Spatially resolved transcriptomics of benign and malignant peripheral nerve sheath tumors.

BACKGROUND

Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells. Here, we aimed at determining the spatial and biological heterogeneity of PNSTs.

METHODS

We performed spatial transcriptomics on formalin-fixed paraffin-embedded diseased peripheral nerve tissue. We used spatial clustering and weighted correlation network analysis to construct niche-similarity networks and gene expression modules. We determined differential expression in primary pathologies, analysed pathways to investigate the biological significance of identified meta-signatures, integrated the transcriptional data with histological features and existing single cell data, and validated expression data by immunohistochemistry.

RESULTS

We identified distinct transcriptional signatures differentiating PNSTs. Immune cell infiltration, APOD and perineurial fibroblast marker expression highlighted the neurofibroma component of hybrid PNSTs (HPNSTs). While APOD was evenly expressed in neurofibromatous tumor tissue in both, HPNST and pure neurofibromas, perineurial fibroblast markers were evenly expressed in HPNST, but restricted to the periphery in plexiform neurofibromas. Furthermore, we provide a spatial cellular differentiation map for MPNST, locating Schwann cell precursors, neural crest-like cells and those with mesenchymal transition.

CONCLUSIONS

This pilot study shows that applying spatial transcriptomics to PNSTs provides important insight into their biology. It helps establishing new markers and provides spatial information about cellular composition and distribution of cellular differentiation states. By integrating morphological and high-dimensional molecular data it can improve PNSTs classification in the future.