3545 Background: Consensus molecular subtypes (CMSs) can guide precision treatment of colorectal cancer (CRC). Currently available assays can identify CMS1 and CMS4 cases well, while a dedicated test to distinguish CMS2 and 3 is lacking. This study aimed to identify a panel of methylation markers to distinguish between CMS2 and 3 in patients with CRC. Methods: Fresh-frozen tumor tissue of 239 patients with stage I-III CRC was included. CMS classification was performed on RNA-seq data using the single-sample-prediction parameter from the “CMSclassifier” package. Methylation profiles were obtained using the Infinium HumanMethylation450 BeadChip. We performed adaptive group-regularised logistic ridge-regression with post-hoc group-weighted elastic net marker selection to build prediction models for classification of CMS2 and CMS3 based on 15, 10 or 5 markers. Data from TCGAwas used for validation. Results: Overall methylation profiles differed between CMS2 and CMS3. Group-regularisation of the probes was done based on their location either relative to a CpG island or relative to a gene present in the CMS classifier resulting in two different prediction models and subsequently different marker panels. For both panels, even when using only 5 markers, sensitivity, specificity, and accuracy were > 90%. Validation showed comparable performances. Conclusions: Our highly sensitive and specific methylation marker panel can be used to distinguish CMS2 and 3. This enables development of a qPCR DNA methylation assay in patients with CRC to provide a specific and non-invasive classification tool.
Stacking and Chaining of Normalisation Methods in Deep Learning-based Classification of Colorectal Cancer using Gut Microbiome Data
