Transcriptional profiles underpin microsatellite status and associated features in colon cancer.

Introduction: While microsatellite instability is associated with prognosis and distinct clinical phenotypes in colon cancer, the basis for this remains incompletely defined. Novel bioinformatic techniques enable a detailed interrogation of the relationship between gene expression profiles and tumor characteristics.

Aim: We aimed to determine if microsatellite instability high (MSI-H) and microsatellite stable (MSS) tumors could be differentiated by gene expression profiles. We investigated the basis of this using a system and network based algorithmic approach.

Methods: Microsatellite status was established using a polymerase chain reaction (PCR) panel and fragment length analysis. Gene expression was determined using Illumina© microarrays comprising 48,701 transcripts, and scaling normalization was conducted using Limma in R. Following filtration for non-significant changes a meta-gene was established and subjected to unsupervised hierarchical clustering using Chipster©. A supervised learning algorithm (PAM) was used to generate a gene-expression based clinical-outcome predictor that was further tested using an independent validation group. A network based linkage analysis was conducted using Ingenuity© focusing on canonical, functional pathways, and associated therapeutic modalities.

Results: MSI-H and MSS tumors clustered separately following an unsupervised hierarchical clustering analysis. A transcriptomic classifier (with 19 component genes) was generated that reliably and reproducibly predicted microsatellite status. MSI-H associated canonical pathways were predominantly immune or inflammation related converging on increased IL-1B and thymidylate synthase expression. The network linkage analysis identified canakinumab, IL-trap and MDX-1100 as the strongest therapeutic candidates that remain to be assessed in the colon cancer setting.

Conclusions: Microsatellite status is underpinned by transcriptional events and can be accurately and reliably defined by differential gene expression. A specific transcriptomic profile is pathognomonic and provides insight into the differences in biology between MSS and MSI-H colon cancers.