Description
Thyroid cancer is one of the most prevalent cancers in the world. The presence of nuclear anomalies, such as subnuclear folds and grooves, is a vital feature of thyroid cancer biopsies for diagnostic purposes. However, the accuracy and categorization of thyroid cancer are reliant on the pathologist’s experience and a significant portion of cases yield inconclusive results. Therefore, there is a need for the technologies that provide quantitative and robust readouts to differentiate thyroid cancer malignancy. In this study, we utilized nanopillar arrays to guide the nuclear morphology aberrations into ordered and quantifiable nanoscale patterns. These patterns effectively distinguish different phenotypes of thyroid cancer cells. In-depth examination of these nanoscale deformations via expansion microscopy reveals differential spatial arrangement of lamin proteins on nanopillars. Additionally, the nanopillar-guided deformation patterns are correlated to cancer metastatic behaviour, such as migration, adhesion. We envision that this nanopillar-based platform will act as an effective tool in quantifying the nuclear irregularities, improving the diagnosis of thyroid cancer.
