Primot, A. | Search bioRxiv

bioRxiv2021

Canine oral melanoma genomic and transcriptomic study defines two molecular subgroups with different therapeutical targets

Mucosal melanoma (MM) is a rare and aggressive clinical cancer that occurs mostly in the head, neck, and anogenital regions. Despite recent advances in genetics and the development of revolutionary treatments, such as immunotherapy, the prognosis for MM remains poor. Canine MM shares several clinical, histological, and genetic features with its human counterpart, offering a relevant spontaneous and immunocompetent model to decipher the genetic bases and explore treatment options for human MM. We performed an integrative genomic and transcriptomic analysis of 32 canine MM samples, which allowed us to identify two molecular subgroups differing in microenvironment and structural variant (SV) content. The overexpression of genes related to the microenvironment and T-cell response was associated with tumors harboring a lower content of structural variants, whereas the overexpression of pigmentation-related pathways and oncogenes such as TERT were associated with a high SV burden. To detail the SVs, especially those with focal amplifications, whole-genome sequencing was performed on four canine MM cell lines. We showed that focal amplifications characterized complex chromosomal rearrangements targeting oncogenes such as MDM2 or CDK4 and a recurrently amplified region on canine chromosome 30, comprising the genes TRPM7, GABPB1, USP8, and SPPL2A, were candidate oncogenes for MM. We showed that the copy numbers of these genes were significantly correlated with their expression levels. Finally, we demonstrated that the genes TRPM7, GABPB1, and SPPL2A play a role in cell proliferation; thus, these may be considered new candidate oncogenes for human MM. Our findings suggest the existence of two MM molecular subgroups that may benefit from dedicated therapies, such as immune checkpoint inhibitors or targeted therapies. These results illustrate the relevance of dog models for deciphering genetic mechanisms in spontaneous MM, along with the potential to screen for efficient targeted therapies for rare and aggressive cancers in humans.

cancer biology↗

bioRxiv2019

Cross-species analysis of melanoma enhancer logic using deep learning

Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. Deciphering the combinatorial code by which these binding sites are assembled within enhancers is indispensable to understand their regulatory involvement in establishing a cells phenotype, especially within biological systems with dysregulated gene regulatory networks, such as melanoma. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators. To perform an in-depth analysis of the enhancer architecture, we trained a deep neural network, called DeepMEL, to classify melanoma enhancers not only in the human genome, but also in other species. DeepMEL revealed the presence, organisation and positional specificity of important transcription factor binding sites. Together, this extensive analysis of the melanoma enhancer code allowed us to propose the concept of a core regulatory complex binding to melanocytic enhancers, consisting of SOX10, TFAP2A, MITF and RUNX, and to disentangle their individual roles in regulating enhancer accessibility and activity.

genomics↗