Bringing Spatial Biology to the Clinic

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Spatial biology is gaining steam as new tools emerge and researchers clamor to produce cellular atlases of tissues at various developmental stages. Tools for resolving spatial biology are poised to transform clinical research in several key ways. In cancer immunotherapy, a recent meta-analysis1 demonstrated that Multiplex Immunofluorescence (mIF) performed better than any single modality, and in fact performed comparably to combinations of any two of the other assays.

From discovery to translation

Cancer immunotherapy achieves remarkable remissions in a subset of patients, but predictive markers are urgently needed to identify patients most likely to benefit. mIF provides information not only about multiple markers on a cell, but spatial information about where different cells or cellular neighborhoods are located and how they interact to influence disease progression. Analysis of the proximity of different immune cell neighborhoods to each other, and to the tumor, can strongly influence disease pathology, progression, and treatment response.

As The Spatial Biology Company®, Akoya Biosciences’ mission is to bring context to the world of biology and human health through the power of spatial phenotyping. Akoya offers two distinct solutions, the CODEX® and Phenoptics™ platforms, to serve the diverse needs of researchers across discovery, translational, and clinical research.

Combining astronomy and pathology to build a new biomarker signature

Astronomers face some of the same challenges as cell biologists when it comes to processing huge amounts of 3D data. Investigators at the John Hopkins University (JHU) combined the Phenoptics mIF platform with sky-mapping algorithms for managing astronomical-scale datasets with Akoya’s Phenoptics mIF platform to create AstroPath™, a novel platform for deep whole slide imaging and spatially mapping microscopic sections of tumors.

Through this study, researchers identified a new spatial phenotypic signature that is highly predictive of response to anti-PD-1 agents and of long-term outcomes for metastatic melanoma, and potentially other cancers. By analyzing the spatial map of six protein markers, PD-1, PD-L1, CD8, CD163, FOXP3, and SOX10, in the tumor microenvironment, the researchers were able to study how tumor and immune cells are spatially organized and how they interact to influence cancer progression and treatment response. JHU researchers are already testing lung cancer using AstroPath™, and the system could be similarly applied to other cancers.

Phenoptics provides reproducible, high-throughput results

mitre phenoptics spatial proximity analysis
Representative image showing a TMA core with proximity map overlay, where orange dots represent PD-1+ cells, and green dots represent PD-L1+ cells. White lines display distance from all PD-L1+ cells to neighboring PD-1+ cells. Only those within 25 µm are counted (scale bar represents 200 µm).

Akoya gathered immuno-oncology and pathology experts from Johns Hopkins University School of Medicine, Yale University School of Medicine, Earle A. Chiles Research Institute, The University of Texas MD Anderson Cancer Center, and Bristol Myers Squibb, to take part in the “Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study,” which was published in the Journal for ImmunoTherapy of Cancer in July 2021, representing the first multi-institutional analytical demonstration of a spatial biology workflow.

The MITRE study optimized an automated 6-plex biomarker assay focused on the PD-1/PD-L1 axis and assessed the inter- and intra-site reproducibility of the assay by measuring spatial biology parameters of tumor and immune cells within tissue samples. The study also measured the proximity of PD-1+ and PD-L1+ cells, which can enhance the predictive value of immunotherapy biomarkers. Akoya’s Phenoptics™ workflow demonstrated high concordance across multiple institutions. The study confirmed that quantitative measures of multiple biomarkers in a tissue section are reproducible at levels aligned with typical clinical testing standards.


1. Lu S, 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

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