Stratification in COloRectal cancer: from biology to Treatment prediction (S:CORT) – Transcriptional profiling of RNA from FFPE tissue


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Keara Redmond1,Susan Richman2,Andrew Blake3,Enric Domingo3,Michael Youdell3,Tim Maughan3,Mark Lawler1,Philip Dunne1
1Queen's University Belfast,2University of Leeds,3University of Oxford

Abstract

Background

Colorectal cancer (CRC) is the fourth most commonly diagnosed cancer, and third most common cause of cancer deaths in the UK. Transcriptional stratification has been used to identify prognostic and predictive subtypes in CRC, including consensus molecular subtypes (CMS) and colorectal intrinsic subtypes (CRIS); this ‘stratified medicine’ approach offers the potential to improve patient outcomes.

Method

The S:CORT consortium aims to utilise archival clinical trial and non-trial tissue (n=2000) to validate classifiers that predict response to common and emerging CRC treatment strategies, as well as attempting to identify new clinically-relevant prognostic/predictive biomarkers in CRC. The multi-omics data produced includes pathological characterisation, gene expression profiles from Xcel microarray, DNA mutations and copy number from NGS panels, methylation data from Illumina EPIC-arrays and patient clinicopathological data.

Results

Molecular processing within S:CORT has highlighted the importance of comprehensive histological assessment and annotation, prior to nucleic acid extraction, in order to avoid sample failure downstream. Following macrodissection and RNA extraction, from blocks up to and exceeding 20 years old, RNA integrity and quantity QA/QC assessments indicate that (1) overall yield and (2) 260/230 ratio assessment are important indicators of eventual transcriptional profiling success from FFPE-derived tissue using microarrays. Transcriptional profiles have been successfully aligned to CMS, CRIS and multiple other tumour-specific and microenvironmental signature classifications for each sample.

Following extensive optimisation of the RNA pipeline, including reduced input (30ng FFPE-derived RNA) and increased amplification steps, the S:CORT consortium now consistently achieves >97% success from even the smallest FFPE biopsy samples (1064 successful from 1089 samples).

Conclusion

It is possible to generate high quality data using transcriptional, mutational and epigenetic profiling on (highly degraded) DNA and RNA extracted from FFPE tissues up to and exceeding 20 years old. This data feeds-forward into our S:CORT multi-omics platform, giving a comprehensive overview of the molecular landscape of archived colorectal tumours.