mRNA Profiling of a Well-differentiated G1 Pancreatic NET Correlates with Immunohistochemistry Profile

Abstract

Background: Neuroendocrine neoplasms (NENs) are a complex group of tumours that occur in many organs. The routinely used IHC markers for NEN diagnosis include CgA, synaptophysin, Ki67 and CD56. These have limitations, including lack of correlation to clinical outcomes and their presence in non-tumour tissue. Identification of additional markers and more quantitative analyses of tumour tissue has the potential to contribute to improved clinical outcomes. We used qRT-PCR to profile the expression levels of a panel of markers in tumour and matched non-tumour tissue from a G1 pancreatic neuroendocrine tumour. Differences in mRNA levels between tumour and non-tumour tissue were compared with IHC analyses of the same sample.Methods: Twenty-seven markers associated with tumour characteristics including neuroendocrine status, proliferation, stem cell phenotype, angiogenesis, epithelial to mesenchymal transition, cell adhesion, differentiation and tumour suppression were selected from previous studies. mRNA levels of these markers were measured in tumour and non-tumour in a G1 pancreatic tissue sample using qRT-PCR. IHC was carried out on the same tissue to detect the corresponding marker proteins. Results: Of the twenty seven markers analysed, seven showed higher mRNA levels in tumour relative to non-tumour while thirteen markers had lower expression in tumour relative to non-tumour tissue. The most substantial difference in mRNA levels were a gain of CgA, CD56, b-catenin, CK20, PDX1 and p53 and loss of Ki67, PCAD, CK7, CD31, MENA, ECAD, EPCAM, CDX2 and CK6. Comparison of qRT-PCR data with IHC showed correlation between fifteen markers.Conclusions: qRT-PCR is a relatively quantitative diagnostic tool with the advantage of being reproducible, fast, cheap and accurate. Differential marker profiling using qRT-PCR has the potential to improve the defining of tumour phenotypes and, in combination with IHC may have clinical utility towards improving stratification or distinguishing tumour grades. The inclusion of matched controls enables a more comparative analysis in comparison to many previous studies that report tumour data only. The results need to be validated in a cohort of different grades of NENs and related to clinical outcomes.