ABSTRACT
Colorectal cancer patients with the BRAF(p.V600E) mutation have poor prognosis in metastatic setting. Personalized treatment options and companion diagnostics are needed to better treat these patients. Previously, we developed a 58-gene signature to characterize the distinct gene expression pattern of BRAF-mutation-like subtype (accuracy 91.1%). Further experiments repurposed drug Vinorelbine as specifically lethal to this BRAF-mutation-like subtype. The aim of this study is to translate this 58-gene signature from a research setting to a robust companion diagnostic that can use formalin-fixed, paraffin-embedded (FFPE) samples to select patients with the BRAF-mutation-like subtype. BRAF mutation and gene expression data of 302 FFPE samples were measured (mutants = 57, wild-type = 245). The performance of the 58-gene signature in FFPE samples showed a high sensitivity of 89.5%. In the identified BRAF-mutation-like subtype group, 50% of tumours were known BRAF mutants, and 50% were BRAF wild-type. The stability of the 58-gene signature in FFPE samples was evaluated by two control samples over 40 independent experiments. The standard deviations (SD) were within the predefined criteria (control 1: SD = 0.091, SD/Range = 3.0%; control 2: SD = 0.169, SD/Range = 5.5%). The fresh frozen version and translated FFPE version of this 58-gene signature were compared using 170 paired fresh frozen and FFPE samples and the result showed high consistency (agreement = 99.3%). In conclusion, we translated this 58-gene signature to a robust companion diagnostic that can use FFPE samples.
ABSTRACT
Recently, the number of genome-wide transcriptome profiles of pure populations of glia cells has drastically increased, resulting in an unprecedented amount of data that offer opportunities to study glia phenotypes and functions in health and disease. To make genome-wide transcriptome data easily accessible, we developed the Glia Open Access Database (GOAD), available via www.goad.education. GOAD contains a collection of previously published and unpublished transcriptome data, including datasets from isolated microglia, astrocytes and oligodendrocytes both at homeostatic and pathological conditions. It contains an intuitive web-based interface that consists of three features that enable searching, browsing, analyzing, and downloading of the data. The first feature is differential gene expression (DE) analysis that provides genes that are significantly up and down-regulated with the associated fold changes and p-values between two conditions of interest. In addition, an interactive Venn diagram is generated to illustrate the overlap and differences between several DE gene lists. The second feature is quantitative gene expression (QE) analysis, to investigate which genes are expressed in a particular glial cell type and to what degree. The third feature is a search utility, which can be used to find a gene of interest and depict its expression in all available expression data sets by generating a gene card. In addition, quality guidelines and relevant concepts for transcriptome analysis are discussed. Finally, GOAD is discussed in relation to several online transcriptome tools developed in neuroscience and immunology. In conclusion, GOAD is a unique platform to facilitate integration of bioinformatics in glia biology.
