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 toolsthe 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.