Background Brain tumors present a complex and highly heterogeneous form of cancer that is challenging to treat, with a median survival of just over one year after diagnosis for primary glioblastomas. Identifying the complex spatial composition of the brain tumor microenvironment (TME) remains challenging, yet it is vital for accurate interpretation of disease origin, progression and treatment. Imaging Mass Cytometry (IMC™) approach address these challenges by enabling quantitative evaluation of multiparametric structural and cellular features of the TME. An IMC based analysis facilitates exploration of tissue heterogeneity across patient samples without the complications of multiplexed fluorescence-based approaches, such as autofluorescence, tissue degradation and spectral overlap. Here, we showcase the application of a neuro-oncology-themed IMC panel on various brain neoplasms.

Methods A 40-plus-marker neuro-oncology IMC antibody panel, consisting of neurophenotyping and human immuno-oncology panels, was used to determine the cellular and structural landscapes of the brain TME. We applied the panel on a tissue microarray (TMA) containing dozens of human brain tumor cores and performed imaging using two whole slide scanning workflows enabled by preview mode, cell mode and tissue mode of the Hyperion XTi™ Imaging System, followed by quantitative data analysis.

Results We mapped the spatial location of brain-specific cell populations making up human tumors – such as NeuN+ neurons, GFAP+ astrocytes, Iba1+ microglia and Olig2+ oligodendrocytes – across the entire TMA using neurophenotyping markers. We classified the distinct states of neurons and quantified CD68+ and CD163+ myeloid and CD8+ and CD57+ lymphoid immune cell infiltration across astrocytoma and glioblastoma tissues. In addition, cells expressing clinically relevant biomarkers, such as PD-L1, HLA-DR, CD66b and CD44, were detected in tumor and infiltrated immune cells in subsets of tumors. Relative quantities of these cells were measured using single-cell analysis utilizing machine learning-based cell segmentation tools and cell-clustering algorithms. Pixel-clustering analysis on Tissue Mode data segregated tissue compartments containing glioma-initiating cells, stem cells, activated astrocytes, cancer-associated fibroblasts, infiltrating immune cells, immune-tumor interfaces, microglia, tumor-associated macrophages, angiogenic vasculature and other biologically relevant tissue structures.

Conclusions Quantitative spatial evaluation of cellular heterogeneity in the brain TME using IMC approach provides insights into the complexity of gliomas and other brain tumors without the artifacts commonly seen in fluorescence-based imaging techniques. In-depth deciphering of the glioma landscape can help in guiding potential personalized therapeutic strategies and effective future clinical solutions to improve patient outcomes.

For Research Use Only. Not for use in diagnostic procedures.