Project title: Revealing the complexities of the pituitary tumor microenvironment via single cell and spatial transcriptomic sequencing
Project No.: lzp-2025/1-0304
Period: 1 January 2026 – 31 December 2028
Project costs: 300 000,00 EUR
Principal Investigator: Dr. biol. Raitis Pečulis
Project summary:
Pituitary neuroendocrine tumors (PitNETs) are most common intracranial tumor, yet they seldom metastasize. Pituitary tissue shows plasticity, but mechanisms preventing cancer are unclear. The study will use spatial transcriptomics to investigate samples from SF1 lineage PitNETs, cell line models and cancer cells to: 1) identify spatial features preventing malignancy, analyzing cell interactions, cell types and pituitary plasticity; 2) explore clinical applicability of markers from gonadotroph PitNET spatial transcriptomic data, comparing PitNETs to other tumors, seeking novel therapeutic targets and functional investigating recent literature reports; 3) perform integrated data analysis linking spatial cell distribution with tumor cell composition from single cell data and identifying clinical group, lineage and PitNET specific localizations, biomarkers and common trends when comparing to other groups as well as reference data including PitNET, cancerous tumor and other neuroendocrine tumors. The project will characterize PitNET cell types and their spatial distribution. Functional experiments will use antisense RNA to investigate roles of recent literature finds and confirm novel discoveries of this project to advance understanding of therapeutic targets for SF1 lineage PitNETs and beyond. This aims to reveal PitNET biology, improve treatment, and potentially inform other cancer research.
Information published 05.01.2026.
Progress of the project:
1 January 2026 – 31 March 2026
The project lzp-2025/1-0304 “Pituitary tumor microenvironment research”, amendments have been made to obtain research ethics permission. Information has been compiled and a database has been created on pituitary tumor patients in the Latvian Genome Database, which includes sample availability, research permits, and analysis data accumulated in previous projects. Theoretical research, compilation, and issue resolution of technical details of data analysis methods are underway. Training is being conducted with the Aviti24 tool to enable the maximum number of necessary spatial transcriptomics analyses to be performed at the Latvian Biomedical Studies and Research Centre. Cooperation is being established with industry representatives to obtain high-resolution tumor cross-section images from FFPE pituitary tumor samples for further development of machine learning methods.
Information published 31.03.2026.