Cancer cells have the ability to migrate from the primary (original) site to other places in the body. The extracellular matrix affects cancer cell migratory capacity and has been correlated with tissue-specific spreading patterns. However, how the matrix orchestrates these behaviors remains unclear. Here, we investigated how both higher collagen concentrations and TGF-β regulate the formation of H1299 cell (a non-small cell lung cancer cell line) spheroids within 3D collagen-based matrices and promote cancer cell invasive capacity. We show that at low collagen concentrations, tumor cells move individually and have moderate invasive capacity, whereas when the collagen concentration is increased, the formation of cell clusters is promoted. In addition, when the concentration of TGF-β in the microenvironment is lower, most of the clusters are aggregates of cancer cells with a spheroid-like morphology and poor migratory capacity. In contrast, higher concentrations of TGF-β induced the formation of clusters with a notably higher invasive capacity, resulting in clear strand-like collective cell migration. Our results show that the concentration of the extracellular matrix is a key regulator of the formation of tumor clusters that affects their development and growth. In addition, chemical factors create a microenvironment that promotes the transformation of idle tumor clusters into very active, invasive tumor structures. These results collectively demonstrate the relevant regulatory role of the mechano-chemical microenvironment in leading the preferential metastasis of tumor cells to specific tissues with high collagen concentrations and TFG-β activity.

The composition and intercellular interactions of tumor cells in the tissues dictate the biochemical and metabolic properties of the tumor microenvironment. The metabolic rewiring has a profound impact on the properties of the microenvironment, to an extent that monitoring such perturbations could harbor diagnostic and therapeutic relevance. A growing interest in these phenomena has inspired the development of novel technologies with sufficient sensitivity and resolution to monitor metabolic alterations in the tumor microenvironment. In this context, surface-enhanced Raman scattering (SERS) can be used for the label-free detection and imaging of diverse molecules of interest among extracellular components. Herein, the application of nanostructured plasmonic substrates comprising Au nanoparticles, self-assembled as ordered superlattices, to the precise SERS detection of selected tumor metabolites, is presented. The potential of this technology is first demonstrated through the analysis of kynurenine, a secreted immunomodulatory derivative of the tumor metabolism and the related molecules tryptophan and purine derivatives. SERS facilitates the unambiguous identification of trace metabolites and allows the multiplex detection of their characteristic fingerprints under different conditions. Finally, the effective plasmonic SERS substrate is combined with a hydrogel-based three-dimensional cancer model, which recreates the tumor microenvironment, for the real-time imaging of metabolite alterations and cytotoxic effects on tumor cells.

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.