OS10.1 Genomic landscape of pituitary adenomas

Abstract

Abstract Introduction: The clinical outcome of pituitary adenomas can be difficult to predict consistently. Methods: We characterized the genomic profile of 150 pituitary adenomas using next-generation sequencing and assessed the correlation between molecular and pathologic characteristics. These adenomas included hormonally active and inactive tumors, ones with typical or atypical histology, and ones that were primary or recurrent. Results: We observed two classes of pituitary adenomas based on their molecular profile. One class, encompassing one-third of the samples, had widespread genomic disruption with high rates of chromosome arm-level copy-number alterations. The other class tumors exhibited somatic copy-number alterations involving less than 6% of the genome. Levels of genomic disruption correlated with tumor pathology, as 75% of the disrupted subclass were functional adenomas or atypical null-cell adenomas, compared to only 13% of the less disrupted groups. Despite these high rates of genomic disruption, we detected relatively few focal events, which is unusual among highly disrupted cancers. We validated our finding by looking at previously published gene-expression data, and found that the disrupted subtype harbored more variable gene-expression profiles than quiet tumors. We validated these observations from the discovery cohort in an independent cohort of over 100 pituitary adenomas using a targeted clinical sequencing panel, and confirmed that a CLIA-approved assay was able to discriminate between disrupted and non-disrupted tumors. Furthermore, we recapitulated the known patterns of GNAS driver alterations in growth hormone secreting adenomas. Conclusion: Sporadic pituitary adenomas have distinct copy number profiles that associate with hormonal and histologic subtypes and influence gene expression. These results contribute to the growing repertoire of molecular data that have been generated in pituitary tumors, and will guide future efforts of pituitary tumor classification and risk-stratification.