Spatial biology is adding new dimensions to translational medicine.
Spatial biology is adding new dimensions to translational medicine.
Advances in biomarker analysis have led to major breakthroughs in cancer research, translational medicine, therapy and diagnostics. Progress has been powered by rapid data generation using high throughput methods such as next generation sequencing and proteomics based on mass spectrometry. With much of the low hanging fruit already harvested, innovative therapies, such as immunotherapy, are offering new ways to combat cancer at the level of the individual patient. Biomarkers help to fine tune this personalized, or precision, medicine in the form of companion diagnostics that guide the use of personalized treatment options, for example in breast cancer.
Progress now depends on going to the next level – mapping biomarkers in space and time to elucidate the evolving tumor microenvironment (TME). This is where spatial biology and spatial-omics (or spatial multiomics), which integrates and visualizes transcriptomic and proteomic data, are providing invaluable insights.
Spatial-omics – mapping the progress of cancer in 3D
The diverse individual cell types and cell states of tumors create a highly complex and dynamic TME that can only be fully understood by spatial analysis. Spatial-omics is now extending the resolution of cancer research and diagnosis from the tissue or organ to the cellular and subcellular level. Spatial phenotyping, based on techniques such as multiplex immunohistochemistry (IHC) or multiplex immunofluorescence (mIF), is proving to be particularly valuable and involves tissue imaging with single cell resolution to visualize and quantitate biomarker expression to map the organization of cells in the tissue and how they interact.
Combining spatial-omics data, such as gene expression data and protein co-detection data can add a new dimension of information – for example, patterns and associations formed by individual cells with specific phenotypes and functions that are not evident with other methodologies (cellular neighborhoods), and posttranslational modification and subcellular localization of proteins and their dysregulation in disease. Spatial transcriptomics and spatial phenotyping are also supporting the drive toward personalized (individualized) medicine.
Spatial phenotyping brings new insights and increases predictive power
There are many examples of how combining biomarkers can bring new insights into disease and therapeutic mechanisms and increase predictive power. Spatial phenotyping, for example based on multiplex IHC, promises to bring this to a new level. For example, cancer immunotherapy is revolutionizing cancer treatment by boosting the natural immune response and advances depend on a deep understanding of the TME. Multiplexed immunophenotyping can give new insights into this and are essential in the identification of predictive biomarkers of response and gaining insights into therapeutic mechanisms of action1.
One example that illustrates the predictive power of spatial phenotyping is the meta-analysis by Steve Lu and coworkers, based at Johns Hopkins Medical Institutions, Baltimore, USA aimed at determining the value of this approach in predicting patient response to anti-PD-1/PD-L1 (programmed cell death ligand 1) therapy2. Their analysis built on reports on the diagnostic accuracy of tumor mutational burden (TMB), gene expression profiling (GEP), and spatial phenotyping using multiplex immunohistochemistry/immunofluorescence (mIHC/IF). The meta-analysis showed that while TMB, PD-L1 IHC, and GEP alone had comparable areas under the curve (AUC) in predicting response to treatment, mIHC/IF and multimodality biomarker strategies were better at predicting response (Figure 1).
Figure 1. Receiver operating characteristic (sROC) curves used to compare performance of the different biomarker approaches in predicting response to treatment. The relative area under the curves (AUCs) measure each approach’s ability to distinguish responders from non-responders. Spatial phenotyping using multiplex immunohistochemistry (mIF) is the best approach to predicting response. Figure reproduced from ref. 3, based on figure 2B, ref. 2.
The researchers concluded that, “The relative success of mIHC/IF in predicting patient response also provides insight into the spatial importance of tumor-immune interactions and the contribution of protein marker co-expression”. What was striking was that this success was achieved with an average of only 2–3 biomarkers, which suggests that exploiting the large multiplexing capacity of mIHC/mIF has the potential of improving diagnostic accuracy even more.
Staying ahead with spatial phenotyping
Spatial phenotyping certainly highlights the wealth of valuable data that can be extracted from a tissue slide. The power of this approach is so great that it has been suggested by Matt Humphries, scientific lead for Tissue Hybridization and Digital Pathology at Queen’s University Belfast, Northern Ireland, that labs that fail to include spatial phenotyping into their research run the risk of being left behind4.
Spatial phenotyping using multiplex IHC
Several multiplex imaging techniques have been developed to generate spatial multi-biomarker data from biopsies5. One approach is the automated multiplex system PhenoCycler-Fusion (formerly CODEX®; Akoya Technologies; https://www.leinco.com/codex_technology/) that uses DNA barcode technology comprised of unique oligonucleotide sequences conjugated to an antibody. An initial step involving staining tissue with up to 100 uniquely barcoded antibodies is followed by cycles of visualizing three antibodies at a time with reporter dyes linked to complimentary barcodes. This enables the visualization and quantification of dozens of biomarkers in a single tissue sample while maintaining cellular and sub-cellular detai