Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/25241
Title: Molecular classification of breast cancer patients using formalin-fixed paraffin-embedded derived RNA samples
Authors: Grech, Godfrey
Baldacchino, Shawn
Saliba, Christian
Sacco, Keith
Yee, Brendan
Scerri, Christian A.
Keywords: Breast -- Cancer -- Patients
Breast -- Cancer -- Molecular aspects
Biochemical markers
RNA
Gene expression
Issue Date: 2016
Publisher: OMICS International
Citation: Grech, G., Baldacchino, S., Saliba, C., Sacco, K., Yee, B., & Scerri, C. (2016). Molecular Classification of Breast Cancer Patients using Formalin-Fixed Paraffin-Embedded derived RNA Samples. Journal of Molecular Biomarkers & Diagnosis, 7, S8:016.
Abstract: The use of archival formalin-fixed paraffin-embedded (FFPE) material to analyse gene expression is limited by the low quality of extracted RNA. In this paper, we utilised an RNA based assay to quantify expression of luminal and basal markers, together with ERBB2 probes, in FFPE archival tissue from 2009 to 2010, all of which had clinical and therapeutic information of more than 5 years. Receptor status of the patients was characterised using the QuantiGene® Plex assay with 100% concordance to immunohistochemical (IHC) and fluorescence in situ hybridisation (FISH) results. A panel of molecular markers known to classify luminal and basal tumours were used and correlated with receptor status of the tumours. As expected, the triple negative breast cancer (TNBC) samples were classified as basal and oestrogen receptor (ER) positive cases as luminal. In summary, the QuantiGene® Plex technology provides a platform to quantitate novel panels of biomarkers on archival material. Moreover, multiplex analysis allows the use of minimal amounts of material providing an opportunity to utilise laser micro-dissected material. FFPE tissue samples are an invaluable resource for retrospective studies to interrogate current novel biomarkers, particularly to generate disease free survival and overall survival graphs to measure predictive value using well annotated retrospective samples with full clinical and pharmacological outcomes.
URI: https://www.um.edu.mt/library/oar//handle/123456789/25241
Appears in Collections:Scholarly Works - FacM&SPat



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