Achterhalen van de rol van synonieme mutaties in kanker

The role of synonymous mutations in cancer pathogenesis is currently underexplored. Furthermore, they are often considered as a separate mutation category distinct from missense mutations, whereas the nucleotide changes associated with both mutation types can cause similar gene expression dysregulation. In this work, we addressed these problems from two angles. First we performed an in-depth investigation of the effects of a synonymous and a missense mutation targeting the same codon on programmed ribosomal frameshifting in the Janus Kinase (JAK2) oncogene. The missense JAK2p.V617F and synonymous JAK2p.V617V mutations were introduced in relevant cell lines K-562 and SKM-1 cells using CRISPR-Cas9 genome editing. Subsequent experiments investigated the effects of these mutations on JAK2 mRNA and (phospho)protein expression as well as mRNA half-life, but no significant differences were observed in either cell line. Further research is still needed to conclusively determine whether ribosomal frameshifting is involved in the regulation of the JAK2 transcript. Secondly, we developed a method to detect novel clusters of synonymous and missense mutations in public cancer genomics data. In melanoma, we show that 22% (11/50) of these mutation clusters are misannotated as coding mutations because the reference transcripts used for their annotation are not expressed. Instead, these mutations are actually non-coding. This, for instance, applies to the mutation clusters targeting known cancer genes kinetochore localized astrin (SPAG5) binding protein (KNSTRN) and BCL2 like 12 (BCL2L12), each affecting 4-5% of melanoma patients. For the latter, we show that these mutations are functional non-coding mutations that target the shared promoter region of the interferon regulatory factor 3 (IRF3) and BCL2L12 genes. This results in downregulation of IRF3, BCL2L12 and tumour protein p53 (TP53) expression in a CRISPR-Cas9 primary melanocyte model and in melanoma patients, in which these mutations are also associated to a worse response to immunotherapy. Finally, we propose a simple automated method to more accurately annotate cancer mutations based on expressed transcripts. This work shows the importance of integrating DNA- and RNA-sequencing data to properly annotate mutations, and identifies a number of previously overlooked and wrongly annotated functional non-coding mutations in melanoma.