Early detection and prediction of cancer metastasis – Unravelling metastasis initiating cell as a dynamic marker using self- functionalized nanosensors

Abstract

The ability of cancer to metastasize to distant organs is an urgent problem to be solved clinically and continue to pose a greater challenge to researchers. Current treatments for cancer are ineffective for metastatic cancer due to inability of conventional imaging techniques to detect at early stages. Additionally, the ability to predict the ability of cancer to metastasize in advance will prove to be a valuable tool to improve patient prognosis. However, current techniques of prediction rely on molecular expression profiles which are highly tumor- specific, patient-specific and does not account for tumor heterogeneity. These factors critically impede the ability to use molecular expression profiles for early diagnosis and prediction of cancer metastasis. Hence, there is a need to concentrate on using tumor cells' phenotypic heterogeneity, which can serve as a diagnostic and predictive marker for cancer metastasis. Here, we have identified a rare subpopulation of cells within the tumor, which can signal metastasis initiation, known as metastasis-initiating cells (MICs) which are cancer non-specific, independent of epigenetic makeup, thereby becoming an excellent, unprejudiced marker for cancer metastasis irrespective of cancer type, the tissue of origin, and cancer stage. However, it is challenging to use MICs as a cellular marker for metastasis from a clinical perspective due to their rare, undetectable nature and the lack of sensitive methods to detect this elusive population of cells. To provide the ultra-sensitivity necessary for the early detection and prediction of cancer metastasis using MICs here we have created a self- functionalised nanosensor which is highly SERS active. The self- functionalized nanosensor enabled the detection of MIC using properties of intracellular biological processes and characters of the tumor microenvironment. The nanosensor enabled a detection sensitivity of 98%. It was able to identify MIC in a heterogeneous population of TICs with an unparalleled specificity of 99.62%. Further, the accuracy of MIC as a predictive marker for metastasis in a heterogeneous population of tumor spheroids was found to be 84.6%. This work contributed to the utilization of cancer cell heterogeneity to identify MIC, which can serve as a universal marker for diagnosis, prediction, and prognosis of tumor metastasis. To the best of our knowledge, this study is the first to design a probe that can provide both the diagnostic signature and predictive signature of cancer metastasis as early as the single cellular stage. This approach holds immense potential in the early diagnosis of metastatic tumors in a clinical setting and considerably improve patient prognosis.