SFEIES24 Poster Presentations Neuroendocrinology (30 abstracts)
1Precision Medicine Centre for Excellence, Patrick G Jonston Centre for Cancer Research, Queens University Belfast, Belfast, United Kingdom; 2Queens University Belfast, Belfast, United Kingdom; 3Regional Centre for Endocrinology and Diabetes, Belfast Health and Social Care Trust, Belfast, United Kingdom; 4Department of Neurosurgery, Belfast Health and Social Care Trust, Belfast, United Kingdom; 5Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; 6Illumina Centre, Illumina, Cambridge, United Kingdom; 7Centre for Public Health, Queens University Belfast, Belfast, United Kingdom; 8Northern Ireland Biobank, Belfast, United Kingdom
Background: Transcription factors TPIT, SF-1 and PIT-1 correlate to differentiation of the pituitary cell lineages. In 2017 the World Health Organisation updated the classification of pituitary neuroendocrine tumours (PitNETs) to include transcription factor immunohistochemical (IHC) staining. This has been reiterated in the 2022 classification of PitNETs.
Aim: To further refine the diagnostic classification of non-functioning PitNETs using transcriptomics.
Methods: Clinicopathological data were extracted from a retrospective pseudoanonymised database of 350 non-functioning PitNET patients who underwent surgery in Northern Ireland. Ethical approval for access to and use of corresponding PitNET samples in research was granted by the Northern Ireland Biobank. RNA extraction was performed with Illuminas TruSeq RNA exome from formalin-fixed paraffin-embedded tissue samples. RNA-seq was aligned to hg38 and sequenced on the Illumina NovaSeq 6000 system. The World Health Organisation 2022 classification of pituitary tumours was used to inform transcript selection, namely of TPIT, SF-1 and PIT1. Two hundred and fifty-seven nonfunctioning PitNETs successfully surpassed sequencing QC thresholds. Transcriptomic data was analysed using Bioconductors DeSeq2 Package for differential gene expression analysis and Pheatmap for data visualisation.
Results: TPIT, SF-1 and PIT1 concurred with 18/21 (90%) of the ACTH positive, 50/51 (98%) of the gonadotrophin positive and 3/11 (27%) of the growth hormone, thyrotrophin and prolactin positive tumours as described by IHC. Twenty-two hormone-negative tumours were reclassified as corticotrophinoma, 141 hormone-negative tumours were reclassified gonadotrophinomas, and 10 hormone-negative tumours were reclassified as PIT1 lineage. Hormonal-IHC and RNA-seq were discordant on three samples.
Conclusion: The results are proportionally equivalent to the literature, with SF-1 (199 (77%)) the most prevalent subtype, followed by TPIT (44 (17%)) and the PIT1 lineage (14 (4%)). This method refines the non-functioning PitNETs diagnosis in this Northern Ireland cohort, most notably reclassifying 22 tumours as the high risk silent corticotroph subtype that have a higher risk of recurrence.