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The complexity of cancer is clearly demonstrated in the diverse ecosystem of the tumor microenvironment (TME). The TME is made up of numerous cell types and its development begins with the changes that happen during oncogenesis. “Genomic mutations, copy number changes, epigenetic alterations, and alternative gene expression occur to varying degrees within the affected tumor cells,” explained Andrea O’Hara, Ph.D., Strategic Technical Specialist at Azenta. “As this happens, immune cells infiltrate into the tissue, along with cancer-associated fibroblasts (CAFs) and all of these different oncogenic and non-oncogenic cells secrete cytokines, ultimately remodeling the extra-cellular matrix.” O’Hara noted that the TME encompasses the oncogenic cells, infiltrating and supporting cells, and the extracellular matrix, all of which combine to form the tumor.
Understanding the makeup of cells within the TME is an important area of research. “A tumor’s distinct TME composition impacts the aggressiveness of the tumor and our ability to successfully treat the tumor,” stated Jacob Tesdorpf, Senior Director of Life Sciences Markets at Revvity. “In recent years, CAR-T cell therapies have shown great promise with blood cancers, but not yet with solid tumors.” Investigating the TME, and specifically the roles of immune cells and signaling, Tesdorpf shared, is key to creating cell therapies for solid tumors.
Strategies for Exploring the TME
There are currently three primary approaches for effectively studying the TME. Tesdorpf outlined these methods as analyzing tumor samples from patients, utilizing animal models, and recreating the TME in vitro. He explained that patient-derived tissues offer relevant insights in their native context and may translate better into clinical benefits, but they are limited by scarcity, heterogeneity, and the invasive nature of biopsies. Whereas animal models, like mice, enable detailed in vivo studies and therapeutic testing, and can be engineered to replicate human diseases or support non-invasive analysis. However, they may not fully replicate human disease due to species-specific differences. In contrast, in vitro models, such as organoids and 3D cultures, provide controlled study conditions and are scalable for drug development. Even with their benefits, Tesdorpf noted that these models struggle to fully capture the complexity of the TME and pose standardization challenges.
Within these different approaches, spatial and single-cell technologies play an essential role in examining the TME. “Single-cell technologies allow insights into the different cell types that may be present within the TME, including details of the amounts and types of infiltrating cells,” explained O’Hara. These technologies can also be utilized to determine the precise immune repertoire within the infiltrating T and B cells. However, O’Hara pointed out that while traditional single-cell sequencing identifies which cells are present, it lacks spatial context and therefore fails to show where they are situated within the TME. “Spatial technology takes this to the next level, by evaluating RNA and protein expression within the 3-D context of the tissue, in a slide-by-slide manner,” she noted.
Recent Advancements
The introduction of spatial and single-cell technologies has greatly advanced the way we investigate and understand the TME. Before these technologies were introduced, O’Hara recounted that most research relied on bulk techniques such as RNA-Seq, DNA-seq (WGS/WES), and western blotting, often paired with traditional H&E staining or more complex ELISA assays. “Spatial and single-cell technologies allow for a much deeper understanding of the TME, as it allows for fine-grained detail of the cells and cell types present, in a way that bulk approaches cannot fully appreciate,” emphasized O’Hara.
Additionally, Tesdorpf discussed how advancements in the development of TME model systems in vitrohave led to more accurately representing in vivo conditions. “These advances include gene editing techniques such as CRISPR, novel matrices both of natural and synthetic origin, and physical methods of arranging cells and matrices like bio-printing and novel sample carriers”, he stated. With these tumor-on-chip devices, researchers can achieve real-time imaging, regulate key environmental factors (cell types, matrix), and utilize active fluidics. Tesdorpf further explained that the analysis of these in vitro models has been enhanced by improvements in imaging techniques (such as clearing protocols, two-photon, and light-sheet microscopy) and the previously mentioned spatial technologies.
For researchers interested in learning more about advances in the TME, Tesdorpf recommended a thorough review that discusses the recent progress in understanding the TME's role in disease and treatment, along with another review that covers the contributions of novel model systems.
Challenges and the Road Ahead
Despite the significant progress made in the field, working with tumor samples is still quite challenging. As O’Hara explained, the “size and complexity of the sample are major concerns: very small samples may not yield enough information while large samples may have wide variability in TME regions.” In addition, certain sample types are highly susceptible to degradation, influenced by their tissue of origin and the possibility of regional necrosis.
Another major challenge of the TME is the attempt to recreate the dynamic behavior of tumors within in vivo or in vitro model systems. Tesdorpf stressed that although this process remains difficult, patient-derived tumoroids that mimic the patients’ TME are a promising development. He also highlighted that the diverse composition of the TME, which varies greatly between tumors and patients, complicates these challenges even further. Spatial omics techniques can provide some insights into this intricate network of interactions, but they also accumulate large volumes of data and lack sufficient temporal resolution.
Similarly, O’Hara emphasized that managing the massive data from these studies presents a significant challenge, but she believes machine learning and AI are at the forefront of analyzing the vast amounts of information gathered from multiple analyses. With recent publications demonstrating their utility in analyzing healthy tissues, O’Hara suggested that the next step is to apply these techniques to tumor samples, enhancing our understanding of the TME and improving personalized medicine approaches for diagnosis and treatment.
“The large amounts of data generated by spatial omics technologies and the rapid growth of AI technology will hopefully accelerate our
understanding of the TME and show ways to deliver more efficient ways to treat cancer,” concluded Tesdorpf.
References
- Johnson, A., Reimer, S., Childres, R. et al. The Applications and Challenges of the Development of In Vitro Tumor Microenvironment Chips. Cel. Mol. Bioeng. 16, 3–21 (2023). https://doi.org/10.1007/s12195-022-00755-7
- de Visser, KE. & Joyce, JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 41, 3, 374–403 (2023). https://doi.org/10.1016/j.ccell.2023.02.016
- Tiwari, A., Trivedi, R. & Lin, SY. Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci 29, 83 (2022). https://doi.org/10.1186/s12929-022-00866-3