bioRxivCancer Biology
Epigenetic mutational landscape in breast cancer: role of the histone methyltransferase gene KMT2D in triple negative tumors
S. Morcillo Garcia, M. d. M. Noblejas Lopez, C. Nieto Jimenez, J. Perez Pena, M. Nuncia Cantarero, B. Györffy, E. Amir, A. Pandiella, E. M. Galan Moya, A. Ocana
Abstract
PurposeEpigenetic regulating proteins like histone methyltransferases produce variations in several functions, some of them associated with the generation of oncogenic processes. Mutations of genes involved in these functions have been recently associated with cancer, and strategies to modulate their activity are currently in clinical development.\n\nMethodsBy using data extracted from the METABRIC study, we searched for mutated genes linked with detrimental outcome in invasive breast carcinoma (n = 772). Then, we used downstream signatures for each mutated gene to associate that signature with clinical prognosis using the online tool \"Genotype-2-Outcome\" (http://www.g-2-o.com). Next, we performed functional annotation analyses to classify genes by functions, and focused on those associated with the epigenetic machinery.\n\nResultsWe identified KMT2D, SETD1A and SETD2, included in the lysine methyltransferase activity function, as linked with poor prognosis in invasive breast cancer. KMT2D, which codes for a histone methyltransferase that acts as a transcriptional regulator, was mutated in 6% of triple negative breast tumors and found to be linked to poor survival. Genes regulated by KMT2D included RAC3, KRT23, or KRT14, among others, which are involved in cell communication and signal transduction. Finally, low expression of KMT2D at the transcriptomic level, which mirror what happens when KMT2D is mutated and functionally inactive, confirmed its prognostic value.\n\nConclusionIn the present work, we describe epigenetic modulating genes which are found to be mutated in breast cancer. We identify the histone methyltransferase KMT2D, which is mutated in 6% of triple negative tumors and linked with poor survival.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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