Predicting Metastasis from Cues of Metastatic Cancer Stem-like Cells-3D-Ultrasensitive Metasensor at a Single-Cell Level.

Metastasis is the primary reason for treatment failure and cancer-related deaths. Hence forecasting the disease in its primary state can advance the prognosis. However, existing techniques fail to reveal the tumor heterogeneity or its evolutionary cascades; hence they are not feasible to predict the onset of metastatic cancer. The key to metastasis originates from the primary tumor cells, evolving by inheriting multistep sequential cue signals. We have identified this specific population, termed metastatic cancer stem-like cells (MCSCs), to foresee cancer's ability to metastasize. An invasive property renders MCSCs nonadherent, summoning a powerful technique to forecast metastasis. Thus, we have generated an ultrasensitive 3D-metasensor to efficiently capture and investigate MCSCs and magnify the vital premetastatic signals from a single cell. We developed 3D-metasensor by an ultrafast laser ionization technique, consisting of self-assembled three-dimensionally organized nanoprobes incorporated with dopant functionalities. This distinct methodology establishes attachment with nonadherent MCSCs, elevates Raman activity, and enables probing of consequent signals (metabolic, proliferation, and metastatic) specifically altered in MCSCs. Extensive analysis using prediction tools-the area under the curve (AUC) and principal component analysis (PCA)-revealed high sensitivity (100%) and specificity (80%) to differentiate the MCSCs from other populations. Further, investigation reveals that the cue signal level from MCSCs of primary cancer is analogous to MCSCs from higher-level tumors, disclosing the relative dependence to estimate the primary tumor's capacity to metastasize. Multiple spectrum evaluation using the metasensor pinpoint the dynamic cues in MCSCs predict the onset of metastasis; thus, exploring these metastasis hallmarks can enhance prognosis and revolutionize therapy strategies.