Different cancer indications and even individual tumors show significant heterogeneity due to the many contributing parameters including a patient’s genetics, tumor mutations, lifestyle, chemical exposure, smoking status and comorbidities.
Some of those parameters influence the genetic makeup of the tumor, others influence the patient’s immune system, and some may influence both.
The complex interplay between mutated tumor cells and the patient’s immune cells takes place in the tumor microenvironment (TME) and a more comprehensive understanding of the TME may be the key to improving drug development, prognosis, and prediction of therapy response for patients with solid tumors.
The TME has two parts: the stroma and the immune component
The TME can be broadly divided into two categories: the stromal component which includes fibroblasts, endothelial cells, and extracellular matrix components, and the immune component, which includes a variety of immune cells such as macrophages, polymorphonuclear cells, mast cells, DCs, and T, B, and NK cells with the last three referred to as TIL (Tumor Infiltrating Lymphocytes).
The TME has a profound impact on cancer growth and progression. For example, cancer-associated fibroblasts can promote tumor growth by secreting growth factors and extracellular matrix components that support tumor cell proliferation and migration. Immune cells also play a critical role in the TME, with some promoting tumor growth (such as regulatory T cells), while others inhibit tumor growth and promote tumor cell death (such as cytotoxic T cells).
Given the important role of the TME in cancer, there is a growing interest in comprehensive characterization to better understand its influence on tumor growth and response to therapy.
Objectives of TME characterization
Depending on the clinical trial and the drug under investigation the objectives of TME characterization are different and may include:
- Quantifying biomarkers, for example HER2 or PD-L1 expression
- Monitoring infiltrating immune cells like NK cells or Cytotoxic T cells
- Measuring the activation status of infiltrating immune cells
- Characterizing the location of biomarkers and cells within the tumor
Precision for Medicine offers a wide range of techniques for tissue analysis, including immunohistochemistry (IHC), multiplex immunofluorescence (MIF), qPCR immunophenotyping (Epiontis ID), in situ hybridization (ISH), and spatial transcriptomics.
Some of these techniques can address most or all objectives of tissue analysis, however, the most effective approach for a certain study will depend on a project’s specific requirements.
Table 1. Tissue analysis objectives and methods most commonly used to address each
| Technology Use | Immunohistochemistry | Multiplex Immunofluorescence | qPCR Immunophenotyping (Epiontis ID) | In Situ Hybridization | Spatial Transcriptomics |
| In-situ protein/RNA detection | Yes; for protein | Yes; for protein | No; limited to cell type detection | Yes; for RNA | Yes; for RNA |
| Monitoring specific immune cells | Limited; can only visualize 1-2 markers at a time | Yes; can characterize complex phenotypes using multiple markers at once | Yes; monitors relative level of immune cell infiltration | Limited; target selection depends on probe availability | Yes; identifies cell types based on gene expression cluster profiles |
| Measuring cellular activation or exhaustion status | Limited; may need sequential slides for multiple markers | Yes; quantitative measurement within specific cell types | Yes; can monitor level of overall activation or exhaustion of immune cell infiltration | Limited; target selection depends on probe availability | Yes; gene expression can be used to reveal activation or exhaustion state |
| Providing spatial context | Yes; single-cell resolution but limited to one or two markers | Yes; provides single-cell resolution with spatial coordinates for multiple markers | No; lacks spatial context | Yes; provides basic spatial understanding of gene expression | Yes; provides spatial context for clusters of genes expressed in the tissue/cells |
| Quantitative Detection | Semi-quantitative; depends on scoring system | Yes; for multiple markers simultaneously | Yes; quantitative detection of immune cell content | Semi-quantitative; depends on probe design, imaging and scoring method | Yes; at transcriptome level |
| High-Throughput Analysis | Moderate; can be automated but each marker requires separate slide | Moderate; requires sophisticated imaging and analysis tools | High throughput; due to fully automated platform | Low to moderate; individual probe development can be labor-intensive | Moderate to high; depending on the technology used |
Precision for Medicine combines and customizes all outlined methods in close collaboration with the clinical partner to fit the level of detail required for efficient characterization of the TME in a given trial.
Methods for tissue analysis
Immunohistochemistry
IHC can be used to detect a wide range of proteins, including those expressed by tumor cells as well as those expressed by cells within the TME. One use of IHC is to assess the expression of predictive biomarkers, such as estrogen receptor or HER2, in breast cancer. IHC is considered a gold standard and is often validated to CLIA level for purposes like patient enrollment or primary endpoints. Occasionally, the assay might even be further validated and used as a companion diagnostic (CDx).
Multiplex Immunofluorescence
MIF, on the other hand, uses fluorescently-labeled primary antibodies to detect multiple proteins of interest simultaneously within the same tissue sample. The resulting fluorescence signal can be visualized and quantified using specialized imaging equipment, allowing for a more precise and quantitative analysis of protein expression and spatial distribution within the tissue sample.
Concurrently, the RNAscope probe reveals the presence of the target mRNA as discrete red dots.
Both MIF and IHC can also be used to detect markers of immune cells such as CD3 (for T cells) or CD68 (for macrophages) within tumor tissue.
qPCR Immunophenotyping (Epiontis ID)
Another method to quantify the number of immune cells within tumor tissue is qPCR immunophenotyping.1,2 This method uses immune cell specific epigenetic DNA-based biomarkers and allows reliable quantification of the immune cells present in any biological sample including frozen whole blood or tissue samples. This is a price-effective approach to quantify infiltrating immune cells as well as markers of immune activation and exhaustion but does not offer information of spatial distribution of these immune cells.
In Situ Hybridization
Another technique to address cellular activation states is ISH which can be used to visualize and quantify specific RNA transcripts within tissue samples. Unlike IHC, which detects proteins, ISH allows for the detection of RNA, including both coding and non-coding RNA. ISH can be used to study gene expression within the TME, which can be important for understanding the signaling pathways that are active within tumors, or even to detect specific sequences used in in cell therapy products. However, quantification and/or enumeration might not provide us with a more detailed understanding of the interactions between different cell types within the tumor microenvironment. To accomplish this, researchers need a tool that can help them gain a more comprehensive understanding of the distribution and organization of immune cells and other cell types within the TME, spatial context.
Spatial context can be used to assess the proximity of immune cells to tumor cells, which can be important for understanding the potential interactions between these cell types. Quantitative image analysis of IHC or MIF images allows for the objective assessment of protein expression levels or cellular distributions within the TME.
Spatial Transcriptomics
Spatial transcriptomics is a field of genomics that aims to accurately resolve mRNA expression at the cellular level in structurally preserved tissues, generating spatially resolved gene expression data from thousands to millions of individual cells within a tissue. Traditional transcriptomics methods measure the gene expression levels of individual cells or bulk tissue samples, providing the overall gene expression profile of the sample but lacking spatial information where in the tissue the genes are expressed. With spatial transcriptomics we are able, for the first time, to combine transcriptional profiles with location, which will reveal genes that may be important for regulating cell–cell interactions and the killing of tumor cells.
Choosing the most effective tissue analysis method
Each clinical trial requires a balance between the necessary level of detail of sample characterization and the associated cost. Choosing from a wide range of methods offered by Precision for Medicine together with careful consideration of advantages and disadvantages optimizes the chances for a project’s success. Explore Precision for Medicine’s solutions for tissue analysis.
References
- Baron et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci Transl Med. 2018 Aug 1;10(452)
- Blokland et al. Epigenetically quantified immune cells in salivary glands of Sjögren’s syndrome patients: a novel tool that detects robust correlations of T follicular helper cells with immunopathology. Rheumatology (Oxford). 2020 Feb 1;59(2):335-343.
- Giraldo el al. The clinical role of the TME in solid cancer. Br J Cancer. 2019 Jan;120(1):45-53.
- Goltsev Y, Samusik N, Kennedy-Darling J, et al. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell. 2018;174(4):968-981.e15.
- Saltz J, Gupta R, Hou L, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 2018;23(1):181-193.e7.
- Schalper KA. Tumor-Infiltrating Lymphocytes and PD-L1: Assessing the Intersection of Immune Biomarkers to Guide Clinical Decision Making in Cancer. Annals of oncology: official journal of the European Society for Medical Oncology. 2019;30(5):684-687.
- Sehouli et al. Epigenetic quantification of tumor-infiltrating T-lymphocytes. Epigenetics. 2011 Feb;6(2):236-46.
