Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient’s treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently <1% of published biomarkers become clinically validated tests, often due to poor study design or lack of independent validation. Researchers from Queen’s University Belfast reviewed a large number of CRC transcriptional studies and identified recurrent sources of technical variability that encompass collection, preservation and storage of malignant tissue, nucleic acid extraction, methods to quantitate RNA transcripts and data analysis pipelines. They propose a series of defined steps for removal of these confounding issues, to ultimately aid in the development of more robust clinical biomarkers.
Flow diagram of the RNA profiling technical variables
This diagram depicts the nine different categories of technical variables that can affect results derived from high throughput RNA profiling technologies, such as microarray and RNA-Seq. It is recommended that investigators review the nine different categories and identify specific variables which are relevant to their particular RNA profiling studies.