Novel diagnostic and prognostic factors for the advanced melanoma based on the glycosylation-related changes studied by biophysical profiling methods

Melanoma is a life-threatening disease due to the early onset of metastasis and frequent resistance to the applied treatment. For now, no single histological, immunohistochemical or serological biomarker was able to provide a precise predictive value for the aggressive behavior in melanoma patients. Thus, the search for quantifying methods allowing a simultaneous diagnosis and prognosis of melanoma patients is highly desirable. By investigating specific molecular interactions with some biosensor-based techniques, one can determine novel prognostic factors for this tumor. In our previous study, we have shown the possibility of a qualitative in vitro distinguishing the commercially available melanoma cells at different progression stages based on the measurements of the lectin Concanavalin A interacting with surface glycans present on cells. Here, we present the results of the quantitative diagnostic and prognostic study of both commercial and patient-derived melanoma cells based on the evaluation of two novel factors: lectin affinity and glycan viscoelastic index obtained from the quartz crystal microbalance with dissipation monitoring (QCM-D) measurements. Two approaches to the QCM-D measurements were applied, the first uses the ability of melanoma cells to grow as a monolayer of cells on the sensor (cell-based sensors), and the second shortens the time of the analysis (suspension cell based-sensors). The results were confirmed by the complementary label-free (atomic force microscopy, AFM; and surface plasmon resonance, SPR) and labeling (lectin-ELISA; and microscale thermophoresis, MST) techniques. This new approach provides additional quantitative diagnosis and a personalized prognosis which can be done simultaneously to the traditional histopathological analysis.

Automated summary

Melanoma is a life-threatening disease with frequent resistance to treatment. Currently, no single biomarker can accurately predict the aggressiveness of melanoma. Therefore, it is highly important to search for a quantifying method for simultaneous diagnosis and prognosis of melanoma patients.