Transcriptome Analysis Reveals Distinct Patterns Between the Invasive and Noninvasive Pituitary Neuroendocrine Tumors.

Although most pituitary neuroendocrine tumors (PitNETs)/pituitary adenomas remain intrasellar, a significant proportion of tumors show parasellar invasive growth and 6% to 8% infiltrate the bone structures, thus affecting the prognosis. There is an unmet need to identify novel markers that can predict the parasellar growth of PitNETs. Furthermore, mechanisms that regulate bone invasiveness of PitNETs and factors related to tumor vascularization are largely unknown. We used genome-wide mRNA analysis in a cohort of 77 patients with PitNETs of different types to explore the differences in gene expression patterns between invasive and noninvasive tumors with respect to the parasellar growth and regarding the rare phenomenon of bone invasiveness. Additionally, we studied the genes correlated to the contrast enhancement quotient, a novel radiological parameter of tumor vascularization. Most of the genes differentially expressed related to the parasellar growth were genes involved in tumor invasiveness. Differentially expressed genes associated with bone invasiveness are involved in NF-κB pathway and antitumoral immune response. Lack of clear clustering regarding the parasellar and bone invasiveness may be explained by the influence of the cell lineage-related genes in this heterogeneous cohort of PitNETs. Our transcriptomics analysis revealed differences in the molecular fingerprints between invasive, including bone invasive, and noninvasive PitNETs, although without clear clustering. The contrast enhancement quotient emerged as a radiological parameter of tumor vascularization, correlating with several angiogenesis-related genes. Several of the top genes related to the PitNET invasiveness and vascularization have potential prognostic and therapeutic application requiring further research.

Gastrointestinal Biopsy Obtained During Cancer Screening, a Biological Marker for α-Synucleinopathy?

The hallmark alteration in α-synucleinopathies, α-synuclein, is observed not only in the brain but also in the peripheral tissues, particularly in the intestine. This suggests that endoscopic biopsies performed for colon cancer screening could facilitate the assessment of α-synuclein in the gastrointestinal (GI) tract. Using immunohistochemistry for α-synuclein, we assessed whether GI biopsies could be used to confirm an ongoing α-synucleinopathy. Seventy-four subjects with cerebral α-synucleinopathy in various Braak stages with concomitant GI biopsies were available for study. In 81% of the subjects, α-synuclein was seen in the mucosal/submucosal GI biopsies. Two subjects with severe cerebral α-synucleinopathy and a long delay between biopsy and death displayed no α-synuclein pathology in the gut, and 11 subjects with sparse cerebral α-synucleinopathy displayed GI α-synuclein up to 36 years prior to death. The finding that there was no GI α-synuclein in 19% of the subjects with cerebral α-synucleinopathy, and α-synuclein was observed in the gut of 11 subjects (15%) with sparse cerebral α-synucleinopathy even many years prior to death is unexpected and jeopardizes the use of assessment of α-synuclein in the peripheral tissue for confirmation of an ongoing cerebral α-synucleinopathy.

Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis.

Pituitary neuroendocrine tumors (PitNETs) are common, generally benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNETs can be classified based on the expression pattern of anterior pituitary hormones and three main transcriptions factors (TF), SF1, PIT1 and TPIT that regulate differentiation of adenohypophysial cells. Here, we have extended this classification based on the global transcriptomics landscape using tumor tissue from a well-defined cohort comprising 51 PitNETs of different clinical and histological types. The molecular profiles were compared with current classification schemes based on immunohistochemistry. Our results identified three main clusters of PitNETs that were aligned with the main pituitary TFs expression patterns. Our analyses enabled further identification of specific genes and expression patterns, including both known and unknown genes, that could distinguish the three different classes of PitNETs. We conclude that the current classification of PitNETs based on the expression of SF1, PIT1 and TPIT reflects three distinct subtypes of PitNETs with different underlying biology and partly independent from the expression of corresponding hormones. The transcriptomic analysis reveals several potentially targetable tumor-driving genes with previously unknown role in pituitary tumorigenesis.