While these techniques are extremely powerful, they still lack an essential component – the spatial context of the cells analyzed. Human biology is spatial by nature, and tumors result from a heterogeneous population of cells and complex microenvironments. The molecular analysis of tumors, even at the single-cell level, only tells part of the story. We believe that the characterization of tissues at the spatial level could complement uni-dimensional data obtained from genomics, proteomics, RNA bulk measurement, and single-cell data, and could shed light on the spatial interactions between those basic building blocks and their effects on cancer development, prognosis, and treatment response.
Primetime for the spatial revolution
The two advancements that motivated the genomic revolution at the beginning of the 21st century were: 1. the ability to test many genes from the same tissue, in parallel using a multiplex test; 2. a dramatic drop in the cost of genetic sequencing.
A similar trend is now seen in spatial biology: novel technologies that were invented over the last years now allow for a multiplex analysis of tissue slides that can recognize the presence of 50-100 proteins, in parallel. Novel in situ analysis of DNA and RNA data, known as spatial genomics and transcriptomics, can provide deep biological information that also preserves the tissue, while providing spatial context regarding the molecular data.
At the same time, the cost of running these complex tests has dramatically decreased in recent years; the spatial analysis of high-plex stained panels now costs ~$1,000 per slide, similar to the cost of WGS by NGS.
As a result, we have seen a dramatic growth of the field of spatial biology in recent years, in terms of basic, translational, and clinical research. Indeed, an increasing number of publications now use spatial analysis tools to explore the tumor microenvironment.
Not surprisingly, spatially-resolved transcriptomics was named the “Method of the Year” by Nature in in 2020, underscoring the rise of spatial biology as the next frontier in cancer research. As technologies improve each year, reducing the costs of these methods, we should expect to see spatial analysis implemented as a standard cancer research tool in the coming years.
While spatial biology research can be applied in many ways, one of the medical fields that could benefit the most is immuno-oncology. The importance of cells’ spatial arrangement to immune response has been suggested since the early days of immunotherapy development. In 2014, around the time that the first anti-PD-1 inhibitor received FDA approval, Tumeh et al. (Nature, 2014) found a significant correlation between CD8 expression at the invasive margin and response to immunotherapy in patients with metastatic melanoma. Over the years, it became evidently more clear that the advanced, thorough examination of multiple biomarkers and their spatial organization must be reached for better accuracy of likely ‘responders’. For example, Lu et al. (JAMA, 2019) compared different biomarker modalities for PD-1/PD-L1 immune checkpoint therapy. Not surprisingly, they found that multiplex immunohistochemistry/immunofluorescence (mIHC/IF) demonstrated a significantly higher specificity and sensitivity for treatment response than PD-L1 single staining (PD-L1 IHC), tumor mutational burden analysis (TMB), and gene expression profiling (GEP). Remarkably, differences were observed in a relatively small multiplexed staining, using an average of 2 to 3 markers. Today, these staining methods can reach up to dozens of markers, likely enhancing predictive capabilities.
Nucleai offers a unique AI platform to make spatial biology actionable
Although new technologies for high-plex spatial imaging are now available, the extraction, analysis, and interpretation of biological data from these images is still challenging and labour intensive.
To build spatial biology atlasses that are not only large but also applicable, one would need advanced data analysis tools that can extract the desired data points and spatial features from the complex images. Said data points and spatial features would then need to be fused with clinical data to enable the generation of novel discoveries.
At Nucleai, we are committed to making spatial analysis actionable. Over the last four years, Nucleai has developed one of the most advanced AI platforms for analyzing spatial data. Through our partnership with leading cancer centers, we have been able to access thousands of slide images, tissue blocks and corresponding clinical data, and build computational models that accurately analyze diverse types of spatial imaging inputs. Our models support different stainings (H&E, IHC, chromogenic, mIF, IMC), labs, microscopes, and tissue types. Not only can our models extract biological data from slide images but they can also interpret the data to generate novel clinical insights.
Spatial biology and its significance will change the way we understand, investigate, and treat cancer in the near future. Leveraging our access to large amounts of spatial data and integrating it with our advanced AI spatial analysis algorithms, we aim to generate the first-ever comprehensive spatial biology atlas and lead the spatial revolution in biology.
References:
- Method of the Year. Nat Methods 5, 1 (2008). https://doi.org/10.1038/nmeth1153
- Method of the Year 2020: spatially resolved transcriptomics. Nat Methods 18, 1 (2021). https://doi.org/10.1038/s41592-020-01042-x
- Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568-571. doi:10.1038/nature13954
- Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195-1204. doi:10.1001/jamaoncol.2019.1549
