Feasibility of Spatial Genomic Analysis on Pituitary Adenomas for Understanding Tumor Microenvironment

Pituitary adenomas represent ~15% of all intracranial tumors, and surgical treatment is pursued for medically refractory lesions when symptomatic mass effect is present on the optic chiasm and other basal structures that may result in visual deficits, hydrocephalus, and endocrine abnormalities. Transnasal resection of pituitary tumors (TSRPT) is the standard approach for surgical treatment of symptomatic pituitary adenomas; however, the 10-year recurrence rate is often ~20% even after gross total resection, and better understanding of heterogeneity amongst the tumor micro-environment may better predict lesions at high risk for recurrence and which are susceptible to adjuvant medical or radiation therapies.

Herein, we demonstrate the feasibility of utilizing “CurioSeeker” spatial mRNA analysis of pituitary tumor single cell sections with cellular phenotype determined by concurrent immunofluorescent staining to detect transcriptional heterogeneity within the tumor micro-environment and aim to correlate this with tumor recurrence and response to adjuvant treatments. In particular, within the same tumor we demonstrate with samples from 5 gonadotrophic pituitary tumors that single cell expression profiles differ between phenotypic cell types (PRL and GH expressing cells), but even within phenotypic lines there is heterogeneity that is spatially clustered. Methodologically, processing is as depicted in [Fig. 1]: frozen samples are sectioned into single cell thickness slides, and sequential slides are analyzed either with PRL and GH co-labeling for phenotypic cellular analysis, or the CurioSeeker spatial mRNA on-slide mRNA sequencing system with subsequent digital merging of data from each slide to correlate mRNA findings with phenotypic expression patterns for each cell cluster. This preliminary study showcases the workflow for single cell spatial mRNA sequencing of pituitary tumors after TSRPT resection that we hope to use for future personalized prognostication regarding recurrence and treatment decisions.

Publikationsverlauf

Artikel online veröffentlicht:
07. Februar 2025