metastasis from another perspective by observing the phenotype or observable cell characteristics and behaviors of cancer cells Under the guidance of Konstantinos konstantopoulos, a core member and professor of nanobiology Research Institute, yankaskaskas and a group of researchers created microfluidic analysis to quantify cell invasion, or maqci, a diagnostic tool and method for predicting breast cancer metastasis by observing the behavior required for two key cell metastasis (rather than tumor genetics) "The complexity of cancer progression and the differences between cancer cells in each patient make it difficult to predict metastasis on a case by case basis," yankaskaskas said Our goal is to continue to use cells from patient biopsies to study breast cancer, and we hope to extend this technology to other types of cancer "Cancer treatment is laborious for the body and can be expensive Some patients need chemotherapy, radiotherapy, surgery, targeted treatment, or more comprehensive treatment MAqCI can help clinicians and patients to determine the most appropriate way to treat invasive cancer and avoid over treatment of less invasive cancer In order to develop their devices, yankaskas must first train maqci to recognize the characteristic behaviors of normal breast epithelial cells (control group), non-invasive breast cancer cells and invasive / metastatic breast cancer cells Once these parameters were determined, the team used independent cell populations, including samples from breast cancer patients, to verify that maqci was able to measure and characterize cells correctly The test measured two key cell behaviors necessary for metastasis: cell viability - the ability to measure cell distant metastasis to the body, and proliferation - the degree of cell proliferation The results, published in the journal Nature biomedical engineering, show that the accuracy, sensitivity and specificity of maqci are enough to predict whether breast cancer patients will have metastasis This technique has potential clinical application value because it uses a small sample size, provides results within one to two days, and can isolate these cells for further characterization Another advantage of maqci is that it can observe the observable characteristics of cells, and it is relatively simple and easy to explain, unlike gene screening It is difficult to predict whether cancer population can metastasize, and this behavior method provides a simpler and more effective prediction method Konstantopoulos said: "maqci has the potential to diagnose the metastasis tendency of tumor, and screen the treatment methods for the metastasis initiating cells according to the specific conditions of patients, for personalized treatment We are currently testing methods to predict life expectancy in patients with brain cancer We believe that maqci will become an important tool for diagnosis, prognosis and accurate treatment of solid tumor patients "Reference: Christopher L yankaskas et al A microfluidic asset for the quantification of the metric dependency of breast cancer specifications, nature Biomedical Engineering (2019) Doi: 10.1038/s41551-019-0400-9
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